, Luca Scrucca [aut,
cre] , Thomas Brendan
Murphy [ctb] , Michael
Fop [ctb] | Title: | Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation |
|---|---|
| Description: | Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference. |
| Authors: | Chris Fraley [aut], Adrian E. Raftery [aut]
, Luca Scrucca [aut,
cre] , Thomas Brendan
Murphy [ctb] , Michael
Fop [ctb] |
| Maintainer: | Luca Scrucca <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 6.1.1 |
| Built: | 2024-12-01 08:53:11 UTC |
| Source: | CRAN |
Gaussian finite mixture models estimated via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization and dimension reduction.
For a quick introduction to mclust see the vignette A quick tour of mclust.
See also:
Mclust for clustering;
MclustDA for supervised classification;
MclustSSC for semi-supervised classification;
densityMclust for density estimation.
Chris Fraley, Adrian Raftery and Luca Scrucca.
Maintainer: Luca Scrucca [email protected]
Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.
# Clustering mod1 <- Mclust(iris[,1:4]) summary(mod1) plot(mod1, what = c("BIC", "classification")) # Classification data(banknote) mod2 <- MclustDA(banknote[,2:7], banknote$Status) summary(mod2) plot(mod2) # Density estimation mod3 <- densityMclust(faithful$waiting) summary(mod3)# Clustering mod1 <- Mclust(iris[,1:4]) summary(mod1) plot(mod1, what = c("BIC", "classification")) # Classification data(banknote) mod2 <- MclustDA(banknote[,2:7], banknote$Status) summary(mod2) plot(mod2) # Density estimation mod3 <- densityMclust(faithful$waiting) summary(mod3)
Acidity index measured in a sample of 155 lakes in the Northeastern United States. Following Crawford et al. (1992, 1994), the data are expressed as log(ANC+50), where ANC is the acidity neutralising capacity value. The data were also used to fit mixture of gaussian distributions by Richardson and Green (1997), and by McLachlan and Peel (2000, Sec. 6.6.2).
data(acidity)data(acidity)
https://www.stats.bris.ac.uk/~peter/mixdata
Crawford, S. L. (1994) An application of the Laplace method to finite mixture distribution. Journal of the American Statistical Association, 89, 259–267.
Crawford, S. L., DeGroot, M. H., Kadane, J. B., and Small, M. J. (1994) Modeling lake chemistry distributions: Approximate Bayesian methods for estimating a finite mixture model. Technometrics, 34, 441–453.
McLachlan, G. and Peel, D. (2000) Finite Mixture Models. Wiley, New York.
Richardson, S. and Green, P. J. (1997) On Bayesian analysis of mixtures with unknown number of components (with discussion). Journal of the Royal Statistical Society, Series B, 59, 731–792.
Computes the adjusted Rand index comparing two classifications.
adjustedRandIndex(x, y)adjustedRandIndex(x, y)
x |
A numeric or character vector of class labels. |
y |
A numeric or character vector of class labels.
The length of |
The adjusted Rand index comparing the two partitions (a scalar). This index has zero expected value in the case of random partition, and it is bounded above by 1 in the case of perfect agreement between two partitions.
L. Hubert and P. Arabie (1985) Comparing Partitions, Journal of the Classification, 2, pp. 193-218.
a <- rep(1:3, 3) a b <- rep(c("A", "B", "C"), 3) b adjustedRandIndex(a, b) a <- sample(1:3, 9, replace = TRUE) a b <- sample(c("A", "B", "C"), 9, replace = TRUE) b adjustedRandIndex(a, b) a <- rep(1:3, 4) a b <- rep(c("A", "B", "C", "D"), 3) b adjustedRandIndex(a, b) irisHCvvv <- hc(modelName = "VVV", data = iris[,-5]) cl3 <- hclass(irisHCvvv, 3) adjustedRandIndex(cl3,iris[,5]) irisBIC <- mclustBIC(iris[,-5]) adjustedRandIndex(summary(irisBIC,iris[,-5])$classification,iris[,5]) adjustedRandIndex(summary(irisBIC,iris[,-5],G=3)$classification,iris[,5])a <- rep(1:3, 3) a b <- rep(c("A", "B", "C"), 3) b adjustedRandIndex(a, b) a <- sample(1:3, 9, replace = TRUE) a b <- sample(c("A", "B", "C"), 9, replace = TRUE) b adjustedRandIndex(a, b) a <- rep(1:3, 4) a b <- rep(c("A", "B", "C", "D"), 3) b adjustedRandIndex(a, b) irisHCvvv <- hc(modelName = "VVV", data = iris[,-5]) cl3 <- hclass(irisHCvvv, 3) adjustedRandIndex(cl3,iris[,5]) irisBIC <- mclustBIC(iris[,-5]) adjustedRandIndex(summary(irisBIC,iris[,-5])$classification,iris[,5]) adjustedRandIndex(summary(irisBIC,iris[,-5],G=3)$classification,iris[,5])
The data set contains six measurements made on 100 genuine and 100 counterfeit old-Swiss 1000-franc bank notes.
data(banknote)data(banknote)
A data frame with the following variables:
the status of the banknote: genuine or counterfeit
Length of bill (mm)
Width of left edge (mm)
Width of right edge (mm)
Bottom margin width (mm)
Top margin width (mm)
Length of diagonal (mm)
Flury, B. and Riedwyl, H. (1988). Multivariate Statistics: A practical approach. London: Chapman & Hall, Tables 1.1 and 1.2, pp. 5-8.
Simulated datasets used in Baudry et al. (2010) to illustrate the proposed mixture components combining method for clustering.
Please see the cited article for a detailed presentation of these datasets. The data frame with name exN.M is presented in Section N.M in the paper.
Test1D (not in the article) has been simulated from a Gaussian mixture distribution in R.
ex4.1 and ex4.2 have been simulated from a Gaussian mixture distribution in R^2.
ex4.3 has been simulated from a mixture of a uniform distribution on a square and a spherical Gaussian distribution in R^2.
ex4.4.1 has been simulated from a Gaussian mixture model in R^2
ex4.4.2 has been simulated from a mixture of two uniform distributions in R^3.
data(Baudry_etal_2010_JCGS_examples)data(Baudry_etal_2010_JCGS_examples)
ex4.1 is a data frame with 600 observations on 2 real variables.
ex4.2 is a data frame with 600 observations on 2 real variables.
ex4.3 is a data frame with 200 observations on 2 real variables.
ex4.4.1 is a data frame with 800 observations on 2 real variables.
ex4.4.2 is a data frame with 300 observations on 3 real variables.
Test1D is a data frame with 200 observations on 1 real variable.
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
data(Baudry_etal_2010_JCGS_examples) output <- clustCombi(data = ex4.4.1) output # is of class clustCombi # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes plot(output)data(Baudry_etal_2010_JCGS_examples) output <- clustCombi(data = ex4.4.1) output # is of class clustCombi # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes plot(output)
Computes the BIC (Bayesian Information Criterion) for parameterized mixture models given the loglikelihood, the dimension of the data, and number of mixture components in the model.
bic(modelName, loglik, n, d, G, noise=FALSE, equalPro=FALSE, ...)bic(modelName, loglik, n, d, G, noise=FALSE, equalPro=FALSE, ...)
modelName |
A character string indicating the model. The help file for
|
loglik |
The log-likelihood for a data set with respect to the Gaussian mixture model
specified in the |
n |
The number of observations in the data used to compute |
d |
The dimension of the data used to compute |
G |
The number of components in the Gaussian mixture model used to compute
|
noise |
A logical variable indicating whether or not the model includes an optional Poisson noise component. The default is to assume no noise component. |
equalPro |
A logical variable indicating whether or not the components in the model are assumed to be present in equal proportion. The default is to assume unequal mixing proportions. |
... |
Catches unused arguments in an indirect or list call via |
The BIC or Bayesian Information Criterion for the given input arguments.
mclustBIC,
nVarParams,
mclustModelNames.
n <- nrow(iris) d <- ncol(iris)-1 G <- 3 emEst <- me(modelName="VVI", data=iris[,-5], unmap(iris[,5])) names(emEst) args(bic) bic(modelName="VVI", loglik=emEst$loglik, n=n, d=d, G=G) # do.call("bic", emEst) ## alternative calln <- nrow(iris) d <- ncol(iris)-1 G <- 3 emEst <- me(modelName="VVI", data=iris[,-5], unmap(iris[,5])) names(emEst) args(bic) bic(modelName="VVI", loglik=emEst$loglik, n=n, d=d, G=G) # do.call("bic", emEst) ## alternative call
The Brier score is a proper score function that measures the accuracy of probabilistic predictions.
BrierScore(z, class)BrierScore(z, class)
z |
a matrix containing the predicted probabilities of each observation
to be classified in one of the classes.
Thus, the number of rows must match the length of |
class |
a numeric, character vector or factor containing the known class labels
for each observation.
If |
The Brier Score is the mean square difference between the true classes and the predicted probabilities.
This function implements the original multi-class definition by Brier (1950), normalized to as in Kruppa et al (2014). The formula is the following:
where is the number of observations, the number of classes, the indicator of class for observation , and is the predicted probability of observation to belong to class .
The above formulation is applicable to multi-class predictions, including the binary case. A small value of the Brier Score indicates high prediction accuracy.
The Brier Score is a strictly proper score (Gneiting and Raftery, 2007), which means that it takes its minimal value only when the predicted probabilities match the empirical probabilities.
Brier, G.W. (1950) Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78 (1): 1-3.
Gneiting, G. and Raftery, A. E. (2007) Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102 (477): 359-378.
Kruppa, J., Liu, Y., Diener, H.-C., Holste, T., Weimar, C., Koonig, I. R., and Ziegler, A. (2014) Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications. Biometrical Journal, 56 (4): 564-583.
# multi-class case class <- factor(c(5,5,5,2,5,3,1,2,1,1), levels = 1:5) probs <- matrix(c(0.15, 0.01, 0.08, 0.23, 0.01, 0.23, 0.59, 0.02, 0.38, 0.45, 0.36, 0.05, 0.30, 0.46, 0.15, 0.13, 0.06, 0.19, 0.27, 0.17, 0.40, 0.34, 0.18, 0.04, 0.47, 0.34, 0.32, 0.01, 0.03, 0.11, 0.04, 0.04, 0.09, 0.05, 0.28, 0.27, 0.02, 0.03, 0.12, 0.25, 0.05, 0.56, 0.35, 0.22, 0.09, 0.03, 0.01, 0.75, 0.20, 0.02), nrow = 10, ncol = 5) cbind(class, probs, map = map(probs)) BrierScore(probs, class) # two-class case class <- factor(c(1,1,1,2,2,1,1,2,1,1), levels = 1:2) probs <- matrix(c(0.91, 0.4, 0.56, 0.27, 0.37, 0.7, 0.97, 0.22, 0.68, 0.43, 0.09, 0.6, 0.44, 0.73, 0.63, 0.3, 0.03, 0.78, 0.32, 0.57), nrow = 10, ncol = 2) cbind(class, probs, map = map(probs)) BrierScore(probs, class) # two-class case when predicted probabilities are constrained to be equal to # 0 or 1, then the (normalized) Brier Score is equal to the classification # error rate probs <- ifelse(probs > 0.5, 1, 0) cbind(class, probs, map = map(probs)) BrierScore(probs, class) classError(map(probs), class)$errorRate # plot Brier score for predicted probabilities in range [0,1] class <- factor(rep(1, each = 100), levels = 0:1) prob <- seq(0, 1, by = 0.01) brier <- sapply(prob, function(p) { z <- matrix(c(1-p,p), nrow = length(class), ncol = 2, byrow = TRUE) BrierScore(z, class) }) plot(prob, brier, type = "l", main = "Scoring all one class", xlab = "Predicted probability", ylab = "Brier score") # brier score for predicting balanced data with constant prob class <- factor(rep(c(1,0), each = 50), levels = 0:1) prob <- seq(0, 1, by = 0.01) brier <- sapply(prob, function(p) { z <- matrix(c(1-p,p), nrow = length(class), ncol = 2, byrow = TRUE) BrierScore(z, class) }) plot(prob, brier, type = "l", main = "Scoring balanced classes", xlab = "Predicted probability", ylab = "Brier score") # brier score for predicting unbalanced data with constant prob class <- factor(rep(c(0,1), times = c(90,10)), levels = 0:1) prob <- seq(0, 1, by = 0.01) brier <- sapply(prob, function(p) { z <- matrix(c(1-p,p), nrow = length(class), ncol = 2, byrow = TRUE) BrierScore(z, class) }) plot(prob, brier, type = "l", main = "Scoring unbalanced classes", xlab = "Predicted probability", ylab = "Brier score")# multi-class case class <- factor(c(5,5,5,2,5,3,1,2,1,1), levels = 1:5) probs <- matrix(c(0.15, 0.01, 0.08, 0.23, 0.01, 0.23, 0.59, 0.02, 0.38, 0.45, 0.36, 0.05, 0.30, 0.46, 0.15, 0.13, 0.06, 0.19, 0.27, 0.17, 0.40, 0.34, 0.18, 0.04, 0.47, 0.34, 0.32, 0.01, 0.03, 0.11, 0.04, 0.04, 0.09, 0.05, 0.28, 0.27, 0.02, 0.03, 0.12, 0.25, 0.05, 0.56, 0.35, 0.22, 0.09, 0.03, 0.01, 0.75, 0.20, 0.02), nrow = 10, ncol = 5) cbind(class, probs, map = map(probs)) BrierScore(probs, class) # two-class case class <- factor(c(1,1,1,2,2,1,1,2,1,1), levels = 1:2) probs <- matrix(c(0.91, 0.4, 0.56, 0.27, 0.37, 0.7, 0.97, 0.22, 0.68, 0.43, 0.09, 0.6, 0.44, 0.73, 0.63, 0.3, 0.03, 0.78, 0.32, 0.57), nrow = 10, ncol = 2) cbind(class, probs, map = map(probs)) BrierScore(probs, class) # two-class case when predicted probabilities are constrained to be equal to # 0 or 1, then the (normalized) Brier Score is equal to the classification # error rate probs <- ifelse(probs > 0.5, 1, 0) cbind(class, probs, map = map(probs)) BrierScore(probs, class) classError(map(probs), class)$errorRate # plot Brier score for predicted probabilities in range [0,1] class <- factor(rep(1, each = 100), levels = 0:1) prob <- seq(0, 1, by = 0.01) brier <- sapply(prob, function(p) { z <- matrix(c(1-p,p), nrow = length(class), ncol = 2, byrow = TRUE) BrierScore(z, class) }) plot(prob, brier, type = "l", main = "Scoring all one class", xlab = "Predicted probability", ylab = "Brier score") # brier score for predicting balanced data with constant prob class <- factor(rep(c(1,0), each = 50), levels = 0:1) prob <- seq(0, 1, by = 0.01) brier <- sapply(prob, function(p) { z <- matrix(c(1-p,p), nrow = length(class), ncol = 2, byrow = TRUE) BrierScore(z, class) }) plot(prob, brier, type = "l", main = "Scoring balanced classes", xlab = "Predicted probability", ylab = "Brier score") # brier score for predicting unbalanced data with constant prob class <- factor(rep(c(0,1), times = c(90,10)), levels = 0:1) prob <- seq(0, 1, by = 0.01) brier <- sapply(prob, function(p) { z <- matrix(c(1-p,p), nrow = length(class), ncol = 2, byrow = TRUE) BrierScore(z, class) }) plot(prob, brier, type = "l", main = "Scoring unbalanced classes", xlab = "Predicted probability", ylab = "Brier score")
Computes component densities for observations in MVN mixture models parameterized by eigenvalue decomposition.
cdens(data, modelName, parameters, logarithm = FALSE, warn = NULL, ...)cdens(data, modelName, parameters, logarithm = FALSE, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
parameters |
The parameters of the model:
|
logarithm |
A logical value indicating whether or not the logarithm of the component densities should be returned. The default is to return the component densities, obtained from the log component densities by exponentiation. |
warn |
A logical value indicating whether or not a warning should be issued
when computations fail. The default is |
... |
Catches unused arguments in indirect or list calls via |
A numeric matrix whose [i,k]th entry is the
density or log density of observation i in component k.
The densities are not scaled by mixing proportions.
When one or more component densities are very large in magnitude, it may be possible to compute the logarithm of the component densities but not the component densities themselves due to overflow.
cdensE, ...,
cdensVVV,
dens,
estep,
mclustModelNames,
mclustVariance,
mclust.options,
do.call
z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case model <- me(modelName = "EEE", data = faithful, z = z2) cdens(modelName = "EEE", data = faithful, logarithm = TRUE, parameters = model$parameters)[1:5,] data(cross) odd <- seq(1, nrow(cross), by = 2) oddBIC <- mclustBIC(cross[odd,-1]) oddModel <- mclustModel(cross[odd,-1], oddBIC) ## best parameter estimates names(oddModel) even <- odd + 1 densities <- cdens(modelName = oddModel$modelName, data = cross[even,-1], parameters = oddModel$parameters) cbind(class = cross[even,1], densities)[1:5,]z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case model <- me(modelName = "EEE", data = faithful, z = z2) cdens(modelName = "EEE", data = faithful, logarithm = TRUE, parameters = model$parameters)[1:5,] data(cross) odd <- seq(1, nrow(cross), by = 2) oddBIC <- mclustBIC(cross[odd,-1]) oddModel <- mclustModel(cross[odd,-1], oddBIC) ## best parameter estimates names(oddModel) even <- odd + 1 densities <- cdens(modelName = oddModel$modelName, data = cross[even,-1], parameters = oddModel$parameters) cbind(class = cross[even,1], densities)[1:5,]
Computes component densities for points in a parameterized MVN mixture model.
cdensE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensX(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEII(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVII(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEEI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVEI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEVI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVVI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEEE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEEV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVEV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVVV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEVE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEVV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVEE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVVE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensXII(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensXXI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensXXX(data, logarithm = FALSE, parameters, warn = NULL, ...)cdensE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensX(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEII(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVII(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEEI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVEI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEVI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVVI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEEE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEEV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVEV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVVV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEVE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensEVV(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVEE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensVVE(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensXII(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensXXI(data, logarithm = FALSE, parameters, warn = NULL, ...) cdensXXX(data, logarithm = FALSE, parameters, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
logarithm |
A logical value indicating whether or not the logarithm of the component densities should be returned. The default is to return the component densities, obtained from the log component densities by exponentiation. |
parameters |
The parameters of the model:
|
warn |
A logical value indicating whether or not a warning should be issued
when computations fail. The default is |
... |
Catches unused arguments in indirect or list calls via |
A numeric matrix whose [i,j]th
entry is the density of observation i in component j.
The densities are not scaled by mixing proportions.
When one or more component densities are very large in magnitude, then it may be possible to compute the logarithm of the component densities but not the component densities themselves due to overflow.
cdens,
dens,
mclustVariance,
mstep,
mclust.options,
do.call.
z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case model <- meVVV(data=faithful, z=z2) cdensVVV(data=faithful, logarithm = TRUE, parameters = model$parameters) data(cross) z2 <- unmap(cross[,1]) model <- meEEV(data = cross[,-1], z = z2) EEVdensities <- cdensEEV( data = cross[,-1], parameters = model$parameters) cbind(cross[,-1],map(EEVdensities))z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case model <- meVVV(data=faithful, z=z2) cdensVVV(data=faithful, logarithm = TRUE, parameters = model$parameters) data(cross) z2 <- unmap(cross[,1]) model <- meEEV(data = cross[,-1], z = z2) EEVdensities <- cdensEEV( data = cross[,-1], parameters = model$parameters) cbind(cross[,-1],map(EEVdensities))
Compute the cumulative density function (cdf) or quantiles from an estimated one-dimensional Gaussian mixture fitted using densityMclust.
cdfMclust(object, data, ngrid = 100, ...) quantileMclust(object, p, ...)cdfMclust(object, data, ngrid = 100, ...) quantileMclust(object, p, ...)
object |
a |
data |
a numeric vector of evaluation points. |
ngrid |
the number of points in a regular grid to be used as evaluation points if no |
p |
a numeric vector of probabilities. |
... |
further arguments passed to or from other methods. |
The cdf is evaluated at points given by the optional argument data. If not provided, a regular grid of length ngrid for the evaluation points is used.
The quantiles are computed using bisection linear search algorithm.
cdfMclust returns a list of x and y values providing, respectively, the evaluation points and the estimated cdf.
quantileMclust returns a vector of quantiles.
Luca Scrucca
densityMclust,
plot.densityMclust.
x <- c(rnorm(100), rnorm(100, 3, 2)) dens <- densityMclust(x, plot = FALSE) summary(dens, parameters = TRUE) cdf <- cdfMclust(dens) str(cdf) q <- quantileMclust(dens, p = c(0.01, 0.1, 0.5, 0.9, 0.99)) cbind(quantile = q, cdf = cdfMclust(dens, q)$y) plot(cdf, type = "l", xlab = "x", ylab = "CDF") points(q, cdfMclust(dens, q)$y, pch = 20, col = "red3") par(mfrow = c(2,2)) dens.waiting <- densityMclust(faithful$waiting) plot(cdfMclust(dens.waiting), type = "l", xlab = dens.waiting$varname, ylab = "CDF") dens.eruptions <- densityMclust(faithful$eruptions) plot(cdfMclust(dens.eruptions), type = "l", xlab = dens.eruptions$varname, ylab = "CDF") par(mfrow = c(1,1))x <- c(rnorm(100), rnorm(100, 3, 2)) dens <- densityMclust(x, plot = FALSE) summary(dens, parameters = TRUE) cdf <- cdfMclust(dens) str(cdf) q <- quantileMclust(dens, p = c(0.01, 0.1, 0.5, 0.9, 0.99)) cbind(quantile = q, cdf = cdfMclust(dens, q)$y) plot(cdf, type = "l", xlab = "x", ylab = "CDF") points(q, cdfMclust(dens, q)$y, pch = 20, col = "red3") par(mfrow = c(2,2)) dens.waiting <- densityMclust(faithful$waiting) plot(cdfMclust(dens.waiting), type = "l", xlab = dens.waiting$varname, ylab = "CDF") dens.eruptions <- densityMclust(faithful$eruptions) plot(cdfMclust(dens.eruptions), type = "l", xlab = dens.eruptions$varname, ylab = "CDF") par(mfrow = c(1,1))
A set of simulated bivariate minefield data (1104 observations).
data(chevron)data(chevron)
A. Dasgupta and A. E. Raftery (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association 93:294-302.
C. Fraley and A.E. Raftery (1998). Computer Journal 41:578-588.
G. J. McLachlan and D. Peel (2000). Finite Mixture Models, Wiley, pages 110-112.
Computes the errore rate of a given classification relative to the known classes, and the location of misclassified data points.
classError(classification, class)classError(classification, class)
classification |
A numeric, character vector or factor specifying the predicted class
labels. Must have the same length as |
class |
A numeric, character vector or factor of known true class labels.
Must have the same length as |
If more than one mapping between predicted classification and the known truth corresponds to the minimum number of classification errors, only one possible set of misclassified observations is returned.
A list with the following two components:
misclassified |
The indexes of the misclassified data points in a minimum error mapping between the predicted classification and the known true classes. |
errorRate |
The error rate corresponding to a minimum error mapping between the predicted classification and the known true classes. |
(a <- rep(1:3, 3)) (b <- rep(c("A", "B", "C"), 3)) classError(a, b) (a <- sample(1:3, 9, replace = TRUE)) (b <- sample(c("A", "B", "C"), 9, replace = TRUE)) classError(a, b) class <- factor(c(5,5,5,2,5,3,1,2,1,1), levels = 1:5) probs <- matrix(c(0.15, 0.01, 0.08, 0.23, 0.01, 0.23, 0.59, 0.02, 0.38, 0.45, 0.36, 0.05, 0.30, 0.46, 0.15, 0.13, 0.06, 0.19, 0.27, 0.17, 0.40, 0.34, 0.18, 0.04, 0.47, 0.34, 0.32, 0.01, 0.03, 0.11, 0.04, 0.04, 0.09, 0.05, 0.28, 0.27, 0.02, 0.03, 0.12, 0.25, 0.05, 0.56, 0.35, 0.22, 0.09, 0.03, 0.01, 0.75, 0.20, 0.02), nrow = 10, ncol = 5) cbind(class, probs, map = map(probs)) classError(map(probs), class)(a <- rep(1:3, 3)) (b <- rep(c("A", "B", "C"), 3)) classError(a, b) (a <- sample(1:3, 9, replace = TRUE)) (b <- sample(c("A", "B", "C"), 9, replace = TRUE)) classError(a, b) class <- factor(c(5,5,5,2,5,3,1,2,1,1), levels = 1:5) probs <- matrix(c(0.15, 0.01, 0.08, 0.23, 0.01, 0.23, 0.59, 0.02, 0.38, 0.45, 0.36, 0.05, 0.30, 0.46, 0.15, 0.13, 0.06, 0.19, 0.27, 0.17, 0.40, 0.34, 0.18, 0.04, 0.47, 0.34, 0.32, 0.01, 0.03, 0.11, 0.04, 0.04, 0.09, 0.05, 0.28, 0.27, 0.02, 0.03, 0.12, 0.25, 0.05, 0.56, 0.35, 0.22, 0.09, 0.03, 0.01, 0.75, 0.20, 0.02), nrow = 10, ncol = 5) cbind(class, probs, map = map(probs)) classError(map(probs), class)
A simple procedure to improve the estimation of class prior probabilities when the training data does not reflect the true a priori probabilities of the target classes. The EM algorithm used is described in Saerens et al (2002).
classPriorProbs(object, newdata = object$data, itmax = 1e3, eps = sqrt(.Machine$double.eps))classPriorProbs(object, newdata = object$data, itmax = 1e3, eps = sqrt(.Machine$double.eps))
object |
an object of class |
newdata |
a data frame or matrix giving the data. If missing the train data obtained from the call to |
itmax |
an integer value specifying the maximal number of EM iterations. |
eps |
a scalar specifying the tolerance associated with deciding when to terminate the EM iterations. |
The estimation procedure employes an EM algorithm as described in Saerens et al (2002).
A vector of class prior estimates which can then be used in the predict.MclustDA to improve predictions.
Saerens, M., Latinne, P. and Decaestecker, C. (2002) Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure, Neural computation, 14 (1), 21–41.
