plsDA

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PLS Discriminant Analysis

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)
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
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

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

Aliases
  • plsDA
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)
Documentation reproduced from package DiscriMiner, version 0.1-29, License: GPL-3

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