| Title: | Model Based Clustering of Binary Dissimilarity Measurements |
|---|---|
| Description: | Functions for fitting a Bayesian model for grouping binary dissimilarity matrices in homogeneous clusters. Currently, it includes methods only for binary data (<doi:10.18637/jss.v100.i16>). |
| Authors: | Sergio Venturini [aut, cre], Raffaella Piccarreta [ctb] |
| Maintainer: | Sergio Venturini <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.3 |
| Built: | 2024-11-24 06:55:15 UTC |
| Source: | CRAN |
adjust_x adjusts the center and orientation of a latent configuration
in Bayesian (metric) multidimensional scaling (BMDS).
adjust_x(x)adjust_x(x)
x |
Numeric matrix containing the latent configuration. |
A list with elements:
xA real matrix containing the adjusted latent configuration.
Sig_xThe variance and covariance matrix of the adjusted latent configuration.
Sergio Venturini [email protected]
bmds for (one-way) Bayesian (metric) multidimensional
scaling.
n <- 100 nr <- 20 nc <- floor(n/nr) x <- matrix(rnorm(1:n), nrow = nr, ncol = nc) adj_x <- adjust_x(x) adj_x$x adj_x$Sig_xn <- 100 nr <- 20 nc <- floor(n/nr) x <- matrix(rnorm(1:n), nrow = nr, ncol = nc) adj_x <- adjust_x(x) adj_x$x adj_x$Sig_x
To illustrate the MDS analysis of sorting data, Takane et al. (2009) refer to judgments on the similarity between n = 18 animals expressed by S = 20 subjects. Each subject was asked to divide the animals into as many groups as needed, based on their similarity. We converted these values to 0 or 1 depending on whether a pair of animals is placed or not in the same group by a subject.
data(animals)data(animals)
A dmbc_data object whose diss element is a list of 20
binary dissimilarity matrices. Each matrix is defined as a dist
object measuring whether each pair of the 18 animals has is placed in the
same group (1) or not (0).
The dist objects have rows and columns that are named as follows:
bear
camel
cat
cow
dog
elephant
giraffe
fox
horse
lion
monkey
mouse
pig
rabbit
sheep
squirrel
tiger
wolf
Takane, Y., Jung, S., Takane, Y. O. (2009). "Multidimensional Scaling". In Millsap, R. E., Maydeu-Olivares, A. (eds.), The SAGE Handbook of Quantitative Methods in Psychology, chapter 10, pp. 217–242,.
data(animals) library(bayesplot) cols <- color_scheme_set("teal") plot(animals, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)data(animals) library(bayesplot) cols <- color_scheme_set("teal") plot(animals, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)
bmds computes the Bayesian multidimensional scaling (BMDS) solutions
using Markov Chain Monte Carlo for a range of specified latent space
dimensions.
bmds( D, min_p = 1, max_pm1 = 6, burnin = 0, nsim = 13000, ic = TRUE, verbose = TRUE )bmds( D, min_p = 1, max_pm1 = 6, burnin = 0, nsim = 13000, ic = TRUE, verbose = TRUE )
D |
Observed dissimilarities (provided as a distance matrix). |
min_p |
A length-one numeric vector providing the minimum value of the latent space dimension to use. |
max_pm1 |
A length-one numeric vector providing the maximum value of the latent space dimension to use (minus 1). |
burnin |
A length-one numeric vector providing the number of iterations to use for burnin. |
nsim |
A length-one numeric vector providing the number of iterations to use in the MCMC simulation after burnin. |
ic |
Logical scalar. If |
verbose |
Logical scalar. If |
A list with the following elements:
x.chainMCMC chain of the latent configuration coordinates.
sigma.chainMCMC chain of the random error.
lambda.chainMCMC chain of the latent configuration variances.
stressNumeric vector of the stress function values.
mdsICList with two elements, the MDSIC and BIC values for the required solutions.
acceptNumeric matrix of acceptance rates.
Sergio Venturini [email protected]
Oh, M.-S., Raftery, A. E. (2001), "Bayesian Multidimensional Scaling and Choice of Dimension", Journal of the American Statistical Association, 96, 1031-1044.
cmdscale for classical (metric) multidimensional scaling.
## Not run: # Airline Distances Between Cities airline <- read.csv(file = system.file("extdata", "airline.csv", package = "dmbc")) airline.nm <- airline[, 1] airline <- airline[, 2:31] colnames(airline) <- airline.nm airline <- as.dist(airline) min_p <- 1 max_p <- 4 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim airline.mds <- cmdscale(airline, max_p) airline.bmds <- bmds(airline, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot(min_p:max_p, airline.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(airline.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], airline.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) airline.bmds.x.mode <- bmds_get_x_mode(airline, airline.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) airline.bmds.d <- dist(airline.bmds.x.mode) airline.mds.d <- dist(airline.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(airline, airline.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Airline Distances \n Between Cities", xlim = c(0, max(airline, airline.bmds.d)), ylim = c(0, max(airline, airline.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(airline, airline.mds.d, pch = 19, col = "cyan", cex = .5) points(airline, airline.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "bottomright", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) # Careers of Lloyds Bank Employees lloyds <- read.csv(file = system.file("extdata", "lloyds.csv", package = "dmbc")) lloyds.nm <- lloyds[, 1] lloyds <- lloyds[, 2:81] colnames(lloyds) <- lloyds.nm lloyds <- as.dist(lloyds) min_p <- 1 max_p <- 12 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim lloyds.mds <- cmdscale(lloyds, max_p) lloyds.bmds <- bmds(lloyds, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot((min_p:max_p), lloyds.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(lloyds.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], lloyds.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) lloyds.bmds.x.mode <- bmds_get_x_mode(lloyds, lloyds.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) lloyds.bmds.d <- dist(lloyds.bmds.x.mode) lloyds.mds.d <- dist(lloyds.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(lloyds, lloyds.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Careers of Lloyds \n Bank Employees, 1905-1950", xlim = c(0, max(lloyds, lloyds.bmds.d)), ylim = c(0, max(lloyds, lloyds.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(lloyds, lloyds.mds.d, pch = 19, col = "cyan", cex = .5) points(lloyds, lloyds.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "topleft", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) # Road distances (in km) between 21 cities in Europe data(eurodist, package = "datasets") min_p <- 1 max_p <- 10 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim eurodist.mds <- cmdscale(eurodist, max_p) eurodist.bmds <- bmds(eurodist, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot((min_p:max_p), eurodist.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(eurodist.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], eurodist.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) eurodist.bmds.x.mode <- bmds_get_x_mode(eurodist, eurodist.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) eurodist.bmds.d <- dist(eurodist.bmds.x.mode) eurodist.mds.d <- dist(eurodist.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(eurodist, eurodist.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Road distances (in km) \n between 21 cities in Europe", xlim = c(0, max(eurodist, eurodist.bmds.d)), ylim = c(0, max(eurodist, eurodist.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(eurodist, eurodist.mds.d, pch = 19, col = "cyan", cex = .5) points(eurodist, eurodist.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "topleft", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) ## End(Not run)## Not run: # Airline Distances Between Cities airline <- read.csv(file = system.file("extdata", "airline.csv", package = "dmbc")) airline.nm <- airline[, 1] airline <- airline[, 2:31] colnames(airline) <- airline.nm airline <- as.dist(airline) min_p <- 1 max_p <- 4 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim airline.mds <- cmdscale(airline, max_p) airline.bmds <- bmds(airline, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot(min_p:max_p, airline.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(airline.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], airline.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) airline.bmds.x.mode <- bmds_get_x_mode(airline, airline.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) airline.bmds.d <- dist(airline.bmds.x.mode) airline.mds.d <- dist(airline.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(airline, airline.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Airline Distances \n Between Cities", xlim = c(0, max(airline, airline.bmds.d)), ylim = c(0, max(airline, airline.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(airline, airline.mds.d, pch = 19, col = "cyan", cex = .5) points(airline, airline.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "bottomright", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) # Careers of Lloyds Bank Employees lloyds <- read.csv(file = system.file("extdata", "lloyds.csv", package = "dmbc")) lloyds.nm <- lloyds[, 1] lloyds <- lloyds[, 2:81] colnames(lloyds) <- lloyds.nm lloyds <- as.dist(lloyds) min_p <- 1 max_p <- 12 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim lloyds.mds <- cmdscale(lloyds, max_p) lloyds.bmds <- bmds(lloyds, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot((min_p:max_p), lloyds.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(lloyds.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], lloyds.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) lloyds.bmds.x.mode <- bmds_get_x_mode(lloyds, lloyds.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) lloyds.bmds.d <- dist(lloyds.bmds.x.mode) lloyds.mds.d <- dist(lloyds.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(lloyds, lloyds.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Careers of Lloyds \n Bank Employees, 1905-1950", xlim = c(0, max(lloyds, lloyds.bmds.d)), ylim = c(0, max(lloyds, lloyds.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(lloyds, lloyds.mds.d, pch = 19, col = "cyan", cex = .5) points(lloyds, lloyds.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "topleft", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) # Road distances (in km) between 21 cities in Europe data(eurodist, package = "datasets") min_p <- 1 max_p <- 10 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim eurodist.mds <- cmdscale(eurodist, max_p) eurodist.bmds <- bmds(eurodist, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot((min_p:max_p), eurodist.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(eurodist.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], eurodist.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) eurodist.bmds.x.mode <- bmds_get_x_mode(eurodist, eurodist.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) eurodist.bmds.d <- dist(eurodist.bmds.x.mode) eurodist.mds.d <- dist(eurodist.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(eurodist, eurodist.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Road distances (in km) \n between 21 cities in Europe", xlim = c(0, max(eurodist, eurodist.bmds.d)), ylim = c(0, max(eurodist, eurodist.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(eurodist, eurodist.mds.d, pch = 19, col = "cyan", cex = .5) points(eurodist, eurodist.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "topleft", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) ## End(Not run)
bmds_get_x_mode returns the latent configuration that produced the
largest posterior value during the MCMC.
