Description Details Author(s) Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ## Not run:
## First example: R package VarSelLCM
# 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)
## 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)
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