# generate data from a mixture f(x) = 0.9 * N(0,1) + 0.1 * N(3,1) n <- 10000 mixpro <- c(0.9, 0.1) class <- factor(sample(0:1, size = n, prob = mixpro, replace = TRUE)) x <- ifelse(class == 1, rnorm(n, mean = 3, sd = 1), rnorm(n, mean = 0, sd = 1)) hist(x[class==0], breaks = 11, xlim = range(x), main = "", xlab = "x", col = adjustcolor("dodgerblue2", alpha.f = 0.5), border = "white") hist(x[class==1], breaks = 11, add = TRUE, col = adjustcolor("red3", alpha.f = 0.5), border = "white") box() # generate training data from a balanced case-control sample, i.e. # f(x) = 0.5 * N(0,1) + 0.5 * N(3,1) n_train <- 1000 class_train <- factor(sample(0:1, size = n_train, prob = c(0.5, 0.5), replace = TRUE)) x_train <- ifelse(class_train == 1, rnorm(n_train, mean = 3, sd = 1), rnorm(n_train, mean = 0, sd = 1)) hist(x_train[class_train==0], breaks = 11, xlim = range(x_train), main = "", xlab = "x", col = adjustcolor("dodgerblue2", alpha.f = 0.5), border = "white") hist(x_train[class_train==1], breaks = 11, add = TRUE, col = adjustcolor("red3", alpha.f = 0.5), border = "white") box() # fit a MclustDA model mod <- MclustDA(x_train, class_train) summary(mod, parameters = TRUE) # test set performance pred <- predict(mod, newdata = x) classError(pred$classification, class)$error BrierScore(pred$z, class) # compute performance over a grid of prior probs priorProp <- seq(0.01, 0.99, by = 0.01) CE <- BS <- rep(as.double(NA), length(priorProp)) for(i in seq(priorProp)) { pred <- predict(mod, newdata = x, prop = c(1-priorProp[i], priorProp[i])) CE[i] <- classError(pred$classification, class = class)$error BS[i] <- BrierScore(pred$z, class) } # estimate the optimal class prior probs (priorProbs <- classPriorProbs(mod, x)) pred <- predict(mod, newdata = x, prop = priorProbs) # compute performance at the estimated class prior probs classError(pred$classification, class = class)$error BrierScore(pred$z, class) matplot(priorProp, cbind(CE,BS), type = "l", lty = 1, lwd = 2, xlab = "Class prior probability", ylab = "", ylim = c(0,max(CE,BS)), panel.first = { abline(h = seq(0,1,by=0.05), col = "grey", lty = 3) abline(v = seq(0,1,by=0.05), col = "grey", lty = 3) }) abline(v = mod$prop[2], lty = 2) # training prop abline(v = mean(class==1), lty = 4) # test prop (usually unknown) abline(v = priorProbs[2], lty = 3, lwd = 2) # estimated prior probs legend("topleft", legend = c("ClassError", "BrierScore"), col = 1:2, lty = 1, lwd = 2, inset = 0.02) # Summary of results: priorProp[which.min(CE)] # best prior of class 1 according to classification error priorProp[which.min(BS)] # best prior of class 1 according to Brier score priorProbs # optimal estimated class prior probabilities# generate data from a mixture f(x) = 0.9 * N(0,1) + 0.1 * N(3,1) n <- 10000 mixpro <- c(0.9, 0.1) class <- factor(sample(0:1, size = n, prob = mixpro, replace = TRUE)) x <- ifelse(class == 1, rnorm(n, mean = 3, sd = 1), rnorm(n, mean = 0, sd = 1)) hist(x[class==0], breaks = 11, xlim = range(x), main = "", xlab = "x", col = adjustcolor("dodgerblue2", alpha.f = 0.5), border = "white") hist(x[class==1], breaks = 11, add = TRUE, col = adjustcolor("red3", alpha.f = 0.5), border = "white") box() # generate training data from a balanced case-control sample, i.e. # f(x) = 0.5 * N(0,1) + 0.5 * N(3,1) n_train <- 1000 class_train <- factor(sample(0:1, size = n_train, prob = c(0.5, 0.5), replace = TRUE)) x_train <- ifelse(class_train == 1, rnorm(n_train, mean = 3, sd = 1), rnorm(n_train, mean = 0, sd = 1)) hist(x_train[class_train==0], breaks = 11, xlim = range(x_train), main = "", xlab = "x", col = adjustcolor("dodgerblue2", alpha.f = 0.5), border = "white") hist(x_train[class_train==1], breaks = 11, add = TRUE, col = adjustcolor("red3", alpha.f = 0.5), border = "white") box() # fit a MclustDA model mod <- MclustDA(x_train, class_train) summary(mod, parameters = TRUE) # test set performance pred <- predict(mod, newdata = x) classError(pred$classification, class)$error BrierScore(pred$z, class) # compute performance over a grid of prior probs priorProp <- seq(0.01, 0.99, by = 0.01) CE <- BS <- rep(as.double(NA), length(priorProp)) for(i in seq(priorProp)) { pred <- predict(mod, newdata = x, prop = c(1-priorProp[i], priorProp[i])) CE[i] <- classError(pred$classification, class = class)$error BS[i] <- BrierScore(pred$z, class) } # estimate the optimal class prior probs (priorProbs <- classPriorProbs(mod, x)) pred <- predict(mod, newdata = x, prop = priorProbs) # compute performance at the estimated class prior probs classError(pred$classification, class = class)$error BrierScore(pred$z, class) matplot(priorProp, cbind(CE,BS), type = "l", lty = 1, lwd = 2, xlab = "Class prior probability", ylab = "", ylim = c(0,max(CE,BS)), panel.first = { abline(h = seq(0,1,by=0.05), col = "grey", lty = 3) abline(v = seq(0,1,by=0.05), col = "grey", lty = 3) }) abline(v = mod$prop[2], lty = 2) # training prop abline(v = mean(class==1), lty = 4) # test prop (usually unknown) abline(v = priorProbs[2], lty = 3, lwd = 2) # estimated prior probs legend("topleft", legend = c("ClassError", "BrierScore"), col = 1:2, lty = 1, lwd = 2, inset = 0.02) # Summary of results: priorProp[which.min(CE)] # best prior of class 1 according to classification error priorProp[which.min(BS)] # best prior of class 1 according to Brier score priorProbs # optimal estimated class prior probabilities
Creates a scatter plot for each pair of variables in given data. Observations in different classes are represented by different colors and symbols.
clPairs(data, classification, symbols = NULL, colors = NULL, cex = NULL, labels = dimnames(data)[[2]], cex.labels = 1.5, gap = 0.2, grid = FALSE, ...) clPairsLegend(x, y, class, col, pch, cex, box = TRUE, ...)clPairs(data, classification, symbols = NULL, colors = NULL, cex = NULL, labels = dimnames(data)[[2]], cex.labels = 1.5, gap = 0.2, grid = FALSE, ...) clPairsLegend(x, y, class, col, pch, cex, box = TRUE, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
classification |
A numeric or character vector representing a classification of observations
(rows) of |
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique class in |
colors |
Either an integer or character vector assigning a color to each
unique class in |
cex |
A vector of numerical values specifying the size of the plotting
symbol for each unique class in |
labels |
A vector of character strings for labelling the variables. The default
is to use the column dimension names of |
cex.labels |
A numerical value specifying the size of the text labels. |
gap |
An argument specifying the distance between subplots (see |
grid |
A logical specifying if grid lines should be added to panels (see |
x, y
|
The x and y co-ordinates with respect to a graphic device having
plotting region coordinates |
class |
The class labels. |
box |
A logical, if |
col, pch
|
The colors and plotting symbols appearing in the legend. |
... |
For a |
The function clPairs() draws scatter plots on the current graphics device for each combination of variables in data. Observations of different classifications are labeled with different symbols.
The function clPairsLegend() can be used to add a legend. See examples below.
The function clPairs() invisibly returns a list with the following components:
class |
A character vector of class labels. |
col |
A vector of colors used for each class. |
pch |
A vector of plotting symbols used for each class. |
pairs,
coordProj,
mclust.options
clPairs(iris[,1:4], cl = iris$Species) clp <- clPairs(iris[,1:4], cl = iris$Species, lower.panel = NULL) clPairsLegend(0.1, 0.4, class = clp$class, col = clp$col, pch = clp$pch, title = "Iris data")clPairs(iris[,1:4], cl = iris$Species) clp <- clPairs(iris[,1:4], cl = iris$Species, lower.panel = NULL) clPairsLegend(0.1, 0.4, class = clp$class, col = clp$col, pch = clp$pch, title = "Iris data")
Provides a hierarchy of combined clusterings from the EM/BIC Gaussian mixture solution to one class, following the methodology proposed in the article cited in the references.
clustCombi(object = NULL, data = NULL, ...)clustCombi(object = NULL, data = NULL, ...)
object |
An object returned by |
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. If the |
... |
Optional arguments to be passed to called functions. Notably, any argument (such as the numbers of components for which the BIC is computed; the models to be fitted by EM; initialization parameters for the EM algorithm, etc.) to be passed to |
Mclust provides a Gaussian mixture fitted to the data by maximum likelihood through the EM algorithm, for the model and number of components selected according to BIC. The corresponding components are hierarchically combined according to an entropy criterion, following the methodology described in the article cited in the references section. The solutions with numbers of classes between the one selected by BIC and one are returned as a clustCombi class object.
A list of class clustCombi giving the hierarchy of combined solutions from the number of components selected by BIC to one. The details of the output components are as follows:
classification |
A list of the data classifications obtained for each combined solution of the hierarchy through a MAP assignment |
combiM |
A list of matrices. |
combiz |
A list of matrices. |
MclustOutput |
A list of class |
J.-P. Baudry, A. E. Raftery, L. Scrucca
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
data(Baudry_etal_2010_JCGS_examples) # run Mclust using provided data output <- clustCombi(data = ex4.1) # or run Mclust and then clustcombi on the returned object mod <- Mclust(ex4.1) output <- clustCombi(mod) output summary(output) # run Mclust using provided data and any further optional argument provided output <- clustCombi(data = ex4.1, modelName = "EEV", G = 1:15) # plot the hierarchy of combined solutions plot(output, what = "classification") # plot some "entropy plots" which may help one to select the number of classes plot(output, what = "entropy") # plot the tree structure obtained from combining mixture components plot(output, what = "tree") # the selected model and number of components obtained from Mclust using BIC output$MclustOutput # the matrix whose [i,k]th entry is the probability that i-th observation in # the data belongs to the k-th class according to the BIC solution head( output$combiz[[output$MclustOutput$G]] ) # the matrix whose [i,k]th entry is the probability that i-th observation in # the data belongs to the k-th class according to the first combined solution head( output$combiz[[output$MclustOutput$G-1]] ) # the matrix describing how to merge the 6-classes solution to get the # 5-classes solution output$combiM[[5]] # for example the following code returns the label of the class (in the # 5-classes combined solution) to which the 4th class (in the 6-classes # solution) is assigned. Only two classes in the (K+1)-classes solution # are assigned the same class in the K-classes solution: the two which # are merged at this step output$combiM[[5]] # recover the 5-classes soft clustering from the 6-classes soft clustering # and the 6 -> 5 combining matrix all( output$combiz[[5]] == t( output$combiM[[5]] %*% t(output$combiz[[6]]) ) ) # the hard clustering under the 5-classes solution head( output$classification[[5]] )data(Baudry_etal_2010_JCGS_examples) # run Mclust using provided data output <- clustCombi(data = ex4.1) # or run Mclust and then clustcombi on the returned object mod <- Mclust(ex4.1) output <- clustCombi(mod) output summary(output) # run Mclust using provided data and any further optional argument provided output <- clustCombi(data = ex4.1, modelName = "EEV", G = 1:15) # plot the hierarchy of combined solutions plot(output, what = "classification") # plot some "entropy plots" which may help one to select the number of classes plot(output, what = "entropy") # plot the tree structure obtained from combining mixture components plot(output, what = "tree") # the selected model and number of components obtained from Mclust using BIC output$MclustOutput # the matrix whose [i,k]th entry is the probability that i-th observation in # the data belongs to the k-th class according to the BIC solution head( output$combiz[[output$MclustOutput$G]] ) # the matrix whose [i,k]th entry is the probability that i-th observation in # the data belongs to the k-th class according to the first combined solution head( output$combiz[[output$MclustOutput$G-1]] ) # the matrix describing how to merge the 6-classes solution to get the # 5-classes solution output$combiM[[5]] # for example the following code returns the label of the class (in the # 5-classes combined solution) to which the 4th class (in the 6-classes # solution) is assigned. Only two classes in the (K+1)-classes solution # are assigned the same class in the K-classes solution: the two which # are merged at this step output$combiM[[5]] # recover the 5-classes soft clustering from the 6-classes soft clustering # and the 6 -> 5 combining matrix all( output$combiz[[5]] == t( output$combiM[[5]] %*% t(output$combiz[[6]]) ) ) # the hard clustering under the 5-classes solution head( output$classification[[5]] )
Return the optimal number of clusters by combining mixture components based on the entropy method discussed in the reference given below.
clustCombiOptim(object, reg = 2, plot = FALSE, ...)clustCombiOptim(object, reg = 2, plot = FALSE, ...)
object |
An object of class |
reg |
The number of parts of the piecewise linear regression for the entropy plots. Choose 2 for a two-segment piecewise linear regression model (i.e. 1 change-point), and 3 for a three-segment piecewise linear regression model (i.e. 3 change-points). |
plot |
Logical, if |
... |
Further arguments passed to or from other methods. |
The function returns a list with the following components:
numClusters.combi |
The estimated number of clusters. |
z.combi |
A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth cluster. |
cluster.combi |
The clustering labels. |
J.-P. Baudry, A. E. Raftery, L. Scrucca
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
combiPlot, entPlot, clustCombi
data(Baudry_etal_2010_JCGS_examples) output <- clustCombi(data = ex4.1) combiOptim <- clustCombiOptim(output) str(combiOptim) # plot optimal clustering with alpha color transparency proportional to uncertainty zmax <- apply(combiOptim$z.combi, 1, max) col <- mclust.options("classPlotColors")[combiOptim$cluster.combi] vadjustcolor <- Vectorize(adjustcolor) alphacol = (zmax - 1/combiOptim$numClusters.combi)/(1-1/combiOptim$numClusters.combi) col <- vadjustcolor(col, alpha.f = alphacol) plot(ex4.1, col = col, pch = mclust.options("classPlotSymbols")[combiOptim$cluster.combi])data(Baudry_etal_2010_JCGS_examples) output <- clustCombi(data = ex4.1) combiOptim <- clustCombiOptim(output) str(combiOptim) # plot optimal clustering with alpha color transparency proportional to uncertainty zmax <- apply(combiOptim$z.combi, 1, max) col <- mclust.options("classPlotColors")[combiOptim$cluster.combi] vadjustcolor <- Vectorize(adjustcolor) alphacol = (zmax - 1/combiOptim$numClusters.combi)/(1-1/combiOptim$numClusters.combi) col <- vadjustcolor(col, alpha.f = alphacol) plot(ex4.1, col = col, pch = mclust.options("classPlotSymbols")[combiOptim$cluster.combi])
Plot classifications corresponding to successive combined solutions.
combiPlot(data, z, combiM, ...)combiPlot(data, z, combiM, ...)
data |
The data. |
z |
A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class, for the initial solution (ie before any combining). Typically, the one returned by |
combiM |
A "combining matrix" (as provided by |
... |
Other arguments to be passed to the |
Plot the classifications obtained by MAP from the matrix t(combiM %*% t(z)), which is the matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class, according to the combined solution obtained by merging (according to combiM) the initial solution described by z.
J.-P. Baudry, A. E. Raftery, L. Scrucca
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
clustCombi, combMat, clustCombi
data(Baudry_etal_2010_JCGS_examples) MclustOutput <- Mclust(ex4.1) MclustOutput$G # Mclust/BIC selected 6 classes par(mfrow=c(2,2)) combiM0 <- diag(6) # is the identity matrix # no merging: plot the initial solution, given by z combiPlot(ex4.1, MclustOutput$z, combiM0, cex = 3) title("No combining") combiM1 <- combMat(6, 1, 2) # let's merge classes labeled 1 and 2 combiM1 combiPlot(ex4.1, MclustOutput$z, combiM1) title("Combine 1 and 2") # let's merge classes labeled 1 and 2, and then components labeled (in this # new 5-classes combined solution) 1 and 2 combiM2 <- combMat(5, 1, 2) %*% combMat(6, 1, 2) combiM2 combiPlot(ex4.1, MclustOutput$z, combiM2) title("Combine 1, 2 and then 1 and 2 again") plot(0,0,type="n", xlab = "", ylab = "", axes = FALSE) legend("center", legend = 1:6, col = mclust.options("classPlotColors"), pch = mclust.options("classPlotSymbols"), title = "Class labels:")data(Baudry_etal_2010_JCGS_examples) MclustOutput <- Mclust(ex4.1) MclustOutput$G # Mclust/BIC selected 6 classes par(mfrow=c(2,2)) combiM0 <- diag(6) # is the identity matrix # no merging: plot the initial solution, given by z combiPlot(ex4.1, MclustOutput$z, combiM0, cex = 3) title("No combining") combiM1 <- combMat(6, 1, 2) # let's merge classes labeled 1 and 2 combiM1 combiPlot(ex4.1, MclustOutput$z, combiM1) title("Combine 1 and 2") # let's merge classes labeled 1 and 2, and then components labeled (in this # new 5-classes combined solution) 1 and 2 combiM2 <- combMat(5, 1, 2) %*% combMat(6, 1, 2) combiM2 combiPlot(ex4.1, MclustOutput$z, combiM2) title("Combine 1, 2 and then 1 and 2 again") plot(0,0,type="n", xlab = "", ylab = "", axes = FALSE) legend("center", legend = 1:6, col = mclust.options("classPlotColors"), pch = mclust.options("classPlotSymbols"), title = "Class labels:")
The method implemented in clustCombi can be used for combining Gaussian mixture components for clustering. This provides a hierarchical structure which can be graphically represented as a tree.
combiTree(object, type = c("triangle", "rectangle"), yaxis = c("entropy", "step"), edgePar = list(col = "darkgray", lwd = 2), ...)combiTree(object, type = c("triangle", "rectangle"), yaxis = c("entropy", "step"), edgePar = list(col = "darkgray", lwd = 2), ...)
object |
An object of class |
type |
A string specifying the dendrogram's type. Possible values are |
yaxis |
A string specifying the quantity used to draw the vertical axis. Possible values are |
edgePar |
A list of plotting parameters. See |
... |
Further arguments passed to or from other methods. |
The function always draw a tree and invisibly returns an object of class 'dendrogram' for fine tuning.
L. Scrucca
data(Baudry_etal_2010_JCGS_examples) output <- clustCombi(data = ex4.1) combiTree(output) combiTree(output, type = "rectangle") combiTree(output, yaxis = "step") combiTree(output, type = "rectangle", yaxis = "step")data(Baudry_etal_2010_JCGS_examples) output <- clustCombi(data = ex4.1) combiTree(output) combiTree(output, type = "rectangle") combiTree(output, yaxis = "step") combiTree(output, type = "rectangle", yaxis = "step")
Create a combining matrix
combMat(K, l1, l2)combMat(K, l1, l2)
K |
The original number of classes: the matrix will define a combining from K to (K-1) classes. |
l1 |
Label of one of the two classes to be combined. |
l2 |
Label of the other class to be combined. |
If z is a vector (length K) whose kth entry is the probability that an observation belongs to the kth class in a K-classes classification, then combiM %*% z is the vector (length K-1) whose kth entry is the probability that the observation belongs to the kth class in the K-1-classes classification obtained by merging classes l1 and l2 in the initial classification.
J.-P. Baudry, A. E. Raftery, L. Scrucca
Plots coordinate projections given multidimensional data and parameters of an MVN mixture model for the data.
coordProj(data, dimens = c(1,2), parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "error", "uncertainty"), addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"), symbols = NULL, colors = NULL, scale = FALSE, xlim = NULL, ylim = NULL, cex = 1, PCH = ".", main = FALSE, ...)coordProj(data, dimens = c(1,2), parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "error", "uncertainty"), addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"), symbols = NULL, colors = NULL, scale = FALSE, xlim = NULL, ylim = NULL, cex = 1, PCH = ".", main = FALSE, ...)
data |
A numeric matrix or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
dimens |
A vector of length 2 giving the integer dimensions of the
desired coordinate projections. The default is
|
parameters |
A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows:
|
z |
A matrix in which the |
classification |
A numeric or character vector representing a classification of
observations (rows) of |
truth |
A numeric or character vector giving a known
classification of each data point.
If |
uncertainty |
A numeric vector of values in (0,1) giving the
uncertainty of each data point. If present argument |
what |
Choose from one of the following three options: |
addEllipses |
A logical indicating whether or not to add ellipses with axes
corresponding to the within-cluster covariances in case of
|
fillEllipses |
A logical specifying whether or not to fill ellipses with transparent
colors when |
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique class in |
colors |
Either an integer or character vector assigning a color to each
unique class in |
scale |
A logical variable indicating whether or not the two chosen
dimensions should be plotted on the same scale, and
thus preserve the shape of the distribution.
Default: |
xlim, ylim
|
Arguments specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots. |
cex |
A numerical value specifying the size of the plotting symbols. The default value is 1. |
PCH |
An argument specifying the symbol to be used when a classification has not been specified for the data. The default value is a small dot ".". |
main |
A logical variable or |
... |
Other graphics parameters. |
A plot showing a two-dimensional coordinate projection of the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.
clPairs,
randProj,
mclust2Dplot,
mclust.options
est <- meVVV(iris[,-5], unmap(iris[,5])) par(pty = "s", mfrow = c(1,1)) coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z, what = "classification", main = TRUE) coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z, truth = iris[,5], what = "error", main = TRUE) coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z, what = "uncertainty", main = TRUE)est <- meVVV(iris[,-5], unmap(iris[,5])) par(pty = "s", mfrow = c(1,1)) coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z, what = "classification", main = TRUE) coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z, truth = iris[,5], what = "error", main = TRUE) coordProj(iris[,-5], dimens=c(2,3), parameters = est$parameters, z = est$z, what = "uncertainty", main = TRUE)
Compute efficiently (via Fortran code) the means, covariance and scattering matrices conditioning on a weighted or indicator matrix
covw(X, Z, normalize = TRUE)covw(X, Z, normalize = TRUE)
X |
A |
Z |
A |
normalize |
A logical indicating if rows of |
A list with the following components:
mean |
A |
S |
A |
W |
A |
M. Fop and L. Scrucca
# Z as an indicator matrix X <- iris[,1:4] Z <- unmap(iris$Species) str(covw(X, Z)) # Z as a matrix of weights mod <- Mclust(X, G = 3, modelNames = "VVV") str(covw(X, mod$z))# Z as an indicator matrix X <- iris[,1:4] Z <- unmap(iris$Species) str(covw(X, Z)) # Z as a matrix of weights mod <- Mclust(X, G = 3, modelNames = "VVV") str(covw(X, mod$z))
Compute the discriminant coordinates or crimcoords obtained by projecting the observed data from multiple groups onto the discriminant subspace. The optimal projection subspace is given by the linear transformation of the original variables that maximizes the ratio of the between-groups covariance (which represents groups separation) to the pooled within-group covariance (which represents within-group dispersion).
crimcoords(data, classification, numdir = NULL, unbiased = FALSE, ...) ## S3 method for class 'crimcoords' summary(object, numdir, ...) ## S3 method for class 'crimcoords' plot(x, ...)crimcoords(data, classification, numdir = NULL, unbiased = FALSE, ...) ## S3 method for class 'crimcoords' summary(object, numdir, ...) ## S3 method for class 'crimcoords' plot(x, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
classification |
A vector (numerical, character string, or factor) giving the groups classification (either the known class labels or the estimated clusters) for the observed data. |
numdir |
An integer value specifying the number of directions of the discriminant subspace to return. If not provided, the maximal number of directions are returned (which is given by the number of non-null eigenvalues, the minimum among the number of variables and the number of groups minus one). However, since the effectiveness of the discriminant coordinates in highlighting the separation of groups is decreasing, it might be useful to provide a smaller value, say 2 or 3. |
unbiased |
A logical specifying if unbiased estimates should be used for the
between-groups and within-groups covariances. By default
|
object, x
|
An object of class |
... |
further arguments passed to or from other methods. |
A list of class crimcoords with the following components:
means |
A matrix of within-groups means. |
B |
The between-groups covariance matrix. |
W |
The pooled within-groups covariance matrix. |
evalues |
A vector of eigenvalues. |
basis |
A matrix of eigenvectors specifying the basis of the discriminant subspace. |
projection |
A matrix of projected data points onto the discriminant subspace. |
classification |
A vector giving the groups classification. |
Luca Scrucca [email protected]
Gnanadesikan, R. (1977) Methods for Statistical Data Analysis of Multivariate Observations. John Wiley 1& Sons, Sec. 4.2.
Flury, B. (1997) A First Course in Multivariate Statistics. Springer, Sec. 7.3.
# discriminant coordinates for the iris data using known classes data("iris") CRIMCOORDS = crimcoords(iris[,-5], iris$Species) summary(CRIMCOORDS) plot(CRIMCOORDS) # banknote data data("banknote") # discriminant coordinate on known classes CRIMCOORDS = crimcoords(banknote[,-1], banknote$Status) summary(CRIMCOORDS) plot(CRIMCOORDS) # discriminant coordinates on estimated clusters mod = Mclust(banknote[,-1]) CRIMCOORDS = crimcoords(banknote[,-1], mod$classification) summary(CRIMCOORDS) plot(CRIMCOORDS) plot(CRIMCOORDS$projection, type = "n") text(CRIMCOORDS$projection, cex = 0.8, labels = strtrim(banknote$Status, 2), col = mclust.options("classPlotColors")[1:mod$G][mod$classification])# discriminant coordinates for the iris data using known classes data("iris") CRIMCOORDS = crimcoords(iris[,-5], iris$Species) summary(CRIMCOORDS) plot(CRIMCOORDS) # banknote data data("banknote") # discriminant coordinate on known classes CRIMCOORDS = crimcoords(banknote[,-1], banknote$Status) summary(CRIMCOORDS) plot(CRIMCOORDS) # discriminant coordinates on estimated clusters mod = Mclust(banknote[,-1]) CRIMCOORDS = crimcoords(banknote[,-1], mod$classification) summary(CRIMCOORDS) plot(CRIMCOORDS) plot(CRIMCOORDS$projection, type = "n") text(CRIMCOORDS$projection, cex = 0.8, labels = strtrim(banknote$Status, 2), col = mclust.options("classPlotColors")[1:mod$G][mod$classification])
A 500 by 3 matrix in which the first column is the classification and the remaining columns are two data from a simulation of two crossed elliptical Gaussians.
data(cross)data(cross)
# This dataset was created as follows n <- 250 set.seed(0) cross <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)), matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1]) cross <- cbind(c(rep(1,n),rep(2,n)), cross)# This dataset was created as follows n <- 250 set.seed(0) cross <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)), matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1]) cross <- cbind(c(rep(1,n),rep(2,n)), cross)
V-fold cross-validation for classification models based on Gaussian finite mixture modelling.
cvMclustDA(object, nfold = 10, prop = object$prop, verbose = interactive(), ...)cvMclustDA(object, nfold = 10, prop = object$prop, verbose = interactive(), ...)
object |
An object of class |
nfold |
An integer specifying the number of folds (by defaul 10-fold CV is used). |
prop |
A vector of class prior probabilities, which if not provided default to the class proportions in the training data. |
verbose |
A logical controlling if a text progress bar is displayed during
the cross-validation procedure. By default is |
... |
Further arguments passed to or from other methods. |
The function implements V-fold cross-validation for classification
models fitted by MclustDA.
Classification error and Brier score are the metrics returned, but other
metrics can be computed using the output returned by this function
(see Examples section below).
The function returns a list with the following components:
classification |
a factor of cross-validated class labels. |
z |
a matrix containing the cross-validated probabilites for class assignment. |
ce |
the cross-validation classification error. |
se.ce |
the standard error of the cross-validated classification error. |
brier |
the cross-validation Brier score. |
se.brier |
the standard error of the cross-validated Brier score. |
Luca Scrucca
MclustDA,
predict.MclustDA,
classError,
BrierScore
# Iris data Class <- iris$Species X <- iris[,1:4] ## EDDA model with common covariance (essentially equivalent to linear discriminant analysis) irisEDDA <- MclustDA(X, Class, modelType = "EDDA", modelNames = "EEE") cv <- cvMclustDA(irisEDDA) # 10-fold CV (default) str(cv) cv <- cvMclustDA(irisEDDA, nfold = length(Class)) # LOO-CV str(cv) ## MclustDA model selected by BIC irisMclustDA <- MclustDA(X, Class) cv <- cvMclustDA(irisMclustDA) # 10-fold CV (default) str(cv) # Banknote data data("banknote") Class <- banknote$Status X <- banknote[,2:7] ## EDDA model selected by BIC banknoteEDDA <- MclustDA(X, Class, modelType = "EDDA") cv <- cvMclustDA(banknoteEDDA) # 10-fold CV (default) str(cv) (ConfusionMatrix <- table(Pred = cv$classification, Class)) TP <- ConfusionMatrix[1,1] FP <- ConfusionMatrix[1,2] FN <- ConfusionMatrix[2,1] TN <- ConfusionMatrix[2,2] (Sensitivity <- TP/(TP+FN)) (Specificity <- TN/(FP+TN))# Iris data Class <- iris$Species X <- iris[,1:4] ## EDDA model with common covariance (essentially equivalent to linear discriminant analysis) irisEDDA <- MclustDA(X, Class, modelType = "EDDA", modelNames = "EEE") cv <- cvMclustDA(irisEDDA) # 10-fold CV (default) str(cv) cv <- cvMclustDA(irisEDDA, nfold = length(Class)) # LOO-CV str(cv) ## MclustDA model selected by BIC irisMclustDA <- MclustDA(X, Class) cv <- cvMclustDA(irisMclustDA) # 10-fold CV (default) str(cv) # Banknote data data("banknote") Class <- banknote$Status X <- banknote[,2:7] ## EDDA model selected by BIC banknoteEDDA <- MclustDA(X, Class, modelType = "EDDA") cv <- cvMclustDA(banknoteEDDA) # 10-fold CV (default) str(cv) (ConfusionMatrix <- table(Pred = cv$classification, Class)) TP <- ConfusionMatrix[1,1] FP <- ConfusionMatrix[1,2] FN <- ConfusionMatrix[2,1] TN <- ConfusionMatrix[2,2] (Sensitivity <- TP/(TP+FN)) (Specificity <- TN/(FP+TN))
Converts covariances from a parameterization by eigenvalue decomposition or cholesky factorization to representation as a 3-D array.
decomp2sigma(d, G, scale, shape, orientation, ...)decomp2sigma(d, G, scale, shape, orientation, ...)
d |
The dimension of the data. |
G |
The number of components in the mixture model. |
scale |
Either a G-vector giving the scale of the covariance (the dth root of its determinant) for each component in the mixture model, or a single numeric value if the scale is the same for each component. |
shape |
Either a G by d matrix in which the kth column is the shape of the covariance matrix (normalized to have determinant 1) for the kth component, or a d-vector giving a common shape for all components. |
orientation |
Either a d by d by G array whose |
... |
Catches unused arguments from an indirect or list call via |
A 3-D array whose [,,k]th component is the
covariance matrix of the kth component in an MVN mixture model.
meEst <- meVEV(iris[,-5], unmap(iris[,5])) names(meEst) meEst$parameters$variance dec <- meEst$parameters$variance decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale, orientation = dec$orientation) do.call("decomp2sigma", dec) ## alternative callmeEst <- meVEV(iris[,-5], unmap(iris[,5])) names(meEst) meEst$parameters$variance dec <- meEst$parameters$variance decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale, orientation = dec$orientation) do.call("decomp2sigma", dec) ## alternative call
Default conjugate prior specification for Gaussian mixtures.
defaultPrior(data, G, modelName, ...)defaultPrior(data, G, modelName, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
G |
The number of mixture components. |
modelName |
A character string indicating the model: |
... |
One or more of the following:
|
defaultPrior is a function whose default is to output the
default prior specification for EM within MCLUST.
Furthermore, defaultPrior can be used as a template to specify
alternative parameters for a conjugate prior.
A list giving the prior degrees of freedom, scale, shrinkage, and mean.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley and A. E. Raftery (2005, revised 2009). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.