bmds_get_x_mode(D, res, p.i, min_p, max_p, start, end)bmds_get_x_mode(D, res, p.i, min_p, max_p, start, end)
D |
Observed dissimilarities (provided as a distance matrix). |
res |
Results of a BMDS analysis as obtained with the
|
p.i |
A length-one numeric vector providing the index of the solution to use. |
min_p |
A length-one numeric vector providing the minimum value of the latent space dimension to use. |
max_p |
A length-one numeric vector providing the maximum value of the latent space dimension to use. |
start |
A length-one numeric vector providing the iteration number to start from. |
end |
A length-one numeric vector providing the iteration number where to end. |
A real matrix containing the posterior mode latent configuration.
Sergio Venturini [email protected]
bmds for (one-way) Bayesian (metric) multidimensional
scaling.
## Not run: # Airline Distances Between Cities airline <- read.csv(file = system.file("extdata", "airline.csv", package = "dmbc")) airline.nm <- airline[, 1] airline <- airline[, 2:31] colnames(airline) <- airline.nm airline <- as.dist(airline) min_p <- 1 max_p <- 4 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim airline.mds <- cmdscale(airline, max_p) airline.bmds <- bmds(airline, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot(min_p:max_p, airline.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(airline.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], airline.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) airline.bmds.x.mode <- bmds_get_x_mode(airline, airline.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) airline.bmds.d <- dist(airline.bmds.x.mode) airline.mds.d <- dist(airline.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(airline, airline.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Airline Distances \n Between Cities", xlim = c(0, max(airline, airline.bmds.d)), ylim = c(0, max(airline, airline.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(airline, airline.mds.d, pch = 19, col = "cyan", cex = .5) points(airline, airline.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "bottomright", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) ## End(Not run)## Not run: # Airline Distances Between Cities airline <- read.csv(file = system.file("extdata", "airline.csv", package = "dmbc")) airline.nm <- airline[, 1] airline <- airline[, 2:31] colnames(airline) <- airline.nm airline <- as.dist(airline) min_p <- 1 max_p <- 4 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim airline.mds <- cmdscale(airline, max_p) airline.bmds <- bmds(airline, min_p, max_p, burnin, nsim) opar <- par(mfrow = c(1, 2)) plot(min_p:max_p, airline.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(airline.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], airline.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) airline.bmds.x.mode <- bmds_get_x_mode(airline, airline.bmds, MDSICmin, min_p, max_p, start = (burnin + 1), end = totiter) airline.bmds.d <- dist(airline.bmds.x.mode) airline.mds.d <- dist(airline.mds[, 1:((min_p:max_p)[MDSICmin])]) plot(airline, airline.bmds.d, type = "n", xlab = "observed", ylab = "estimated", main = "Airline Distances \n Between Cities", xlim = c(0, max(airline, airline.bmds.d)), ylim = c(0, max(airline, airline.bmds.d))) abline(0, 1, lty = 2, col = "gray") points(airline, airline.mds.d, pch = 19, col = "cyan", cex = .5) points(airline, airline.bmds.d, pch = 19, col = "magenta", cex = .5) legend(x = "bottomright", legend = c("Classical MDS", "Bayesian MDS"), pch = c(19, 19), col = c("cyan", "magenta")) par(opar) ## End(Not run)
check_list_na() compares two lists and fills in the missing
elements in the first with those included in the second. The
comparison is recursive in the sense that the process is repeated for
all lists included in those given.
check_list_na(orig, des)check_list_na(orig, des)
orig |
A list whose content must be checked. |
des |
A list to use as a reference with which compare the first one. |
A list with all elements added.
Sergio Venturini [email protected]
G <- 5 prior <- list(eta = list(a = rep(1, G), b = rep(2, G))) check_list_na(prior, dmbc_prior())G <- 5 prior <- list(eta = list(a = rep(1, G), b = rep(2, G))) check_list_na(prior, dmbc_prior())
dmbc_config class instance.Extract the final cluster memberships from a dmbc_config class instance.
## S4 method for signature 'dmbc_config' clusters(object, newdata = NULL, ...)## S4 method for signature 'dmbc_config' clusters(object, newdata = NULL, ...)
object |
An object of class |
newdata |
An object of no explicit specification (currently ignored). |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
comp_ssr computes the sum of squared residuals (SSR) from the
observed distances (diss) and the given latent coordinates
(x).
comp_ssr(x, diss)comp_ssr(x, diss)
x |
Real matrix containing the latent configuration. |
diss |
Observed dissimilarities (provided as a distance matrix). |
A length-one numeric vector providing the SSR for its arguments.
Sergio Venturini [email protected]
bmds for (one-way) Bayesian (metric) multidimensional
scaling.
n <- 10000 nr <- 200 nc <- floor(n/nr) x <- matrix(rnorm(1:n), nrow = nr, ncol = nc) obsdiss <- dist(x) ssr <- numeric(ncol(x)) for (i in 1:ncol(x)) { ssr[i] <- comp_ssr(x[, 1:i], obsdiss) } plot(ssr, xlab = "number of dimensions", ylab = "SSR", type = "b")n <- 10000 nr <- 200 nc <- floor(n/nr) x <- matrix(rnorm(1:n), nrow = nr, ncol = nc) obsdiss <- dist(x) ssr <- numeric(ncol(x)) for (i in 1:ncol(x)) { ssr[i] <- comp_ssr(x[, 1:i], obsdiss) } plot(ssr, xlab = "number of dimensions", ylab = "SSR", type = "b")
dmbc(), the main function of the package, estimates a DMBC model
for a given set of S dissimilarity matrices.
