Component Density for Parameterized MVN Mixture Models
Computes component densities for observations in MVN mixture models parameterized by eigenvalue decomposition.
cdens(data, modelName, parameters, logarithm = FALSE, warn = NULL, ...)
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
|
parameters |
The parameters of the model:
|
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 |
... |
Catches unused arguments in indirect or list calls via |
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.
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.
cdensE
, ...,
cdensVVV
,
dens
,
estep
,
mclustModelNames
,
mclustVariance
,
mclust.options
,
do.call
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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