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mclust (version 5.0.2)

cdens: Component Density for Parameterized MVN Mixture Models

Description

Computes component densities for observations in MVN mixture models parameterized by eigenvalue decomposition.

Usage

cdens(modelName, data, logarithm = FALSE, parameters, warn = NULL, ...)

Arguments

modelName
A character string indicating the model. The help file for mclustModelNames describes the available models.
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]
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,k]th entry is the density or log density of observation i in component k. 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, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

cdensE, ..., cdensVVV, dens, estep, mclustModelNames, mclustVariance, mclust.options, do.call

Examples

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

model <- me(modelName = "EEE", data = faithful, z = z2)
cdens(modelName = "EEE", data = faithful, logarithm = TRUE, 
      parameters = model$parameters)[1:5,]

data(cross)
odd <- seq(1, nrow(cross), by = 2)
oddBIC <- mclustBIC(cross[odd,-1]) 
oddModel <- mclustModel(cross[odd,-1], oddBIC) ## best parameter estimates
names(oddModel)

even <- odd + 1
densities <- cdens(modelName = oddModel$modelName, data = cross[even,-1], 
                   parameters = oddModel$parameters)
cbind(class = cross[even,1], densities)[1:5,]

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