dmbc( data, p = 2, G = 3, control = dmbc_control(), prior = NULL, cl = NULL, post_all = FALSE )dmbc( data, p = 2, G = 3, control = dmbc_control(), prior = NULL, cl = NULL, post_all = FALSE )
data |
An object of class |
p |
A length-one numeric vector indicating the number of dimensions of the latent space. |
G |
A length-one numeric vector indicating the number of cluster to partition the S subjects. |
control |
A list of control parameters that affect the sampling
but do not affect the posterior distribution. See
|
prior |
A list containing the prior hyperparameters. See
|
cl |
An optional parallel or
snow
cluster for use if |
post_all |
A length-one logical vector, which if TRUE applies a further post-processing to the simulated chains (in case these are more than one). |
A dmbc_fit_list object.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
bmds for Bayesian (metric) multidimensional scaling.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 20000 nsim <- 10000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) summary(sim.dmbc, include.burnin = FALSE) library(bayesplot) library(ggplot2) color_scheme_set("teal") plot(sim.dmbc, what = "trace", regex_pars = "eta") z <- dmbc_get_configuration(sim.dmbc, chain = 1, est = "mean", labels = 1:16) summary(z) color_scheme_set("mix-pink-blue") graph <- plot(z, size = 2, size_lbl = 3) graph + panel_bg(fill = "gray90", color = NA) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 20000 nsim <- 10000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) summary(sim.dmbc, include.burnin = FALSE) library(bayesplot) library(ggplot2) color_scheme_set("teal") plot(sim.dmbc, what = "trace", regex_pars = "eta") z <- dmbc_get_configuration(sim.dmbc, chain = 1, est = "mean", labels = 1:16) summary(z) color_scheme_set("mix-pink-blue") graph <- plot(z, size = 2, size_lbl = 3) graph + panel_bg(fill = "gray90", color = NA) ## End(Not run)
dmbc_check_groups() is an auxiliary function for checking whether
the cluster membership estimates provided by the individual chains of the
fitted model provided agree or not.
dmbc_check_groups(res, est = "mean")dmbc_check_groups(res, est = "mean")
res |
An object of class |
est |
A length-one character vector indicating the estimate type to use. |
A length-one logical vector, which is equal to TRUE if all simulated chains provide the same cluster membership estimates, and FALSE otherwise.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_get_configuration() for a description of the
configuration extractor function.
dmbc_fit_list for a description of a fitted
DMBC model.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_check_groups(sim.dmbc) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_check_groups(sim.dmbc) ## End(Not run)
An S4 class to represent the the latent configuration estimate for a DMBC model.
Z.estAn array containing the estimate of the latent configuration for a DMBC model.
Z.sdAn array containing the standard deviation of the latent configuration for a DMBC model.
clusterA numeric vector providing the estimated group membership for the S subjects in the data.
estA length-one character vector providing the estimate type
returned in Z.est. Possible values are mean (posterior
mean), median (posterior median), ml (maximum likelihood)
and map (maximum-a-posteriori).
nA length-one numeric vector providing the number of objects.
pA length-one numeric vector providing the number of latent dimensions.
SA length-one numeric vector providing the number of subjects.
GA length-one numeric vector providing the number of clusters.
familyAn object of class list; named list with
elements representing the parameter estimates corresponding to different
values of p and G.
chainA length-one numeric vector representing the ID of the MCMC chain used to compute the estimates.
labelsA character vector for the (optional) strings to use in the plots for labeling the objects.
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
showClass("dmbc_config")showClass("dmbc_config")
dmbc_control() is an auxiliary function as user interface for
dmbc() fitting. Typically only used when calling the dmbc()
function. It is used to set parameters that affect the sampling but do
not affect the posterior distribution.
control_dmbc() is an alias for dmbc_control().
check_control() is an auxiliary function that verifies the
correctness of the controls provided before a DMBC is fitted with
dmbc().
dmbc_control( nsim = 5000, burnin = 10000, thin = 1, nchains = 1, threads = 1, seed = NULL, parallel = "no", z.prop = 1.5, alpha.prop = 0.75, random.start = TRUE, partition = NULL, method = "manhattan", procrustes = TRUE, relabel = TRUE, store.burnin = TRUE, verbose = FALSE ) control_dmbc( nsim = 5000, burnin = 10000, thin = 1, nchains = 1, threads = 1, seed = NULL, parallel = "no", z.prop = 1.5, alpha.prop = 0.75, random.start = TRUE, partition = NULL, method = "manhattan", procrustes = TRUE, relabel = TRUE, store.burnin = TRUE, verbose = FALSE ) check_control(control)dmbc_control( nsim = 5000, burnin = 10000, thin = 1, nchains = 1, threads = 1, seed = NULL, parallel = "no", z.prop = 1.5, alpha.prop = 0.75, random.start = TRUE, partition = NULL, method = "manhattan", procrustes = TRUE, relabel = TRUE, store.burnin = TRUE, verbose = FALSE ) control_dmbc( nsim = 5000, burnin = 10000, thin = 1, nchains = 1, threads = 1, seed = NULL, parallel = "no", z.prop = 1.5, alpha.prop = 0.75, random.start = TRUE, partition = NULL, method = "manhattan", procrustes = TRUE, relabel = TRUE, store.burnin = TRUE, verbose = FALSE ) check_control(control)
nsim |
A length-one numeric vector for the number of draws to be taken from the posterior distribution. |
burnin |
A length-one numeric vector for the number of initial MCMC iterations (usually to be discarded). |
thin |
A length-one numeric vector for the number of iterations between consecutive draws. |
nchains |
A length-one numeric vector for the number of parallel chains to run. |
threads |
A length-one numeric vector for the number of chains to run. If greater than 1, package parallel is used to take advantage of any multiprocessing or distributed computing capabilities that may be available. |
seed |
An integer scalar. If supplied, provides the random number seed. |
parallel |
A length-one character vector indicating the type of parallel
operation to be used (if any). Possible values are |
z.prop |
A length-one numeric vector providing the standard deviation of the proposal distribution for the jump in the individual latent space position. |
alpha.prop |
A length-one numeric vector providing the standard deviation of the proposal distribution for the jump in the individual random effect value. |
random.start |
A length-one logical vector. If |
partition |
A length-one numeric vector providing the user-defined starting partition. |
method |
A length-one character vector that specifies the distance
measure to use in determining the initial partition. Allowed values are
those from the |
procrustes |
A length-one logical vector. If |
relabel |
A length-one logical vector. If |
store.burnin |
A logical scalar. If |
verbose |
A logical scalar. If |
control |
A list of control options. |
A named list with the control options as components.
Sergio Venturini [email protected]
dmbc()
## Not run: data(simdiss, package = "dmbc") # Shorter run than default. sim.fit <- dmbc(simdiss, control = dmbc_control(burnin = 1000, nsim = 2000, thin = 5, verbose = TRUE)) ## End(Not run)## Not run: data(simdiss, package = "dmbc") # Shorter run than default. sim.fit <- dmbc(simdiss, control = dmbc_control(burnin = 1000, nsim = 2000, thin = 5, verbose = TRUE)) ## End(Not run)
An S4 class to represent the data to use in a DMBC model.
dissA list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a dist object.
nA length-one character vector representing the number of objects compared by each subject.
