DiscriMiner (version 0.1-22)

plsDA: PLS Discriminant Analysis

Description

Performs a Partial Least Squares (PLS) Discriminant Analysis

Usage

plsDA(variables, group, autosel = TRUE, comps = 2, validation = NULL, learn = NULL, test = NULL)

Arguments

variables

matrix or data frame with explanatory variables

group

vector or factor with group memberships

autosel

logical indicating automatic selection of PLS components by cross-validation. Default autosel=TRUE

comps

integer greater than one indicating the number of PLS components to retain. Used only when autosel=FALSE

validation

type of validation, either NULL or "learntest". Default NULL

learn

optional vector of indices for a learn-set. Only used when validation="learntest". Default NULL

test

optional vector of indices for a test-set. Only used when validation="learntest". Default NULL

Value

• An object of class "plsda", basically a list with the following elements
• functions
  table with discriminant functions
• confusion
  confusion matrix
• scores
  discriminant scores for each observation
• classification
  assigned class
• error_rate
  misclassification error rate
• components
  PLS components
• Q2
  quality of loo cross-validation
• R2
  R-squared coefficients
• VIP
  Variable Importance for Projection
• comp_vars
  correlations between components and variables
• comp_group
  correlations between components and groups

Details

When validation=NULL leave-one-out (loo) cross-validation is performed. When validation="learntest" validation is performed by providing a learn-set and a test-set of observations.

References

Tenenhaus M. (1998) La Regression PLS. Editions Technip, Paris. Perez-Enciso M., Tenenhaus M. (2003) Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics 112: 581-592.

See Also

classify, geoDA, linDA, quaDA

Examples

# load iris dataset
  data(iris)

  # PLS discriminant analysis specifying number of components = 2
  my_pls1 = plsDA(iris[,1:4], iris$Species, autosel=FALSE, comps=2)
  my_pls1$confusion
  my_pls1$error_rate
  # plot circle of correlations
  plot(my_pls1)

  # PLS discriminant analysis with automatic selection of components
  my_pls2 = plsDA(iris[,1:4], iris$Species, autosel=TRUE)
  my_pls2$confusion
  my_pls2$error_rate
  
  # linear discriminant analysis with learn-test validation
  learning = c(1:40, 51:90, 101:140)
  testing = c(41:50, 91:100, 141:150)
  my_pls3 = plsDA(iris[,1:4], iris$Species, validation="learntest", learn=learning, test=testing)
  my_pls3$confusion
  my_pls3$error_rate