caret (version 6.0-24)

classDist: Compute and predict the distances to class centroids

Description

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

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)

Value

• for classDist, an object of class classDist with elements:
• values
  a list with elements for each class. Each element contains a mean vector for the class centroid and the inverse of the class covariance matrix
• classes
  a character vector of class labels
• pca
  the results of prcomp when pca = TRUE
• call
  the function call
• p
  the number of variables
• n
  a 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.

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.

References

Forina et al. CAIMAN brothers: A family of powerful classification and class modeling techniques. Chemometrics and Intelligent Laboratory Systems (2009) vol. 96 (2) pp. 239-245

See Also

mahalanobis

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])