| Title: | Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering |
|---|---|
| Description: | Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification. |
| Authors: | Christophe Biernacki [aut], Matthieu Marbac [aut, cre], Vincent Vandewalle [aut] |
| Maintainer: | Matthieu Marbac <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.2.0 |
| Built: | 2024-12-09 06:46:28 UTC |
| Source: | CRAN |
The main function for parameter inference is clusvis. Moreover, specific functions clusvisVarSelLCM and clusvisMixmod are implemented to visualize the results of the R package VarSelLCM and Rmixmod. After parameter inference, visualization is done with function plotDensityClusVisu.
| Package: | ClusVis |
| Type: | Package |
| Version: | 1.1.0 |
| Date: | 2018-04-18 |
| License: | GPL-3 |
| LazyLoad: | yes |
Biernacki, C. and Marbac, M. and Vandewalle, V.
## Not run: ## First example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Second example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data(birds) # Model-based clustering with 3 components resmixmod <- mixmodCluster(birds, 3) # Inference of the parameters used for results visualization (general approach) # Probabilities of classification are not sampled from the model parameter, # but observed probabilities of classification are used for parameter estimation resvisu <- clusvis(log(resmixmod@bestResult@proba), resmixmod@bestResult@parameters@proportions) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(resmixmod) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Third example: R package VarSelLCM # Package loading require(VarSelLCM) # Data loading (categorical data) data("heart") # Model-based clustering with 3 components res <- VarSelCluster(heart[,-13], 3) # Inference of the parameters used for results visualization # (specific for VarSelLCM results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisVarSelLCM(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)## Not run: ## First example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Second example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data(birds) # Model-based clustering with 3 components resmixmod <- mixmodCluster(birds, 3) # Inference of the parameters used for results visualization (general approach) # Probabilities of classification are not sampled from the model parameter, # but observed probabilities of classification are used for parameter estimation resvisu <- clusvis(log(resmixmod@bestResult@proba), resmixmod@bestResult@parameters@proportions) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(resmixmod) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Third example: R package VarSelLCM # Package loading require(VarSelLCM) # Data loading (categorical data) data("heart") # Model-based clustering with 3 components res <- VarSelCluster(heart[,-13], 3) # Inference of the parameters used for results visualization # (specific for VarSelLCM results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisVarSelLCM(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)
This function estimates the parameters used for visualization
clusvis(logtik.estim, prop = rep(1/ncol(logtik.estim), ncol(logtik.estim)), logtik.obs = NULL, maxit = 10^3, nbrandomInit = 12, nbcpu = 1)clusvis(logtik.estim, prop = rep(1/ncol(logtik.estim), ncol(logtik.estim)), logtik.obs = NULL, maxit = 10^3, nbrandomInit = 12, nbcpu = 1)
logtik.estim |
matrix. It contains the probabilities of classification used for parameter inference (should be sampled from the model parameter or computed from the observations). |
prop |
vector. It contains the class proportions (by default, classes have same proportion). |
logtik.obs |
matrix. It contains the probabilities of classification of the clustered sample. If missing, logtik.estim is used. |
maxit |
numeric. It limits the number of iterations for the Quasi-Newton algorithm (default 1000). |
nbrandomInit |
numeric. It defines the number of random initialization of the Quasi-Newton algorithm. |
nbcpu |
numeric. It specifies the number of CPU (only for linux) |
Returns a list
## Not run: ## First example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Second example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data(birds) # Model-based clustering with 3 components resmixmod <- mixmodCluster(birds, 3) # Inference of the parameters used for results visualization (general approach) # Probabilities of classification are not sampled from the model parameter, # but observed probabilities of classification are used for parameter estimation resvisu <- clusvis(log(resmixmod@bestResult@proba), resmixmod@bestResult@parameters@proportions) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(resmixmod) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Third example: R package VarSelLCM # Package loading require(VarSelLCM) # Data loading (categorical data) data("heart") # Model-based clustering with 3 components res <- VarSelCluster(heart[,-13], 3) # Inference of the parameters used for results visualization # (specific for VarSelLCM results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisVarSelLCM(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)## Not run: ## First example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Second example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data(birds) # Model-based clustering with 3 components resmixmod <- mixmodCluster(birds, 3) # Inference of the parameters used for results visualization (general approach) # Probabilities of classification are not sampled from the model parameter, # but observed probabilities of classification are used for parameter estimation resvisu <- clusvis(log(resmixmod@bestResult@proba), resmixmod@bestResult@parameters@proportions) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(resmixmod) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Third example: R package VarSelLCM # Package loading require(VarSelLCM) # Data loading (categorical data) data("heart") # Model-based clustering with 3 components res <- VarSelCluster(heart[,-13], 3) # Inference of the parameters used for results visualization # (specific for VarSelLCM results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisVarSelLCM(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)
This function estimates the parameters used for visualization of model-based clustering performs with R package Rmixmod. To achieve the parameter infernece, it automatically samples probabilities of classification from the model parameters
