R: Component Density for Parameterized MVN Mixture Models

cdens {mclust} | R Documentation

## 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: `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.
`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,]

    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,]

[Package mclust version 5.4.2 Index]