predict.hdmda: Prediction method for ‘hdmda’ class objects.

Description

This function computes the class prediction of a dataset with respect to the model-based supervised classification method hdmda.

Usage

# S3 method for hdmda
predict(object, X, …)

Arguments

object

An object of class ‘hdmda’.

A matrix or a data frame of observations, assuming the rows are the observations and the columns the variables. Note that NAs are not allowed.

…

Arguments based from or to other methods. Not currently used.

Value

class

vector of the predicted class.

posterior

The matrix of the probabilities to belong to a class for each observation and each class.

References

C. Bouveyron and C. Brunet (2014), “Model-based clustering of high-dimensional data: A review”, Computational Statistics and Data Analysis, vol. 71, pp. 52-78.

Bouveyron, C. Girard, S. and Schmid, C. (2007), “High Dimensional Discriminant Analysis”, Communications in Statistics: Theory and Methods, vol. 36 (14), pp. 2607-2623.

Bouveyron, C. Celeux, G. and Girard, S. (2011), “Intrinsic dimension estimation by maximum likelihood in probabilistic PCA”, Pattern Recognition Letters, vol. 32 (14), pp. 1706-1713.

Berge, L. Bouveyron, C. and Girard, S. (2012), “HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data”, Journal of Statistical Software, 46(6), pp. 1-29, url: http://www.jstatsoft.org/v46/i06/.

Hastie, T., & Tibshirani, R. (1996), “Discriminant analysis by Gaussian mixtures”, Journal of the Royal Statistical Society, Series B (Methodological), pp. 155-176.

Examples

Run this code

# NOT RUN {
# Load the Wine data set
data(wine)
cls = wine[,1]; X = scale(wine[,-1])

# A simple use...
out = hdmda(X[1:100,],cls[1:100])
res = predict(out,X[101:nrow(X),])
# }