C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.
mclustBIC,
me,
mstep,
priorControl
# default prior irisBIC <- mclustBIC(iris[,-5], prior = priorControl()) summary(irisBIC, iris[,-5]) # equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName = "defaultPrior")) summary(irisBIC, iris[,-5]) # no prior on the mean; default prior on variance irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0)) summary(irisBIC, iris[,-5]) # equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName="defaultPrior", shrinkage=0)) summary(irisBIC, iris[,-5]) defaultPrior( iris[-5], G = 3, modelName = "VVV")# default prior irisBIC <- mclustBIC(iris[,-5], prior = priorControl()) summary(irisBIC, iris[,-5]) # equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName = "defaultPrior")) summary(irisBIC, iris[,-5]) # no prior on the mean; default prior on variance irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0)) summary(irisBIC, iris[,-5]) # equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName="defaultPrior", shrinkage=0)) summary(irisBIC, iris[,-5]) defaultPrior( iris[-5], G = 3, modelName = "VVV")
Computes densities of observations in parameterized MVN mixtures.
dens(data, modelName, parameters, logarithm = FALSE, warn=NULL, ...)dens(data, modelName, parameters, logarithm = FALSE, warn=NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
parameters |
The parameters of the model:
|
logarithm |
A logical value indicating whether or not the logarithm of the component densities should be returned. The default is to return the component densities, obtained from the log component densities by exponentiation. |
warn |
A logical value indicating whether or not a warning should be issued
when computations fail. The default is |
... |
Catches unused arguments in indirect or list calls via |
A numeric vector whose ith component is the density of the
ith observation in data in the MVN mixture specified
by parameters.
cdens,
mclust.options,
do.call
faithfulModel <- Mclust(faithful) Dens <- dens(modelName = faithfulModel$modelName, data = faithful, parameters = faithfulModel$parameters) Dens ## alternative call do.call("dens", faithfulModel)faithfulModel <- Mclust(faithful) Dens <- dens(modelName = faithfulModel$modelName, data = faithful, parameters = faithfulModel$parameters) Dens ## alternative call do.call("dens", faithfulModel)
Produces a density estimate for each data point using a Gaussian finite
mixture model from Mclust.
densityMclust(data, ..., plot = TRUE)densityMclust(data, ..., plot = TRUE)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
... |
Additional arguments for the |
plot |
A logical value specifying if the estimated density should be
plotted. For more contols on the resulting graph see the associated
|
An object of class densityMclust, which inherits from
Mclust. This contains all the components described in
Mclust and the additional element:
density |
The density evaluated at the input |
Revised version by Luca Scrucca based on the original code by C. Fraley and A.E. Raftery.
Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.
plot.densityMclust,
Mclust,
summary.Mclust,
predict.densityMclust.
dens <- densityMclust(faithful$waiting) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "BIC", legendArgs = list(x = "topright")) plot(dens, what = "density", data = faithful$waiting) dens <- densityMclust(faithful, modelNames = "EEE", G = 3, plot = FALSE) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "density", data = faithful, drawlabels = FALSE, points.pch = 20) plot(dens, what = "density", type = "hdr") plot(dens, what = "density", type = "hdr", prob = c(0.1, 0.9)) plot(dens, what = "density", type = "hdr", data = faithful) plot(dens, what = "density", type = "persp") dens <- densityMclust(iris[,1:4], G = 2) summary(dens, parameters = TRUE) plot(dens, what = "density", data = iris[,1:4], col = "slategrey", drawlabels = FALSE, nlevels = 7) plot(dens, what = "density", type = "hdr", data = iris[,1:4]) plot(dens, what = "density", type = "persp", col = grey(0.9))dens <- densityMclust(faithful$waiting) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "BIC", legendArgs = list(x = "topright")) plot(dens, what = "density", data = faithful$waiting) dens <- densityMclust(faithful, modelNames = "EEE", G = 3, plot = FALSE) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "density", data = faithful, drawlabels = FALSE, points.pch = 20) plot(dens, what = "density", type = "hdr") plot(dens, what = "density", type = "hdr", prob = c(0.1, 0.9)) plot(dens, what = "density", type = "hdr", data = faithful) plot(dens, what = "density", type = "persp") dens <- densityMclust(iris[,1:4], G = 2) summary(dens, parameters = TRUE) plot(dens, what = "density", data = iris[,1:4], col = "slategrey", drawlabels = FALSE, nlevels = 7) plot(dens, what = "density", type = "hdr", data = iris[,1:4]) plot(dens, what = "density", type = "persp", col = grey(0.9))
mclustDensity estimationDiagnostic plots for density estimation. Only available for the one-dimensional case.
densityMclust.diagnostic(object, type = c("cdf", "qq"), col = c("black", "black"), lwd = c(2,1), lty = c(1,1), legend = TRUE, grid = TRUE, ...)densityMclust.diagnostic(object, type = c("cdf", "qq"), col = c("black", "black"), lwd = c(2,1), lty = c(1,1), legend = TRUE, grid = TRUE, ...)
object |
An object of class |
type |
The type of graph requested:
|
col |
A pair of values for the color to be used for plotting, respectively, the estimated CDF and the empirical cdf. |
lwd |
A pair of values for the line width to be used for plotting, respectively, the estimated CDF and the empirical cdf. |
lty |
A pair of values for the line type to be used for plotting, respectively, the estimated CDF and the empirical cdf. |
legend |
A logical indicating if a legend must be added to the plot of fitted CDF vs the empirical CDF. |
grid |
A logical indicating if a |
... |
Additional arguments. |
The two diagnostic plots for density estimation in the one-dimensional case are discussed in Loader (1999, pp- 87-90).
Luca Scrucca
Loader C. (1999), Local Regression and Likelihood. New York, Springer.
Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/
densityMclust,
plot.densityMclust.
x <- faithful$waiting dens <- densityMclust(x, plot = FALSE) plot(dens, x, what = "diagnostic") # or densityMclust.diagnostic(dens, type = "cdf") densityMclust.diagnostic(dens, type = "qq")x <- faithful$waiting dens <- densityMclust(x, plot = FALSE) plot(dens, x, what = "diagnostic") # or densityMclust.diagnostic(dens, type = "cdf") densityMclust.diagnostic(dens, type = "qq")
The data set contains three measurements made on 145 non-obese adult patients classified into three groups.
data(diabetes)data(diabetes)
A data frame with the following variables:
The type of diabete: Normal, Overt, and Chemical.
Area under plasma glucose curve after a three hour oral glucose tolerance test (OGTT).
Area under plasma insulin curve after a three hour oral glucose tolerance test (OGTT).
Steady state plasma glucose.
This dataset is flawed (compare with the reference) and it is provided here only for backward compatibility. A 5-variable version of the Reaven and Miller data is available in package rrcov. The glucose and sspg columns in this dataset are identical to the fpg and insulin columns, respectively in the rrcov version. The insulin column in this dataset differs from the glucose column in the rrcov version in one entry: observation 104 has the value 45 in the insulin column in this data, and 455 in the corresponding glucose column of the rrcov version.
Reaven, G. M. and Miller, R. G. (1979). An attempt to define the nature of chemical diabetes using a multidimensional analysis. Diabetologia 16:17-24.
Efficiently computes the density of observations for a generic multivariate Gaussian distribution.
dmvnorm(data, mean, sigma, log = FALSE)dmvnorm(data, mean, sigma, log = FALSE)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
mean |
A vector of means for each variable. |
sigma |
A positive definite covariance matrix. |
log |
A logical value indicating whether or not the logarithm of the densities should be returned. |
A numeric vector whose ith element gives the density of the
ith observation in data for the multivariate Gaussian
distribution with parameters mean and sigma.
# univariate ngrid <- 101 x <- seq(-5, 5, length = ngrid) dens <- dmvnorm(x, mean = 1, sigma = 5) plot(x, dens, type = "l") # bivariate ngrid <- 101 x1 <- x2 <- seq(-5, 5, length = ngrid) mu <- c(1,0) sigma <- matrix(c(1,0.5,0.5,2), 2, 2) dens <- dmvnorm(as.matrix(expand.grid(x1, x2)), mu, sigma) dens <- matrix(dens, ngrid, ngrid) image(x1, x2, dens) contour(x1, x2, dens, add = TRUE)# univariate ngrid <- 101 x <- seq(-5, 5, length = ngrid) dens <- dmvnorm(x, mean = 1, sigma = 5) plot(x, dens, type = "l") # bivariate ngrid <- 101 x1 <- x2 <- seq(-5, 5, length = ngrid) mu <- c(1,0) sigma <- matrix(c(1,0.5,0.5,2), 2, 2) dens <- dmvnorm(as.matrix(expand.grid(x1, x2)), mu, sigma) dens <- matrix(dens, ngrid, ngrid) image(x1, x2, dens) contour(x1, x2, dens, add = TRUE)
Duplicated data are grouped together to form a basic partition that can be used to start hierarchical agglomeration.
dupPartition(data)dupPartition(data)
data |
A numeric vector, matrix, or data frame of observations.
If a matrix or data frame, rows correspond to observations ( |
A vector of indices indicating the partition.
dupPartition(iris[,1:4]) dupPartition(iris) dupPartition(iris$Species)dupPartition(iris[,1:4]) dupPartition(iris) dupPartition(iris$Species)
Implements the EM algorithm for parameterized Gaussian mixture models, starting with the expectation step.
em(data, modelName, parameters, prior = NULL, control = emControl(), warn = NULL, ...)em(data, modelName, parameters, prior = NULL, control = emControl(), warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
parameters |
A names list giving the parameters of the model. The components are as follows:
|
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
control |
A list of control parameters for EM. The defaults are set by the call
|
warn |
A logical value indicating whether or not a warning should be issued
when computations fail. The default is |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
The number of mixture components. |
z |
A matrix whose |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
control |
The list of control parameters for EM used. |
prior |
The specification of a conjugate prior on the means and variances used,
|
Attributes: |
|
emE, ...,
emVVV,
estep,
me,
mstep,
mclust.options,
do.call
msEst <- mstep(modelName = "EEE", data = iris[,-5], z = unmap(iris[,5])) names(msEst) em(modelName = msEst$modelName, data = iris[,-5], parameters = msEst$parameters) do.call("em", c(list(data = iris[,-5]), msEst)) ## alternative callmsEst <- mstep(modelName = "EEE", data = iris[,-5], z = unmap(iris[,5])) names(msEst) em(modelName = msEst$modelName, data = iris[,-5], parameters = msEst$parameters) do.call("em", c(list(data = iris[,-5]), msEst)) ## alternative call
Supplies a list of values including tolerances for singularity and convergence assessment, for use functions involving EM within MCLUST.
emControl(eps, tol, itmax, equalPro)emControl(eps, tol, itmax, equalPro)
eps |
A scalar tolerance associated with deciding when to terminate
computations due to computational singularity in
covariances. Smaller values of |
tol |
A vector of length two giving relative convergence tolerances for the
log-likelihood and for parameter convergence in the inner loop for models
with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"), respectively.
The default is |
itmax |
A vector of length two giving integer limits on the number of EM
iterations and on the number of iterations in the inner loop for
models with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"),
respectively. The default is
|
equalPro |
Logical variable indicating whether or not the mixing proportions are
equal in the model. Default: |
emControl is provided for assigning values and defaults
for EM within MCLUST.
A named list in which the names are the names of the arguments and the values are the values supplied to the arguments.
em,
estep,
me,
mstep,
mclustBIC
irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6)) summary(irisBIC, iris[,-5])irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6)) summary(irisBIC, iris[,-5])
Implements the EM algorithm for a parameterized Gaussian mixture model, starting with the expectation step.
emE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emX(data, prior = NULL, warn = NULL, ...) emEII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emXII(data, prior = NULL, warn = NULL, ...) emXXI(data, prior = NULL, warn = NULL, ...) emXXX(data, prior = NULL, warn = NULL, ...)emE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emX(data, prior = NULL, warn = NULL, ...) emEII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVII(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVEI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVVI(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVEE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVVE(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVEV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emEVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emVVV(data, parameters, prior = NULL, control = emControl(), warn = NULL, ...) emXII(data, prior = NULL, warn = NULL, ...) emXXI(data, prior = NULL, warn = NULL, ...) emXXX(data, prior = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
parameters |
The parameters of the model:
|
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
warn |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is given in |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
z |
A matrix whose |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
|
me,
mstep,
mclustVariance,
mclust.options.
msEst <- mstepEEE(data = iris[,-5], z = unmap(iris[,5])) names(msEst) emEEE(data = iris[,-5], parameters = msEst$parameters)msEst <- mstepEEE(data = iris[,-5], z = unmap(iris[,5])) names(msEst) emEEE(data = iris[,-5], parameters = msEst$parameters)
Plot "entropy plots" to help select the number of classes from a hierarchy of combined clusterings.
entPlot(z, combiM, abc = c("standard", "normalized"), reg = 2, ...)entPlot(z, combiM, abc = c("standard", "normalized"), reg = 2, ...)
z |
A matrix whose |
combiM |
A list of "combining matrices" (as provided by |
abc |
Choose one or more of: "standard", "normalized", to specify whether the number of observations involved in each combining step should be taken into account to scale the plots or not. |
reg |
The number of parts of the piecewise linear regression for the entropy plots. Choose one or more of: 2 (for 1 change-point), 3 (for 2 change-points). |
... |
Other graphical arguments to be passed to the plot functions. |
Please see the article cited in the references for more details. A clear elbow in the "entropy plot" should suggest the user to consider the corresponding number(s) of class(es).
if abc = "standard", plots the entropy against the number of clusters and the difference between the entropy of successive combined solutions against the number of clusters.
if abc = "normalized", plots the entropy against the cumulated number of observations involved in the successive combining steps and the difference between the entropy of successive combined solutions divided by the number of observations involved in the corresponding combining step against the number of clusters.
J.-P. Baudry, A. E. Raftery, L. Scrucca
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
plot.clustCombi, combiPlot, clustCombi
data(Baudry_etal_2010_JCGS_examples) # run Mclust to get the MclustOutput output <- clustCombi(data = ex4.2, modelNames = "VII") entPlot(output$MclustOutput$z, output$combiM, reg = c(2,3)) # legend: in red, the single-change-point piecewise linear regression; # in blue, the two-change-point piecewise linear regression.data(Baudry_etal_2010_JCGS_examples) # run Mclust to get the MclustOutput output <- clustCombi(data = ex4.2, modelNames = "VII") entPlot(output$MclustOutput$z, output$combiM, reg = c(2,3)) # legend: in red, the single-change-point piecewise linear regression; # in blue, the two-change-point piecewise linear regression.
Draw error bars at x from upper to lower. If horizontal = FALSE (default)
bars are drawn vertically, otherwise horizontally.
errorBars(x, upper, lower, width = 0.1, code = 3, angle = 90, horizontal = FALSE, ...)errorBars(x, upper, lower, width = 0.1, code = 3, angle = 90, horizontal = FALSE, ...)
x |
A vector of values where the bars must be drawn. |
upper |
A vector of upper values where the bars must end. |
lower |
A vector of lower values where the bars must start. |
width |
A value specifying the width of the end-point segment. |
code |
An integer code specifying the kind of arrows to be drawn. For details see |
angle |
A value specifying the angle at the arrow edge. For details see |
horizontal |
A logical specifying if bars should be drawn vertically (default) or horizontally. |
... |
Further arguments are passed to |
par(mfrow=c(2,2)) # Create a simple example dataset x <- 1:5 n <- c(10, 15, 12, 6, 3) se <- c(1, 1.2, 2, 1, .5) # upper and lower bars b <- barplot(n, ylim = c(0, max(n)*1.5)) errorBars(b, lower = n-se, upper = n+se, lwd = 2, col = "red3") # one side bars b <- barplot(n, ylim = c(0, max(n)*1.5)) errorBars(b, lower = n, upper = n+se, lwd = 2, col = "red3", code = 1) # plot(x, n, ylim = c(0, max(n)*1.5), pch = 0) errorBars(x, lower = n-se, upper = n+se, lwd = 2, col = "red3") # dotchart(n, labels = x, pch = 19, xlim = c(0, max(n)*1.5)) errorBars(x, lower = n-se, upper = n+se, col = "red3", horizontal = TRUE)par(mfrow=c(2,2)) # Create a simple example dataset x <- 1:5 n <- c(10, 15, 12, 6, 3) se <- c(1, 1.2, 2, 1, .5) # upper and lower bars b <- barplot(n, ylim = c(0, max(n)*1.5)) errorBars(b, lower = n-se, upper = n+se, lwd = 2, col = "red3") # one side bars b <- barplot(n, ylim = c(0, max(n)*1.5)) errorBars(b, lower = n, upper = n+se, lwd = 2, col = "red3", code = 1) # plot(x, n, ylim = c(0, max(n)*1.5), pch = 0) errorBars(x, lower = n-se, upper = n+se, lwd = 2, col = "red3") # dotchart(n, labels = x, pch = 19, xlim = c(0, max(n)*1.5)) errorBars(x, lower = n-se, upper = n+se, col = "red3", horizontal = TRUE)
Implements the expectation step of EM algorithm for parameterized Gaussian mixture models.
estep(data, modelName, parameters, warn = NULL, ...)estep(data, modelName, parameters, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
parameters |
A names list giving the parameters of the model. The components are as follows:
|
warn |
A logical value indicating whether or not a warning should be issued
when computations fail. The default is |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
z |
A matrix whose |
parameters |
The input parameters. |
loglik |
The log-likelihood for the data in the mixture model. |
Attributes |
|
estepE, ...,
estepVVV,
em,
mstep,
mclust.options
mclustVariance
msEst <- mstep(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5])) names(msEst) estep(modelName = msEst$modelName, data = iris[,-5], parameters = msEst$parameters)msEst <- mstep(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5])) names(msEst) estep(modelName = msEst$modelName, data = iris[,-5], parameters = msEst$parameters)
Implements the expectation step in the EM algorithm for a parameterized Gaussian mixture model.
estepE(data, parameters, warn = NULL, ...) estepV(data, parameters, warn = NULL, ...) estepEII(data, parameters, warn = NULL, ...) estepVII(data, parameters, warn = NULL, ...) estepEEI(data, parameters, warn = NULL, ...) estepVEI(data, parameters, warn = NULL, ...) estepEVI(data, parameters, warn = NULL, ...) estepVVI(data, parameters, warn = NULL, ...) estepEEE(data, parameters, warn = NULL, ...) estepEEV(data, parameters, warn = NULL, ...) estepVEV(data, parameters, warn = NULL, ...) estepVVV(data, parameters, warn = NULL, ...) estepEVE(data, parameters, warn = NULL, ...) estepEVV(data, parameters, warn = NULL, ...) estepVEE(data, parameters, warn = NULL, ...) estepVVE(data, parameters, warn = NULL, ...)estepE(data, parameters, warn = NULL, ...) estepV(data, parameters, warn = NULL, ...) estepEII(data, parameters, warn = NULL, ...) estepVII(data, parameters, warn = NULL, ...) estepEEI(data, parameters, warn = NULL, ...) estepVEI(data, parameters, warn = NULL, ...) estepEVI(data, parameters, warn = NULL, ...) estepVVI(data, parameters, warn = NULL, ...) estepEEE(data, parameters, warn = NULL, ...) estepEEV(data, parameters, warn = NULL, ...) estepVEV(data, parameters, warn = NULL, ...) estepVVV(data, parameters, warn = NULL, ...) estepEVE(data, parameters, warn = NULL, ...) estepEVV(data, parameters, warn = NULL, ...) estepVEE(data, parameters, warn = NULL, ...) estepVVE(data, parameters, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
parameters |
The parameters of the model:
|
warn |
A logical value indicating whether or certain warnings should be issued.
The default is given by |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
Character string identifying the model. |
z |
A matrix whose |
parameters |
The input parameters. |
loglik |
The logliklihood for the data in the mixture model. |
Attribute |
|
estep,
em,
mstep,
do.call,
mclustVariance,
mclust.options.
msEst <- mstepEII(data = iris[,-5], z = unmap(iris[,5])) names(msEst) estepEII(data = iris[,-5], parameters = msEst$parameters)msEst <- mstepEII(data = iris[,-5], z = unmap(iris[,5])) names(msEst) estepEII(data = iris[,-5], parameters = msEst$parameters)
The data set contains unemployment rates for 31 European countries for the year 2014.
data(EuroUnemployment)data(EuroUnemployment)
A data frame with the following variables:
Total unemployment rate, i.e. percentage of unemployed persons aged 15-74 in the economically active population.
Youth unemployment rate, i.e. percentage of unemployed persons aged 15-24 in the economically active population.
Long-term unemployment rate, i.e. percentage of unemployed persons who have been unemployed for 12 months or more.
Dataset downloaded from EUROSTAT https://ec.europa.eu/eurostat.
Starting with the density estimate obtained from a fitted Gaussian finite mixture model, cluster cores are identified from the connected components at a given density level. Once cluster cores are identified, the remaining observations are allocated to those cluster cores for which the probability of cluster membership is the highest.
gmmhd(object, ngrid = min(round((log(nrow(data)))*10), nrow(data)), dr = list(d = 3, lambda = 1, cumEvalues = NULL, mindir = 2), classify = list(G = 1:5, modelNames = mclust.options("emModelNames")[-c(8, 10)]), ...) ## S3 method for class 'gmmhd' plot(x, what = c("mode", "cores", "clusters"), ...)gmmhd(object, ngrid = min(round((log(nrow(data)))*10), nrow(data)), dr = list(d = 3, lambda = 1, cumEvalues = NULL, mindir = 2), classify = list(G = 1:5, modelNames = mclust.options("emModelNames")[-c(8, 10)]), ...) ## S3 method for class 'gmmhd' plot(x, what = c("mode", "cores", "clusters"), ...)
object |
An object returned by |
ngrid |
An integer specifying the number of grid points used to compute the density levels. |
dr |
A list of parameters used in the dimension reduction step. |
classify |
A list of parameters used in the classification step. |
x |
An object of class |
what |
A string specifying the type of plot to be produced. See Examples section. |
... |
further arguments passed to or from other methods. |
Model-based clustering associates each component of a finite mixture distribution to a group or cluster. An underlying implicit assumption is that a one-to-one correspondence exists between mixture components and clusters. However, a single Gaussian density may not be sufficient, and two or more mixture components could be needed to reasonably approximate the distribution within a homogeneous group of observations.
This function implements the methodology proposed by Scrucca (2016) based on the identification of high density regions of the underlying density function. Starting with an estimated Gaussian finite mixture model, the corresponding density estimate is used to identify the cluster cores, i.e. those data points which form the core of the clusters.
These cluster cores are obtained from the connected components at a given density level . A mode function gives the number of connected components as the level is varied.
Once cluster cores are identified, the remaining observations are allocated to those cluster cores for which the probability of cluster membership is the highest.
The method usually improves the identification of non-Gaussian clusters compared to a fully parametric approach. Furthermore, it enables the identification of clusters which cannot be obtained by merging mixture components, and it can be straightforwardly extended to cases of higher dimensionality.
A list of class gmmhd with the following components:
Mclust |
The input object of class |
MclustDA |
An object of class |
MclustDR |
An object of class |
x |
The data used in the algorithm. This can be the input data or a projection if a preliminary dimension reduction step is performed. |
density |
The density estimated from the input Gaussian finite mixture model evaluated at the input data. |
con |
A list of connected components at each step. |
nc |
A vector giving the number of connected components (i.e. modes) at each step. |
pn |
Vector of values over a uniform grid of proportions of length |
qn |
Vector of density quantiles corresponding to proportions |
pc |
Vector of empirical proportions corresponding to quantiles |
clusterCores |
Vector of cluster cores numerical labels; |
clusterCores |
Vector of numerical labels giving the final clustering. |
numClusters |
An integer giving the number of clusters. |
Luca Scrucca [email protected]
Scrucca, L. (2016) Identifying connected components in Gaussian finite mixture models for clustering. Computational Statistics & Data Analysis, 93, 5-17.
data(faithful) mod <- Mclust(faithful) summary(mod) plot(as.densityMclust(mod), faithful, what = "density", points.pch = mclust.options("classPlotSymbols")[mod$classification], points.col = mclust.options("classPlotColors")[mod$classification]) GMMHD <- gmmhd(mod) summary(GMMHD) plot(GMMHD, what = "mode") plot(GMMHD, what = "cores") plot(GMMHD, what = "clusters")data(faithful) mod <- Mclust(faithful) summary(mod) plot(as.densityMclust(mod), faithful, what = "density", points.pch = mclust.options("classPlotSymbols")[mod$classification], points.col = mclust.options("classPlotColors")[mod$classification]) GMMHD <- gmmhd(mod) summary(GMMHD) plot(GMMHD, what = "mode") plot(GMMHD, what = "cores") plot(GMMHD, what = "clusters")
GvHD (Graft-versus-Host Disease) data of Brinkman et al. (2007). Two samples of this flow cytometry data, one from a patient with the GvHD, and the other from a control patient. The GvHD positive and control samples consist of 9083 and 6809 observations, respectively. Both samples include four biomarker variables, namely, CD4, CD8b, CD3, and CD8. The objective of the analysis is to identify CD3+ CD4+ CD8b+ cell sub-populations present in the GvHD positive sample.
A treatment of this data by combining mixtures is proposed in Baudry et al. (2010).
data(GvHD)data(GvHD)
GvHD.pos (positive patient) is a data frame with 9083 observations on the following 4 variables, which are biomarker measurements.
GvHD.control (control patient) is a data frame with 6809 observations on the following 4 variables, which are biomarker measurements.
R. R. Brinkman, M. Gasparetto, S.-J. J. Lee, A. J. Ribickas, J. Perkins, W. Janssen, R. Smiley and C. Smith (2007). High-content flow cytometry and temporal data analysis for defining a cellular signature of Graft-versus-Host Disease. Biology of Blood and Marrow Transplantation, 13: 691-700.
K. Lo, R. R. Brinkman, R. Gottardo (2008). Automated gating of flow cytometry data via robust model-based clustering. Cytometry A, 73: 321-332.
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
data(GvHD) dat <- GvHD.pos[1:500,] # only a few lines for a quick example output <- clustCombi(data = dat) output # is of class clustCombi # plot the hierarchy of combined solutions plot(output, what = "classification") # plot some "entropy plots" which may help one to select the number of classes plot(output, what = "entropy") # plot the tree structure obtained from combining mixture components plot(output, what = "tree")data(GvHD) dat <- GvHD.pos[1:500,] # only a few lines for a quick example output <- clustCombi(data = dat) output # is of class clustCombi # plot the hierarchy of combined solutions plot(output, what = "classification") # plot some "entropy plots" which may help one to select the number of classes plot(output, what = "entropy") # plot the tree structure obtained from combining mixture components plot(output, what = "tree")
Agglomerative hierarchical clustering based on maximum likelihood criteria for Gaussian mixture models parameterized by eigenvalue decomposition.
hc(data, modelName = "VVV", use = "VARS", partition = dupPartition(data), minclus = 1, ...) ## S3 method for class 'hc' as.hclust(x, ...)hc(data, modelName = "VVV", use = "VARS", partition = dupPartition(data), minclus = 1, ...) ## S3 method for class 'hc' as.hclust(x, ...)
data |
A numeric vector, matrix, or data frame of observations.
Categorical variables are not allowed.
If a matrix or data frame, rows correspond to observations ( |
modelName |
A character string indicating the model to be used in model-based agglomerative hierarchical clustering.
If |
use |
A character string specifying the type of input variables/data transformation to be used for model-based agglomerative hierarchical clustering.
If |
partition |
A numeric or character vector representing a partition of
observations (rows) of |
minclus |
A number indicating the number of clusters at which to stop the agglomeration. The default is to stop when all observations have been merged into a single cluster. |
... |
Arguments for the method-specific |
x |
An object of class |
Most models have memory usage of the order of the square of the
number groups in the initial partition for fast execution.
Some models, such as equal variance or "EEE",
do not admit a fast algorithm under the usual agglomerative
hierarchical clustering paradigm.
These use less memory but are much slower to execute.
The function hc() returns a numeric two-column matrix in which
the ith row gives the minimum index for observations in each of
the two clusters merged at the ith stage of agglomerative
hierarchical clustering. Several other informations are also returned
as attributes.
The method as.hclust.hc() can be used to convert the input
object from class 'hc' to class 'hclust'.
If modelName = "E" (univariate with equal variances) or
modelName = "EII" (multivariate with equal spherical
covariances), then underlying model is the same as that for
Ward's method for hierarchical clustering.
Banfield J. D. and Raftery A. E. (1993). Model-based Gaussian and non-Gaussian Clustering. Biometrics, 49:803-821.
Fraley C. (1998). Algorithms for model-based Gaussian hierarchical clustering. SIAM Journal on Scientific Computing, 20:270-281.
Fraley C. and Raftery A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97:611-631.
Scrucca L. and Raftery A. E. (2015). Improved initialisation of model-based clustering using Gaussian hierarchical partitions. Advances in Data Analysis and Classification, 9/4:447-460.
hcE, ...,
hcVVV,
plot.hc,
hclass,
mclust.options
hcTree <- hc(modelName = "VVV", data = iris[,-5]) hcTree cl <- hclass(hcTree,c(2,3)) table(cl[,"2"]) table(cl[,"3"]) clPairs(iris[,-5], classification = cl[,"2"]) clPairs(iris[,-5], classification = cl[,"3"])hcTree <- hc(modelName = "VVV", data = iris[,-5]) hcTree cl <- hclass(hcTree,c(2,3)) table(cl[,"2"]) table(cl[,"3"]) clPairs(iris[,-5], classification = cl[,"2"]) clPairs(iris[,-5], classification = cl[,"3"])
Agglomerative hierarchical clustering based on maximum likelihood for a Gaussian mixture model parameterized by eigenvalue decomposition.
hcE(data, partition = NULL, minclus=1, ...) hcV(data, partition = NULL, minclus = 1, alpha = 1, ...) hcEII(data, partition = NULL, minclus = 1, ...) hcVII(data, partition = NULL, minclus = 1, alpha = 1, ...) hcEEE(data, partition = NULL, minclus = 1, ...) hcVVV(data, partition = NULL, minclus = 1, alpha = 1, beta = 1, ...)hcE(data, partition = NULL, minclus=1, ...) hcV(data, partition = NULL, minclus = 1, alpha = 1, ...) hcEII(data, partition = NULL, minclus = 1, ...) hcVII(data, partition = NULL, minclus = 1, alpha = 1, ...) hcEEE(data, partition = NULL, minclus = 1, ...) hcVVV(data, partition = NULL, minclus = 1, alpha = 1, beta = 1, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
partition |
A numeric or character vector representing a partition of
observations (rows) of |
minclus |
A number indicating the number of clusters at which to stop the agglomeration. The default is to stop when all observations have been merged into a single cluster. |
alpha, beta
|
Additional tuning parameters needed for initializatiion in some models. For details, see Fraley 1998. The defaults provided are usually adequate. |
... |
Catch unused arguments from a |
Most models have memory usage of the order of the square of the
number groups in the initial partition for fast execution.
