FLXMCregnorm {flexord} | R Documentation |
FlexMix Driver for Regularized Multivariate Normal Mixtures
Description
This model driver implements the regularization method as
introduced by Fraley and Raftery (2007) for univariate normal
mixtures. Default parameters for the regularization according to
that paper may be obtained using FLXMCregnorm_defaults()
. We
extend this to the multivariate case assuming independence between
variables within components, i.e., we only implement the special
case where the covariance matrix is diagonal. For more general
applications of normal mixtures see package mclust.
Usage
FLXMCregnorm(formula = . ~ ., params)
Arguments
formula |
A formula which is interpreted relative to the formula
specified in the call to |
params |
Prior parameters for normal mixtures. You may obtain
default values according to Fraley and Raftery (2007) using
|
Details
For the regularization the conjugate prior distributions for the normal distribution are used, which are:
Normal prior with parameter
mu_p
andsigma^2/kappa_p
for the mean.Inverse Gamma prior with parameters
nu_p/2
andzeta_p^2/2
for the variance.
Value
An object of class "FLXC"
.
References
Ernst, D, Ortega Menjivar, L, Scharl, T, GrĂ¼n, B (2025). Ordinal Clustering with the flex-Scheme. Austrian Journal of Statistics. Submitted manuscript.
Fraley, C, Raftery, AE (2007) Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Journal of Classification, 24(2), 155-181
See Also
FLXMCregnorm_defaults
Examples
library("flexmix")
library("flexord")
library("flexclust")
# example data
data("iris", package = "datasets")
my_iris <- subset(iris, select=setdiff(colnames(iris), "Species")) |>
as.matrix()
# cluster one model with a scale parameter similar to the default for 3 components
params <- FLXMCregnorm_defaults(my_iris, zeta_p = c(0.23, 0.06, 1.04, 0.19))
m1 <- stepFlexmix(my_iris ~ 1, k = 3,
model=FLXMCregnorm(params = params))
summary(m1)
# rand index of clusters vs species
randIndex(clusters(m1), iris$Species)
# cluster one model with default scale parameter
params <- FLXMCregnorm_defaults(my_iris, k = 3)
m2 <- stepFlexmix(my_iris ~ 1, k = 3,
model = FLXMCregnorm(params = params))
summary(m2)
# rand index of clusters vs species
randIndex(clusters(m2), iris$Species)
# rand index between both models (should be >= 0.8)
randIndex(clusters(m1), clusters(m2))