clusvisMixmod(mixmodResult, sample.size = 5000, maxit = 10^3, nbrandomInit = 4 * mixmodResult@bestResult@nbCluster, nbcpu = 1, loccont = NULL)clusvisMixmod(mixmodResult, sample.size = 5000, maxit = 10^3, nbrandomInit = 4 * mixmodResult@bestResult@nbCluster, nbcpu = 1, loccont = NULL)
mixmodResult |
[ |
sample.size |
numeric. Number of probabilities of classification sampled for parameter inference. |
maxit |
numeric. It limits the number of iterations for the Quasi-Newton algorithm (default 1000). |
nbrandomInit |
numeric. It defines the number of random initialization of the Quasi-Newton algorithm. |
nbcpu |
numeric. It specifies the number of CPU (only for linux). |
loccont |
numeric. Index of the column containing continuous variables (only for mixed-type data). |
Returns a list
## Not run: ## First example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Second example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data(birds) # Model-based clustering with 3 components resmixmod <- mixmodCluster(birds, 3) # Inference of the parameters used for results visualization (general approach) # Probabilities of classification are not sampled from the model parameter, # but observed probabilities of classification are used for parameter estimation resvisu <- clusvis(log(resmixmod@bestResult@proba), resmixmod@bestResult@parameters@proportions) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(resmixmod) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)## Not run: ## First example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## Second example: R package Rmixmod # Package loading require(Rmixmod) # Data loading (categorical data) data(birds) # Model-based clustering with 3 components resmixmod <- mixmodCluster(birds, 3) # Inference of the parameters used for results visualization (general approach) # Probabilities of classification are not sampled from the model parameter, # but observed probabilities of classification are used for parameter estimation resvisu <- clusvis(log(resmixmod@bestResult@proba), resmixmod@bestResult@parameters@proportions) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(resmixmod) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)
This function estimates the parameters used for visualization of model-based clustering performs with R package Rmixmod. To achieve the parameter infernece, it automatically samples probabilities of classification from the model parameters
clusvisVarSelLCM(varselResult, sample.size = 5000, maxit = 10^3, nbrandomInit = 4 * varselResult@model@g, nbcpu = 1, loccont = NULL)clusvisVarSelLCM(varselResult, sample.size = 5000, maxit = 10^3, nbrandomInit = 4 * varselResult@model@g, nbcpu = 1, loccont = NULL)
varselResult |
[ |
sample.size |
numeric. Number of probabilities of classification sampled for parameter inference. |
maxit |
numeric. It limits the number of iterations for the Quasi-Newton algorithm (default 1000). |
nbrandomInit |
numeric. It defines the number of random initialization of the Quasi-Newton algorithm. |
nbcpu |
numeric. It specifies the number of CPU (only for linux). |
loccont |
numeric. Index of the column containing continuous variables (only for mixed-type data). |
Returns a list
## Not run: # Package loading require(VarSelLCM) # Data loading (categorical data) data("heart") # Model-based clustering with 3 components res <- VarSelCluster(heart[,-13], 3) # Inference of the parameters used for results visualization # (specific for VarSelLCM results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisVarSelLCM(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)## Not run: # Package loading require(VarSelLCM) # Data loading (categorical data) data("heart") # Model-based clustering with 3 components res <- VarSelCluster(heart[,-13], 3) # Inference of the parameters used for results visualization # (specific for VarSelLCM results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisVarSelLCM(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)
This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition).
Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc. Washington, D.C., 1985.
Schlimmer, J. C. (1987). Concept acquisition through representational adjustment. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine, CA.
Website: https://archive.ics.uci.edu/ml/datasets/congressional+voting+records
data(congress)data(congress)
Function for visualizing the clustering results
plotDensityClusVisu(res, dim = c(1, 2), threshold = 0.95, add.obs = FALSE, positionlegend = "topright", xlim = NULL, ylim = NULL, colset = c("darkorange1", "dodgerblue2", "black", "chartreuse2", "darkorchid2", "gold2", "deeppink2", "deepskyblue1", "firebrick2", "cyan1", "red", "yellow"))plotDensityClusVisu(res, dim = c(1, 2), threshold = 0.95, add.obs = FALSE, positionlegend = "topright", xlim = NULL, ylim = NULL, colset = c("darkorange1", "dodgerblue2", "black", "chartreuse2", "darkorchid2", "gold2", "deeppink2", "deepskyblue1", "firebrick2", "cyan1", "red", "yellow"))
res |
|
dim |
numeric. This vector of size two choose the axes to represent. |
threshold |
numeric. It contains the thersholds used for computing the level curves. |
add.obs |
boolean. If TRUE, coordinnates of the observations are plotted. |
positionlegend |
character. It specifies the legend location. |
xlim |
numeric. It specifies the range of x-axis. |
ylim |
numeric. It specifies the range of y-axis. |
colset |
character. It specifies the colors of the observations per class. |
## Not run: # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)## Not run: # Package loading require(Rmixmod) # Data loading (categorical data) data("congress") # Model-based clustering with 4 components set.seed(123) res <- mixmodCluster(congress[,-1], 4, strategy = mixmodStrategy(nbTryInInit = 500, nbTry=25)) # Inference of the parameters used for results visualization # (specific for Rmixmod results) # It is better because probabilities of classification are generated # by using the model parameters resvisu <- clusvisMixmod(res) # Component interpretation graph plotDensityClusVisu(resvisu) # Scatter-plot of the observation memberships plotDensityClusVisu(resvisu, add.obs = TRUE) ## End(Not run)