mclust (version 2.1-14)

dens: Density for Parameterized MVN Mixtures

Description

Computes densities of obseravations in parameterized MVN mixtures.

Usage

dens(modelName, data, mu, logarithm, ...)

Arguments

modelName

A character string indicating the model. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, eq

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.

mu

The mean for each component. If there is more than one component, mu is a matrix whose columns are the means of the components.

logarithm

Return logarithm of the density, rather than the density itself. Default: FALSE

...

Other arguments, such as an argument describing the variance. See cdens.

Value

• A numeric vector whose ith component is the density of observation i in the MVN mixture specified by mu and ....

References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust. C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

See Also

grid1, cdens, mclustOptions, do.call

Examples

n <- 100 ## create artificial data

set.seed(0)
x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
           matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
xclass <- c(rep(1,n),rep(2,n))
clPairs(x, cl = xclass, sym = c("1","2")) ## display the data

set.seed(0)
I <- sample(1:(2*n))
x <- x[I, ]
xclass <- xclass[I]

odd <- seq(1, 2*n, by = 2)
oddBic <- EMclust(x[odd, ]) 
oddSumry <- summary(oddBic, x[odd, ]) ## best parameter estimates
names(oddSumry)

oddDens <- dens(modelName = oddSumry$modelName, data = x,
   mu = oddSumry$mu, decomp = oddSumry$decomp, pro = oddSumry$pro)

oddDens <- do.call("dens", c(list(data = x), oddSumry))  ## alternative call

even <- odd + 1
evenBic <- EMclust(x[even, ]) 
evenSumry <- summary(evenBic, x[even, ]) ## best parameter estimates
evenDens <- do.call( "dens", c(list(data = x), evenSumry))

cbind(class = xclass, odd = oddDens, even = evenDens)