FlexMix Clustering Demo Driver
These are demo drivers for flexmix implementing
model-based clustering of Gaussian data.
FLXMCmvnorm(formula = . ~ ., diagonal = TRUE) FLXMCnorm1(formula = . ~ .)
formula |
A formula which is interpreted relative to the formula
specified in the call to |
diagonal |
If |
This is mostly meant as a demo for FlexMix driver programming, you
should also look at package mclust for real
applications. FLXMCmvnorm clusters multivariate data,
FLXMCnorm1 univariate data. In the latter case smart
initialization is important, see the example below.
FLXMCmvnorm returns an object of class FLXMC.
Friedrich Leisch and Bettina Gruen
Friedrich Leisch. FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11(8), 2004. doi:10.18637/jss.v011.i08
data("Nclus", package = "flexmix")
require("MASS")
eqscplot(Nclus)
## This model is wrong (one component has a non-diagonal cov matrix)
ex1 <- flexmix(Nclus ~ 1, k = 4, model = FLXMCmvnorm())
print(ex1)
plotEll(ex1, Nclus)
## True model, wrong number of components
ex2 <- flexmix(Nclus ~ 1, k = 6, model = FLXMCmvnorm(diagonal = FALSE))
print(ex2)
plotEll(ex2, Nclus)
## Get parameters of first component
parameters(ex2, component = 1)
## Have a look at the posterior probabilies of 10 random observations
ok <- sample(1:nrow(Nclus), 10)
p <- posterior(ex2)[ok, ]
p
## The following two should be the same
max.col(p)
clusters(ex2)[ok]
## Now try the univariate case
plot(density(Nclus[, 1]))
ex3 <- flexmix(Nclus[, 1] ~ 1, cluster = cut(Nclus[, 1], 3),
model = FLXMCnorm1())
ex3
parameters(ex3)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.