RVAideMemoire (version 0.9-35)

DA.confusion: Classification error rate of a Discriminant Analysis

Description

Compute the classification error rate of a Discriminant Analysis. The function divides the data in a training and a testing dataset, and predict the class of the individuals of the testing dataset by using the model fitted with the training dataset. Linear Discriminant Analysis (by using lda) and Partial Least Squares - Discriminant Analysis (by using plsda) are handled.

Usage

DA.confusion(model, train = 2/3, crit.lda = c("plug-in", "predictive","debiased"),
  crit.plsda = c("mahalanobis.dist", "centroids.dist", "max.dist"))

Arguments

model

object of class "lda" (from lda) or "plsda" (from plsda).

train

proportion of the total number of individuals to be used to build the training dataset.

crit.lda

this determines how the parameter estimation is handled in LDA cross-validation. With "plug-in" (the default) the usual unbiased parameter estimates are used and assumed to be correct. With "debiased" an unbiased estimator of the

crit.plsda

prediction method to be applied for PLS-DA cross-validation. Should be a subset of "mahalanobis.dist" (default), "centroids.dist" or "max.dist".

Value

• model
  type of discriminant model used.
• crit.lda
  method for parameter estimation when working on a LDA.
• crit.plsda
  distance used when working on a PLS-DA.
• prop.train
  a vector giving the number of individuals used to build the training dataset (used) and the total number of individuals (total).
• ind.for.train
  individuals used to build the training dataset.
• predicted
  vector of predicted classes.
• confusion
  confusion matrix.
• prop.confusion
  classification error rate.

Details

When working on a LDA, the prior probabilities for the model fitted on the training dataset are extracted from model (they are not evaluated from the training dataset itself).

See Also

lda, predict.lda, plsda, predict.plsda

Examples

# LDA model
require(MASS)
data(iris)
model.LDA <- lda(iris[,1:4],iris$Species)
DA.confusion(model.LDA)

# PLS-DA model
require(mixOmics)
data(yeast)
model.PLSDA <- plsda(t(yeast$data),yeast$strain.cond)
DA.confusion(model.PLSDA)