SA length-one numeric vector representing the number of subjects.
familyA length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
showClass("dmbc_data")showClass("dmbc_data")
dmbc_fit() is the main function that estimates a DMBC model.
dmbc_fit(D, p, G, family, control, prior, start)dmbc_fit(D, p, G, family, control, prior, start)
D |
A list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a |
p |
A length-one numeric vector indicating the number of dimensions of the latent space. |
G |
A length-one numeric vector indicating the number of cluster to partition the S subjects. |
family |
A length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data. |
control |
A list of control parameters that affect the sampling
but do not affect the posterior distribution See
|
prior |
A list containing the prior hyperparameters. See
|
start |
A named list of starting values for the MCMC algorithm (see
|
A dmbc_fit_list object.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 20000 nsim <- 10000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) summary(sim.dmbc, include.burnin = FALSE) library(bayesplot) library(ggplot2) color_scheme_set("teal") plot(sim.dmbc, what = "trace", regex_pars = "eta") z <- dmbc_get_configuration(sim.dmbc, chain = 1, est = "mean", labels = 1:16) summary(z) color_scheme_set("mix-pink-blue") graph <- plot(z, size = 2, size_lbl = 3) graph + panel_bg(fill = "gray90", color = NA) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 20000 nsim <- 10000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) summary(sim.dmbc, include.burnin = FALSE) library(bayesplot) library(ggplot2) color_scheme_set("teal") plot(sim.dmbc, what = "trace", regex_pars = "eta") z <- dmbc_get_configuration(sim.dmbc, chain = 1, est = "mean", labels = 1:16) summary(z) color_scheme_set("mix-pink-blue") graph <- plot(z, size = 2, size_lbl = 3) graph + panel_bg(fill = "gray90", color = NA) ## End(Not run)
dmbc_fit_list object to a list.dmbc_fit_list_to_list converts an object of class
dmbc_fit_list to a list of arrays including all the parameter.
chains. It is intended for internal use mainly.
dmbc_fit_list_to_list(res, include.burnin = FALSE, verbose = TRUE)dmbc_fit_list_to_list(res, include.burnin = FALSE, verbose = TRUE)
res |
An object of type |
include.burnin |
A logical scalar. If |
verbose |
A logical scalar. If |
An object of type mcmc.list.
Sergio Venturini [email protected]
dmbc() for for fitting a DMBC model;
dmbc_fit_list-class.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], nchains = 2, verbose = TRUE) sim.dmbc <- dmbc(simdiss, p, G, control) sim.list <- dmbc_fit_list_to_list(sim.dmbc, TRUE) library(bayesplot) mcmc_trace(sim.list, regex_pars = "lambda") ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], nchains = 2, verbose = TRUE) sim.dmbc <- dmbc(simdiss, p, G, control) sim.list <- dmbc_fit_list_to_list(sim.dmbc, TRUE) library(bayesplot) mcmc_trace(sim.list, regex_pars = "lambda") ## End(Not run)
dmbc_fit_list object to an object of class
mcmc.list.dmbc_fit_list_to_mcmc.list converts an object of class
dmbc_fit_list to an object with class mcmc.list.
dmbc_fit_list_to_mcmc.list(res, include.burnin = FALSE, verbose = TRUE)dmbc_fit_list_to_mcmc.list(res, include.burnin = FALSE, verbose = TRUE)
res |
An object of type |
include.burnin |
A logical scalar. If |
verbose |
A logical scalar. If |
An object of type mcmc.list.
Sergio Venturini [email protected]
dmbc() for for fitting a DMBC model;
dmbc_fit_list-class;
mcmc.list.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], nchains = 2, verbose = TRUE) sim.dmbc <- dmbc(simdiss, p, G, control) sim.mcmc <- dmbc_fit_list_to_mcmc.list(sim.dmbc, TRUE) plot(sim.mcmc) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], nchains = 2, verbose = TRUE) sim.dmbc <- dmbc(simdiss, p, G, control) sim.mcmc <- dmbc_fit_list_to_mcmc.list(sim.dmbc, TRUE) plot(sim.mcmc) ## End(Not run)
An S4 class to represent the results of fitting DMBC model using multiple Markov Chain Monte Carlo chains.
resultsAn object of class list; list of dmbc_fit
objects corresponding to the parallel MCMC chains simulated during the
estimation.
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_fit for more details on the components of each element of
the list.
showClass("dmbc_fit_list")showClass("dmbc_fit_list")
dmbc_fit object to an object of class mcmc.dmbc_fit_to_mcmc converts an object of class dmbc_fit
to an object with class mcmc.
dmbc_fit_to_mcmc(res, include.burnin = FALSE, verbose = TRUE)dmbc_fit_to_mcmc(res, include.burnin = FALSE, verbose = TRUE)
res |
An object of type |
include.burnin |
A logical scalar. If |
verbose |
A logical scalar. If |
An object of type mcmc.
Sergio Venturini [email protected]
dmbc() for for fitting a DMBC model;
dmbc_fit-class;
mcmc.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], verbose = TRUE) sim.dmbc <- dmbc(simdiss, p, G, control) sim.mcmc <- dmbc_fit_to_mcmc(sim.dmbc@results[[1]], TRUE) plot(sim.mcmc) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], verbose = TRUE) sim.dmbc <- dmbc(simdiss, p, G, control) sim.mcmc <- dmbc_fit_to_mcmc(sim.dmbc@results[[1]], TRUE) plot(sim.mcmc) ## End(Not run)
An S4 class to represent the results of fitting DMBC model using a single Markov Chain Monte Carlo chain.
z.chainAn object of class array; posterior draws from
the MCMC algorithm for the (untransformed) latent configuration .
z.chain.pAn object of class array; posterior draws from
the MCMC algorithm for the (Procrustes-transformed) latent configuration
.
alpha.chainAn object of class matrix; posterior draws
from the MCMC algorithm for the parameters.
eta.chainAn object of class matrix; posterior draws
from the MCMC algorithm for the parameters.
sigma2.chainAn object of class matrix; posterior draws
from the MCMC algorithm for the parameters.
lambda.chainAn object of class matrix; posterior draws
from the MCMC algorithm for the parameters.
prob.chainAn object of class array; posterior draws
from the MCMC algorithm for the cluster membership probabilities.
x.ind.chainAn object of class array; posterior draws
from the MCMC algorithm for the cluster membership indicators.
x.chainAn object of class matrix; posterior draws from
the MCMC algorithm for the cluster membership labels.
acceptAn object of class matrix; final acceptance rates
for the MCMC algorithm.
dissAn object of class list; list of observed
dissimilarity matrices.
densAn object of class list; list of log-likelihood,
log-prior and log-posterior values at each iteration of the MCMC simulation.
controlAn object of class list; list of the control
parameters (number of burnin and sample iterations, number of MCMC chains,
etc.). See dmbc_control() for more information.
priorAn object of class list; list of the prior
hyperparameters. See dmbc_prior() for more information.
dimAn object of class list; list of dimensions for
the estimated model, i.e. number of objects (n), number of latent
dimensions (p), number of clusters (G), and number of
subjects (S).
modelAn object of class dmbc_model.
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
showClass("dmbc_fit")showClass("dmbc_fit")
dmbc_get_configuration() is an extractor function for extracting the
latent configuration estimates of a fitted DMBC model.
dmbc_get_configuration(res, chain = 1, est = "mean", labels = character(0))dmbc_get_configuration(res, chain = 1, est = "mean", labels = character(0))
res |
An object of class |
chain |
A length-one numeric vector indicating the MCMC chain number to use. |
est |
A length-one character vector indicating the estimate type to use. |
labels |
An optional character vector with the object labels. |
A dmbc_config object.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) z <- dmbc_get_configuration(sim.dmbc, chain = 1, est = "mean") summary(z) library(bayesplot) library(ggplot2) color_scheme_set("mix-pink-blue") graph <- plot(z, size = 2, size_lbl = 3) graph + panel_bg(fill = "gray90", color = NA) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) z <- dmbc_get_configuration(sim.dmbc, chain = 1, est = "mean") summary(z) library(bayesplot) library(ggplot2) color_scheme_set("mix-pink-blue") graph <- plot(z, size = 2, size_lbl = 3) graph + panel_bg(fill = "gray90", color = NA) ## End(Not run)
dmbc_get_map() is an extractor function for extracting the
maximum-a-posterior estimates of the parameters for a fitted DMBC model.
dmbc_get_map(res, chain = 1)dmbc_get_map(res, chain = 1)
res |
An object of class |
chain |
A length-one numeric vector indicating the MCMC chain number to use. |
A named list with the following elements:
z: array of latent coordinates posterior mean estimates
alpha: numeric vector of alpha posterior mean estimates
eta: numeric vector of eta posterior mean estimates
sigma2: numeric vector of sigma2 posterior mean estimates
lambda: numeric vector of lambda posterior mean estimates
prob: numeric matrix of probability posterior mean estimates
cluster: numeric vector of cluster membership posterior mean estimates
logpost: length-one numeric vector of the maximum log-posterior value
chain: length-one numeric vector of the MCMC chain number used
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_map(sim.dmbc, chain = 1) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_map(sim.dmbc, chain = 1) ## End(Not run)
dmbc_get_ml() is an extractor function for extracting the
maximum likelihood estimates of the parameters for a fitted DMBC model.
