plsDA_old: PLS Discriminant Analysis

In DiscriMiner: Tools of the Trade for Discriminant Analysis

Description

Performs a Partial Least Squares (PLS) Discriminant Analysis

Usage

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

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
scaled	logical indicating whether to scale the data (default TRUE)

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.

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

Note

This is a previous version of plsDA. Not used anymore.

Author(s)

Gaston Sanchez

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)

Example output

            predicted
original     setosa versicolor virginica
  setosa         49          1         0
  versicolor      0         30        20
  virginica       0          7        43
[1] 0.1866667
            predicted
original     setosa versicolor virginica
  setosa         49          1         0
  versicolor      0         30        20
  virginica       0          7        43
[1] 0.1866667
            predicted
original     setosa versicolor virginica
  setosa          9          1         0
  versicolor      0          8         2
  virginica       0          2         8
[1] 0.1666667

DiscriMiner documentation built on May 1, 2019, 10:32 p.m.