Some models, such as equal variance or "EEE",
do not admit a fast algorithm under the usual agglomerative
hierachical clustering paradigm.
These use less memory but are much slower to execute.
A numeric two-column matrix in which the ith row gives the minimum index for observations in each of the two clusters merged at the ith stage of agglomerative hierarchical clustering.
J. D. Banfield and A. E. Raftery (1993). Model-based Gaussian and non-Gaussian Clustering. Biometrics 49:803-821.
C. Fraley (1998). Algorithms for model-based Gaussian hierarchical clustering. SIAM Journal on Scientific Computing 20:270-281.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
hcTree <- hcEII(data = iris[,-5]) cl <- hclass(hcTree,c(2,3)) par(pty = "s", mfrow = c(1,1)) clPairs(iris[,-5],cl=cl[,"2"]) clPairs(iris[,-5],cl=cl[,"3"]) par(mfrow = c(1,2)) dimens <- c(1,2) coordProj(iris[,-5], classification=cl[,"2"], dimens=dimens) coordProj(iris[,-5], classification=cl[,"3"], dimens=dimens)hcTree <- hcEII(data = iris[,-5]) cl <- hclass(hcTree,c(2,3)) par(pty = "s", mfrow = c(1,1)) clPairs(iris[,-5],cl=cl[,"2"]) clPairs(iris[,-5],cl=cl[,"3"]) par(mfrow = c(1,2)) dimens <- c(1,2) coordProj(iris[,-5], classification=cl[,"2"], dimens=dimens) coordProj(iris[,-5], classification=cl[,"3"], dimens=dimens)
Determines the classifications corresponding to different numbers of groups given merge pairs from hierarchical agglomeration.
hclass(hcPairs, G)hclass(hcPairs, G)
hcPairs |
A numeric two-column matrix in which the ith row gives the minimum index for observations in each of the two clusters merged at the ith stage of agglomerative hierarchical clustering. |
G |
An integer or vector of integers giving the number of clusters for which the corresponding classfications are wanted. |
A matrix with length(G) columns, each column
corresponding to a classification. Columns are indexed by the character
representation of the integers in G.
hcTree <- hc(modelName="VVV", data = iris[,-5]) cl <- hclass(hcTree,c(2,3)) par(pty = "s", mfrow = c(1,1)) clPairs(iris[,-5],cl=cl[,"2"]) clPairs(iris[,-5],cl=cl[,"3"])hcTree <- hc(modelName="VVV", data = iris[,-5]) cl <- hclass(hcTree,c(2,3)) par(pty = "s", mfrow = c(1,1)) clPairs(iris[,-5],cl=cl[,"2"]) clPairs(iris[,-5],cl=cl[,"3"])
Create a hierarchical structure using a random hierarchical partition of the data.
hcRandomPairs(data, seed = NULL, ...)hcRandomPairs(data, seed = NULL, ...)
data |
A numeric matrix or data frame of observations. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
seed |
Optional single value, interpreted as an integer, specifying the seed for random partition. |
... |
Catches unused arguments in indirect or list calls via |
A numeric two-column matrix in which the ith row gives the minimum index for observations in each of the two clusters merged at the ith stage of a random agglomerative hierarchical clustering.
data <- iris[,1:4] randPairs <- hcRandomPairs(data) str(randPairs) # start model-based clustering from a random partition mod <- Mclust(data, initialization = list(hcPairs = randPairs)) summary(mod)data <- iris[,1:4] randPairs <- hcRandomPairs(data) str(randPairs) # start model-based clustering from a random partition mod <- Mclust(data, initialization = list(hcPairs = randPairs)) summary(mod)
Compute the levels of Highest Density Regions (HDRs) for any density and probability levels.
hdrlevels(density, prob)hdrlevels(density, prob)
density |
A vector of density values computed on a set of (observed) evaluation points. |
prob |
A vector of probability levels in the range |
From Hyndman (1996), let be the density function of a random
variable . Then the HDR is the subset
of the sample space of such that
where is the largest constant such that
The function returns a vector of density values corresponding to HDRs at given probability levels.
L. Scrucca
Rob J. Hyndman (1996) Computing and Graphing Highest Density Regions. The American Statistician, 50(2):120-126.
# Example: univariate Gaussian x <- rnorm(1000) f <- dnorm(x) a <- c(0.5, 0.25, 0.1) (f_a <- hdrlevels(f, prob = 1-a)) plot(x, f) abline(h = f_a, lty = 2) text(max(x), f_a, labels = paste0("f_", a), pos = 3) mean(f > f_a[1]) range(x[which(f > f_a[1])]) qnorm(1-a[1]/2) mean(f > f_a[2]) range(x[which(f > f_a[2])]) qnorm(1-a[2]/2) mean(f > f_a[3]) range(x[which(f > f_a[3])]) qnorm(1-a[3]/2) # Example 2: univariate Gaussian mixture set.seed(1) cl <- sample(1:2, size = 1000, prob = c(0.7, 0.3), replace = TRUE) x <- ifelse(cl == 1, rnorm(1000, mean = 0, sd = 1), rnorm(1000, mean = 4, sd = 1)) f <- 0.7*dnorm(x, mean = 0, sd = 1) + 0.3*dnorm(x, mean = 4, sd = 1) a <- 0.25 (f_a <- hdrlevels(f, prob = 1-a)) plot(x, f) abline(h = f_a, lty = 2) text(max(x), f_a, labels = paste0("f_", a), pos = 3) mean(f > f_a) # find the regions of HDR ord <- order(x) f <- f[ord] x <- x[ord] x_a <- x[f > f_a] j <- which.max(diff(x_a)) region1 <- x_a[c(1,j)] region2 <- x_a[c(j+1,length(x_a))] plot(x, f, type = "l") abline(h = f_a, lty = 2) abline(v = region1, lty = 3, col = 2) abline(v = region2, lty = 3, col = 3)# Example: univariate Gaussian x <- rnorm(1000) f <- dnorm(x) a <- c(0.5, 0.25, 0.1) (f_a <- hdrlevels(f, prob = 1-a)) plot(x, f) abline(h = f_a, lty = 2) text(max(x), f_a, labels = paste0("f_", a), pos = 3) mean(f > f_a[1]) range(x[which(f > f_a[1])]) qnorm(1-a[1]/2) mean(f > f_a[2]) range(x[which(f > f_a[2])]) qnorm(1-a[2]/2) mean(f > f_a[3]) range(x[which(f > f_a[3])]) qnorm(1-a[3]/2) # Example 2: univariate Gaussian mixture set.seed(1) cl <- sample(1:2, size = 1000, prob = c(0.7, 0.3), replace = TRUE) x <- ifelse(cl == 1, rnorm(1000, mean = 0, sd = 1), rnorm(1000, mean = 4, sd = 1)) f <- 0.7*dnorm(x, mean = 0, sd = 1) + 0.3*dnorm(x, mean = 4, sd = 1) a <- 0.25 (f_a <- hdrlevels(f, prob = 1-a)) plot(x, f) abline(h = f_a, lty = 2) text(max(x), f_a, labels = paste0("f_", a), pos = 3) mean(f > f_a) # find the regions of HDR ord <- order(x) f <- f[ord] x <- x[ord] x_a <- x[f > f_a] j <- which.max(diff(x_a)) region1 <- x_a[c(1,j)] region2 <- x_a[c(j+1,length(x_a))] plot(x, f, type = "l") abline(h = f_a, lty = 2) abline(v = region1, lty = 3, col = 2) abline(v = region2, lty = 3, col = 3)
Computes a simple approximation to the hypervolume of a multivariate data set.
hypvol(data, reciprocal=FALSE)hypvol(data, reciprocal=FALSE)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
reciprocal |
A logical variable indicating whether or not the reciprocal hypervolume is desired rather than the hypervolume itself. The default is to return the hypervolume. |
Returns the minimum of the hypervolume computed from simple variable bounds
and that computed from variable bounds of the principal component scores.
Used for the default hypervolume parameter for the noise
component when observations are designated as noise in Mclust
and mclustBIC.
A. Dasgupta and A. E. Raftery (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association 93:294-302.
C. Fraley and A.E. Raftery (1998). Computer Journal 41:578-588.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
hypvol(iris[,-5])hypvol(iris[,-5])
Computes the ICL (Integrated Complete-data Likelihood) for criterion for a Gaussian Mixture Model fitted by Mclust.
icl(object, ...)icl(object, ...)
object |
An object of class |
... |
Further arguments passed to or from other methods. |
The ICL for the given input MCLUST model.
Biernacki, C., Celeux, G., Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Analysis and Machine Intelligence, 22 (7), 719-725.
Mclust,
mclustBIC,
mclustICL,
bic.
mod <- Mclust(iris[,1:4]) icl(mod)mod <- Mclust(iris[,1:4]) icl(mod)
Imputes missing data using the mix package.
imputeData(data, categorical = NULL, seed = NULL, verbose = interactive())imputeData(data, categorical = NULL, seed = NULL, verbose = interactive())
data |
A numeric vector, matrix, or data frame of observations containing missing values. Categorical variables are allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
categorical |
A logical vectors whose ith entry is |
seed |
A seed for the function |
verbose |
A logical, if |
A dataset of the same dimensions as data with missing values
filled in.
Schafer J. L. (1997). Analysis of Imcomplete Multivariate Data, Chapman and Hall.
# Note that package 'mix' must be installed data(stlouis, package = "mix") # impute the continuos variables in the stlouis data stlimp <- imputeData(stlouis[,-(1:3)]) # plot imputed values imputePairs(stlouis[,-(1:3)], stlimp)# Note that package 'mix' must be installed data(stlouis, package = "mix") # impute the continuos variables in the stlouis data stlimp <- imputeData(stlouis[,-(1:3)]) # plot imputed values imputePairs(stlouis[,-(1:3)], stlimp)
Creates a scatter plot for each pair of variables in given data, allowing display of imputations for missing values in different colors and symbols than non missing values.
imputePairs(data, dataImp, symbols = c(1,16), colors = c("black", "red"), labels, panel = points, ..., lower.panel = panel, upper.panel = panel, diag.panel = NULL, text.panel = textPanel, label.pos = 0.5 + has.diag/3, cex.labels = NULL, font.labels = 1, row1attop = TRUE, gap = 0.2)imputePairs(data, dataImp, symbols = c(1,16), colors = c("black", "red"), labels, panel = points, ..., lower.panel = panel, upper.panel = panel, diag.panel = NULL, text.panel = textPanel, label.pos = 0.5 + has.diag/3, cex.labels = NULL, font.labels = 1, row1attop = TRUE, gap = 0.2)
data |
A numeric vector, matrix, or data frame of observations containing missing values. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
dataImp |
The dataset |
symbols |
Either an integer or character vector assigning plotting symbols to the nonmissing data and impued values, respectively. The default is a closed circle for the nonmissing data and an open circle for the imputed values. |
colors |
Either an integer or character vector assigning colors to the nonmissing data and impued values, respectively. The default is black for the nonmissing data and red for the imputed values. |
labels |
As in function |
panel |
As in function |
... |
As in function |
lower.panel |
As in function |
upper.panel |
As in function |
diag.panel |
As in function |
text.panel |
As in function |
label.pos |
As in function |
cex.labels |
As in function |
font.labels |
As in function |
row1attop |
As in function |
gap |
As in function |
A pairs plot displaying the location of missing and nonmissing values.
Schafer J. L. (1997). Analysis of Imcomplete Multivariate Data, Chapman and Hall.
# Note that package 'mix' must be installed data(stlouis, package = "mix") # impute the continuos variables in the stlouis data stlimp <- imputeData(stlouis[,-(1:3)]) # plot imputed values imputePairs(stlouis[,-(1:3)], stlimp)# Note that package 'mix' must be installed data(stlouis, package = "mix") # impute the continuos variables in the stlouis data stlimp <- imputeData(stlouis[,-(1:3)]) # plot imputed values imputePairs(stlouis[,-(1:3)], stlimp)
Mclust objectReturns the log-likelihood for a 'Mclust' object.
## S3 method for class 'Mclust' logLik(object, ...)## S3 method for class 'Mclust' logLik(object, ...)
object |
an object of class |
... |
further arguments passed to or from other methods. |
Returns an object of class 'logLik' with an element providing the maximized log-likelihood, and further arguments giving the number of (estimated) parameters in the model ("df") and the sample size ("nobs").
Luca Scrucca
irisMclust <- Mclust(iris[,1:4]) summary(irisMclust) logLik(irisMclust)irisMclust <- Mclust(iris[,1:4]) summary(irisMclust) logLik(irisMclust)
MclustDA objectReturns the log-likelihood for a MclustDA object.
## S3 method for class 'MclustDA' logLik(object, data, ...)## S3 method for class 'MclustDA' logLik(object, data, ...)
object |
an object of class |
data |
the data for which the log-likelihood must be computed. If missing, the observed data from the |
... |
further arguments passed to or from other methods. |
Returns an object of class 'logLik' with an element providing the maximized log-likelihood, and further arguments giving the number of (estimated) parameters in the model ("df") and the sample size ("nobs").
Luca Scrucca
irisMclustDA <- MclustDA(iris[,1:4], iris$Species) summary(irisMclustDA) logLik(irisMclustDA)irisMclustDA <- MclustDA(iris[,1:4], iris$Species) summary(irisMclustDA) logLik(irisMclustDA)
Efficient implementation (via Fortran) of the log-sum-exp function.
logsumexp(x, v = NULL)logsumexp(x, v = NULL)
x |
a matrix of dimension |
v |
an optional vector of length |
Given the matrix x, for each row (with ), the log-sum-exp (LSE) function calculates
where .
Returns a vector of values of length equal to the number of rows of x.
Luca Scrucca
Blanchard P., Higham D. J., Higham N. J. (2021). Accurately computing the log-sum-exp and softmax functions. IMA Journal of Numerical Analysis, 41/4:2311–2330. doi:10.1093/imanum/draa038
x = matrix(rnorm(15), 5, 3) v = log(c(0.5, 0.3, 0.2)) logsumexp(x, v)x = matrix(rnorm(15), 5, 3) v = log(c(0.5, 0.3, 0.2)) logsumexp(x, v)
A function to compute the majority vote (some would say plurality) label in a vector of labels, breaking ties at random.
majorityVote(x)majorityVote(x)
x |
A vector of values, either numerical or not. |
A list with the following components:
table |
A table of votes for each unique value of |
ind |
An integer specifying which unique value of |
majority |
A string specifying the majority vote label. |
L. Scrucca
x <- c("A", "C", "A", "B", "C", "B", "A") majorityVote(x)x <- c("A", "C", "A", "B", "C", "B", "A") majorityVote(x)
Converts a matrix in which each row sums to 1 to an integer vector specifying for each row the column index of the maximum.
map(z, warn = mclust.options("warn"), ...)map(z, warn = mclust.options("warn"), ...)
z |
A matrix (for example a matrix of conditional probabilities in which each row sums to 1 as produced by the E-step of the EM algorithm). |
warn |
A logical variable indicating whether or not a warning should be
issued when there are some columns of |
... |
Provided to allow lists with elements other than the arguments can
be passed in indirect or list calls with |
A integer vector with one entry for each row of z,
in which the i-th value is the column index at which the
i-th row of z attains a maximum.
emEst <- me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5])) map(emEst$z)emEst <- me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5])) map(emEst$z)
Best correspondence between classes given two vectors viewed as alternative classifications of the same object.
mapClass(a, b)mapClass(a, b)
a |
A numeric or character vector of class labels. |
b |
A numeric or character vector of class labels.
Must have the same length as
|
A list with two named elements,
aTOb and
bTOa which are themselves lists.
The aTOb list has a component corresponding
to each unique element of a, which gives
the element or elements of b
that result in the closest class correspondence.
The bTOa list has a component corresponding
to each unique element of b, which gives
the element or elements of a
that result in the closest class correspondence.
a <- rep(1:3, 3) a b <- rep(c("A", "B", "C"), 3) b mapClass(a, b) a <- sample(1:3, 9, replace = TRUE) a b <- sample(c("A", "B", "C"), 9, replace = TRUE) b mapClass(a, b)a <- rep(1:3, 3) a b <- rep(c("A", "B", "C"), 3) b mapClass(a, b) a <- sample(1:3, 9, replace = TRUE) a b <- sample(c("A", "B", "C"), 9, replace = TRUE) b mapClass(a, b)
Model-based clustering based on parameterized finite Gaussian mixture models. Models are estimated by EM algorithm initialized by hierarchical model-based agglomerative clustering. The optimal model is then selected according to BIC.
Mclust(data, G = NULL, modelNames = NULL, prior = NULL, control = emControl(), initialization = NULL, warn = mclust.options("warn"), x = NULL, verbose = interactive(), ...)Mclust(data, G = NULL, modelNames = NULL, prior = NULL, control = emControl(), initialization = NULL, warn = mclust.options("warn"), x = NULL, verbose = interactive(), ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical
variables are not allowed. If a matrix or data frame, rows
correspond to observations ( |
G |
An integer vector specifying the numbers of mixture components
(clusters) for which the BIC is to be calculated.
The default is |
modelNames |
A vector of character strings indicating the models to be fitted in the EM phase of clustering. The default is:
The help file for |
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
initialization |
A list containing zero or more of the following components:
|
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued.
The default is controlled by |
x |
An object of class |
verbose |
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is |
... |
Catches unused arguments in indirect or list calls via |
An object of class 'Mclust' providing the optimal (according to BIC)
mixture model estimation.
The details of the output components are as follows:
call |
The matched call |
data |
The input data matrix. |
modelName |
A character string denoting the model at which the optimal BIC occurs. |
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
The optimal number of mixture components. |
BIC |
All BIC values. |
loglik |
The log-likelihood corresponding to the optimal BIC. |
df |
The number of estimated parameters. |
bic |
BIC value of the selected model. |
icl |
ICL value of the selected model. |
hypvol |
The hypervolume parameter for the noise component if required, otherwise set to |
parameters |
A list with the following components:
|
z |
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class. |
classification |
The classification corresponding to |
uncertainty |
The uncertainty associated with the classification. |
Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.
C. Fraley and A. E. Raftery (2007) Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification, 24, 155-181.
summary.Mclust,
plot.Mclust,
priorControl,
emControl,
hc,
mclustBIC,
mclustModelNames,
mclust.options
mod1 <- Mclust(iris[,1:4]) summary(mod1) mod2 <- Mclust(iris[,1:4], G = 3) summary(mod2, parameters = TRUE) # Using prior mod3 <- Mclust(iris[,1:4], prior = priorControl()) summary(mod3) mod4 <- Mclust(iris[,1:4], prior = priorControl(functionName="defaultPrior", shrinkage=0.1)) summary(mod4) # Clustering of faithful data with some artificial noise added nNoise <- 100 set.seed(0) # to make it reproducible Noise <- apply(faithful, 2, function(x) runif(nNoise, min = min(x)-.1, max = max(x)+.1)) data <- rbind(faithful, Noise) plot(faithful) points(Noise, pch = 20, cex = 0.5, col = "lightgrey") set.seed(0) NoiseInit <- sample(c(TRUE,FALSE), size = nrow(faithful)+nNoise, replace = TRUE, prob = c(3,1)/4) mod5 <- Mclust(data, initialization = list(noise = NoiseInit)) summary(mod5, parameter = TRUE) plot(mod5, what = "classification")mod1 <- Mclust(iris[,1:4]) summary(mod1) mod2 <- Mclust(iris[,1:4], G = 3) summary(mod2, parameters = TRUE) # Using prior mod3 <- Mclust(iris[,1:4], prior = priorControl()) summary(mod3) mod4 <- Mclust(iris[,1:4], prior = priorControl(functionName="defaultPrior", shrinkage=0.1)) summary(mod4) # Clustering of faithful data with some artificial noise added nNoise <- 100 set.seed(0) # to make it reproducible Noise <- apply(faithful, 2, function(x) runif(nNoise, min = min(x)-.1, max = max(x)+.1)) data <- rbind(faithful, Noise) plot(faithful) points(Noise, pch = 20, cex = 0.5, col = "lightgrey") set.seed(0) NoiseInit <- sample(c(TRUE,FALSE), size = nrow(faithful)+nNoise, replace = TRUE, prob = c(3,1)/4) mod5 <- Mclust(data, initialization = list(noise = NoiseInit)) summary(mod5, parameter = TRUE) plot(mod5, what = "classification")
These functions are provided for compatibility with older versions of the mclust package only, and may be removed eventually.
cv.MclustDA(...) cv1EMtrain(data, labels, modelNames=NULL) bicEMtrain(data, labels, modelNames=NULL)cv.MclustDA(...) cv1EMtrain(data, labels, modelNames=NULL) bicEMtrain(data, labels, modelNames=NULL)
... |
pass arguments down. |
data |
A numeric vector or matrix of observations. |
labels |
Labels for each element or row in the dataset. |
modelNames |
Vector of model names that should be tested. The default is to select all available model names. |
Set or retrieve default values for use with MCLUST package.
mclust.options(...)mclust.options(...)
... |
one or more arguments provided in the |
mclust.options() is provided for assigning or retrieving default values used by various functions in MCLUST.
Available options are:
emModelNamesA vector of 3-character strings that are associated with multivariate
models for which EM estimation is available in MCLUST.
The current default is all of the multivariate mixture models
supported in MCLUST.
The help file for mclustModelNames describes the
available models.
hcModelNameA character string specifying the multivariate model to be used in model-based agglomerative hierarchical clustering for initialization of EM algorithm.
The available models are the following:
"EII"spherical, equal volume;
"EEE"ellipsoidal, equal volume, shape, and orientation;
"VII"spherical, unequal volume;
"VVV"ellipsoidal, varying volume, shape, and orientation (default).
hcUseA character string specifying the type of input variables/transformation to be used in model-based agglomerative hierarchical clustering for initialization of EM algorithm.
Possible values are:
"VARS"original variables;
"STD"standardized variables (centered and scaled);
"SPH"sphered variables (centered, scaled and uncorrelated) computed using SVD;
"PCS"principal components computed using SVD on centered variables (i.e. using the covariance matrix);
"PCR"principal components computed using SVD on standardized (center and scaled) variables (i.e. using the correlation matrix);
"SVD"scaled SVD transformation (default);
"RND"no transformation is applied but a random hierarchical structure is returned (see hcRandomPairs).
For further details see Scrucca and Raftery (2015), Scrucca et al. (2016).
subsetA value specifying the maximal sample size to be used in the model-based
hierarchical clustering to start the EM algorithm.
If data sample size exceeds this value, a random sample is drawn of size
specified by subset.
fillEllipsesA logical value specifying whether or not to fill with transparent
colors ellipses corresponding to the within-cluster covariances in case
of "classification" plot for 'Mclust' objects, or
"scatterplot" graphs for 'MclustDA' objects.
bicPlotSymbolsA vector whose entries correspond to graphics symbols for plotting the
BIC values output from Mclust and mclustBIC.
These are displayed in the legend which appears at the lower right
of the BIC plots.
bicPlotColorsA vector whose entries correspond to colors for plotting the
BIC curves from output from Mclust and
mclustBIC.
These are displayed in the legend which appears at the lower right
of the BIC plots.
classPlotSymbolsA vector whose entries are either integers corresponding to graphics symbols or single characters for indicating classifications when plotting data. Classes are assigned symbols in the given order.
classPlotColorsA vector whose entries correspond to colors for indicating classifications when plotting data. Classes are assigned colors in the given order.
warnA logical value indicating whether or not to issue certain warnings.
Most of these warnings have to do with situations in which
singularities are encountered.
The default is warn = FALSE.
The parameter values set via a call to this function will remain in effect for the rest of the session, affecting the subsequent behaviour of the functions for which the given parameters are relevant.
If the argument list is empty the function returns the current list of values. If the argument list is not empty, the returned list is invisible.
Scrucca L. and Raftery A. E. (2015) Improved initialisation of model-based clustering using Gaussian hierarchical partitions. Advances in Data Analysis and Classification, 9/4, pp. 447-460.
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
Mclust,
MclustDA,
densityMclust,
emControl
opt <- mclust.options() # save default values irisBIC <- mclustBIC(iris[,-5]) summary(irisBIC, iris[,-5]) mclust.options(emModelNames = c("EII", "EEI", "EEE")) irisBIC <- mclustBIC(iris[,-5]) summary(irisBIC, iris[,-5]) mclust.options(opt) # restore default values mclust.options() oldpar <- par(mfrow = c(2,1), no.readonly = TRUE) n <- with(mclust.options(), max(sapply(list(bicPlotSymbols, bicPlotColors),length))) plot(seq(n), rep(1,n), ylab = "", xlab = "", yaxt = "n", pch = mclust.options("bicPlotSymbols"), col = mclust.options("bicPlotColors")) title("mclust.options(\"bicPlotSymbols\") \n mclust.options(\"bicPlotColors\")") n <- with(mclust.options(), max(sapply(list(classPlotSymbols, classPlotColors),length))) plot(seq(n), rep(1,n), ylab = "", xlab = "", yaxt = "n", pch = mclust.options("classPlotSymbols"), col = mclust.options("classPlotColors")) title("mclust.options(\"classPlotSymbols\") \n mclust.options(\"classPlotColors\")") par(oldpar)opt <- mclust.options() # save default values irisBIC <- mclustBIC(iris[,-5]) summary(irisBIC, iris[,-5]) mclust.options(emModelNames = c("EII", "EEI", "EEE")) irisBIC <- mclustBIC(iris[,-5]) summary(irisBIC, iris[,-5]) mclust.options(opt) # restore default values mclust.options() oldpar <- par(mfrow = c(2,1), no.readonly = TRUE) n <- with(mclust.options(), max(sapply(list(bicPlotSymbols, bicPlotColors),length))) plot(seq(n), rep(1,n), ylab = "", xlab = "", yaxt = "n", pch = mclust.options("bicPlotSymbols"), col = mclust.options("bicPlotColors")) title("mclust.options(\"bicPlotSymbols\") \n mclust.options(\"bicPlotColors\")") n <- with(mclust.options(), max(sapply(list(classPlotSymbols, classPlotColors),length))) plot(seq(n), rep(1,n), ylab = "", xlab = "", yaxt = "n", pch = mclust.options("classPlotSymbols"), col = mclust.options("classPlotColors")) title("mclust.options(\"classPlotSymbols\") \n mclust.options(\"classPlotColors\")") par(oldpar)
Plot one-dimensional data given parameters of an MVN mixture model for the data.
mclust1Dplot(data, parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "density", "error", "uncertainty"), symbols = NULL, colors = NULL, ngrid = length(data), xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, cex = 1, main = FALSE, ...)mclust1Dplot(data, parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "density", "error", "uncertainty"), symbols = NULL, colors = NULL, ngrid = length(data), xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, cex = 1, main = FALSE, ...)
data |
A numeric vector of observations. Categorical variables are not allowed. |
parameters |
A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows:
|
z |
A matrix in which the |
classification |
A numeric or character vector representing a classification of
observations (rows) of |
truth |
A numeric or character vector giving a known
classification of each data point.
If |
uncertainty |
A numeric vector of values in (0,1) giving the
uncertainty of each data point. If present argument |
what |
Choose from one of the following options: |
symbols |
Either an integer or character vector assigning a plotting symbol to
each unique class |
colors |
Either an integer or character vector assigning a color to each
unique class |
ngrid |
Number of grid points to use for density computation over the interval spanned by the data. The default is the length of the data set. |
xlab, ylab
|
An argument specifying a label for the axes. |
xlim, ylim
|
An argument specifying bounds of the plot. This may be useful for when comparing plots. |
cex |
An argument specifying the size of the plotting symbols. The default value is 1. |
main |
A logical variable or |
... |
Other graphics parameters. |
A plot showing location of the mixture components, classification, uncertainty, density and/or classification errors. Points in the different classes are shown in separated levels above the whole of the data.
mclust2Dplot,
clPairs,
coordProj
n <- 250 ## create artificial data set.seed(1) y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) yclass <- c(rep(1,n), rep(2,n), rep(3,n)) yModel <- Mclust(y) mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, what = "classification") mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, what = "error", truth = yclass) mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, what = "density") mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters, what = "uncertainty")n <- 250 ## create artificial data set.seed(1) y <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) yclass <- c(rep(1,n), rep(2,n), rep(3,n)) yModel <- Mclust(y) mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, what = "classification") mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, what = "error", truth = yclass) mclust1Dplot(y, parameters = yModel$parameters, z = yModel$z, what = "density") mclust1Dplot(y, z = yModel$z, parameters = yModel$parameters, what = "uncertainty")
Plot two-dimensional data given parameters of an MVN mixture model for the data.
mclust2Dplot(data, parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "uncertainty", "error"), addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"), symbols = NULL, colors = NULL, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, scale = FALSE, cex = 1, PCH = ".", main = FALSE, swapAxes = FALSE, ...)mclust2Dplot(data, parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "uncertainty", "error"), addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"), symbols = NULL, colors = NULL, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, scale = FALSE, cex = 1, PCH = ".", main = FALSE, swapAxes = FALSE, ...)
data |
A numeric matrix or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. In this case the data are two dimensional, so there are two columns. |
parameters |
A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows:
|
z |
A matrix in which the |
classification |
A numeric or character vector representing a classification of
observations (rows) of |
truth |
A numeric or character vector giving a known
classification of each data point.
