predict.densityMclust: Density estimate of multivariate observations by Gaussian...

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

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 class 'densityMclust'
predict(object, newdata, what = c("dens", "cdens", "z"), logarithm = FALSE, ...)

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); "z" returns a matrix of conditional probabilities of each data point to belong to a mixture component.
logarithm	A logical value indicating whether or not the logarithm of the density or component densities should be returned.
...	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).

Author(s)

Luca Scrucca

See Also

Mclust.

Examples

x <- faithful$waiting
dens <- densityMclust(x, plot = FALSE)
x0 <- seq(50, 100, by = 10)
d0 <- predict(dens, x0)
plot(dens, what = "density")
points(x0, d0, pch = 20)

mclust documentation built on Nov. 16, 2023, 5:10 p.m.