classDist

From caret v4.39 by Max Kuhn

Compute and predict the distances to class centroids

This function computes the class centroids and covariance matrix for a training set for determining Mahalanobis distances of samples to each class centroid.

Keywords: manip

Usage

classDist(x, ...)
## S3 method for class 'default':
classDist(x, y, groups = 5, pca = FALSE, keep = NULL, ...)
## S3 method for class 'classDist':
predict(object, newdata, trans = log, ...)

Arguments

x: a matrix or data frame of predictor variables
y: a numeric or factor vector of class labels
groups: an integer for the number of bins for splitting a numeric outcome
pca: a logical: should principal components analysis be applied to the dataset prior to splitting the data by class?
keep: an integer for the number of PCA components that should by used to predict new samples (NULL uses all within a tolerance of sqrt(.Machine$double.eps))
object: an object of class classDist
newdata: a matrix or data frame. If vars was previously specified, these columns should be in newdata
trans: an optional function that can be applied to each class distance. trans = NULL will not apply a function
...: optional arguments to pass (not currently used)

Details

For factor outcomes, the data are split into groups for each class and the mean and covariance matrix are calculated. These are then used to compute Mahalanobis distances to the class centers (using predict.classDist The function will check for non-singular matrices.

For numeric outcomes, the data are split into roughly equal sized bins based on groups. Percentiles are used to split the data.

Value

for classDist, an object of class classDist with elements:
valuesa list with elements for each class. Each element contains a mean vector for the class centroid and the inverse of the class covariance matrix
classesa character vector of class labels
pcathe results of prcomp when pca = TRUE
callthe function call
pthe number of variables
na vector of samples sizes per class
For predict.classDist, a matrix with columns for each class. The columns names are the names of the class with the prefix dist.. In the case of numeric y, the class labels are the percentiles. For example, of groups = 9, the variable names would be dist.11.11, dist.22.22, etc.

Examples

trainSet <- sample(1:150, 100)

distData <- classDist(iris[trainSet, 1:4], 
                      iris$Species[trainSet])

newDist <- predict(distData,
                   iris[-trainSet, 1:4])

splom(newDist, groups = iris$Species[-trainSet])

Documentation reproduced from package caret, version 4.39, License: GPL-2

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