ManlyMix (version 0.1.15.1)

Manly.model: Manly mixture model

Description

Runs all the functionality of a Manly mixture model.

Usage

Manly.model(X, K = 1:5, Gaussian = FALSE, initial = "k-means", 
nstart = 100, method = "ward.D",  short.iter = 5, 
select = "none", silent = TRUE, plot = FALSE, var1 = NULL, 
var2 = NULL, VarAssess = FALSE, conf.CI = NULL, overlap = FALSE, N = 1000, 
tol = 1e-5, max.iter = 1000, ...)

Value

Model

best mixture model obtained

VarAssess

estimated variance-covariance matrix for model parameter estimates

Overlap

estimated overlap of Manly mixture components

Arguments

X

dataset matrix (n x p)

K

number of components tested

Gaussian

whether Gaussian mixture models are run or not

initial

initialization strategy of the EM algorithm ("k-means" - partition obtained by k-means clustering, "hierarchical" - partition obtained by hierarchical clustering, "emEM" - parameters estimated by the emEM algorithm)

nstart

number of random starts for the k-means or the emEM initialization

method

linkage method for the hierarchical initialization

short.iter

number of short emEM iterations to run

select

control to run Manly.select or not ("none" - do not run Manly.select , "forward" - run forward selection, "backward" - run backward selection)

silent

control the output from Manly.select

plot

control to construct the density or contour plot or not

var1

x-axis variable for contour plot or variable for density plot

var2

y-axis variable for contour plot

VarAssess

run the variability assessment of the Manly mixture model or not

conf.CI

specify the confidence level of parameter estimates

overlap

estimate the overlap of Manly mixture components or not

N

number of Monte Carlo simulations to run in the Manly.overlap function

tol

tolerance level

max.iter

maximum number of iterations

...

further arguments related to Manly.plot

Details

Wrapper function that incorporates all functionality associated with Manly mixture modeling.

See Also

Manly.EM

Examples

set.seed(123)

K <- 3; p <- 4
X <- as.matrix(iris[,-5])
id.true <- rep(1:K, each = 50)

Obj <- Manly.model(X, K = 1:5, initial = "emEM", nstart = 1, short.iter = 5)