predict.MclustSSC: Classification of multivariate observations by...

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

Classification of multivariate observations by semi-supervised Gaussian finite mixtures

Description

Classify multivariate observations based on Gaussian finite mixture models estimated by MclustSSC.

Usage

  ## S3 method for class 'MclustSSC'
predict(object, newdata, ...)

Arguments

object	an object of class 'MclustSSC' resulting from a call to MclustSSC.
newdata	a data frame or matrix giving the data. If missing the train data obtained from the call to MclustSSC are classified.
...	further arguments passed to or from other methods.

Value

Returns a list of with the following components:

classification	a factor of predicted class labels for newdata.
z	a matrix whose [i,k]th entry is the probability that observation i in newdata belongs to the kth class.

Author(s)

Luca Scrucca

See Also

MclustSSC.

Examples

X <- iris[,1:4]
class <- iris$Species
# randomly remove class labels
set.seed(123)
class[sample(1:length(class), size = 120)] <- NA
table(class, useNA = "ifany")
clPairs(X, ifelse(is.na(class), 0, class),
        symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3),
        main = "Partially classified data")

# Fit semi-supervised classification model
mod_SSC  <- MclustSSC(X, class)

pred_SSC <- predict(mod_SSC)
table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany")

X_new = data.frame(Sepal.Length = c(5, 8),
                   Sepal.Width  = c(3.1, 4),
                   Petal.Length = c(2, 5),
                   Petal.Width  = c(0.5, 2))
predict(mod_SSC, newdata = X_new)

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