If |
uncertainty |
A numeric vector of values in (0,1) giving the
uncertainty of each data point. If present argument |
what |
Choose from one of the following three options: |
addEllipses |
A logical indicating whether or not to add ellipses with axes corresponding to the within-cluster covariances. |
fillEllipses |
A logical specifying whether or not to fill ellipses with transparent
colors when |
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique class in |
colors |
Either an integer or character vector assigning a color to each
unique class in |
xlim, ylim
|
Optional argument specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots. |
xlab, ylab
|
Optional argument specifying labels for the x-axis and y-axis. |
scale |
A logical variable indicating whether or not the two chosen
dimensions should be plotted on the same scale, and
thus preserve the shape of the distribution.
Default: |
cex |
An argument specifying the size of the plotting symbols. The default value is 1. |
PCH |
An argument specifying the symbol to be used when a classificatiion has not been specified for the data. The default value is a small dot ".". |
main |
A logical variable or |
swapAxes |
A logical variable indicating whether or not the axes should be swapped for the plot. |
... |
Other graphics parameters. |
A plot showing the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.
surfacePlot,
clPairs,
coordProj,
mclust.options
faithfulModel <- Mclust(faithful) mclust2Dplot(faithful, parameters=faithfulModel$parameters, z=faithfulModel$z, what = "classification", main = TRUE) mclust2Dplot(faithful, parameters=faithfulModel$parameters, z=faithfulModel$z, what = "uncertainty", main = TRUE)faithfulModel <- Mclust(faithful) mclust2Dplot(faithful, parameters=faithfulModel$parameters, z=faithfulModel$z, what = "classification", main = TRUE) mclust2Dplot(faithful, parameters=faithfulModel$parameters, z=faithfulModel$z, what = "uncertainty", main = TRUE)
BIC for parameterized Gaussian mixture models fitted by EM algorithm initialized by model-based hierarchical clustering.
mclustBIC(data, G = NULL, modelNames = NULL, prior = NULL, control = emControl(), initialization = list(hcPairs = NULL, subset = NULL, noise = NULL), Vinv = NULL, warn = mclust.options("warn"), x = NULL, verbose = interactive(), ...)mclustBIC(data, G = NULL, modelNames = NULL, prior = NULL, control = emControl(), initialization = list(hcPairs = NULL, subset = NULL, noise = NULL), Vinv = NULL, warn = mclust.options("warn"), x = NULL, verbose = interactive(), ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
G |
An integer vector specifying the numbers of mixture components
(clusters) for which the BIC is to be calculated.
The default is |
modelNames |
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. The help file for
unless the argument |
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
initialization |
A list containing zero or more of the following components:
|
Vinv |
An estimate of the reciprocal hypervolume of the data region.
The default is determined by applying function |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when
estimation fails.
The default is controlled by |
x |
An object of class |
verbose |
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is |
... |
Catches unused arguments in indirect or list calls via |
Return an object of class 'mclustBIC' containing the Bayesian Information
Criterion for the specified mixture models numbers of clusters.
Auxiliary information returned as attributes.
The corresponding print method shows the matrix of values and the top models according to the BIC criterion.
summary.mclustBIC,
priorControl,
emControl,
mclustModel,
hc,
me,
mclustModelNames,
mclust.options
irisBIC <- mclustBIC(iris[,-5]) irisBIC plot(irisBIC) subset <- sample(1:nrow(iris), 100) irisBIC <- mclustBIC(iris[,-5], initialization=list(subset = subset)) irisBIC plot(irisBIC) irisBIC1 <- mclustBIC(iris[,-5], G=seq(from=1,to=9,by=2), modelNames=c("EII", "EEI", "EEE")) irisBIC1 plot(irisBIC1) irisBIC2 <- mclustBIC(iris[,-5], G=seq(from=2,to=8,by=2), modelNames=c("VII", "VVI", "VVV"), x= irisBIC1) irisBIC2 plot(irisBIC2) nNoise <- 450 set.seed(0) poissonNoise <- apply(apply( iris[,-5], 2, range), 2, function(x, n) runif(n, min = x[1]-.1, max = x[2]+.1), n = nNoise) set.seed(0) noiseInit <- sample(c(TRUE,FALSE),size=nrow(iris)+nNoise,replace=TRUE, prob=c(3,1)) irisNdata <- rbind(iris[,-5], poissonNoise) irisNbic <- mclustBIC(data = irisNdata, G = 1:5, initialization = list(noise = noiseInit)) irisNbic plot(irisNbic)irisBIC <- mclustBIC(iris[,-5]) irisBIC plot(irisBIC) subset <- sample(1:nrow(iris), 100) irisBIC <- mclustBIC(iris[,-5], initialization=list(subset = subset)) irisBIC plot(irisBIC) irisBIC1 <- mclustBIC(iris[,-5], G=seq(from=1,to=9,by=2), modelNames=c("EII", "EEI", "EEE")) irisBIC1 plot(irisBIC1) irisBIC2 <- mclustBIC(iris[,-5], G=seq(from=2,to=8,by=2), modelNames=c("VII", "VVI", "VVV"), x= irisBIC1) irisBIC2 plot(irisBIC2) nNoise <- 450 set.seed(0) poissonNoise <- apply(apply( iris[,-5], 2, range), 2, function(x, n) runif(n, min = x[1]-.1, max = x[2]+.1), n = nNoise) set.seed(0) noiseInit <- sample(c(TRUE,FALSE),size=nrow(iris)+nNoise,replace=TRUE, prob=c(3,1)) irisNdata <- rbind(iris[,-5], poissonNoise) irisNbic <- mclustBIC(data = irisNdata, G = 1:5, initialization = list(noise = noiseInit)) irisNbic plot(irisNbic)
Update the BIC (Bayesian Information Criterion) for parameterized Gaussian
mixture models by taking the best from BIC results as returned by mclustBIC.
mclustBICupdate(BIC, ...)mclustBICupdate(BIC, ...)
BIC |
Object of class |
... |
Further objects of class |
An object of class 'mclustBIC' containing the best values obtained from
merging the input arguments. Attributes are also updated according to the best
BIC found, so calling Mclust on the resulting ouput will return
the corresponding best model (see example).
data(galaxies, package = "MASS") galaxies <- galaxies / 1000 # use several random starting points BIC <- NULL for(j in 1:100) { rBIC <- mclustBIC(galaxies, verbose = FALSE, initialization = list(hcPairs = hcRandomPairs(galaxies))) BIC <- mclustBICupdate(BIC, rBIC) } pickBIC(BIC) plot(BIC) mod <- Mclust(galaxies, x = BIC) summary(mod)data(galaxies, package = "MASS") galaxies <- galaxies / 1000 # use several random starting points BIC <- NULL for(j in 1:100) { rBIC <- mclustBIC(galaxies, verbose = FALSE, initialization = list(hcPairs = hcRandomPairs(galaxies))) BIC <- mclustBICupdate(BIC, rBIC) } pickBIC(BIC) plot(BIC) mod <- Mclust(galaxies, x = BIC) summary(mod)
Bootstrap or jackknife estimation of standard errors and percentile bootstrap confidence intervals for the parameters of a Gaussian mixture model.
MclustBootstrap(object, nboot = 999, type = c("bs", "wlbs", "pb", "jk"), alpha = 1, max.nonfit = 10*nboot, verbose = interactive(), ...)MclustBootstrap(object, nboot = 999, type = c("bs", "wlbs", "pb", "jk"), alpha = 1, max.nonfit = 10*nboot, verbose = interactive(), ...)
object |
An object of class |
nboot |
The number of bootstrap replications. |
type |
A character string specifying the type of resampling to use:
|
alpha |
A numerical value used when |
max.nonfit |
The maximum number of non-estimable models allowed. |
verbose |
A logical controlling if a text progress bar is displayed during the resampling procedure. By default is |
... |
Further arguments passed to or from other methods. |
For a fitted Gaussian mixture model with object$G mixture components and covariances parameterisation object$modelName, this function returns either the bootstrap distribution or the jackknife distribution of mixture parameters. In the former case, the nonparametric bootstrap or the weighted likelihood bootstrap approach could be used, so the the bootstrap procedure generates nboot bootstrap samples of the same size as the original data by resampling with replacement from the observed data. In the jackknife case, the procedure considers all the samples obtained by omitting one observation at time.
The resulting resampling distribution can then be used to obtain standard errors and percentile confidence intervals by the use of summary.MclustBootstrap function.
An object of class 'MclustBootstrap' with the following components:
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
A value specifying the number of mixture components. |
modelName |
A character string specifying the mixture model covariances
parameterisation (see |
parameters |
A list of estimated parameters for the mixture components with the following components:
|
nboot |
The number of bootstrap replications if |
type |
The type of resampling approach used. |
nonfit |
The number of resamples that did not convergence during the procedure. |
pro |
A matrix of dimension ( |
mean |
An array of dimension ( |
variance |
An array of dimension ( |
Davison, A. and Hinkley, D. (1997) Bootstrap Methods and Their Applications. Cambridge University Press.
McLachlan, G.J. and Peel, D. (2000) Finite Mixture Models. Wiley.
O'Hagan A., Murphy T. B., Gormley I. C. and Scrucca L. (2015) On Estimation of Parameter Uncertainty in Model-Based Clustering. Submitted to Computational Statistics.
summary.MclustBootstrap, plot.MclustBootstrap, Mclust, densityMclust.
data(diabetes) X <- diabetes[,-1] modClust <- Mclust(X) bootClust <- MclustBootstrap(modClust) summary(bootClust, what = "se") summary(bootClust, what = "ci") data(acidity) modDens <- densityMclust(acidity, plot = FALSE) modDens <- MclustBootstrap(modDens) summary(modDens, what = "se") summary(modDens, what = "ci")data(diabetes) X <- diabetes[,-1] modClust <- Mclust(X) bootClust <- MclustBootstrap(modClust) summary(bootClust, what = "se") summary(bootClust, what = "ci") data(acidity) modDens <- densityMclust(acidity, plot = FALSE) modDens <- MclustBootstrap(modDens) summary(modDens, what = "se") summary(modDens, what = "ci")
Perform the likelihood ratio test (LRT) for assessing the number of mixture components in a specific finite mixture model parameterisation. The observed significance is approximated by using the (parametric) bootstrap for the likelihood ratio test statistic (LRTS).
mclustBootstrapLRT(data, modelName = NULL, nboot = 999, level = 0.05, maxG = NULL, verbose = interactive(), ...) ## S3 method for class 'mclustBootstrapLRT' print(x, ...) ## S3 method for class 'mclustBootstrapLRT' plot(x, G = 1, hist.col = "grey", hist.border = "lightgrey", breaks = "Scott", col = "forestgreen", lwd = 2, lty = 3, main = NULL, ...)mclustBootstrapLRT(data, modelName = NULL, nboot = 999, level = 0.05, maxG = NULL, verbose = interactive(), ...) ## S3 method for class 'mclustBootstrapLRT' print(x, ...) ## S3 method for class 'mclustBootstrapLRT' plot(x, G = 1, hist.col = "grey", hist.border = "lightgrey", breaks = "Scott", col = "forestgreen", lwd = 2, lty = 3, main = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the mixture model to be fitted.
The help file for |
nboot |
The number of bootstrap replications to use (by default 999). |
level |
The significance level to be used to terminate the sequential bootstrap procedure. |
maxG |
The maximum number of mixture components |
verbose |
A logical controlling if a text progress bar is displayed during the bootstrap procedure. By default is |
... |
Further arguments passed to or from other methods. In particular, see the optional arguments in |
x |
An |
G |
A value specifying the number of components for which to plot the bootstrap distribution. |
hist.col |
The colour to be used to fill the bars of the histogram. |
hist.border |
The color of the border around the bars of the histogram. |
breaks |
See the argument in function |
col, lwd, lty
|
The color, line width and line type to be used to represent the observed LRT statistic. |
main |
The title for the graph. |
The implemented algorithm for computing the LRT observed significance using the bootstrap is the following.
Let be the number of mixture components under the null hypothesis versus under the alternative. Bootstrap samples are drawn by simulating data under the null hypothesis. Then, the p-value may be approximated using eq. (13) on McLachlan and Rathnayake (2014). Equivalently, using the notation of Davison and Hinkley (1997) it may be computed as
where = number of bootstrap samples = LRTS computed on the observed data = LRTS computed on the th bootstrap sample.
An object of class 'mclustBootstrapLRT' with the following components:
G |
A vector of number of components tested under the null hypothesis. |
modelName |
A character string specifying the mixture model as provided in the function call (see above). |
obs |
The observed values of the LRTS. |
boot |
A matrix of dimension |
p.value |
A vector of p-values. |
Davison, A. and Hinkley, D. (1997) Bootstrap Methods and Their Applications. Cambridge University Press.
McLachlan G.J. (1987) On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Applied Statistics, 36, 318-324.
McLachlan, G.J. and Peel, D. (2000) Finite Mixture Models. Wiley.
McLachlan, G.J. and Rathnayake, S. (2014) On the number of components in a Gaussian mixture model. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5), pp. 341-355.
data(faithful) faithful.boot = mclustBootstrapLRT(faithful, model = "VVV") faithful.boot plot(faithful.boot, G = 1) plot(faithful.boot, G = 2)data(faithful) faithful.boot = mclustBootstrapLRT(faithful, model = "VVV") faithful.boot plot(faithful.boot, G = 1) plot(faithful.boot, G = 2)
Discriminant analysis based on Gaussian finite mixture modeling.
MclustDA(data, class, G = NULL, modelNames = NULL, modelType = c("MclustDA", "EDDA"), prior = NULL, control = emControl(), initialization = NULL, warn = mclust.options("warn"), verbose = interactive(), ...)MclustDA(data, class, G = NULL, modelNames = NULL, modelType = c("MclustDA", "EDDA"), prior = NULL, control = emControl(), initialization = NULL, warn = mclust.options("warn"), verbose = interactive(), ...)
data |
A data frame or matrix giving the training data. |
class |
A vector giving the known class labels (either a numerical value or a character string) for the observations in the training data. |
G |
An integer vector specifying the numbers of mixture components
(clusters) for which the BIC is to be calculated within each class.
The default is |
modelNames |
A vector of character strings indicating the models to be fitted
by EM within each class (see the description in
|
modelType |
A character string specifying whether the models given in
|
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
initialization |
A list containing zero or more of the following components:
|
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when
estimation fails.
The default is controlled by |
verbose |
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is |
... |
Further arguments passed to or from other methods. |
The "EDDA" method for discriminant analysis is described in Bensmail and Celeux (1996), while "MclustDA" in Fraley and Raftery (2002).
An object of class 'MclustDA' providing the optimal (according
to BIC) mixture model.
The details of the output components are as follows:
call |
The matched call. |
data |
The input data matrix. |
class |
The input class labels. |
type |
A character string specifying the |
models |
A list of |
n |
The total number of observations in the data. |
d |
The dimension of the data. |
bic |
Optimal BIC value. |
loglik |
Log-likelihood for the selected model. |
df |
Number of estimated parameters. |
Luca Scrucca
Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.
Bensmail, H., and Celeux, G. (1996) Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition.Journal of the American Statistical Association, 91, 1743-1748.
summary.MclustDA,
plot.MclustDA,
predict.MclustDA,
classError
odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] # common EEE covariance structure (which is essentially equivalent to linear discriminant analysis) irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA", modelNames = "EEE") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # common covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # general covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train) summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) plot(irisMclustDA) plot(irisMclustDA, dimens = 3:4) plot(irisMclustDA, dimens = 4) plot(irisMclustDA, what = "classification") plot(irisMclustDA, what = "classification", newdata = X.test) plot(irisMclustDA, what = "classification", dimens = 3:4) plot(irisMclustDA, what = "classification", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "classification", dimens = 4) plot(irisMclustDA, what = "classification", dimens = 4, newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 4) plot(irisMclustDA, what = "error") plot(irisMclustDA, what = "error", dimens = 3:4) plot(irisMclustDA, what = "error", dimens = 4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 3:4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 4) # simulated 1D data n <- 250 set.seed(1) triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) triClass <- c(rep(1,n), rep(2,n), rep(3,n)) odd <- seq(from = 1, to = length(triModal), by = 2) even <- odd + 1 triMclustDA <- MclustDA(triModal[odd], triClass[odd]) summary(triMclustDA, parameters = TRUE) summary(triMclustDA, newdata = triModal[even], newclass = triClass[even]) plot(triMclustDA, what = "scatterplot") plot(triMclustDA, what = "classification") plot(triMclustDA, what = "classification", newdata = triModal[even]) plot(triMclustDA, what = "train&test", newdata = triModal[even]) plot(triMclustDA, what = "error") plot(triMclustDA, what = "error", newdata = triModal[even], newclass = triClass[even]) # simulated 2D cross data data(cross) odd <- seq(from = 1, to = nrow(cross), by = 2) even <- odd + 1 crossMclustDA <- MclustDA(cross[odd,-1], cross[odd,1]) summary(crossMclustDA, parameters = TRUE) summary(crossMclustDA, newdata = cross[even,-1], newclass = cross[even,1]) plot(crossMclustDA, what = "scatterplot") plot(crossMclustDA, what = "classification") plot(crossMclustDA, what = "classification", newdata = cross[even,-1]) plot(crossMclustDA, what = "train&test", newdata = cross[even,-1]) plot(crossMclustDA, what = "error") plot(crossMclustDA, what = "error", newdata =cross[even,-1], newclass = cross[even,1])odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] # common EEE covariance structure (which is essentially equivalent to linear discriminant analysis) irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA", modelNames = "EEE") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # common covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # general covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train) summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) plot(irisMclustDA) plot(irisMclustDA, dimens = 3:4) plot(irisMclustDA, dimens = 4) plot(irisMclustDA, what = "classification") plot(irisMclustDA, what = "classification", newdata = X.test) plot(irisMclustDA, what = "classification", dimens = 3:4) plot(irisMclustDA, what = "classification", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "classification", dimens = 4) plot(irisMclustDA, what = "classification", dimens = 4, newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 4) plot(irisMclustDA, what = "error") plot(irisMclustDA, what = "error", dimens = 3:4) plot(irisMclustDA, what = "error", dimens = 4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 3:4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 4) # simulated 1D data n <- 250 set.seed(1) triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) triClass <- c(rep(1,n), rep(2,n), rep(3,n)) odd <- seq(from = 1, to = length(triModal), by = 2) even <- odd + 1 triMclustDA <- MclustDA(triModal[odd], triClass[odd]) summary(triMclustDA, parameters = TRUE) summary(triMclustDA, newdata = triModal[even], newclass = triClass[even]) plot(triMclustDA, what = "scatterplot") plot(triMclustDA, what = "classification") plot(triMclustDA, what = "classification", newdata = triModal[even]) plot(triMclustDA, what = "train&test", newdata = triModal[even]) plot(triMclustDA, what = "error") plot(triMclustDA, what = "error", newdata = triModal[even], newclass = triClass[even]) # simulated 2D cross data data(cross) odd <- seq(from = 1, to = nrow(cross), by = 2) even <- odd + 1 crossMclustDA <- MclustDA(cross[odd,-1], cross[odd,1]) summary(crossMclustDA, parameters = TRUE) summary(crossMclustDA, newdata = cross[even,-1], newclass = cross[even,1]) plot(crossMclustDA, what = "scatterplot") plot(crossMclustDA, what = "classification") plot(crossMclustDA, what = "classification", newdata = cross[even,-1]) plot(crossMclustDA, what = "train&test", newdata = cross[even,-1]) plot(crossMclustDA, what = "error") plot(crossMclustDA, what = "error", newdata =cross[even,-1], newclass = cross[even,1])
A dimension reduction method for visualizing the clustering or classification structure obtained from a finite mixture of Gaussian densities.
MclustDR(object, lambda = 1, normalized = TRUE, Sigma, tol = sqrt(.Machine$double.eps))MclustDR(object, lambda = 1, normalized = TRUE, Sigma, tol = sqrt(.Machine$double.eps))
object |
An object of class |
lambda |
A tuning parameter in the range [0,1] as described in Scrucca (2014). The directions that mostly separate the estimated clusters or classes are recovered using the default value 1. Users can set this parameter to balance the relative importance of information derived from cluster/class means and covariances. For instance, a value of 0.5 gives equal importance to differences in means and covariances among clusters/classes. |
normalized |
Logical. If |
Sigma |
Marginal covariance matrix of data. If not provided is estimated by the MLE of observed data. |
tol |
A tolerance value. |
The method aims at reducing the dimensionality by identifying a set of linear combinations, ordered by importance as quantified by the associated eigenvalues, of the original features which capture most of the clustering or classification structure contained in the data.
Information on the dimension reduction subspace is obtained from the variation on group means and, depending on the estimated mixture model, on the variation on group covariances (see Scrucca, 2010).
Observations may then be projected onto such a reduced subspace, thus providing summary plots which help to visualize the underlying structure.
The method has been extended to the supervised case, i.e. when the true classification is known (see Scrucca, 2014).
This implementation doesn't provide a formal procedure for the selection of dimensionality. A future release will include one or more methods.
An object of class 'MclustDR' with the following components:
call |
The matched call |
type |
A character string specifying the type of model for which the dimension reduction is computed. Currently, possible values are |
x |
The data matrix. |
Sigma |
The covariance matrix of the data. |
mixcomp |
A numeric vector specifying the mixture component of each data observation. |
class |
A factor specifying the classification of each data observation. For model-based clustering this is equivalent to the corresponding mixture component. For model-based classification this is the known classification. |
G |
The number of mixture components. |
modelName |
The name of the parameterization of the estimated mixture model(s). See |
mu |
A matrix of means for each mixture component. |
sigma |
An array of covariance matrices for each mixture component. |
pro |
The estimated prior for each mixture component. |
M |
The kernel matrix. |
lambda |
The tuning parameter. |
evalues |
The eigenvalues from the generalized eigen-decomposition of the kernel matrix. |
raw.evectors |
The raw eigenvectors from the generalized eigen-decomposition of the kernel matrix, ordered according to the eigenvalues. |
basis |
The basis of the estimated dimension reduction subspace. |
std.basis |
The basis of the estimated dimension reduction subspace standardized to variables having unit standard deviation. |
numdir |
The dimension of the projection subspace. |
dir |
The estimated directions, i.e. the data projected onto the estimated dimension reduction subspace. |
Luca Scrucca
Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.
Scrucca, L. (2014) Graphical Tools for Model-based Mixture Discriminant Analysis. Advances in Data Analysis and Classification, 8(2), pp. 147-165.
summary.MclustDR, plot.MclustDR, Mclust, MclustDA.
# clustering data(diabetes) mod <- Mclust(diabetes[,-1]) summary(mod) dr <- MclustDR(mod) summary(dr) plot(dr, what = "scatterplot") plot(dr, what = "evalues") dr <- MclustDR(mod, lambda = 0.5) summary(dr) plot(dr, what = "scatterplot") plot(dr, what = "evalues") # classification data(banknote) da <- MclustDA(banknote[,2:7], banknote$Status, modelType = "EDDA") dr <- MclustDR(da) summary(dr) da <- MclustDA(banknote[,2:7], banknote$Status) dr <- MclustDR(da) summary(dr)# clustering data(diabetes) mod <- Mclust(diabetes[,-1]) summary(mod) dr <- MclustDR(mod) summary(dr) plot(dr, what = "scatterplot") plot(dr, what = "evalues") dr <- MclustDR(mod, lambda = 0.5) summary(dr) plot(dr, what = "scatterplot") plot(dr, what = "evalues") # classification data(banknote) da <- MclustDA(banknote[,2:7], banknote$Status, modelType = "EDDA") dr <- MclustDR(da) summary(dr) da <- MclustDA(banknote[,2:7], banknote$Status) dr <- MclustDR(da) summary(dr)
Implements a subset selection method for selecting the relevant directions spanning the dimension reduction subspace for visualizing the clustering or classification structure obtained from a finite mixture of Gaussian densities.
MclustDRsubsel(object, G = 1:9, modelNames = mclust.options("emModelNames"), ..., bic.stop = 0, bic.cutoff = 0, mindir = 1, verbose = interactive())MclustDRsubsel(object, G = 1:9, modelNames = mclust.options("emModelNames"), ..., bic.stop = 0, bic.cutoff = 0, mindir = 1, verbose = interactive())
object |
An object of class |
||||||
G |
An integer vector specifying the numbers of mixture components or clusters. |
||||||
modelNames |
A vector of character strings indicating the models to be fitted. See |
||||||
... |
|||||||
bic.stop |
A criterion to terminate the search. If maximal BIC difference is less than
|
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bic.cutoff |
A value specifying how to select simplest “best” model within |
||||||
mindir |
An integer value specifying the minimum number of directions to be estimated. |
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verbose |
A logical or integer value specifying if and how much detailed information should be reported during the iterations of the algorithm.
|
The GMMDR method aims at reducing the dimensionality by identifying a set of linear combinations, ordered by importance as quantified by the associated eigenvalues, of the original features which capture most of the clustering or classification structure contained in the data. This is implemented in MclustDR.
The MclustDRsubsel function implements the greedy forward search algorithm discussed in Scrucca (2010) to prune the set of all GMMDR directions. The criterion used to select the relevant directions is based on the BIC difference between a clustering model and a model in which the feature proposal has no clustering relevance. The steps are the following:
1. Select the first feature to be the one which maximizes the BIC difference between the best clustering model and the model which assumes no clustering, i.e. a single component.
2. Select the next feature amongst those not previously included, to be the one which maximizes the BIC difference.
3. Iterate the previous step until all the BIC differences for the inclusion of a feature become less than bic.stop.
At each step, the search over the model space is performed with respect to the model parametrisation and the number of clusters.
An object of class 'MclustDRsubsel' which inherits from 'MclustDR', so it has the same components of the latter plus the following:
basisx |
The basis of the estimated dimension reduction subspace expressed in terms of the original variables. |
std.basisx |
The basis of the estimated dimension reduction subspace expressed in terms of the original variables standardized to have unit standard deviation. |
Luca Scrucca
Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.
Scrucca, L. (2014) Graphical Tools for Model-based Mixture Discriminant Analysis. Advances in Data Analysis and Classification, 8(2), pp. 147-165
# clustering data(crabs, package = "MASS") x <- crabs[,4:8] class <- paste(crabs$sp, crabs$sex, sep = "|") mod <- Mclust(x) table(class, mod$classification) dr <- MclustDR(mod) summary(dr) plot(dr) drs <- MclustDRsubsel(dr) summary(drs) table(class, drs$classification) plot(drs, what = "scatterplot") plot(drs, what = "pairs") plot(drs, what = "contour") plot(drs, what = "boundaries") plot(drs, what = "evalues") # classification data(banknote) da <- MclustDA(banknote[,2:7], banknote$Status) table(banknote$Status, predict(da)$class) dr <- MclustDR(da) summary(dr) drs <- MclustDRsubsel(dr) summary(drs) table(banknote$Status, predict(drs)$class) plot(drs, what = "scatterplot") plot(drs, what = "classification") plot(drs, what = "boundaries")# clustering data(crabs, package = "MASS") x <- crabs[,4:8] class <- paste(crabs$sp, crabs$sex, sep = "|") mod <- Mclust(x) table(class, mod$classification) dr <- MclustDR(mod) summary(dr) plot(dr) drs <- MclustDRsubsel(dr) summary(drs) table(class, drs$classification) plot(drs, what = "scatterplot") plot(drs, what = "pairs") plot(drs, what = "contour") plot(drs, what = "boundaries") plot(drs, what = "evalues") # classification data(banknote) da <- MclustDA(banknote[,2:7], banknote$Status) table(banknote$Status, predict(da)$class) dr <- MclustDR(da) summary(dr) drs <- MclustDRsubsel(dr) summary(drs) table(banknote$Status, predict(drs)$class) plot(drs, what = "scatterplot") plot(drs, what = "classification") plot(drs, what = "boundaries")
ICL (Integrated Complete-data Likelihood) for parameterized Gaussian mixture models fitted by EM algorithm initialized by model-based hierarchical clustering.
mclustICL(data, G = NULL, modelNames = NULL, initialization = list(hcPairs = NULL, subset = NULL, noise = NULL), x = NULL, ...) ## S3 method for class 'mclustICL' summary(object, G, modelNames, ...)mclustICL(data, G = NULL, modelNames = NULL, initialization = list(hcPairs = NULL, subset = NULL, noise = NULL), x = NULL, ...) ## S3 method for class 'mclustICL' summary(object, G, modelNames, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
G |
An integer vector specifying the numbers of mixture components
(clusters) for which the criteria should be calculated.
The default is |
modelNames |
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. The help file for
|
initialization |
A list containing zero or more of the following components:
|
x |
An object of class |
... |
Futher arguments used in the call to |
object |
An integer vector specifying the numbers of mixture components
(clusters) for which the criteria should be calculated.
The default is |
Returns an object of class 'mclustICL' containing the the ICL criterion
for the specified mixture models and numbers of clusters.
The corresponding print method shows the matrix of values and the top models according to the ICL criterion. The summary method shows only the top models.
