DiscriMiner (version 0.1-29)

plsDA: PLS Discriminant Analysis

Description

Performs a Partial Least Squares (PLS) Discriminant Analysis by giving the option to include a random leave-k fold out cross validation

Usage

plsDA(variables, group, autosel = TRUE, comps = 2, validation = NULL, learn = NULL, test = NULL, cv = "LOO", k = NULL, retain.models = FALSE)

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

cv

string indicating the type of crossvalidation. Avialable options are "LOO" (Leave-One-Out) and "LKO" (Leave-K fold-Out)

k

fold left out if using LKO (usually 7 or 10)

retain.models

whether to retain lower models (i.e. all lower component results)

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

loadings

loadings

y.loadings

y loadings

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

## Not run: 
# # 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
# ## End(Not run)