Classification of multivariate observations by semi-supervised Gaussian finite mixtures
Classify multivariate observations based on Gaussian finite mixture models estimated by MclustSSC.
## S3 method for class 'MclustSSC' predict(object, newdata, ...)
Returns a list of with the following components:
classification |
a factor of predicted class labels for |
z |
a matrix whose [i,k]th entry is the probability that
observation i in |
Luca Scrucca
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.