Biernacki, C., Celeux, G., Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Analysis and Machine Intelligence, 22 (7), 719-725.
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
plot.mclustICL,
Mclust,
mclustBIC,
mclustBootstrapLRT,
bic,
icl
data(faithful) faithful.ICL <- mclustICL(faithful) faithful.ICL summary(faithful.ICL) plot(faithful.ICL) # compare with faithful.BIC <- mclustBIC(faithful) faithful.BIC plot(faithful.BIC)data(faithful) faithful.ICL <- mclustICL(faithful) faithful.ICL summary(faithful.ICL) plot(faithful.ICL) # compare with faithful.BIC <- mclustBIC(faithful) faithful.BIC plot(faithful.BIC)
Compute the maximal log-likelihood from a table of BIC values contained in a 'mclustBIC' object as returned by function mclustBIC.
mclustLoglik(object, ...)mclustLoglik(object, ...)
object |
An object of class |
... |
Catches unused arguments in an indirect or list call via |
An object of class 'mclustLoglik' containing the maximal log-likelihood values for the Gaussian mixture models provided as input.
BIC <- mclustBIC(iris[,1:4]) mclustLoglik(BIC)BIC <- mclustBIC(iris[,1:4]) mclustLoglik(BIC)
Determines the best model from clustering via mclustBIC
for a given set of model parameterizations and numbers of components.
mclustModel(data, BICvalues, G, modelNames, ...)mclustModel(data, BICvalues, G, modelNames, ...)
data |
The matrix or vector of observations used to generate ‘object’. |
BICvalues |
An |
G |
A vector of integers giving the numbers of mixture components (clusters)
from which the best model according to BIC will be selected
( |
modelNames |
A vector of integers giving the model parameterizations
from which the best model according to BIC will be selected
( |
... |
Not used. For generic/method consistency. |
A list giving the optimal (according to BIC) parameters,
conditional probabilities z, and log-likelihood,
together with the associated classification and its uncertainty.
The details of the output components are as follows:
modelName |
A character string indicating the model. The help file for
|
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
The number of components in the Gaussian mixture model corresponding to the optimal BIC. |
bic |
The optimal BIC value. |
loglik |
The log-likelihood corresponding to the optimal BIC. |
parameters |
A list with the following components:
|
z |
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class. |
irisBIC <- mclustBIC(iris[,-5]) mclustModel(iris[,-5], irisBIC) mclustModel(iris[,-5], irisBIC, G = 1:6, modelNames = c("VII", "VVI", "VVV"))irisBIC <- mclustBIC(iris[,-5]) mclustModel(iris[,-5], irisBIC) mclustModel(iris[,-5], irisBIC, G = 1:6, modelNames = c("VII", "VVI", "VVV"))
Description of model names used in the MCLUST package.
mclustModelNames(model)mclustModelNames(model)
model |
A string specifying the model. |
The following models are available in package mclust:
univariate mixture
"E"equal variance (one-dimensional)
"V"variable/unqual variance (one-dimensional)
multivariate mixture
"EII"spherical, equal volume
"VII"spherical, unequal volume
"EEI"diagonal, equal volume and shape
"VEI"diagonal, varying volume, equal shape
"EVI"diagonal, equal volume, varying shape
"VVI"diagonal, varying volume and shape
"EEE"ellipsoidal, equal volume, shape, and orientation
"VEE"ellipsoidal, equal shape and orientation (*)
"EVE"ellipsoidal, equal volume and orientation (*)
"VVE"ellipsoidal, equal orientation (*)
"EEV"ellipsoidal, equal volume and equal shape
"VEV"ellipsoidal, equal shape
"EVV"ellipsoidal, equal volume (*)
"VVV"ellipsoidal, varying volume, shape, and orientation
single component
"X"univariate normal
"XII"spherical multivariate normal
"XXI"diagonal multivariate normal
"XXX"ellipsoidal multivariate normal
(*) new models in mclust version >= 5.0.0.
Returns a list with the following components:
model |
a character string indicating the model (as in input). |
type |
the description of the indicated model (see Details section). |
mclustModelNames("E") mclustModelNames("EEE") mclustModelNames("VVV") mclustModelNames("XXI")mclustModelNames("E") mclustModelNames("EEE") mclustModelNames("VVV") mclustModelNames("XXI")
Semi-Supervised classification based on Gaussian finite mixture modeling.
MclustSSC(data, class, G = NULL, modelNames = NULL, prior = NULL, control = emControl(), warn = mclust.options("warn"), verbose = interactive(), ...)MclustSSC(data, class, G = NULL, modelNames = NULL, prior = NULL, control = emControl(), warn = mclust.options("warn"), verbose = interactive(), ...)
data |
A data frame or matrix giving the training data. |
class |
A vector giving the known class labels (either a numerical value or
a character string) for the observations in the training data.
Observations with unknown class are encoded as |
G |
An integer value specifying the numbers of mixture components or classes. By default is set equal to the number of known classes. See the examples below. |
modelNames |
A vector of character strings indicating the models to be fitted
by EM (see the description in |
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when
estimation fails.
The default is controlled by |
verbose |
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is |
... |
Further arguments passed to or from other methods. |
The semi-supervised approach implemented in MclustSSC() is a simple Gaussian mixture model for classification where at the first M-step only observations with known class labels are used for parameters estimation. Then, a standard EM algorithm is used for updating the probabiltiy of class membership for unlabelled data while keeping fixed the probabilities for labelled data.
An object of class 'MclustSSC' providing the optimal (according
to BIC) Gaussian mixture model for semi-supervised classification.
The details of the output components are as follows:
call |
The matched call. |
data |
The input data matrix. |
class |
The input class labels (including |
modelName |
A character string specifying the "best" estimated model. |
G |
A numerical value specifying the number of mixture components or classes of the "best" estimated model. |
n |
The total number of observations in the data. |
d |
The dimension of the data. |
BIC |
All BIC values. |
loglik |
Log-likelihood for the selected model. |
df |
Number of estimated parameters. |
bic |
Optimal BIC value. |
parameters |
A list with the following components:
|
z |
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class. |
classification |
The classification corresponding to |
prior |
The prior used (if any). |
control |
A list of control parameters used in the EM algorithm. |
Luca Scrucca
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
summary.MclustSSC,
plot.MclustSSC,
predict.MclustSSC
# Simulate two overlapping groups n <- 200 pars <- list(pro = c(0.5, 0.5), mean = matrix(c(-1,1), nrow = 2, ncol = 2, byrow = TRUE), variance = mclustVariance("EII", d = 2, G = 2)) pars$variance$sigmasq <- 1 data <- sim("EII", parameters = pars, n = n, seed = 12) class <- data[,1] X <- data[,-1] clPairs(X, class, symbols = c(1,2), main = "Full classified data") # Randomly remove labels cl <- class; cl[sample(1:n, size = 195)] <- NA table(cl, useNA = "ifany") clPairs(X, ifelse(is.na(cl), 0, class), symbols = c(0, 16, 17), colors = c("grey", 4, 2), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, cl) summary(mod_SSC, parameters = TRUE) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class) ngrid <- 50 xgrid <- seq(-3, 3, length.out = ngrid) ygrid <- seq(-4, 4.5, length.out = ngrid) xygrid <- expand.grid(xgrid, ygrid) pred_SSC <- predict(mod_SSC, newdata = xygrid) col <- mclust.options("classPlotColors")[class] pch <- class pch[!is.na(cl)] = ifelse(cl[!is.na(cl)] == 1, 19, 17) plot(X, pch = pch, col = col) contour(xgrid, ygrid, matrix(pred_SSC$z[,1], ngrid, ngrid), add = TRUE, levels = 0.5, drawlabels = FALSE, lty = 2, lwd = 2)# Simulate two overlapping groups n <- 200 pars <- list(pro = c(0.5, 0.5), mean = matrix(c(-1,1), nrow = 2, ncol = 2, byrow = TRUE), variance = mclustVariance("EII", d = 2, G = 2)) pars$variance$sigmasq <- 1 data <- sim("EII", parameters = pars, n = n, seed = 12) class <- data[,1] X <- data[,-1] clPairs(X, class, symbols = c(1,2), main = "Full classified data") # Randomly remove labels cl <- class; cl[sample(1:n, size = 195)] <- NA table(cl, useNA = "ifany") clPairs(X, ifelse(is.na(cl), 0, class), symbols = c(0, 16, 17), colors = c("grey", 4, 2), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, cl) summary(mod_SSC, parameters = TRUE) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class) ngrid <- 50 xgrid <- seq(-3, 3, length.out = ngrid) ygrid <- seq(-4, 4.5, length.out = ngrid) xygrid <- expand.grid(xgrid, ygrid) pred_SSC <- predict(mod_SSC, newdata = xygrid) col <- mclust.options("classPlotColors")[class] pch <- class pch[!is.na(cl)] = ifelse(cl[!is.na(cl)] == 1, 19, 17) plot(X, pch = pch, col = col) contour(xgrid, ygrid, matrix(pred_SSC$z[,1], ngrid, ngrid), add = TRUE, levels = 0.5, drawlabels = FALSE, lty = 2, lwd = 2)
Specification of variance parameters for the various types of Gaussian mixture models.
mclustVariance(modelName, d = NULL, G = 2)mclustVariance(modelName, d = NULL, G = 2)
modelName |
A character string specifying the model. |
d |
A integer specifying the dimension of the data. |
G |
An integer specifying the number of components in the mixture model. |
The variance component in the parameters list from the
output to e.g. me or mstep or input to e.g. estep may contain one or more of the following arguments, depending on the model:
modelNameA character string indicating the model.
dThe dimension of the data.
GThe number of components in the mixture model.
sigmasqfor the one-dimensional models ("E", "V") and spherical
models ("EII", "VII"). This is either a vector whose
kth component is the variance for the kth component in
the mixture model ("V" and "VII"), or a scalar giving
the common variance for all components in the mixture model ("E"
and "EII").
SigmaFor the equal variance models "EII", "EEI", and
"EEE".
A d by d matrix giving the common covariance for all
components of the mixture model.
cholSigmaFor the equal variance model "EEE".
A d by d upper triangular matrix giving the
Cholesky factor of the common covariance for all
components of the mixture model.
sigmaFor all multidimensional mixture models. A
d by d by G matrix array whose
[,,k]th entry is the covariance matrix for
the kth component of the mixture model.
cholsigmaFor the unconstrained covariance mixture model "VVV".
A d by d by G matrix array whose
[,,k]th entry is the upper triangular Cholesky factor
of the covariance matrix for the kth component of the
mixture model.
scaleFor diagonal models "EEI", "EVI", "VEI",
"VVI" and constant-shape models "EEV" and "VEV".
Either a G-vector giving the scale of the covariance (the
dth root of its determinant) for each component in the
mixture model, or a single numeric value if the scale is the
same for each component.
shapeFor diagonal models "EEI", "EVI", "VEI",
"VVI" and constant-shape models "EEV" and "VEV".
Either a G by d matrix in which the kth
column is the shape of the covariance matrix (normalized to have
determinant 1) for the kth component, or a
d-vector giving a common shape for all components.
orientationFor the constant-shape models "EEV" and "VEV".
Either a d by d by G array whose
[,,k]th entry is the orthonomal matrix whose
columns are the eigenvectors of the covariance matrix of
the kth component, or a d by d
orthonormal matrix if the mixture components have a
common orientation. The orientation component
is not needed in spherical and diagonal models, since
the principal components are parallel to the coordinate axes
so that the orientation matrix is the identity.
In all cases, the value
-1 is used as a placeholder for unknown nonzero entries.
Implements the EM algorithm for MVN mixture models parameterized by eignevalue decomposition, starting with the maximization step.
me(data, modelName, z, prior = NULL, control = emControl(), Vinv = NULL, warn = NULL, ...)me(data, modelName, z, prior = NULL, control = emControl(), Vinv = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
z |
A matrix whose |
prior |
Specification of a conjugate prior on the means and variances.
See the help file for |
control |
A list of control parameters for EM. The defaults are set by the call
|
Vinv |
If the model is to include a noise term, |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is set in |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
The number of mixture components. |
z |
A matrix whose |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
control |
The list of control parameters for EM used. |
prior |
The specification of a conjugate prior on the means and variances used,
|
Attributes: |
|
meE, ...,
meVVV,
em,
mstep,
estep,
priorControl,
mclustModelNames,
mclustVariance,
mclust.options
me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))me(modelName = "VVV", data = iris[,-5], z = unmap(iris[,5]))
Implements the EM algorithm for fitting Gaussian mixture models parameterized by eigenvalue decomposition, when observations have weights, starting with the maximization step.
me.weighted(data, modelName, z, weights = NULL, prior = NULL, control = emControl(), Vinv = NULL, warn = NULL, ...)me.weighted(data, modelName, z, weights = NULL, prior = NULL, control = emControl(), Vinv = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
z |
A matrix whose |
weights |
A vector of positive weights, where the |
prior |
Specification of a conjugate prior on the means and variances.
See the help file for |
control |
A list of control parameters for EM. The defaults are set by the call
|
Vinv |
If the model is to include a noise term, |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is set by |
... |
Catches unused arguments in indirect or list calls via |
This is a more efficient version made available with mclust using Fortran code internally.
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
z |
A matrix whose |
parameters |
|
loglik |
The log-likelihood for the estimated mixture model. |
bic |
The BIC value for the estimated mixture model. |
Attributes: |
|
T. Brendan Murphy, Luca Scrucca
me,
meE, ...,
meVVV,
em,
mstep,
estep,
priorControl,
mclustModelNames,
mclustVariance,
mclust.options
w = rexp(nrow(iris)) w = w/mean(w) c(summary(w), sum = sum(w)) z = unmap(sample(1:3, size = nrow(iris), replace = TRUE)) MEW = me.weighted(data = iris[,-5], modelName = "VVV", z = z, weights = w) str(MEW,1)w = rexp(nrow(iris)) w = w/mean(w) c(summary(w), sum = sum(w)) z = unmap(sample(1:3, size = nrow(iris), replace = TRUE)) MEW = me.weighted(data = iris[,-5], modelName = "VVV", z = z, weights = w) str(MEW,1)
Implements the EM algorithm for a parameterized Gaussian mixture model, starting with the maximization step.
meE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meX(data, prior = NULL, warn = NULL, ...) meEII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meXII(data, prior = NULL, warn = NULL, ...) meXXI(data, prior = NULL, warn = NULL, ...) meXXX(data, prior = NULL, warn = NULL, ...)meE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meX(data, prior = NULL, warn = NULL, ...) meEII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVII(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVI(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVE(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVEV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meEVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meVVV(data, z, prior=NULL, control=emControl(), Vinv=NULL, warn=NULL, ...) meXII(data, prior = NULL, warn = NULL, ...) meXXI(data, prior = NULL, warn = NULL, ...) meXXX(data, prior = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
z |
A matrix whose |
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
control |
A list of control parameters for EM. The defaults are set by the call
|
Vinv |
An estimate of the reciprocal hypervolume of the data region, when the
model is to include a noise term. Set to a negative value or zero if
a noise term is desired, but an estimate is unavailable — in that
case function |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is given by |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
z |
A matrix whose |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
|
em,
me,
estep,
mclust.options
meVVV(data = iris[,-5], z = unmap(iris[,5]))meVVV(data = iris[,-5], z = unmap(iris[,5]))
Maximization step in the EM algorithm for parameterized Gaussian mixture models.
mstep(data, modelName, z, prior = NULL, warn = NULL, ...)mstep(data, modelName, z, prior = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
modelName |
A character string indicating the model. The help file for
|
z |
A matrix whose |
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is given by |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
Attributes: |
|
This function computes the M-step only for MVN mixtures, so in
analyses involving noise, the conditional probabilities input should
exclude those for the noise component.
In contrast to me for the EM algorithm, computations in mstep
are carried out unless failure due to overflow would occur. To impose
stricter tolerances on a single mstep, use me with the
itmax component of the control argument set to 1.
mstepE, ...,
mstepVVV,
emControl,
me,
estep,
mclust.options.
mstep(modelName = "VII", data = iris[,-5], z = unmap(iris[,5]))mstep(modelName = "VII", data = iris[,-5], z = unmap(iris[,5]))
Maximization step in the EM algorithm for a parameterized Gaussian mixture model.
mstepE( data, z, prior = NULL, warn = NULL, ...) mstepV( data, z, prior = NULL, warn = NULL, ...) mstepEII( data, z, prior = NULL, warn = NULL, ...) mstepVII( data, z, prior = NULL, warn = NULL, ...) mstepEEI( data, z, prior = NULL, warn = NULL, ...) mstepVEI( data, z, prior = NULL, warn = NULL, control = NULL, ...) mstepEVI( data, z, prior = NULL, warn = NULL, ...) mstepVVI( data, z, prior = NULL, warn = NULL, ...) mstepEEE( data, z, prior = NULL, warn = NULL, ...) mstepEEV( data, z, prior = NULL, warn = NULL, ...) mstepVEV( data, z, prior = NULL, warn = NULL, control = NULL,...) mstepVVV( data, z, prior = NULL, warn = NULL, ...) mstepEVE( data, z, prior = NULL, warn = NULL, control = NULL, ...) mstepEVV( data, z, prior = NULL, warn = NULL, ...) mstepVEE( data, z, prior = NULL, warn = NULL, control = NULL, ...) mstepVVE( data, z, prior = NULL, warn = NULL, control = NULL, ...)mstepE( data, z, prior = NULL, warn = NULL, ...) mstepV( data, z, prior = NULL, warn = NULL, ...) mstepEII( data, z, prior = NULL, warn = NULL, ...) mstepVII( data, z, prior = NULL, warn = NULL, ...) mstepEEI( data, z, prior = NULL, warn = NULL, ...) mstepVEI( data, z, prior = NULL, warn = NULL, control = NULL, ...) mstepEVI( data, z, prior = NULL, warn = NULL, ...) mstepVVI( data, z, prior = NULL, warn = NULL, ...) mstepEEE( data, z, prior = NULL, warn = NULL, ...) mstepEEV( data, z, prior = NULL, warn = NULL, ...) mstepVEV( data, z, prior = NULL, warn = NULL, control = NULL,...) mstepVVV( data, z, prior = NULL, warn = NULL, ...) mstepEVE( data, z, prior = NULL, warn = NULL, control = NULL, ...) mstepEVV( data, z, prior = NULL, warn = NULL, ...) mstepVEE( data, z, prior = NULL, warn = NULL, control = NULL, ...) mstepVVE( data, z, prior = NULL, warn = NULL, control = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
z |
A matrix whose |
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when the
estimation fails. The default is given by |
control |
Values controlling termination for models |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
Attributes: |
|
This function computes the M-step only for MVN mixtures, so in
analyses involving noise, the conditional probabilities input should
exclude those for the noise component.
In contrast to me for the EM algorithm, computations in mstep
are carried out unless failure due to overflow would occur. To impose
stricter tolerances on a single mstep, use me with the
itmax component of the control argument set to 1.
mstep,
me,
estep,
mclustVariance,
priorControl,
emControl.
mstepVII(data = iris[,-5], z = unmap(iris[,5]))mstepVII(data = iris[,-5], z = unmap(iris[,5]))
Computes the mean, covariance, and log-likelihood from fitting a single Gaussian to given data (univariate or multivariate normal).
mvn( modelName, data, prior = NULL, warn = NULL, ...)mvn( modelName, data, prior = NULL, warn = NULL, ...)
modelName |
A character string representing a model name. This can be either
|
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
warn |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is given by |
... |
Catches unused arguments in indirect or list calls via |
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
|
mvnX,
mvnXII,
mvnXXI,
mvnXXX,
mclustModelNames
n <- 1000 set.seed(0) x <- rnorm(n, mean = -1, sd = 2) mvn(modelName = "X", x) mu <- c(-1, 0, 1) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)), MARGIN = 2, STATS = mu, FUN = "+") mvn(modelName = "XII", x) mvn(modelName = "Spherical", x) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3), MARGIN = 2, STATS = mu, FUN = "+") mvn(modelName = "XXI", x) mvn(modelName = "Diagonal", x) Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma), MARGIN = 2, STATS = mu, FUN = "+") mvn(modelName = "XXX", x) mvn(modelName = "Ellipsoidal", x)n <- 1000 set.seed(0) x <- rnorm(n, mean = -1, sd = 2) mvn(modelName = "X", x) mu <- c(-1, 0, 1) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)), MARGIN = 2, STATS = mu, FUN = "+") mvn(modelName = "XII", x) mvn(modelName = "Spherical", x) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3), MARGIN = 2, STATS = mu, FUN = "+") mvn(modelName = "XXI", x) mvn(modelName = "Diagonal", x) Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma), MARGIN = 2, STATS = mu, FUN = "+") mvn(modelName = "XXX", x) mvn(modelName = "Ellipsoidal", x)
Computes the mean, covariance, and log-likelihood from fitting a single Gaussian (univariate or multivariate normal).
mvnX(data, prior = NULL, warn = NULL, ...) mvnXII(data, prior = NULL, warn = NULL, ...) mvnXXI(data, prior = NULL, warn = NULL, ...) mvnXXX(data, prior = NULL, warn = NULL, ...)mvnX(data, prior = NULL, warn = NULL, ...) mvnXII(data, prior = NULL, warn = NULL, ...) mvnXXI(data, prior = NULL, warn = NULL, ...) mvnXXX(data, prior = NULL, warn = NULL, ...)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
prior |
Specification of a conjugate prior on the means and variances. The default assumes no prior. |
warn |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is given by |
... |
Catches unused arguments in indirect or list calls via |
mvnXIIcomputes the best fitting Gaussian with the covariance restricted to be a multiple of the identity.
mvnXXIcomputes the best fitting Gaussian with the covariance restricted to be diagonal.
mvnXXXcomputes the best fitting Gaussian with ellipsoidal (unrestricted) covariance.
A list including the following components:
modelName |
A character string identifying the model (same as the input argument). |
parameters |
|
loglik |
The log likelihood for the data in the mixture model. |
Attributes: |
|
n <- 1000 set.seed(0) x <- rnorm(n, mean = -1, sd = 2) mvnX(x) mu <- c(-1, 0, 1) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)), MARGIN = 2, STATS = mu, FUN = "+") mvnXII(x) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3), MARGIN = 2, STATS = mu, FUN = "+") mvnXXI(x) Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma), MARGIN = 2, STATS = mu, FUN = "+") mvnXXX(x)n <- 1000 set.seed(0) x <- rnorm(n, mean = -1, sd = 2) mvnX(x) mu <- c(-1, 0, 1) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% (2*diag(3)), MARGIN = 2, STATS = mu, FUN = "+") mvnXII(x) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% diag(1:3), MARGIN = 2, STATS = mu, FUN = "+") mvnXXI(x) Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3) set.seed(0) x <- sweep(matrix(rnorm(n*3), n, 3) %*% chol(Sigma), MARGIN = 2, STATS = mu, FUN = "+") mvnXXX(x)
Gives the number of estimated parameters for parameterizations of the Gaussian mixture model that are used in MCLUST.
nMclustParams(modelName, d, G, noise = FALSE, equalPro = FALSE, ...)nMclustParams(modelName, d, G, noise = FALSE, equalPro = FALSE, ...)
modelName |
A character string indicating the model. The help file for
|
d |
The dimension of the data. Not used for models in which neither the shape nor the orientation varies. |
G |
The number of components in the Gaussian mixture model used to compute
|
noise |
A logical variable indicating whether or not the model includes an optional Poisson noise component. |
equalPro |
A logical variable indicating whether or not the components in the model are assumed to be present in equal proportion. |
... |
Catches unused arguments in indirect or list calls via |
To get the total number of parameters in model, add G*d for the
means and G-1 for the mixing proportions if they are unequal.
The number of variance parameters in the corresponding Gaussian mixture model.
mapply(nMclustParams, mclust.options("emModelNames"), d = 2, G = 3)mapply(nMclustParams, mclust.options("emModelNames"), d = 2, G = 3)
Gives the number of variance parameters for parameterizations of the Gaussian mixture model that are used in MCLUST.
nVarParams(modelName, d, G, ...)nVarParams(modelName, d, G, ...)
modelName |
A character string indicating the model. The help file for
|
d |
The dimension of the data. Not used for models in which neither the shape nor the orientation varies. |
G |
The number of components in the Gaussian mixture model used to compute
|
... |
Catches unused arguments in indirect or list calls via |
To get the total number of parameters in model, add G*d for the
means and G-1 for the mixing proportions if they are unequal.
The number of variance parameters in the corresponding Gaussian mixture model.
mapply(nVarParams, mclust.options("emModelNames"), d = 2, G = 3)mapply(nVarParams, mclust.options("emModelNames"), d = 2, G = 3)
Converts a vector interpreted as a classification or partitioning into a numeric vector.
partconv(x, consec=TRUE)partconv(x, consec=TRUE)
x |
A vector interpreted as a classification or partitioning. |
consec |
Logical value indicating whether or not to consecutive class numbers should be used . |
Numeric encoding of x.
When consec = TRUE, the distinct values in x are numbered by
the order in which they appear.
When consec = FALSE, each distinct value in x is numbered by
the index corresponding to its first appearance in x.
partconv(iris[,5]) set.seed(0) cl <- sample(LETTERS[1:9], 25, replace=TRUE) partconv(cl, consec=FALSE) partconv(cl, consec=TRUE)partconv(iris[,5]) set.seed(0) cl <- sample(LETTERS[1:9], 25, replace=TRUE) partconv(cl, consec=FALSE) partconv(cl, consec=TRUE)
Gives a one-to-one mapping from unique observations to rows of a data matrix.
partuniq(x)partuniq(x)
x |
Matrix of observations. |
A vector of length nrow(x) with integer entries. An observation
k is assigned an integer i whenever observation i
is the first row of x that is identical to observation k
(note that i <= k).
set.seed(0) mat <- data.frame(lets = sample(LETTERS[1:2],9,TRUE), nums = sample(1:2,9,TRUE)) mat ans <- partuniq(mat) ans partconv(ans,consec=TRUE)set.seed(0) mat <- data.frame(lets = sample(LETTERS[1:2],9,TRUE), nums = sample(1:2,9,TRUE)) mat ans <- partuniq(mat) ans partconv(ans,consec=TRUE)
Plot combined clusterings results: classifications corresponding to Mclust/BIC and to the hierarchically combined classes, "entropy plots" to help to select a number of classes, and the tree structure obtained from combining mixture components.
## S3 method for class 'clustCombi' plot(x, what = c("classification", "entropy", "tree"), ...)## S3 method for class 'clustCombi' plot(x, what = c("classification", "entropy", "tree"), ...)
x |
Object returned by |
what |
Type of plot. |
... |
Other arguments to be passed to other functions: |
Classifications are plotted with combiPlot, which relies on the Mclust plot functions.
Entropy plots are plotted with entPlot and may help to select a number of classes: please see the article cited in the references.
Tree plots are produced by combiTree and graph the tree structure implied by the clusters combining process.
J.-P. Baudry, A. E. Raftery, L. Scrucca
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353.
combiPlot, entPlot, combiTree, clustCombi.
data(Baudry_etal_2010_JCGS_examples) ## 1D Example output <- clustCombi(data = Test1D, G=1:15) # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes (please see the article cited # in the references) plot(output) ## 2D Example output <- clustCombi(data = ex4.1) # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes (please see the article cited # in the references) plot(output) ## 3D Example output <- clustCombi(data = ex4.4.2) # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes (please see the article cited # in the references) plot(output)data(Baudry_etal_2010_JCGS_examples) ## 1D Example output <- clustCombi(data = Test1D, G=1:15) # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes (please see the article cited # in the references) plot(output) ## 2D Example output <- clustCombi(data = ex4.1) # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes (please see the article cited # in the references) plot(output) ## 3D Example output <- clustCombi(data = ex4.4.2) # plots the hierarchy of combined solutions, then some "entropy plots" which # may help one to select the number of classes (please see the article cited # in the references) plot(output)
Plotting methods for an object of class 'mclustDensity'. Available graphs
are plot of BIC values and density for univariate and bivariate data. For
higher data dimensionality a scatterplot matrix of pairwise densities is
drawn.
## S3 method for class 'densityMclust' plot(x, data = NULL, what = c("BIC", "density", "diagnostic"), ...) plotDensityMclust1(x, data = NULL, col = gray(0.3), hist.col = "lightgrey", hist.border = "white", breaks = "Sturges", ...) plotDensityMclust2(x, data = NULL, nlevels = 11, levels = NULL, prob = c(0.25, 0.5, 0.75), points.pch = 1, points.col = 1, points.cex = 0.8, ...) plotDensityMclustd(x, data = NULL, nlevels = 11, levels = NULL, prob = c(0.25, 0.5, 0.75), points.pch = 1, points.col = 1, points.cex = 0.8, gap = 0.2, ...)## S3 method for class 'densityMclust' plot(x, data = NULL, what = c("BIC", "density", "diagnostic"), ...) plotDensityMclust1(x, data = NULL, col = gray(0.3), hist.col = "lightgrey", hist.border = "white", breaks = "Sturges", ...) plotDensityMclust2(x, data = NULL, nlevels = 11, levels = NULL, prob = c(0.25, 0.5, 0.75), points.pch = 1, points.col = 1, points.cex = 0.8, ...) plotDensityMclustd(x, data = NULL, nlevels = 11, levels = NULL, prob = c(0.25, 0.5, 0.75), points.pch = 1, points.col = 1, points.cex = 0.8, gap = 0.2, ...)
x |
An object of class |
data |
Optional data points. |
what |
The type of graph requested:
|
col |
The color to be used to draw the density line in 1-dimension or contours in higher dimensions. |
hist.col |
The color to be used to fill the bars of the histogram. |
hist.border |
The color of the border around the bars of the histogram. |
breaks |
See the argument in function |
points.pch, points.col, points.cex
|
The character symbols, colors, and magnification to be used for plotting |
nlevels |
An integer, the number of levels to be used in plotting contour densities. |
levels |
A vector of density levels at which to draw the contour lines. |
prob |
A vector of probability levels for computing HDR. Only used if |
gap |
Distance between subplots, in margin lines, for the matrix of pairwise scatterplots. |
... |
Additional arguments passed to |
The function plot.densityMclust allows to obtain the plot of
estimated density or the graph of BIC values for evaluated models.
