predict.densityMclust: Density estimate of multivariate observations by Gaussian finite mixture modeling

Description

Compute density estimation for multivariate observations based on Gaussian finite mixture models estimated by densityMclust.

Usage

# S3 method for densityMclust
predict(object, newdata, what = c("dens", "cdens"), …)

Arguments

object

an object of class 'densityMclust' resulting from a call to densityMclust.

newdata

a vector, a data frame or matrix giving the data. If missing the density is computed for the input data obtained from the call to densityMclust.

what

a character string specifying what to retrieve: "dens" returns a vector of values for the mixture density, cdens returns a matrix of component densities for each mixture component (along the columns).

…

further arguments passed to or from other methods.

Value

Returns a vector or a matrix of densities evaluated at newdata depending on the argument what (see above).

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.

Examples

Run this code

# NOT RUN {
x = faithful$waiting
dens = densityMclust(x)
x0 = seq(50, 100, by = 10)
d0 = predict(dens, x0)
plot(dens)
points(x0, d0, pch = 20)
# }