mclust (version 3.4.7)

cdensE: Component Density for a Parameterized MVN Mixture Model

Description

Computes component densities for points in a parameterized MVN mixture model.

Usage

cdensE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEII(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVII(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEEI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVEI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEVI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVVI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEEE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEEV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVEV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVVV(data, logarithm = FALSE, parameters, warn = NULL, ...)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

logarithm

A logical value indicating whether or not the logarithm of the component densities should be returned. The default is to return the component densities, obtained from the log component densities by exponentiation.

parameters

The parameters of the model: [object Object],[object Object],[object Object]

warn

A logical value indicating whether or not a warning should be issued when computations fail. The default is warn=FALSE.

...

Catches unused arguments in indirect or list calls via do.call.

Value

• A numeric matrix whose [i,j]th entry is the density of observation i in component j. The densities are not scaled by mixing proportions.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley and A. E. Raftery (2006, revised 2010). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.

See Also

cdens, dens, mclustBIC, mstep, mclustOptions, do.call

Examples

z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case

model <- meVVV(data=faithful, z=z2)
cdensVVV(data=faithful, logarithm = TRUE, parameters = model$parameters)

z2 <- unmap(cross[,1])

model <- meEEV(data = cross[,-1], z = z2)

EEVdensities <- cdensEEV( data = cross[,-1], parameters = model$parameters)

cbind(cross[,-1],map(EEVdensities))