If what = "density" the produced plot dependes on the dimensionality
of the data.
For one-dimensional data a call with no data provided produces a
plot of the estimated density over a sensible range of values. If
data is provided the density is over-plotted on a histogram for the
observed data.
For two-dimensional data further arguments available are those accepted by
the surfacePlot function. In particular, the density can be
represented through "contour", "hdr", "image", and
"persp" type of graph.
For type = "hdr" Highest Density Regions (HDRs) are plotted for
probability levels prob. See hdrlevels for details.
For higher dimensionality a scatterplot matrix of pairwise projected densities is drawn.
Luca Scrucca
densityMclust,
surfacePlot,
densityMclust.diagnostic,
Mclust.
dens <- densityMclust(faithful$waiting, plot = FALSE) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "BIC", legendArgs = list(x = "topright")) plot(dens, what = "density", data = faithful$waiting) dens <- densityMclust(faithful, plot = FALSE) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "density", data = faithful, drawlabels = FALSE, points.pch = 20) plot(dens, what = "density", type = "hdr") plot(dens, what = "density", type = "hdr", prob = seq(0.1, 0.9, by = 0.1)) plot(dens, what = "density", type = "hdr", data = faithful) plot(dens, what = "density", type = "persp") dens <- densityMclust(iris[,1:4], plot = FALSE) summary(dens, parameters = TRUE) plot(dens, what = "density", data = iris[,1:4], col = "slategrey", drawlabels = FALSE, nlevels = 7) plot(dens, what = "density", type = "hdr", data = iris[,1:4]) plot(dens, what = "density", type = "persp", col = grey(0.9))dens <- densityMclust(faithful$waiting, plot = FALSE) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "BIC", legendArgs = list(x = "topright")) plot(dens, what = "density", data = faithful$waiting) dens <- densityMclust(faithful, plot = FALSE) summary(dens) summary(dens, parameters = TRUE) plot(dens, what = "density", data = faithful, drawlabels = FALSE, points.pch = 20) plot(dens, what = "density", type = "hdr") plot(dens, what = "density", type = "hdr", prob = seq(0.1, 0.9, by = 0.1)) plot(dens, what = "density", type = "hdr", data = faithful) plot(dens, what = "density", type = "persp") dens <- densityMclust(iris[,1:4], plot = FALSE) summary(dens, parameters = TRUE) plot(dens, what = "density", data = iris[,1:4], col = "slategrey", drawlabels = FALSE, nlevels = 7) plot(dens, what = "density", type = "hdr", data = iris[,1:4]) plot(dens, what = "density", type = "persp", col = grey(0.9))
Display two types for dendrograms for model-based hierarchical clustering objects.
## S3 method for class 'hc' plot(x, what=c("loglik","merge"), maxG=NULL, labels=FALSE, hang=0, ...)## S3 method for class 'hc' plot(x, what=c("loglik","merge"), maxG=NULL, labels=FALSE, hang=0, ...)
x |
An object of class |
what |
A character string indicating the type of dendrogram to be displayed.
|
maxG |
The maximum number of clusters for the dendrogram.
For |
labels |
A logical variable indicating whether or not to display leaf (observation) labels for the dendrogram (row names of the data). These are likely to be useful only if the number of observations in fairly small, since otherwise the labels will be too crowded to read. The default is not to display the leaf labels. |
hang |
For |
... |
Additional plotting arguments. |
The plotting input does not share all of the properties of hclust
objects, hence not all plotting arguments associated with hclust
can be expected to work here.
A dendrogram is drawn, with distances based on either the classification likelihood or the merge level (number of clusters).
If modelName = "E" (univariate with equal variances) or
modelName = "EII" (multivariate with equal spherical
covariances), then the underlying model is the same as for
Ward's method for hierarchical clustering.
J. D. Banfield and A. E. Raftery (1993). Model-based Gaussian and non-Gaussian Clustering. Biometrics 49:803-821.
C. Fraley (1998). Algorithms for model-based Gaussian hierarchical clustering. SIAM Journal on Scientific Computing 20:270-281.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
data(EuroUnemployment) hcTree <- hc(modelName = "VVV", data = EuroUnemployment) plot(hcTree, what = "loglik") plot(hcTree, what = "loglik", labels = TRUE) plot(hcTree, what = "loglik", maxG = 5, labels = TRUE) plot(hcTree, what = "merge") plot(hcTree, what = "merge", labels = TRUE) plot(hcTree, what = "merge", labels = TRUE, hang = 0.1) plot(hcTree, what = "merge", labels = TRUE, hang = -1) plot(hcTree, what = "merge", labels = TRUE, maxG = 5)data(EuroUnemployment) hcTree <- hc(modelName = "VVV", data = EuroUnemployment) plot(hcTree, what = "loglik") plot(hcTree, what = "loglik", labels = TRUE) plot(hcTree, what = "loglik", maxG = 5, labels = TRUE) plot(hcTree, what = "merge") plot(hcTree, what = "merge", labels = TRUE) plot(hcTree, what = "merge", labels = TRUE, hang = 0.1) plot(hcTree, what = "merge", labels = TRUE, hang = -1) plot(hcTree, what = "merge", labels = TRUE, maxG = 5)
Plots for model-based clustering results, such as BIC, classification, uncertainty and density.
## S3 method for class 'Mclust' plot(x, what = c("BIC", "classification", "uncertainty", "density"), dimens = NULL, xlab = NULL, ylab = NULL, addEllipses = TRUE, main = FALSE, ...)## S3 method for class 'Mclust' plot(x, what = c("BIC", "classification", "uncertainty", "density"), dimens = NULL, xlab = NULL, ylab = NULL, addEllipses = TRUE, main = FALSE, ...)
x |
Output from |
what |
A string specifying the type of graph requested. Available choices are:
If not specified, in interactive sessions a menu of choices is proposed. |
dimens |
A vector of integers specifying the dimensions of the coordinate projections
in case of |
xlab, ylab
|
Optional labels for the x-axis and the y-axis. |
addEllipses |
A logical indicating whether or not to add ellipses with axes
corresponding to the within-cluster covariances in case of
|
main |
A logical or |
... |
Other graphics parameters. |
For more flexibility in plotting, use mclust1Dplot,
mclust2Dplot, surfacePlot, coordProj, or
randProj.
Mclust,
plot.mclustBIC,
plot.mclustICL,
mclust1Dplot,
mclust2Dplot,
surfacePlot,
coordProj,
randProj.
precipMclust <- Mclust(precip) plot(precipMclust) faithfulMclust <- Mclust(faithful) plot(faithfulMclust) irisMclust <- Mclust(iris[,-5]) plot(irisMclust)precipMclust <- Mclust(precip) plot(precipMclust) faithfulMclust <- Mclust(faithful) plot(faithfulMclust) irisMclust <- Mclust(iris[,-5]) plot(irisMclust)
Plots the BIC values returned by the mclustBIC function.
## S3 method for class 'mclustBIC' plot(x, G = NULL, modelNames = NULL, symbols = NULL, colors = NULL, xlab = NULL, ylab = "BIC", legendArgs = list(x = "bottomright", ncol = 2, cex = 1, inset = 0.01), ...)## S3 method for class 'mclustBIC' plot(x, G = NULL, modelNames = NULL, symbols = NULL, colors = NULL, xlab = NULL, ylab = "BIC", legendArgs = list(x = "bottomright", ncol = 2, cex = 1, inset = 0.01), ...)
x |
Output from |
G |
One or more numbers of components corresponding to models fit in |
modelNames |
One or more model names corresponding to models fit in |
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique class in |
colors |
Either an integer or character vector assigning a color to each
unique class in |
xlab |
Optional label for the horizontal axis of the BIC plot. |
ylab |
Label for the vertical axis of the BIC plot. |
legendArgs |
Arguments to pass to the |
... |
Other graphics parameters. |
A plot of the BIC values.
plot(mclustBIC(precip), legendArgs = list(x = "bottomleft")) plot(mclustBIC(faithful)) plot(mclustBIC(iris[,-5]))plot(mclustBIC(precip), legendArgs = list(x = "bottomleft")) plot(mclustBIC(faithful)) plot(mclustBIC(iris[,-5]))
Plots the bootstrap distribution of parameters as returned by the MclustBootstrap function.
## S3 method for class 'MclustBootstrap' plot(x, what = c("pro", "mean", "var"), show.parest = TRUE, show.confint = TRUE, hist.col = "grey", hist.border = "lightgrey", breaks = NA, col = "forestgreen", lwd = 2, lty = 3, xlab = NULL, xlim = NULL, ylim = NULL, ...)## S3 method for class 'MclustBootstrap' plot(x, what = c("pro", "mean", "var"), show.parest = TRUE, show.confint = TRUE, hist.col = "grey", hist.border = "lightgrey", breaks = NA, col = "forestgreen", lwd = 2, lty = 3, xlab = NULL, xlim = NULL, ylim = NULL, ...)
x |
Object returned by |
what |
Character string specifying if mixing proportions ( |
show.parest |
A logical specifying if the parameter estimate should be drawn as vertical line. |
show.confint |
A logical specifying if the resampling-based confidence interval should be drawn at the bottom of the graph. Confidence level can be provided as further argument |
hist.col |
The color to be used to fill the bars of the histograms. |
hist.border |
The color of the border around the bars of the histograms. |
breaks |
The number of breaks used in histogram to visualize the bootstrap distribution. When |
col, lwd, lty
|
The color, line width and line type to be used to represent the estimated parameters and confidence intervals. |
xlab |
Optional label for the horizontal axis. |
xlim, ylim
|
A two-values vector of axis range for, respectively, horizontal and vertical axis. |
... |
Other graphics parameters. |
A plot for each variable/component of the selected parameters.
data(diabetes) X <- diabetes[,-1] modClust <- Mclust(X, G = 3, modelNames = "VVV") bootClust <- MclustBootstrap(modClust, nboot = 99) par(mfrow = c(1,3), mar = c(4,2,2,0.5)) plot(bootClust, what = "pro") par(mfrow = c(3,3), mar = c(4,2,2,0.5)) plot(bootClust, what = "mean")data(diabetes) X <- diabetes[,-1] modClust <- Mclust(X, G = 3, modelNames = "VVV") bootClust <- MclustBootstrap(modClust, nboot = 99) par(mfrow = c(1,3), mar = c(4,2,2,0.5)) plot(bootClust, what = "pro") par(mfrow = c(3,3), mar = c(4,2,2,0.5)) plot(bootClust, what = "mean")
Plots for model-based mixture discriminant analysis results, such as scatterplot of training and test data, classification of train and test data, and errors.
## S3 method for class 'MclustDA' plot(x, what = c("scatterplot", "classification", "train&test", "error"), newdata, newclass, dimens = NULL, symbols, colors, main = NULL, ...)## S3 method for class 'MclustDA' plot(x, what = c("scatterplot", "classification", "train&test", "error"), newdata, newclass, dimens = NULL, symbols, colors, main = NULL, ...)
x |
An object of class |
what |
A string specifying the type of graph requested. Available choices are:
If not specified, in interactive sessions a menu of choices is proposed. |
newdata |
A data frame or matrix for test data. |
newclass |
A vector giving the class labels for the observations in the test data (if known). |
dimens |
A vector of integers giving the dimensions of the desired coordinate projections for multivariate data. The default is to take all the the available dimensions for plotting. |
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique class. Elements in |
colors |
Either an integer or character vector assigning a color to each
unique class in |
main |
A logical, a character string, or |
... |
further arguments passed to or from other methods. |
For more flexibility in plotting, use mclust1Dplot,
mclust2Dplot, surfacePlot, coordProj, or
randProj.
Luca Scrucca
MclustDA,
surfacePlot,
coordProj,
randProj
odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] # common EEE covariance structure (which is essentially equivalent to linear discriminant analysis) irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA", modelNames = "EEE") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # common covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # general covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train) summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) plot(irisMclustDA) plot(irisMclustDA, dimens = 3:4) plot(irisMclustDA, dimens = 4) plot(irisMclustDA, what = "classification") plot(irisMclustDA, what = "classification", newdata = X.test) plot(irisMclustDA, what = "classification", dimens = 3:4) plot(irisMclustDA, what = "classification", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "classification", dimens = 4) plot(irisMclustDA, what = "classification", dimens = 4, newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 4) plot(irisMclustDA, what = "error") plot(irisMclustDA, what = "error", dimens = 3:4) plot(irisMclustDA, what = "error", dimens = 4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 3:4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 4) # simulated 1D data n <- 250 set.seed(1) triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) triClass <- c(rep(1,n), rep(2,n), rep(3,n)) odd <- seq(from = 1, to = length(triModal), by = 2) even <- odd + 1 triMclustDA <- MclustDA(triModal[odd], triClass[odd]) summary(triMclustDA, parameters = TRUE) summary(triMclustDA, newdata = triModal[even], newclass = triClass[even]) plot(triMclustDA) plot(triMclustDA, what = "classification") plot(triMclustDA, what = "classification", newdata = triModal[even]) plot(triMclustDA, what = "train&test", newdata = triModal[even]) plot(triMclustDA, what = "error") plot(triMclustDA, what = "error", newdata = triModal[even], newclass = triClass[even]) # simulated 2D cross data data(cross) odd <- seq(from = 1, to = nrow(cross), by = 2) even <- odd + 1 crossMclustDA <- MclustDA(cross[odd,-1], cross[odd,1]) summary(crossMclustDA, parameters = TRUE) summary(crossMclustDA, newdata = cross[even,-1], newclass = cross[even,1]) plot(crossMclustDA) plot(crossMclustDA, what = "classification") plot(crossMclustDA, what = "classification", newdata = cross[even,-1]) plot(crossMclustDA, what = "train&test", newdata = cross[even,-1]) plot(crossMclustDA, what = "error") plot(crossMclustDA, what = "error", newdata =cross[even,-1], newclass = cross[even,1])odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] # common EEE covariance structure (which is essentially equivalent to linear discriminant analysis) irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA", modelNames = "EEE") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # common covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA") summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) # general covariance structure selected by BIC irisMclustDA <- MclustDA(X.train, Class.train) summary(irisMclustDA, parameters = TRUE) summary(irisMclustDA, newdata = X.test, newclass = Class.test) plot(irisMclustDA) plot(irisMclustDA, dimens = 3:4) plot(irisMclustDA, dimens = 4) plot(irisMclustDA, what = "classification") plot(irisMclustDA, what = "classification", newdata = X.test) plot(irisMclustDA, what = "classification", dimens = 3:4) plot(irisMclustDA, what = "classification", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "classification", dimens = 4) plot(irisMclustDA, what = "classification", dimens = 4, newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 3:4) plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 4) plot(irisMclustDA, what = "error") plot(irisMclustDA, what = "error", dimens = 3:4) plot(irisMclustDA, what = "error", dimens = 4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 3:4) plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 4) # simulated 1D data n <- 250 set.seed(1) triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5)) triClass <- c(rep(1,n), rep(2,n), rep(3,n)) odd <- seq(from = 1, to = length(triModal), by = 2) even <- odd + 1 triMclustDA <- MclustDA(triModal[odd], triClass[odd]) summary(triMclustDA, parameters = TRUE) summary(triMclustDA, newdata = triModal[even], newclass = triClass[even]) plot(triMclustDA) plot(triMclustDA, what = "classification") plot(triMclustDA, what = "classification", newdata = triModal[even]) plot(triMclustDA, what = "train&test", newdata = triModal[even]) plot(triMclustDA, what = "error") plot(triMclustDA, what = "error", newdata = triModal[even], newclass = triClass[even]) # simulated 2D cross data data(cross) odd <- seq(from = 1, to = nrow(cross), by = 2) even <- odd + 1 crossMclustDA <- MclustDA(cross[odd,-1], cross[odd,1]) summary(crossMclustDA, parameters = TRUE) summary(crossMclustDA, newdata = cross[even,-1], newclass = cross[even,1]) plot(crossMclustDA) plot(crossMclustDA, what = "classification") plot(crossMclustDA, what = "classification", newdata = cross[even,-1]) plot(crossMclustDA, what = "train&test", newdata = cross[even,-1]) plot(crossMclustDA, what = "error") plot(crossMclustDA, what = "error", newdata =cross[even,-1], newclass = cross[even,1])
Graphs data projected onto the estimated subspace for model-based clustering and classification.
## S3 method for class 'MclustDR' plot(x, dimens, what = c("scatterplot", "pairs", "contour", "classification", "boundaries", "density", "evalues"), symbols, colors, col.contour = gray(0.7), col.sep = grey(0.4), ngrid = 200, nlevels = 5, asp = NULL, ...)## S3 method for class 'MclustDR' plot(x, dimens, what = c("scatterplot", "pairs", "contour", "classification", "boundaries", "density", "evalues"), symbols, colors, col.contour = gray(0.7), col.sep = grey(0.4), ngrid = 200, nlevels = 5, asp = NULL, ...)
x |
An object of class |
dimens |
A vector of integers giving the dimensions of the desired coordinate projections for multivariate data. |
what |
The type of graph requested:
|
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique mixture component. Elements in |
colors |
Either an integer or character vector assigning a color to each
unique cluster or known class. Elements in |
col.contour |
The color of contours in case |
col.sep |
The color of classification boundaries in case |
ngrid |
An integer specifying the number of grid points to use in evaluating the classification regions. |
nlevels |
The number of levels to use in case |
asp |
For scatterplots the |
... |
further arguments passed to or from other methods. |
Luca Scrucca
Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.
mod <- Mclust(iris[,1:4], G = 3) dr <- MclustDR(mod, lambda = 0.5) plot(dr, what = "evalues") plot(dr, what = "pairs") plot(dr, what = "scatterplot", dimens = c(1,3)) plot(dr, what = "contour") plot(dr, what = "classification", ngrid = 200) plot(dr, what = "boundaries", ngrid = 200) plot(dr, what = "density") plot(dr, what = "density", dimens = 2) data(banknote) da <- MclustDA(banknote[,2:7], banknote$Status, G = 1:3) dr <- MclustDR(da) plot(dr, what = "evalues") plot(dr, what = "pairs") plot(dr, what = "contour") plot(dr, what = "classification", ngrid = 200) plot(dr, what = "boundaries", ngrid = 200) plot(dr, what = "density") plot(dr, what = "density", dimens = 2)mod <- Mclust(iris[,1:4], G = 3) dr <- MclustDR(mod, lambda = 0.5) plot(dr, what = "evalues") plot(dr, what = "pairs") plot(dr, what = "scatterplot", dimens = c(1,3)) plot(dr, what = "contour") plot(dr, what = "classification", ngrid = 200) plot(dr, what = "boundaries", ngrid = 200) plot(dr, what = "density") plot(dr, what = "density", dimens = 2) data(banknote) da <- MclustDA(banknote[,2:7], banknote$Status, G = 1:3) dr <- MclustDR(da) plot(dr, what = "evalues") plot(dr, what = "pairs") plot(dr, what = "contour") plot(dr, what = "classification", ngrid = 200) plot(dr, what = "boundaries", ngrid = 200) plot(dr, what = "density") plot(dr, what = "density", dimens = 2)
Plots the ICL values returned by the mclustICL function.
## S3 method for class 'mclustICL' plot(x, ylab = "ICL", ...)## S3 method for class 'mclustICL' plot(x, ylab = "ICL", ...)
x |
Output from |
ylab |
Label for the vertical axis of the plot. |
... |
Further arguments passed to the |
A plot of the ICL values.
data(faithful) faithful.ICL = mclustICL(faithful) plot(faithful.ICL)data(faithful) faithful.ICL = mclustICL(faithful) plot(faithful.ICL)
Plots for semi-supervised classification based on Gaussian finite mixture models.
## S3 method for class 'MclustSSC' plot(x, what = c("BIC", "classification", "uncertainty"), ...)## S3 method for class 'MclustSSC' plot(x, what = c("BIC", "classification", "uncertainty"), ...)
x |
An object of class |
what |
A string specifying the type of graph requested. Available choices are:
If not specified, in interactive sessions a menu of choices is proposed. |
... |
further arguments passed to or from other methods. See |
Luca Scrucca
X <- iris[,1:4] class <- iris$Species # randomly remove class labels set.seed(123) class[sample(1:length(class), size = 120)] <- NA table(class, useNA = "ifany") clPairs(X, ifelse(is.na(class), 0, class), symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, class) summary(mod_SSC, parameters = TRUE) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany") plot(mod_SSC, what = "BIC") plot(mod_SSC, what = "classification") plot(mod_SSC, what = "uncertainty")X <- iris[,1:4] class <- iris$Species # randomly remove class labels set.seed(123) class[sample(1:length(class), size = 120)] <- NA table(class, useNA = "ifany") clPairs(X, ifelse(is.na(class), 0, class), symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, class) summary(mod_SSC, parameters = TRUE) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany") plot(mod_SSC, what = "BIC") plot(mod_SSC, what = "classification") plot(mod_SSC, what = "uncertainty")
Compute density estimation for multivariate observations based on Gaussian finite mixture models estimated by densityMclust.
## S3 method for class 'densityMclust' predict(object, newdata, what = c("dens", "cdens", "z"), logarithm = FALSE, ...)## S3 method for class 'densityMclust' predict(object, newdata, what = c("dens", "cdens", "z"), logarithm = FALSE, ...)
object |
an object of class |
newdata |
a vector, a data frame or matrix giving the data. If missing the density is computed for the input data obtained from the call to |
what |
a character string specifying what to retrieve: |
logarithm |
A logical value indicating whether or not the logarithm of the density or component densities should be returned. |
... |
further arguments passed to or from other methods. |
Returns a vector or a matrix of densities evaluated at newdata depending on the argument what (see above).
Luca Scrucca
x <- faithful$waiting dens <- densityMclust(x, plot = FALSE) x0 <- seq(50, 100, by = 10) d0 <- predict(dens, x0) plot(dens, what = "density") points(x0, d0, pch = 20)x <- faithful$waiting dens <- densityMclust(x, plot = FALSE) x0 <- seq(50, 100, by = 10) d0 <- predict(dens, x0) plot(dens, what = "density") points(x0, d0, pch = 20)
Cluster prediction for multivariate observations based on Gaussian finite mixture models estimated by Mclust.
## S3 method for class 'Mclust' predict(object, newdata, ...)## S3 method for class 'Mclust' predict(object, newdata, ...)
object |
an object of class |
newdata |
a data frame or matrix giving the data. If missing the clustering data obtained from the call to |
... |
further arguments passed to or from other methods. |
Returns a list of with the following components:
classification |
a factor of predicted cluster labels for |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
Luca Scrucca
model <- Mclust(faithful) # predict cluster for the observed data pred <- predict(model) str(pred) pred$z # equal to model$z pred$classification # equal to plot(faithful, col = pred$classification, pch = pred$classification) # predict cluster over a grid grid <- apply(faithful, 2, function(x) seq(min(x), max(x), length = 50)) grid <- expand.grid(eruptions = grid[,1], waiting = grid[,2]) pred <- predict(model, grid) plot(grid, col = mclust.options("classPlotColors")[pred$classification], pch = 15, cex = 0.5) points(faithful, pch = model$classification)model <- Mclust(faithful) # predict cluster for the observed data pred <- predict(model) str(pred) pred$z # equal to model$z pred$classification # equal to plot(faithful, col = pred$classification, pch = pred$classification) # predict cluster over a grid grid <- apply(faithful, 2, function(x) seq(min(x), max(x), length = 50)) grid <- expand.grid(eruptions = grid[,1], waiting = grid[,2]) pred <- predict(model, grid) plot(grid, col = mclust.options("classPlotColors")[pred$classification], pch = 15, cex = 0.5) points(faithful, pch = model$classification)
Classify multivariate observations based on Gaussian finite mixture models estimated by MclustDA.
## S3 method for class 'MclustDA' predict(object, newdata, prop = object$prop, ...)## S3 method for class 'MclustDA' predict(object, newdata, prop = object$prop, ...)
object |
an object of class |
newdata |
a data frame or matrix giving the data. If missing the train data obtained from the call to |
prop |
the class proportions or prior class probabilities to belong to each class; by default, this is set at the class proportions in the training data. |
... |
further arguments passed to or from other methods. |
Returns a list of with the following components:
classification |
a factor of predicted class labels for |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
Luca Scrucca
odd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] irisMclustDA <- MclustDA(X.train, Class.train) predTrain <- predict(irisMclustDA) predTrain predTest <- predict(irisMclustDA, X.test) predTestodd <- seq(from = 1, to = nrow(iris), by = 2) even <- odd + 1 X.train <- iris[odd,-5] Class.train <- iris[odd,5] X.test <- iris[even,-5] Class.test <- iris[even,5] irisMclustDA <- MclustDA(X.train, Class.train) predTrain <- predict(irisMclustDA) predTrain predTest <- predict(irisMclustDA, X.test) predTest
Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.
## S3 method for class 'MclustDR' predict(object, dim = 1:object$numdir, newdata, eval.points, ...)## S3 method for class 'MclustDR' predict(object, dim = 1:object$numdir, newdata, eval.points, ...)
object |
an object of class |
dim |
the dimensions of the reduced subspace used for prediction. |
newdata |
a data frame or matrix giving the data. If missing the data obtained from the call to |
eval.points |
a data frame or matrix giving the data projected on the reduced subspace. If provided |
... |
further arguments passed to or from other methods. |
Returns a list of with the following components:
dir |
a matrix containing the data projected onto the |
density |
densities from mixture model for each data point. |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
uncertainty |
The uncertainty associated with the classification. |
classification |
A vector of values giving the MAP classification. |
Luca Scrucca
Scrucca, L. (2010) Dimension reduction for model-based clustering. Statistics and Computing, 20(4), pp. 471-484.
mod = Mclust(iris[,1:4]) dr = MclustDR(mod) pred = predict(dr) str(pred) data(banknote) mod = MclustDA(banknote[,2:7], banknote$Status) dr = MclustDR(mod) pred = predict(dr) str(pred)mod = Mclust(iris[,1:4]) dr = MclustDR(mod) pred = predict(dr) str(pred) data(banknote) mod = MclustDA(banknote[,2:7], banknote$Status) dr = MclustDR(mod) pred = predict(dr) str(pred)
Classify multivariate observations based on Gaussian finite mixture models estimated by MclustSSC.
## S3 method for class 'MclustSSC' predict(object, newdata, ...)## S3 method for class 'MclustSSC' predict(object, newdata, ...)
object |
an object of class |
newdata |
a data frame or matrix giving the data. If missing the train data obtained from the call to |
... |
further arguments passed to or from other methods. |
Returns a list of with the following components:
classification |
a factor of predicted class labels for |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
Luca Scrucca
X <- iris[,1:4] class <- iris$Species # randomly remove class labels set.seed(123) class[sample(1:length(class), size = 120)] <- NA table(class, useNA = "ifany") clPairs(X, ifelse(is.na(class), 0, class), symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, class) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany") X_new = data.frame(Sepal.Length = c(5, 8), Sepal.Width = c(3.1, 4), Petal.Length = c(2, 5), Petal.Width = c(0.5, 2)) predict(mod_SSC, newdata = X_new)X <- iris[,1:4] class <- iris$Species # randomly remove class labels set.seed(123) class[sample(1:length(class), size = 120)] <- NA table(class, useNA = "ifany") clPairs(X, ifelse(is.na(class), 0, class), symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3), main = "Partially classified data") # Fit semi-supervised classification model mod_SSC <- MclustSSC(X, class) pred_SSC <- predict(mod_SSC) table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany") X_new = data.frame(Sepal.Length = c(5, 8), Sepal.Width = c(3.1, 4), Petal.Length = c(2, 5), Petal.Width = c(0.5, 2)) predict(mod_SSC, newdata = X_new)
Specify a conjugate prior for Gaussian mixtures.
priorControl(functionName = "defaultPrior", ...)priorControl(functionName = "defaultPrior", ...)
functionName |
The name of the function specifying the conjugate prior.
By default the function |
... |
Optional named arguments to the function specified in |
The function priorControl is used to specify a conjugate prior
for EM within MCLUST.
Note that, as described in defaultPrior, in the multivariate
case only 10 out of 14 models may be used in conjunction with a prior, i.e.
those available in MCLUST up to version 4.4.
A list with the function name as the first component. The remaining components (if any) consist of a list of arguments to the function with assigned values.
C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.
mclustBIC,
me,
mstep,
defaultPrior
# default prior irisBIC <- mclustBIC(iris[,-5], prior = priorControl()) summary(irisBIC, iris[,-5]) # no prior on the mean; default prior on variance irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0)) summary(irisBIC, iris[,-5])# default prior irisBIC <- mclustBIC(iris[,-5], prior = priorControl()) summary(irisBIC, iris[,-5]) # no prior on the mean; default prior on variance irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0)) summary(irisBIC, iris[,-5])
Generate a random orthogonal basis matrix of dimension using
the method in Heiberger (1978).
randomOrthogonalMatrix(nrow, ncol, n = nrow, d = ncol, seed = NULL)randomOrthogonalMatrix(nrow, ncol, n = nrow, d = ncol, seed = NULL)
nrow |
the number of rows of the resulting orthogonal matrix. |
ncol |
the number of columns of the resulting orthogonal matrix. |
n |
deprecated. See |
d |
deprecated. See |
seed |
an optional integer argument to use in |
The use of arguments n and d is deprecated and they will be removed in the future.