dmbc_get_ml(res, chain = 1)dmbc_get_ml(res, chain = 1)
res |
An object of class |
chain |
A length-one numeric vector indicating the MCMC chain number to use. |
A named list with the following elements:
z: array of latent coordinates posterior mean estimates
alpha: numeric vector of alpha posterior mean estimates
eta: numeric vector of eta posterior mean estimates
sigma2: numeric vector of sigma2 posterior mean estimates
lambda: numeric vector of lambda posterior mean estimates
prob: numeric matrix of probability posterior mean estimates
cluster: numeric vector of cluster membership posterior mean estimates
loglik: length-one numeric vector of the maximum log-likelihood value
chain: length-one numeric vector of the MCMC chain number used
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_ml(sim.dmbc, chain = 1) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_ml(sim.dmbc, chain = 1) ## End(Not run)
dmbc_get_postmean() is an extractor function for extracting the
posterior mean estimates of the parameters for a fitted DMBC model.
dmbc_get_postmean(res, chain = 1)dmbc_get_postmean(res, chain = 1)
res |
An object of class |
chain |
A length-one numeric vector indicating the MCMC chain number to use. |
A named list with the following elements:
z: array of latent coordinates posterior mean estimates
alpha: numeric vector of alpha posterior mean estimates
eta: numeric vector of eta posterior mean estimates
sigma2: numeric vector of sigma2 posterior mean estimates
lambda: numeric vector of lambda posterior mean estimates
prob: numeric matrix of probability posterior mean estimates
cluster: numeric vector of cluster membership posterior mean estimates
chain: length-one numeric vector of the MCMC chain number used
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_postmean(sim.dmbc, chain = 1) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_postmean(sim.dmbc, chain = 1) ## End(Not run)
dmbc_get_postmedian() is an extractor function for extracting the
posterior median estimates of the parameters for a fitted DMBC model.
dmbc_get_postmedian(res, chain = 1)dmbc_get_postmedian(res, chain = 1)
res |
An object of class |
chain |
A length-one numeric vector indicating the MCMC chain number to use. |
A named list with the following elements:
z: array of latent coordinates posterior median estimates
alpha: numeric vector of alpha posterior median estimates
eta: numeric vector of eta posterior median estimates
sigma2: numeric vector of sigma2 posterior median estimates
lambda: numeric vector of lambda posterior median estimates
prob: numeric matrix of probability posterior median estimates
cluster: numeric vector of cluster membership posterior median estimates
chain: length-one numeric vector of the MCMC chain number used
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_data for a description of the data format.
dmbc_fit_list for a description of the elements
included in the returned object.
## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_postmedian(sim.dmbc, chain = 1) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 3 p <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 2, thin = 10, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) dmbc_get_postmedian(sim.dmbc, chain = 1) ## End(Not run)
dmbc_IC() is the main function for simultaneously selecting the
optimal latent space dimension (p) and number of clusters
(G) for a DMBC analysis.
dmbc_IC( data, pmax = 3, Gmax = 5, control = dmbc_control(), prior = NULL, est = "mean" )dmbc_IC( data, pmax = 3, Gmax = 5, control = dmbc_control(), prior = NULL, est = "mean" )
data |
An object of class |
pmax |
A length-one numeric vector indicating the maximum number of dimensions of the latent space to consider. |
Gmax |
A length-one numeric vector indicating the maximum number of cluster to consider. |
control |
A list of control parameters that affect the sampling
but do not affect the posterior distribution See
|
prior |
A list containing the prior hyperparameters. See
|
est |
A length-one character vector indicating the estimate type to
use. Possible values are |
A dmbc_ic object.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc() for fitting a DMBC model.
dmbc_ic for a description of the elements included
in the returned object.
## Not run: data(simdiss, package = "dmbc") pmax <- 2 Gmax <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 1809 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, thin = 10, store.burnin = TRUE) sim.ic <- dmbc_IC(data = simdiss, pmax = pmax, Gmax = Gmax, control = control, est = "mean") pmax <- pmax + 1 Gmax <- Gmax + 2 new.ic <- update(sim.ic, pmax = pmax, Gmax = Gmax) new.ic # plot the results library(bayesplot) library(ggplot2) color_scheme_set("mix-yellow-blue") p <- plot(new.ic, size = c(4, 1.5)) p + panel_bg(fill = "gray90", color = NA) ## End(Not run)## Not run: data(simdiss, package = "dmbc") pmax <- 2 Gmax <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 1809 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, thin = 10, store.burnin = TRUE) sim.ic <- dmbc_IC(data = simdiss, pmax = pmax, Gmax = Gmax, control = control, est = "mean") pmax <- pmax + 1 Gmax <- Gmax + 2 new.ic <- update(sim.ic, pmax = pmax, Gmax = Gmax) new.ic # plot the results library(bayesplot) library(ggplot2) color_scheme_set("mix-yellow-blue") p <- plot(new.ic, size = c(4, 1.5)) p + panel_bg(fill = "gray90", color = NA) ## End(Not run)
An S4 class to represent the comparison of a set of DMBC models through the dissimilarity model-based clustering information criterion (DCIC).
logpriorAn object of class matrix providing the
log-prior values corresponding to different values of p and
G.
logmlikAn object of class matrix providing the
marginal log-likelihood values corresponding to different values of
p and G.
logcorrfactAn object of class matrix providing the
logarithm of the correction factors corresponding to different values of
p and G.
DCICAn object of class matrix providing the values
of the DCIC index corresponding to different values of p and
G.
post.estAn object of class list; named list with
elements representing the parameter estimates corresponding to different
values of p and G.
estA length-one character vector representing the estimate
type used in computing the DCIC index. Possible values are mean,
median, ml and map. See dmbc_ic() for
more details about these values.
res_last_pAn object of class list; list of
dmbc_fit_list objects with the results of fitting the DMBC
models corresponding to the last value of p. This is needed in case
of an update of the DCIC calculations using additional p and/or
G values.
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
showClass("dmbc_ic")showClass("dmbc_ic")
dmbc_init() is the main function that estimates a DMBC model.
dmbc_init(D, p, G, family, random.start, method, partition)dmbc_init(D, p, G, family, random.start, method, partition)
D |
A list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a |
p |
A length-one numeric vector indicating the number of dimensions of the latent space. |
G |
A length-one numeric vector indicating the number of cluster to partition the S subjects. |
family |
A length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data. |
random.start |
A length-one logical vector. If |
method |
A length-one character vector specifying the distance
measure to use in determining the initial partition. Allowed values are
those from the |
partition |
A length-one numeric vector providing the user-defined starting partition. |
A named list with the following items:
z: array of latent coordinates starting values
x: numeric vector of initial cluster memberships
ng: numeric vector of initial cluster sizes
alpha: numeric vector of alpha starting values
eta: numeric vector of eta starting values
sigma2: numeric vector of sigma2 starting values
lambda: numeric vector of lambda starting values
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc() for fitting a DMBC model.
data(simdiss, package = "dmbc") dmbc_init(simdiss@diss, p = 2, G = 3, family = "binomial", random.start = TRUE)data(simdiss, package = "dmbc") dmbc_init(simdiss@diss, p = 2, G = 3, family = "binomial", random.start = TRUE)
dmbc_logLik() computes the log-likelihood value for a DMBC model.
dmbc_logLik(D, Z, alpha, lambda, x)dmbc_logLik(D, Z, alpha, lambda, x)
D |
A list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a |
Z |
A numeric matrix containing the latent configuration. |
alpha |
A numeric vector containing the alpha values. |
lambda |
A numeric vector containing the alpha lambda. |
x |
A numeric vector containing the cluster indicator values. |
A length-one numeric vector of the log-likelihood value.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc().
dmbc_logLik_rbmds() computes the log-likelihood value for a DMBC model.
dmbc_logLik_rbmds(D, Z, alpha)dmbc_logLik_rbmds(D, Z, alpha)
D |
A list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a |
Z |
A numeric matrix containing the latent configuration. |
alpha |
A numeric vector containing the alpha values. |
A length-one numeric vector of the log-likelihood value.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc().
dmbc_match_groups() is an auxiliary function for realigning the
cluster membership estimates provided by the individual chains of the
fitted model if they do not agree.
dmbc_match_groups(res, est = "mean", ref = 1)dmbc_match_groups(res, est = "mean", ref = 1)
res |
An object of class |
est |
A length-one character vector indicating the estimate type to use. |
ref |
A length-one numeric vector indicating the chain number to use as the reference. |
An object of class dmbc_fit_list.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc_check_groups() for checking the consistency
of the cluster memberships across chains for a fitted DMBC model.
dmbc_get_configuration() for a description of the
configuration extractor function.
dmbc_fit_list for a description of a fitted
DMBC model.
