Component Density for Parameterized MVN Mixture Models

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

Usage

cdens(data, modelName, parameters, logarithm = FALSE, 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.

modelName

A character string indicating the model. The help file for mclustModelNames describes the available models.

parameters

The parameters of the model:

mean

The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.

variance

A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for mclustVariance for details.

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.

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.

Note

When one or more component densities are very large in magnitude, it may be possible to compute the logarithm of the component densities but not the component densities themselves due to overflow.

See also

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

Examples

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,]
#>         [,1]       [,2]
#> 1  -4.504988 -15.570952
#> 2 -28.297451  -2.782971
#> 3  -6.042371 -11.151408
#> 4 -18.516948  -3.349049
#> 5  -2.967399 -30.134605

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)
#> [1] "modelName"  "n"          "d"          "G"          "bic"       
#> [6] "loglik"     "parameters" "z"         

even <- odd + 1
densities <- cdens(modelName = oddModel$modelName, data = cross[even,-1], 
                   parameters = oddModel$parameters)
cbind(class = cross[even,1], densities)[1:5,]
#>    class                         
#> 2      1 0.014497007 1.123598e-07
#> 4      1 0.001087501 1.333448e-75
#> 6      1 0.005431090 1.180715e-02
#> 8      1 0.013545695 2.488475e-10
#> 10     1 0.001215779 9.250963e-03