An orthogonal matrix of dimension such that each column is orthogonal to the other and has unit lenght. Because of the latter, it is also called orthonormal.
Heiberger R. (1978) Generation of random orthogonal matrices. Journal of the Royal Statistical Society. Series C (Applied Statistics), 27(2), 199-206.
B <- randomOrthogonalMatrix(10,3) zapsmall(crossprod(B))B <- randomOrthogonalMatrix(10,3) zapsmall(crossprod(B))
Plots random projections given multidimensional data and parameters of an MVN mixture model for the data.
randProj(data, seeds = NULL, parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "error", "uncertainty"), quantiles = c(0.75, 0.95), addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"), symbols = NULL, colors = NULL, scale = FALSE, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, cex = 1, PCH = ".", main = FALSE, ...)randProj(data, seeds = NULL, parameters = NULL, z = NULL, classification = NULL, truth = NULL, uncertainty = NULL, what = c("classification", "error", "uncertainty"), quantiles = c(0.75, 0.95), addEllipses = TRUE, fillEllipses = mclust.options("fillEllipses"), symbols = NULL, colors = NULL, scale = FALSE, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, cex = 1, PCH = ".", main = FALSE, ...)
data |
A numeric matrix or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
seeds |
An integer value or a vector of integer values to be used as seed for
random number generation. If multiple values are provided, then each seed
should produce a different projection.
By default, a single seed is drawn randomnly, so each call of
|
parameters |
A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows:
|
z |
A matrix in which the |
classification |
A numeric or character vector representing a classification of
observations (rows) of |
truth |
A numeric or character vector giving a known
classification of each data point.
If |
uncertainty |
A numeric vector of values in (0,1) giving the
uncertainty of each data point. If present argument |
what |
Choose from one of the following three options: |
quantiles |
A vector of length 2 giving quantiles used in plotting uncertainty. The smallest symbols correspond to the smallest quantile (lowest uncertainty), medium-sized (open) symbols to points falling between the given quantiles, and large (filled) symbols to those in the largest quantile (highest uncertainty). The default is (0.75,0.95). |
addEllipses |
A logical indicating whether or not to add ellipses with axes
corresponding to the within-cluster covariances in case of
|
fillEllipses |
A logical specifying whether or not to fill ellipses with transparent
colors when |
symbols |
Either an integer or character vector assigning a plotting symbol to each
unique class in |
colors |
Either an integer or character vector assigning a color to each
unique class in |
scale |
A logical variable indicating whether or not the two chosen
dimensions should be plotted on the same scale, and
thus preserve the shape of the distribution.
Default: |
xlim, ylim
|
Optional arguments specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots. |
xlab, ylab
|
Optional arguments specifying the labels for, respectively, the horizontal and vertical axis. |
cex |
A numerical value specifying the size of the plotting symbols. The default value is 1. |
PCH |
An argument specifying the symbol to be used when a classificatiion has not been specified for the data. The default value is a small dot ".". |
main |
A logical variable or |
... |
Other graphics parameters. |
A plot showing a random two-dimensional projection of the data, together with the location of the mixture components, classification, uncertainty, and/or classification errors.
The function also returns an invisible list with components basis, the randomnly generated basis of the projection subspace, data, a matrix of projected data, and mu and sigma the component parameters transformed to the projection subspace.
clPairs,
coordProj,
mclust2Dplot,
mclust.options
est <- meVVV(iris[,-5], unmap(iris[,5])) par(pty = "s", mfrow = c(1,1)) randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z, what = "classification", main = TRUE) randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z, truth = iris[,5], what = "error", main = TRUE) randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z, what = "uncertainty", main = TRUE)est <- meVVV(iris[,-5], unmap(iris[,5])) par(pty = "s", mfrow = c(1,1)) randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z, what = "classification", main = TRUE) randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z, truth = iris[,5], what = "error", main = TRUE) randProj(iris[,-5], seeds=1:3, parameters = est$parameters, z = est$z, what = "uncertainty", main = TRUE)
Converts a set of covariance matrices from representation as a 3-D array to a parameterization by eigenvalue decomposition.
sigma2decomp(sigma, G = NULL, tol = sqrt(.Machine$double.eps), ...)sigma2decomp(sigma, G = NULL, tol = sqrt(.Machine$double.eps), ...)
sigma |
Either a 3-D array whose [,,k]th component is the covariance matrix for the kth component in an MVN mixture model, or a single covariance matrix in the case that all components have the same covariance. |
G |
The number of components in the mixture. When
|
tol |
Tolerance for determining whether or not the covariances have equal volume,
shape, and or orientation. The default is the square root of the relative
machine precision, |
... |
Catches unused arguments from an indirect or list call via |
The covariance matrices for the mixture components in decomposition form, including the following components:
modelName |
A character string indicating the infered model. The help file for
|
d |
The dimension of the data. |
G |
The number of components in the mixture model. |
scale |
Either a G-vector giving the scale of the covariance (the dth root of its determinant) for each component in the mixture model, or a single numeric value if the scale is the same for each component. |
shape |
Either a G by d matrix in which the kth column is the shape of the covariance matrix (normalized to have determinant 1) for the kth component, or a d-vector giving a common shape for all components. |
orientation |
Either a d by d by G array whose
|
meEst <- meEEE(iris[,-5], unmap(iris[,5])) names(meEst$parameters$variance) meEst$parameters$variance$Sigma sigma2decomp(meEst$parameters$variance$Sigma, G = length(unique(iris[,5])))meEst <- meEEE(iris[,-5], unmap(iris[,5])) names(meEst$parameters$variance) meEst$parameters$variance$Sigma sigma2decomp(meEst$parameters$variance$Sigma, G = length(unique(iris[,5])))
Simulate data from parameterized MVN mixture models.
sim(modelName, parameters, n, seed = NULL, ...)sim(modelName, parameters, n, seed = NULL, ...)
modelName |
A character string indicating the model. The help file for
|
parameters |
A list with the following components:
|
n |
An integer specifying the number of data points to be simulated. |
seed |
An optional integer argument to |
... |
Catches unused arguments in indirect or list calls via |
This function can be used with an indirect or list call using
do.call, allowing the output of e.g. mstep, em,
me, Mclust to be passed directly without the need to
specify individual parameters as arguments.
A matrix in which first column is the classification and the remaining
columns are the n observations simulated from the specified MVN
mixture model.
Attributes: |
|
simE, ...,
simVVV,
Mclust,
mstep,
do.call
irisBIC <- mclustBIC(iris[,-5]) irisModel <- mclustModel(iris[,-5], irisBIC) names(irisModel) irisSim <- sim(modelName = irisModel$modelName, parameters = irisModel$parameters, n = nrow(iris)) do.call("sim", irisModel) # alternative call par(pty = "s", mfrow = c(1,2)) dimnames(irisSim) <- list(NULL, c("dummy", (dimnames(iris)[[2]])[-5])) dimens <- c(1,2) lim1 <- apply(iris[,dimens],2,range) lim2 <- apply(irisSim[,dimens+1],2,range) lims <- apply(rbind(lim1,lim2),2,range) xlim <- lims[,1] ylim <- lims[,2] coordProj(iris[,-5], parameters=irisModel$parameters, classification=map(irisModel$z), dimens=dimens, xlim=xlim, ylim=ylim) coordProj(iris[,-5], parameters=irisModel$parameters, classification=map(irisModel$z), truth = irisSim[,-1], dimens=dimens, xlim=xlim, ylim=ylim) irisModel3 <- mclustModel(iris[,-5], irisBIC, G=3) irisSim3 <- sim(modelName = irisModel3$modelName, parameters = irisModel3$parameters, n = 500, seed = 1) irisModel3$n <- NULL irisSim3 <- do.call("sim",c(list(n=500,seed=1),irisModel3)) # alternative call clPairs(irisSim3[,-1], cl = irisSim3[,1])irisBIC <- mclustBIC(iris[,-5]) irisModel <- mclustModel(iris[,-5], irisBIC) names(irisModel) irisSim <- sim(modelName = irisModel$modelName, parameters = irisModel$parameters, n = nrow(iris)) do.call("sim", irisModel) # alternative call par(pty = "s", mfrow = c(1,2)) dimnames(irisSim) <- list(NULL, c("dummy", (dimnames(iris)[[2]])[-5])) dimens <- c(1,2) lim1 <- apply(iris[,dimens],2,range) lim2 <- apply(irisSim[,dimens+1],2,range) lims <- apply(rbind(lim1,lim2),2,range) xlim <- lims[,1] ylim <- lims[,2] coordProj(iris[,-5], parameters=irisModel$parameters, classification=map(irisModel$z), dimens=dimens, xlim=xlim, ylim=ylim) coordProj(iris[,-5], parameters=irisModel$parameters, classification=map(irisModel$z), truth = irisSim[,-1], dimens=dimens, xlim=xlim, ylim=ylim) irisModel3 <- mclustModel(iris[,-5], irisBIC, G=3) irisSim3 <- sim(modelName = irisModel3$modelName, parameters = irisModel3$parameters, n = 500, seed = 1) irisModel3$n <- NULL irisSim3 <- do.call("sim",c(list(n=500,seed=1),irisModel3)) # alternative call clPairs(irisSim3[,-1], cl = irisSim3[,1])
Simulate data from a parameterized MVN mixture model.
simE(parameters, n, seed = NULL, ...) simV(parameters, n, seed = NULL, ...) simEII(parameters, n, seed = NULL, ...) simVII(parameters, n, seed = NULL, ...) simEEI(parameters, n, seed = NULL, ...) simVEI(parameters, n, seed = NULL, ...) simEVI(parameters, n, seed = NULL, ...) simVVI(parameters, n, seed = NULL, ...) simEEE(parameters, n, seed = NULL, ...) simVEE(parameters, n, seed = NULL, ...) simEVE(parameters, n, seed = NULL, ...) simVVE(parameters, n, seed = NULL, ...) simEEV(parameters, n, seed = NULL, ...) simVEV(parameters, n, seed = NULL, ...) simEVV(parameters, n, seed = NULL, ...) simVVV(parameters, n, seed = NULL, ...)simE(parameters, n, seed = NULL, ...) simV(parameters, n, seed = NULL, ...) simEII(parameters, n, seed = NULL, ...) simVII(parameters, n, seed = NULL, ...) simEEI(parameters, n, seed = NULL, ...) simVEI(parameters, n, seed = NULL, ...) simEVI(parameters, n, seed = NULL, ...) simVVI(parameters, n, seed = NULL, ...) simEEE(parameters, n, seed = NULL, ...) simVEE(parameters, n, seed = NULL, ...) simEVE(parameters, n, seed = NULL, ...) simVVE(parameters, n, seed = NULL, ...) simEEV(parameters, n, seed = NULL, ...) simVEV(parameters, n, seed = NULL, ...) simEVV(parameters, n, seed = NULL, ...) simVVV(parameters, n, seed = NULL, ...)
parameters |
A list with the following components:
|
n |
An integer specifying the number of data points to be simulated. |
seed |
An optional integer argument to |
... |
Catches unused arguments in indirect or list calls via |
This function can be used with an indirect or list call using
do.call, allowing the output of e.g. mstep, em
me, Mclust, to be passed directly without the need
to specify individual parameters as arguments.
A matrix in which first column is the classification and the remaining
columns are the n observations simulated from the specified MVN
mixture model.
Attributes: |
|
sim,
Mclust,
mstepE,
mclustVariance.
d <- 2 G <- 2 scale <- 1 shape <- c(1, 9) O1 <- diag(2) O2 <- diag(2)[,c(2,1)] O <- array(cbind(O1,O2), c(2, 2, 2)) O variance <- list(d= d, G = G, scale = scale, shape = shape, orientation = O) mu <- matrix(0, d, G) ## center at the origin simdat <- simEEV( n = 200, parameters = list(pro=c(1,1),mean=mu,variance=variance), seed = NULL) cl <- simdat[,1] sigma <- array(apply(O, 3, function(x,y) crossprod(x*y), y = sqrt(scale*shape)), c(2,2,2)) paramList <- list(mu = mu, sigma = sigma) coordProj( simdat, paramList = paramList, classification = cl)d <- 2 G <- 2 scale <- 1 shape <- c(1, 9) O1 <- diag(2) O2 <- diag(2)[,c(2,1)] O <- array(cbind(O1,O2), c(2, 2, 2)) O variance <- list(d= d, G = G, scale = scale, shape = shape, orientation = O) mu <- matrix(0, d, G) ## center at the origin simdat <- simEEV( n = 200, parameters = list(pro=c(1,1),mean=mu,variance=variance), seed = NULL) cl <- simdat[,1] sigma <- array(apply(O, 3, function(x,y) crossprod(x*y), y = sqrt(scale*shape)), c(2,2,2)) paramList <- list(mu = mu, sigma = sigma) coordProj( simdat, paramList = paramList, classification = cl)
Efficient implementation (via Fortran) of the softmax (aka multinomial logistic) function converting a set of numerical values to probabilities summing to 1.
softmax(x, v = NULL)softmax(x, v = NULL)
x |
a matrix of dimension |
v |
an optional vector of length |
Given the matrix x, for each row (with ), the softmax function calculates
Returns a matrix of the same dimension as x with values in the range that sum to 1 along the rows.
Luca Scrucca
Blanchard P., Higham D. J., Higham N. J. (2021). Accurately computing the log-sum-exp and softmax functions. IMA Journal of Numerical Analysis, 41/4:2311–2330. doi:10.1093/imanum/draa038
x = matrix(rnorm(15), 5, 3) v = log(c(0.5, 0.3, 0.2)) (z = softmax(x, v)) rowSums(z)x = matrix(rnorm(15), 5, 3) v = log(c(0.5, 0.3, 0.2)) (z = softmax(x, v)) rowSums(z)
Summary method for class "Mclust".
## S3 method for class 'Mclust' summary(object, classification = TRUE, parameters = FALSE, ...) ## S3 method for class 'summary.Mclust' print(x, digits = getOption("digits"), ...)## S3 method for class 'Mclust' summary(object, classification = TRUE, parameters = FALSE, ...) ## S3 method for class 'summary.Mclust' print(x, digits = getOption("digits"), ...)
object |
An object of class |
x |
An object of class |
classification |
Logical; if |
parameters |
Logical; if |
digits |
The number of significant digits to use when printing. |
... |
Further arguments passed to or from other methods. |
Luca Scrucca
mod1 = Mclust(iris[,1:4]) summary(mod1) summary(mod1, parameters = TRUE, classification = FALSE) mod2 = densityMclust(faithful, plot = FALSE) summary(mod2) summary(mod2, parameters = TRUE)mod1 = Mclust(iris[,1:4]) summary(mod1) summary(mod1, parameters = TRUE, classification = FALSE) mod2 = densityMclust(faithful, plot = FALSE) summary(mod2) summary(mod2, parameters = TRUE)
Optimal model characteristics and classification for model-based
clustering via mclustBIC.
## S3 method for class 'mclustBIC' summary(object, data, G, modelNames, ...)## S3 method for class 'mclustBIC' summary(object, data, G, modelNames, ...)
object |
An |
data |
The matrix or vector of observations used to generate ‘object’. |
G |
A vector of integers giving the numbers of mixture components (clusters)
from which the best model according to BIC will be selected
( |
modelNames |
A vector of integers giving the model parameterizations
from which the best model according to BIC will be selected
( |
... |
Not used. For generic/method consistency. |
A list giving the optimal (according to BIC) parameters,
conditional probabilities z, and log-likelihood,
together with the associated classification and its uncertainty.
The details of the output components are as follows:
modelName |
A character string denoting the model corresponding to the optimal BIC. |
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
The number of mixture components in the model corresponding to the optimal BIC. |
bic |
The optimal BIC value. |
loglik |
The log-likelihood corresponding to the optimal BIC. |
parameters |
A list with the following components:
|
z |
A matrix whose [i,k]th entry is the probability that observation i in the data belongs to the kth class. |
classification |
|
uncertainty |
The uncertainty associated with the classification. |
Attributes: |
|
irisBIC <- mclustBIC(iris[,-5]) summary(irisBIC, iris[,-5]) summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))irisBIC <- mclustBIC(iris[,-5]) summary(irisBIC, iris[,-5]) summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))
Summary of bootstrap distribution for the parameters of a Gaussian mixture model providing either standard errors or percentile bootstrap confidence intervals.
## S3 method for class 'MclustBootstrap' summary(object, what = c("se", "ci", "ave"), conf.level = 0.95, ...)## S3 method for class 'MclustBootstrap' summary(object, what = c("se", "ci", "ave"), conf.level = 0.95, ...)
object |
An object of class |
what |
A character string: |
conf.level |
A value specifying the confidence level of the interval. |
... |
Further arguments passed to or from other methods. |
For details about the procedure used to obtain the bootstrap distribution see MclustBootstrap.
data(diabetes) X = diabetes[,-1] modClust = Mclust(X) bootClust = MclustBootstrap(modClust) summary(bootClust, what = "se") summary(bootClust, what = "ci") data(acidity) modDens = densityMclust(acidity, plot = FALSE) modDens = MclustBootstrap(modDens) summary(modDens, what = "se") summary(modDens, what = "ci")data(diabetes) X = diabetes[,-1] modClust = Mclust(X) bootClust = MclustBootstrap(modClust) summary(bootClust, what = "se") summary(bootClust, what = "ci") data(acidity) modDens = densityMclust(acidity, plot = FALSE) modDens = MclustBootstrap(modDens) summary(modDens, what = "se") summary(modDens, what = "ci")
Summary method for class "MclustDA".
## S3 method for class 'MclustDA' summary(object, parameters = FALSE, newdata, newclass, ...) ## S3 method for class 'summary.MclustDA' print(x, digits = getOption("digits"), ...)## S3 method for class 'MclustDA' summary(object, parameters = FALSE, newdata, newclass, ...) ## S3 method for class 'summary.MclustDA' print(x, digits = getOption("digits"), ...)
object |
An object of class |
x |
An object of class |
parameters |
Logical; if |
newdata |
A data frame or matrix giving the test data. |
newclass |
A vector giving the class labels for the observations in the test data. |
digits |
The number of significant digits to use when printing. |
... |
Further arguments passed to or from other methods. |
The function summary.MclustDA computes and returns a list of summary statistics of the estimated MclustDA or EDDA model for classification.
Luca Scrucca
mod = MclustDA(data = iris[,1:4], class = iris$Species) summary(mod) summary(mod, parameters = TRUE)mod = MclustDA(data = iris[,1:4], class = iris$Species) summary(mod) summary(mod, parameters = TRUE)
Summary method for class "MclustDR".
## S3 method for class 'MclustDR' summary(object, numdir, std = FALSE, ...) ## S3 method for class 'summary.MclustDR' print(x, digits = max(5, getOption("digits") - 3), ...)## S3 method for class 'MclustDR' summary(object, numdir, std = FALSE, ...) ## S3 method for class 'summary.MclustDR' print(x, digits = max(5, getOption("digits") - 3), ...)
object |
An object of class |
x |
An object of class |
numdir |
An integer providing the number of basis directions to be printed. |
std |
if |
digits |
The number of significant digits to use when printing. |
... |
Further arguments passed to or from other methods. |
Luca Scrucca
Summary method for class "MclustSSC".
## S3 method for class 'MclustSSC' summary(object, parameters = FALSE, ...) ## S3 method for class 'summary.MclustSSC' print(x, digits = getOption("digits"), ...)## S3 method for class 'MclustSSC' summary(object, parameters = FALSE, ...) ## S3 method for class 'summary.MclustSSC' print(x, digits = getOption("digits"), ...)
object |
An object of class |
x |
An object of class |
parameters |
Logical; if |
digits |
The number of significant digits to use when printing. |
... |
Further arguments passed to or from other methods. |
The function summary.MclustSSC computes and returns a list of summary statistics of the estimated MclustSSC model for semi-supervised classification.
Luca Scrucca
Plots a density or uncertainty surface given bivariate data and parameters of a MVN mixture model for the data.
surfacePlot(data, parameters, what = c("density", "uncertainty"), type = c("contour", "hdr", "image", "persp"), transformation = c("none", "log", "sqrt"), grid = 200, nlevels = 11, levels = NULL, prob = c(0.25, 0.5, 0.75), col = gray(0.5), col.palette = function(...) hcl.colors(..., "blues", rev = TRUE), hdr.palette = blue2grey.colors, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, main = FALSE, scale = FALSE, swapAxes = FALSE, verbose = FALSE, ...)surfacePlot(data, parameters, what = c("density", "uncertainty"), type = c("contour", "hdr", "image", "persp"), transformation = c("none", "log", "sqrt"), grid = 200, nlevels = 11, levels = NULL, prob = c(0.25, 0.5, 0.75), col = gray(0.5), col.palette = function(...) hcl.colors(..., "blues", rev = TRUE), hdr.palette = blue2grey.colors, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL, main = FALSE, scale = FALSE, swapAxes = FALSE, verbose = FALSE, ...)
data |
A matrix, or data frame of bivariate observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
parameters |
A named list giving the parameters of an MCLUST model, used to produce superimposing ellipses on the plot. The relevant components are as follows:
|
what |
Choose from one of the following options: |
type |
Choose from one of the following three options: |
transformation |
Choose from one of the following three options: |
grid |
The number of grid points (evenly spaced on each axis).
The mixture density and uncertainty is computed at
|
nlevels |
The number of levels to use for a contour plot.
Default: |
levels |
A vector of levels at which to draw the lines in a contour plot. |
prob |
A vector of probability levels for computing HDR.
Only used if |
col |
A string specifying the colour to be used for |
col.palette |
A function which defines a palette of colours to be used for
|
hdr.palette |
A function which defines a palette of colours to be used for
|
xlim, ylim
|
Optional argument specifying bounds for the ordinate, abscissa of the plot. This may be useful for when comparing plots. |
xlab, ylab
|
Optional argument specifying labels for the x-axis and y-axis. |
main |
A logical variable or |
scale |
A logical variable indicating whether or not the two dimensions should be plotted on the same scale, and thus preserve the shape of the distribution. The default is not to scale. |
swapAxes |
A logical variable indicating whether or not the axes should be swapped for the plot. |
verbose |
A logical variable telling whether or not to print an indication that the function is in the process of computing values at the grid points, which typically takes some time to complete. |
... |
Other graphics parameters. |
For an image plot, a color scheme may need to be selected on the display device in order to view the plot.
A plots showing (a transformation of) the density or uncertainty for the given mixture model and data.
The function also returns an invisible list with components x,
y, and z in which x and y are the values used to
define the grid and z is the transformed density or uncertainty at the
grid points.
faithfulModel <- Mclust(faithful) surfacePlot(faithful, parameters = faithfulModel$parameters, type = "contour", what = "density", transformation = "none", drawlabels = FALSE) surfacePlot(faithful, parameters = faithfulModel$parameters, type = "persp", what = "density", transformation = "log") surfacePlot(faithful, parameters = faithfulModel$parameters, type = "contour", what = "uncertainty", transformation = "log")faithfulModel <- Mclust(faithful) surfacePlot(faithful, parameters = faithfulModel$parameters, type = "contour", what = "density", transformation = "none", drawlabels = FALSE) surfacePlot(faithful, parameters = faithfulModel$parameters, type = "persp", what = "density", transformation = "log") surfacePlot(faithful, parameters = faithfulModel$parameters, type = "contour", what = "uncertainty", transformation = "log")
Data on five laboratory tests administered to a sample of 215 patients. The tests are used to predict whether a patient's thyroid can be classified as euthyroidism (normal thyroid gland function), hypothyroidism (underactive thyroid not producing enough thyroid hormone) or hyperthyroidism (overactive thyroid producing and secreting excessive amounts of the free thyroid hormones T3 and/or thyroxine T4). Diagnosis of thyroid operation was based on a complete medical record, including anamnesis, scan, etc.
data(thyroid)data(thyroid)
A data frame with the following variables:
Diagnosis of thyroid operation: Hypo, Normal, and Hyper.
T3-resin uptake test (percentage).
Total Serum thyroxin as measured by the isotopic displacement method.
Total serum triiodothyronine as measured by radioimmuno assay.
Basal thyroid-stimulating hormone (TSH) as measured by radioimmuno assay.
Maximal absolute difference of TSH value after injection of 200 micro grams of thyrotropin-releasing hormone as compared to the basal value.
One of several databases in the Thyroid Disease Data Set (new-thyroid.data, new-thyroid.names) of the UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets/thyroid+disease. Please note the UCI conditions of use.
Coomans, D., Broeckaert, M. Jonckheer M. and Massart D.L. (1983) Comparison of Multivariate Discriminant Techniques for Clinical Data - Application to the Thyroid Functional State, Meth. Inform. Med. 22, pp. 93-101.
Coomans, D. and I. Broeckaert (1986) Potential Pattern Recognition in Cemical and Medical Decision Making, Research Studies Press, Letchworth, England.
Displays the uncertainty in converting a conditional probablility from EM to a classification in model-based clustering.
uncerPlot(z, truth, ...)uncerPlot(z, truth, ...)
z |
A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture. |
truth |
A numeric or character vector giving the true classification of the data. |
... |
Provided to allow lists with elements other than the arguments can
be passed in indirect or list calls with |
When truth is provided and the number of classes is compatible
with z, the function compareClass is used to to find best
correspondence between classes in truth and z.
A plot of the uncertainty profile of the data,
with uncertainties in increasing order of magnitude.
If truth is supplied and the number of
classes is the same as the number of columns of
z, the uncertainty
of the misclassified data is marked by vertical lines on the plot.
irisModel3 <- Mclust(iris[,-5], G = 3) uncerPlot(z = irisModel3$z) uncerPlot(z = irisModel3$z, truth = iris[,5])irisModel3 <- Mclust(iris[,-5], G = 3) uncerPlot(z = irisModel3$z) uncerPlot(z = irisModel3$z, truth = iris[,5])
Converts a classification into a matrix of indicator variables.
unmap(classification, groups=NULL, noise=NULL, ...)unmap(classification, groups=NULL, noise=NULL, ...)
classification |
A numeric or character vector. Typically the distinct entries of this vector would represent a classification of observations in a data set. |
groups |
A numeric or character vector indicating the groups from which
|
noise |
A single numeric or character value used to indicate the value of
|
... |
Catches unused arguments in indirect or list calls via |
An n by m matrix of (0,1) indicator variables,
where n is the length of classification and m is
the number of unique values or symbols in classification.
Columns are labeled by the unique values in classification,
and the [i,j]th entry is 1 if classification[i]
is the jth unique value or symbol in sorted order
classification.
If a noise value of symbol is designated, the corresponding indicator
variables are relocated to the last column of the matrix.
z <- unmap(iris[,5]) z[1:5, ] emEst <- me(modelName = "VVV", data = iris[,-5], z = z) emEst$z[1:5,] map(emEst$z)z <- unmap(iris[,5]) z[1:5, ] emEst <- me(modelName = "VVV", data = iris[,-5], z = z) emEst$z[1:5,] map(emEst$z)
The data set provides data for 569 patients on 30 features of the cell nuclei obtained from a digitized image of a fine needle aspirate (FNA) of a breast mass. For each patient the cancer was diagnosed as malignant or benign.
data(wdbc)data(wdbc)
A data frame with 569 observations on the following variables:
IDID number
Diagnosiscancer diagnosis: M = malignant, B = benign
Radius_meana numeric vector
Texture_meana numeric vector
Perimeter_meana numeric vector
Area_meana numeric vector
Smoothness_meana numeric vector
Compactness_meana numeric vector
Concavity_meana numeric vector
Nconcave_meana numeric vector
Symmetry_meana numeric vector
Fractaldim_meana numeric vector
Radius_sea numeric vector
Texture_sea numeric vector
Perimeter_sea numeric vector
Area_sea numeric vector
Smoothness_sea numeric vector
Compactness_sea numeric vector
Concavity_sea numeric vector
Nconcave_sea numeric vector
Symmetry_sea numeric vector
Fractaldim_sea numeric vector
Radius_extremea numeric vector
Texture_extremea numeric vector
Perimeter_extremea numeric vector
Area_extremea numeric vector
Smoothness_extremea numeric vector
Compactness_extremea numeric vector
Concavity_extremea numeric vector
Nconcave_extremea numeric vector
Symmetry_extremea numeric vector
Fractaldim_extremea numeric vector
The recorded features are:
Radius as mean of distances from center to points on the perimeter
Texture as standard deviation of gray-scale values
Perimeter as cell nucleus perimeter
Area as cell nucleus area
Smoothness as local variation in radius lengths
Compactness as cell nucleus compactness, perimeter^2 / area - 1
Concavity as severity of concave portions of the contour
Nconcave as number of concave portions of the contour
Symmetry as cell nucleus shape
Fractaldim as fractal dimension, "coastline approximation" - 1
For each feature the recorded values are computed from each image as <feature_name>_mean, <feature_name>_se, and <feature_name>_extreme, for the mean, the standard error, and the mean of the three largest values.
The Breast Cancer Wisconsin (Diagnostic) Data Set (wdbc.data, wdbc.names) from the UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic). Please note the UCI conditions of use.
Mangasarian, O. L., Street, W. N., and Wolberg, W. H. (1995) Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pp. 570-577.
A dataset consisting of 1000 observations drawn from a 14-component normal mixture in which the covariances of the components have the same size and shape but differ in orientation.
data(wreath)data(wreath)
C. Fraley, A. E. Raftery and R. Wehrens (2005). Incremental model-based clustering for large datasets with small clusters. Journal of Computational and Graphical Statistics 14:1:18.