## Not run: data(simdiss, package = "dmbc") G <- 5 p <- 3 prm.prop <- list(z = 4, alpha = 2) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 6, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) sim.dmbc_new <- dmbc_match_groups(sim.dmbc) ## End(Not run)## Not run: data(simdiss, package = "dmbc") G <- 5 p <- 3 prm.prop <- list(z = 4, alpha = 2) burnin <- 2000 nsim <- 1000 seed <- 2301 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, nchains = 6, store.burnin = TRUE, threads = 2, parallel = "snow") sim.dmbc <- dmbc(simdiss, p, G, control) sim.dmbc_new <- dmbc_match_groups(sim.dmbc) ## End(Not run)
An S4 class to represent a DMBC model.
pA length-one character vector representing the number of dimensions of the latent space to use in the MDS analysis.
GA length-one numeric vector representing the number of clusters to partition the subjects into.
familyA length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data.
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
showClass("dmbc_model")showClass("dmbc_model")
dmbc_prior() is an auxiliary function as user interface for
dmbc() fitting. Typically only used when calling the dmbc()
function. It is used to set prior hyperparameters.
prior_dmbc() is an alias for dmbc_prior().
check_prior() is an auxiliary function that verifies the
correctness of the prior hyperparameters provided before a DMBC is fitted
with dmbc().
update_prior() is an auxiliary function to modify a set of prior
choices using a new value of p and G. It is intended for
internal use mainly in the dmbc_ic() function.
dmbc_prior( eta = list(a = rep(1.5, .dmbcEnv$current_G), b = rep(0.5, .dmbcEnv$current_G)), sigma2 = list(a = 0.1, b = 0.1), lambda = rep(1, .dmbcEnv$current_G) ) prior_dmbc( eta = list(a = rep(1.5, .dmbcEnv$current_G), b = rep(0.5, .dmbcEnv$current_G)), sigma2 = list(a = 0.1, b = 0.1), lambda = rep(1, .dmbcEnv$current_G) ) check_prior(prior) update_prior(prior, p, G)dmbc_prior( eta = list(a = rep(1.5, .dmbcEnv$current_G), b = rep(0.5, .dmbcEnv$current_G)), sigma2 = list(a = 0.1, b = 0.1), lambda = rep(1, .dmbcEnv$current_G) ) prior_dmbc( eta = list(a = rep(1.5, .dmbcEnv$current_G), b = rep(0.5, .dmbcEnv$current_G)), sigma2 = list(a = 0.1, b = 0.1), lambda = rep(1, .dmbcEnv$current_G) ) check_prior(prior) update_prior(prior, p, G)
eta |
A named list containing the hyperparameters for the prior
distribution of the |
sigma2 |
A named list containing the hyperparameters for the prior
distributions of the |
lambda |
A list containing the hyperparameters for the prior
distribution of the |
prior |
A named list of prior hyperparameters. |
p |
A length-one numeric vector indicating the number of dimensions of the latent space. |
G |
A length-one numeric vector indicating the number of cluster to partition the S subjects. |
A list with the prior hyperparameters as components.
Sergio Venturini [email protected]
dmbc()
## Not run: data(simdiss, package = "dmbc") # Shorter run than default. sim.fit <- dmbc(simdiss, control = dmbc_control(burnin = 1000, nsim = 2000, thin = 1, verbose = TRUE), prior = dmbc_prior(sigma2 = list(a = 1, b = 4))) ## End(Not run)## Not run: data(simdiss, package = "dmbc") # Shorter run than default. sim.fit <- dmbc(simdiss, control = dmbc_control(burnin = 1000, nsim = 2000, thin = 1, verbose = TRUE), prior = dmbc_prior(sigma2 = list(a = 1, b = 4))) ## End(Not run)
dmbc_config class using new/initialize.Create an instance of the dmbc_config class using new/initialize.
## S4 method for signature 'dmbc_config' initialize( .Object, Z.est = array(), Z.sd = array(), cluster = numeric(), est = character(), n = numeric(), S = numeric(), p = numeric(), G = numeric(), family = character(), chain = numeric(), labels = character() )## S4 method for signature 'dmbc_config' initialize( .Object, Z.est = array(), Z.sd = array(), cluster = numeric(), est = character(), n = numeric(), S = numeric(), p = numeric(), G = numeric(), family = character(), chain = numeric(), labels = character() )
.Object |
Prototype object from the class |
Z.est |
An array containing the estimate of the latent configuration for a DMBC model. |
Z.sd |
An array containing the standard deviation of the latent configuration for a DMBC model. |
cluster |
A numeric vector providing the estimated group membership for the S subjects in the data. |
est |
A length-one character vector providing the estimate type
returned in |
n |
A length-one numeric vector providing the number of objects. |
S |
A length-one numeric vector providing the number of subjects. |
p |
A length-one numeric vector providing the number of latent dimensions. |
G |
A length-one numeric vector providing the number of clusters. |
family |
An object of class |
chain |
A length-one numeric vector representing the ID of the MCMC chain used to compute the estimates. |
labels |
A character vector for the (optional) strings to use in the plots for labeling the objects. |
Sergio Venturini [email protected]
dmbc_data class using new/initialize.Create an instance of the dmbc_data class using new/initialize.
## S4 method for signature 'dmbc_data' initialize( .Object, diss = list(), n = numeric(), S = numeric(), family = character() )## S4 method for signature 'dmbc_data' initialize( .Object, diss = list(), n = numeric(), S = numeric(), family = character() )
.Object |
Prototype object from the class |
diss |
A list whose elements are the dissimilarity matrices corresponding
to the judgments expressed by the S subjects/raters. These matrices
must be defined as a |
n |
A length-one character vector representing the number of objects compared by each subject. |
S |
A length-one numeric vector representing the number of subjects. |
family |
A length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data. |
Sergio Venturini [email protected]
dmbc_fit_list class using new/initialize.Create an instance of the dmbc_fit_list class using new/initialize.
## S4 method for signature 'dmbc_fit_list' initialize(.Object, results = list())## S4 method for signature 'dmbc_fit_list' initialize(.Object, results = list())
.Object |
Prototype object from the class |
results |
A list whose elements are the |
Sergio Venturini [email protected]
dmbc_fit class using new/initialize.Create an instance of the dmbc_fit class using new/initialize.
