Plotting method for MclustSSC semi-supervised classification
Plots for semi-supervised classification based on Gaussian finite mixture models.
## S3 method for class 'MclustSSC'
plot(x, what = c("BIC", "classification", "uncertainty"), ...)x |
An object of class |
what |
A string specifying the type of graph requested. Available choices are:
If not specified, in interactive sessions a menu of choices is proposed. |
... |
further arguments passed to or from other methods. See |
Luca Scrucca
X <- iris[,1:4]
class <- iris$Species
# randomly remove class labels
set.seed(123)
class[sample(1:length(class), size = 120)] <- NA
table(class, useNA = "ifany")
clPairs(X, ifelse(is.na(class), 0, class),
symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3),
main = "Partially classified data")
# Fit semi-supervised classification model
mod_SSC <- MclustSSC(X, class)
summary(mod_SSC, parameters = TRUE)
pred_SSC <- predict(mod_SSC)
table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany")
plot(mod_SSC, what = "BIC")
plot(mod_SSC, what = "classification")
plot(mod_SSC, what = "uncertainty")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.