Description Usage Arguments Value Examples
View source: R/clusvisvarsellcm.R
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
1 2 | 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
# Package loading
require(VarSelLCM)
# Data loading (categorical data)
data("congress")
# Model-based clustering with 3 components
res <- VarSelCluster(congress, 3)
# 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 <- clusvisVarSelLCM(res)
# Component interpretation graph
plotDensityClusVisu(resvisu)
# Scatter-plot of the observation memberships
plotDensityClusVisu(resvisu, add.obs = TRUE)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.