## S4 method for signature 'dmbc_fit' initialize( .Object, z.chain = array(), z.chain.p = array(), alpha.chain = matrix(), eta.chain = matrix(), sigma2.chain = matrix(), lambda.chain = matrix(), prob.chain = array(), x.ind.chain = array(), x.chain = matrix(), accept = matrix(), diss = list(), dens = list(), control = list(), prior = list(), dim = list(), model = NA )## S4 method for signature 'dmbc_fit' initialize( .Object, z.chain = array(), z.chain.p = array(), alpha.chain = matrix(), eta.chain = matrix(), sigma2.chain = matrix(), lambda.chain = matrix(), prob.chain = array(), x.ind.chain = array(), x.chain = matrix(), accept = matrix(), diss = list(), dens = list(), control = list(), prior = list(), dim = list(), model = NA )
.Object |
Prototype object from the class |
z.chain |
An object of class |
z.chain.p |
An object of class |
alpha.chain |
An object of class |
eta.chain |
An object of class |
sigma2.chain |
An object of class |
lambda.chain |
An object of class |
prob.chain |
An object of class |
x.ind.chain |
An object of class |
x.chain |
An object of class |
accept |
An object of class |
diss |
An object of class |
dens |
An object of class |
control |
An object of class |
prior |
An object of class |
dim |
An object of class |
model |
An object of class |
Sergio Venturini [email protected]
dmbc_ic class using new/initialize.Create an instance of the dmbc_ic class using new/initialize.
## S4 method for signature 'dmbc_ic' initialize( .Object, logprior = matrix(), logmlik = matrix(), logcorrfact = matrix(), DCIC = matrix(), post.est = list(), est = character(), res_last_p = list() )## S4 method for signature 'dmbc_ic' initialize( .Object, logprior = matrix(), logmlik = matrix(), logcorrfact = matrix(), DCIC = matrix(), post.est = list(), est = character(), res_last_p = list() )
.Object |
Prototype object from the class |
logprior |
An object of class |
logmlik |
An object of class |
logcorrfact |
An object of class |
DCIC |
An object of class |
post.est |
An object of class |
est |
A length-one character vector representing the estimate
type used in computing the DCIC index. Possible values are |
res_last_p |
An object of class |
Sergio Venturini [email protected]
dmbc_model class using new/initialize.Create an instance of the dmbc_model class using new/initialize.
## S4 method for signature 'dmbc_model' initialize(.Object, p = numeric(), G = numeric(), family = character())## S4 method for signature 'dmbc_model' initialize(.Object, p = numeric(), G = numeric(), family = character())
.Object |
Prototype object from the class |
p |
A length-one character vector representing the number of dimensions of the latent space to use in the MDS analysis. |
G |
A length-one numeric vector representing the number of clusters to partition the subjects into. |
family |
A length-one character vector representing the type of data to analyze. Currently, it accepts only the 'binomial' value, but future developments will include the possibility to analyze continuous, multinomial and count data. |
Sergio Venturini [email protected]
Rosenberg and Kim (1975) designed an experiment to analyze the perceived similarities of 15 kinship terms.
Here, we consider the data relative to 85 females made available in Rosenberg (1982). Each subject was asked to group the kinship terms according to the perceived similarity. Thus, S = 85 binary dissimilarity matrices are available whose elements (0 or 1) indicate whether or not two kinship terms were grouped together by each individual.
data(kinship)data(kinship)
A dmbc_data object whose diss element is a list of 85
binary dissimilarity matrices. Each matrix is defined as a dist
object measuring whether each pair of the 15 kinship terms is judged as
similar (1) or not (0).
The dist objects have rows and columns that are named as follows:
grandfather
grandmother
granddaughter
grandson
brother
sister
father
mother
daughter
son
nephew
niece
cousin
aunt
uncle
Rosenberg, S. (1982). The method of sorting in multivariate research with applications selected from cognitive psychology and person perception. In N Hirschberg, LG Humphreys (eds.), Multivariate Applications in the Social Sciences, pp. 117–142. Erlbaum., Hillsdale, NJ.
Rosenberg, S., Kim, M. P. (1975). The method of sorting as a data-gathering procedure in multivariate research. Multivariate Behavioral Research, 10.
data(kinship) library(bayesplot) cols <- color_scheme_set("mix-red-blue") plot(kinship, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)data(kinship) library(bayesplot) cols <- color_scheme_set("mix-red-blue") plot(kinship, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)
mdsic computes the information criterion for a set of Bayesian
multidimensional scaling (BMDS) solutions using the approach in
Oh & Raftery (2001).
mdsic(x_star, rmin_ssr, n, min_p = 1, max_p = 6)mdsic(x_star, rmin_ssr, n, min_p = 1, max_p = 6)
x_star |
An array containing the latent configurations
estimated using |
rmin_ssr |
A numeric vector providing the ratios of SSR for the latent dimensions requested. |
n |
A length-one numeric vector providing the number of objects. |
min_p |
A length-one numeric vector providing the minimum value of the latent space dimension to use. |
max_p |
A length-one numeric vector providing the maximum value of the latent space dimension to use. |
A list with the following elements:
mdsicA numeric vector with the values of MDSIC index.
bicA numeric vector with the values of the BIC index.
Sergio Venturini [email protected]
Oh, M.-S., Raftery, A. E. (2001), "Bayesian Multidimensional Scaling and Choice of Dimension", Journal of the American Statistical Association, 96, 1031-1044.
bmds for Bayesian (metric) multidimensional scaling
and comp_ssr for the computation of SSR.
## Not run: # Road distances (in km) between 21 cities in Europe data(eurodist, package = "datasets") min_p <- 1 max_p <- 10 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim eurodist.mds <- cmdscale(eurodist, max_p) eurodist.bmds <- bmds(eurodist, min_p, max_p, burnin, nsim) plot((min_p:max_p), eurodist.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(eurodist.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], eurodist.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) ## End(Not run)## Not run: # Road distances (in km) between 21 cities in Europe data(eurodist, package = "datasets") min_p <- 1 max_p <- 10 burnin <- 200 nsim <- 1000 totiter <- burnin + nsim eurodist.mds <- cmdscale(eurodist, max_p) eurodist.bmds <- bmds(eurodist, min_p, max_p, burnin, nsim) plot((min_p:max_p), eurodist.bmds$mdsIC$mdsic, type = "b", main = "MDS Information Criterion", xlab = "p", ylab = "MDSIC") MDSICmin <- which.min(eurodist.bmds$mdsIC$mdsic) points((min_p:max_p)[MDSICmin], eurodist.bmds$mdsIC$mdsic[MDSICmin], col = "red", pch = 10, cex = 1.75, lwd = 1.5) ## End(Not run)
dmbc_config class instance.Provide a graphical summary of a dmbc_config class instance.
## S4 method for signature 'dmbc_config,ANY' plot( x, size = NULL, size_lbl = NULL, nudge_x = 0, nudge_y = 0, label_objects = TRUE, ... )## S4 method for signature 'dmbc_config,ANY' plot( x, size = NULL, size_lbl = NULL, nudge_x = 0, nudge_y = 0, label_objects = TRUE, ... )
x |
An object of class |
size |
A length-two numeric vector providing the optional sizes of points and lines in the plot. |
size_lbl |
A length-one numeric vector providing the size of labels. |
nudge_x |
A length-one numeric vector providing the optional horizontal adjustment to nudge labels by. |
nudge_y |
A length-one numeric vector providing the optional vertical adjustment to nudge labels by. |
label_objects |
A length-one logical vector. If |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
dmbc_data class instance.Provide a graphical summary of a dmbc_data class instance.
## S4 method for signature 'dmbc_data,ANY' plot(x, colors = c("white", "black"), font = NA, cex.font = NA, ...)## S4 method for signature 'dmbc_data,ANY' plot(x, colors = c("white", "black"), font = NA, cex.font = NA, ...)
x |
An object of class |
colors |
A character vector providing the colors to use in the plot. |
font |
A length-one numeric vector for the font to use for text.
Can be a vector. |
cex.font |
A length-one numeric vector for the character expansion
factor. |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
data(simdiss) library(bayesplot) cols <- color_scheme_set("brightblue") plot(simdiss, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)data(simdiss) library(bayesplot) cols <- color_scheme_set("brightblue") plot(simdiss, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)
dmbc_fit_list class instance.Provide a graphical summary of a dmbc_fit_list class instance.
## S4 method for signature 'dmbc_fit_list,ANY' plot( x, what = "trace", pars = character(), regex_pars = "lambda", include.burnin = FALSE, combo = NULL, ... )## S4 method for signature 'dmbc_fit_list,ANY' plot( x, what = "trace", pars = character(), regex_pars = "lambda", include.burnin = FALSE, combo = NULL, ... )
x |
An object of class |
what |
A length-one character vector providing the plot type to produce.
