tune.func: Functions for Tuning Appropriate Number of Components

In mt: Metabolomics Data Analysis Toolbox

Description

Tune appropriate number of components (ncomp) for plsc, plslda or pcalda.

Usage

tune.plsc(x,y, pls="simpls",ncomp=10, tune.pars,...)

tune.plslda(x,y, pls="simpls",ncomp=10, tune.pars,...)

tune.pcalda(x,y, ncomp=NULL, tune.pars,...)

Arguments

x	A matrix or data frame containing the explanatory variables if no formula is given as the principal argument.
y	A factor specifying the class for each observation if no formula principal argument is given.
pls	A method for calculating PLS scores and loadings. The following methods are supported: simpls: SIMPLS algorithm. kernelpls: kernel algorithm. oscorespls: orthogonal scores algorithm. For details, see simpls.fit, kernelpls.fit and oscorespls.fit in package pls.
ncomp	The number of components to be used in the classification.
tune.pars	A list of parameters using by the resampling method. See valipars for details.
...	Further parameters passed to tune.

Value

A list including:

ncomp	The best number of components.
acc.tune	Accuracy rate of components.

Author(s)

Wanchang Lin

See Also

plsc, plslda, pcalda,valipars

Examples

## Not run: 
data(abr1)
cl   <- factor(abr1$fact$class)
dat  <- preproc(abr1$pos , y=cl, method=c("log10"),add=1)[,110:500]

## divide data as training and test data
idx <- sample(1:nrow(dat), round((2/3)*nrow(dat)), replace=FALSE) 

## construct train and test data 
train.dat  <- dat[idx,]
train.t    <- cl[idx]
test.dat   <- dat[-idx,]        
test.t     <- cl[-idx] 

## tune the best number of components
ncomp.plsc   <- tune.plsc(dat,cl, pls="simpls",ncomp=20)
ncomp.plslda <- tune.plslda(dat,cl, pls="simpls",ncomp=20)
ncomp.pcalda <- tune.pcalda(dat,cl, ncomp=60)

## model fit
(z.plsc   <- plsc(train.dat,train.t, ncomp=ncomp.plsc$ncomp))
(z.plslda <- plslda(train.dat,train.t, ncomp=ncomp.plslda$ncomp))
(z.pcalda <- pcalda(train.dat,train.t, ncomp=ncomp.pcalda$ncomp))

## or indirect use tune function in model fit
z.plsc   <- plsc(train.dat,train.t, ncomp=20, tune=TRUE)
z.plslda <- plslda(train.dat,train.t, ncomp=20, tune=TRUE)
z.pcalda <- pcalda(train.dat,train.t, ncomp=60, tune=TRUE)

## predict test data
pred.plsc   <- predict(z.plsc, test.dat)$class
pred.plslda <- predict(z.plslda, test.dat)$class
pred.pcalda <- predict(z.pcalda, test.dat)$class

## classification rate and confusion matrix
cl.rate(test.t, pred.plsc)
cl.rate(test.t, pred.plslda)
cl.rate(test.t, pred.pcalda)


## End(Not run)

mt documentation built on Feb. 2, 2022, 1:07 a.m.