Admissible values are those provided by the bayesplot package,
that is: |
pars |
An optional character vector of parameter names. If neither
|
regex_pars |
An optional |
include.burnin |
A length-one logical vector. If |
combo |
A character vector providing the plot types to combine (see
|
... |
Further arguments to pass on. |
Sergio Venturini [email protected]
dmbc_fit class instance.Provide a graphical summary of a dmbc_fit class instance.
## S4 method for signature 'dmbc_fit,ANY' plot( x, what = "trace", pars = character(), regex_pars = "lambda", include.burnin = FALSE, combo = NULL, ... )## S4 method for signature 'dmbc_fit,ANY' plot( x, what = "trace", pars = character(), regex_pars = "lambda", include.burnin = FALSE, combo = NULL, ... )
x |
An object of class |
what |
A length-one character vector providing the plot type to produce.
Admissible values are those provided by the bayesplot package,
that is: |
pars |
An optional character vector of parameter names. If neither
|
regex_pars |
An optional |
include.burnin |
A length-one logical vector. If |
combo |
A character vector providing the plot types to combine (see
|
... |
Further arguments to pass on. |
Sergio Venturini [email protected]
dmbc_ic class instance.Provide a graphical summary of a dmbc_ic class instance.
## S4 method for signature 'dmbc_ic,ANY' plot(x, size = NULL, ...)## S4 method for signature 'dmbc_ic,ANY' plot(x, size = NULL, ...)
x |
An object of class |
size |
A length-two numeric vector providing the optional sizes of points and lines in the plot. |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
dmbc_config class.Show an instance of the dmbc_config class.
## S4 method for signature 'dmbc_config' show(object)## S4 method for signature 'dmbc_config' show(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_data class.Show an instance of the dmbc_data class.
## S4 method for signature 'dmbc_data' show(object)## S4 method for signature 'dmbc_data' show(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_fit_list class.Show an instance of the dmbc_fit_list class.
## S4 method for signature 'dmbc_fit_list' show(object)## S4 method for signature 'dmbc_fit_list' show(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_fit class.Show an instance of the dmbc_fit class.
## S4 method for signature 'dmbc_fit' show(object)## S4 method for signature 'dmbc_fit' show(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_ic class.Show an instance of the dmbc_ic class.
## S4 method for signature 'dmbc_ic' show(object)## S4 method for signature 'dmbc_ic' show(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_model class.Show an instance of the dmbc_model class.
## S4 method for signature 'dmbc_model' show(object)## S4 method for signature 'dmbc_model' show(object)
object |
An object of class |
Sergio Venturini [email protected]
A dataset containing a list of simulated binary dissimilarity matrices.
data(simdiss)data(simdiss)
A dmbc_data object whose diss element is a list
of 10 binary dissimilarity matrices. Each matrix is defined as a dist
object measuring the agreement among 16 different units.
data(simdiss) library(bayesplot) cols <- color_scheme_set("brightblue") plot(simdiss, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)data(simdiss) library(bayesplot) cols <- color_scheme_set("brightblue") plot(simdiss, colors = unlist(cols)[c(1, 6)], font = 1, cex.font = 0.75)
dmbc_fit_list object.Subsetting a dmbc_fit_list object.
## S4 method for signature 'dmbc_fit_list' subset(x, pars = character(), regex_pars = character(), ...)## S4 method for signature 'dmbc_fit_list' subset(x, pars = character(), regex_pars = character(), ...)
x |
An object of class |
pars |
An optional character vector of parameter names. If neither
|
regex_pars |
An optional |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
dmbc_fit object.Subsetting a dmbc_fit object.
## S4 method for signature 'dmbc_fit' subset(x, pars = character(), regex_pars = character(), ...)## S4 method for signature 'dmbc_fit' subset(x, pars = character(), regex_pars = character(), ...)
x |
An object of class |
pars |
An optional character vector of parameter names. If neither
|
regex_pars |
An optional |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
dmbc_config class instance.Provide a summary of a dmbc_config class instance.
## S4 method for signature 'dmbc_config' summary(object)## S4 method for signature 'dmbc_config' summary(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_data class instance.Provide a summary of a dmbc_data class instance.
## S4 method for signature 'dmbc_data' summary(object)## S4 method for signature 'dmbc_data' summary(object)
object |
An object of class |
Sergio Venturini [email protected]
dmbc_fit_list class instance.Provide a summary of a dmbc_fit_list class instance.
## S4 method for signature 'dmbc_fit_list' summary(object, include.burnin = FALSE, summary.Z = FALSE, ...)## S4 method for signature 'dmbc_fit_list' summary(object, include.burnin = FALSE, summary.Z = FALSE, ...)
object |
An object of class |
include.burnin |
A length-one logical vector. If |
summary.Z |
A length-one logical vector. If |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
dmbc_fit class instance.Provide a summary of a dmbc_fit class instance.
## S4 method for signature 'dmbc_fit' summary(object, include.burnin = FALSE, summary.Z = FALSE, ...)## S4 method for signature 'dmbc_fit' summary(object, include.burnin = FALSE, summary.Z = FALSE, ...)
object |
An object of class |
include.burnin |
A length-one logical vector. If |
summary.Z |
A length-one logical vector. If |
... |
Further arguments to pass on (currently ignored). |
Sergio Venturini [email protected]
dmbc_ic class instance.Provide a summary of a dmbc_ic class instance.
## S4 method for signature 'dmbc_ic' summary(object, p = NULL, G = NULL)## S4 method for signature 'dmbc_ic' summary(object, p = NULL, G = NULL)
object |
An object of class |
p |
An optional length-one numeric vector providing the number of latent space dimension to use in the summary. |
G |
An optional length-one numeric vector providing the number of clusters to use in the summary. |
Sergio Venturini [email protected]
dmbc_ic class instance.Provide an update of a dmbc_ic class instance.
## S4 method for signature 'dmbc_ic' update(object, pmax = NULL, Gmax = NULL, ...)## S4 method for signature 'dmbc_ic' update(object, pmax = NULL, Gmax = NULL, ...)
object |
An object of class |
pmax |
A length-one numeric vector indicating the maximum number of dimensions of the latent space to consider. |
Gmax |
A length-one numeric vector indicating the maximum number of cluster to consider. |
... |
Further arguments to pass on (currently ignored). |
A dmbc_ic object.
Sergio Venturini [email protected]
Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based
Clustering of Several Binary Dissimilarity Matrices: the dmbc
Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.
dmbc() for fitting a DMBC model.
dmbc_ic for a description of the elements included
in the returned object.
## Not run: data(simdiss, package = "dmbc") pmax <- 2 Gmax <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 1809 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, thin = 10, store.burnin = TRUE) sim.ic <- dmbc_IC(data = simdiss, pmax = pmax, Gmax = Gmax, control = control, est = "mean") pmax <- pmax + 1 Gmax <- Gmax + 2 new.ic <- update(sim.ic, pmax = pmax, Gmax = Gmax) new.ic # plot the results library(bayesplot) library(ggplot2) color_scheme_set("mix-yellow-blue") p <- plot(new.ic, size = c(4, 1.5)) p + panel_bg(fill = "gray90", color = NA) ## End(Not run)## Not run: data(simdiss, package = "dmbc") pmax <- 2 Gmax <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 1809 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, thin = 10, store.burnin = TRUE) sim.ic <- dmbc_IC(data = simdiss, pmax = pmax, Gmax = Gmax, control = control, est = "mean") pmax <- pmax + 1 Gmax <- Gmax + 2 new.ic <- update(sim.ic, pmax = pmax, Gmax = Gmax) new.ic # plot the results library(bayesplot) library(ggplot2) color_scheme_set("mix-yellow-blue") p <- plot(new.ic, size = c(4, 1.5)) p + panel_bg(fill = "gray90", color = NA) ## End(Not run)