glpls1a.mlogit.cv.error: Leave-one-out cross-validation error using MIRWPLS and...

In Bioconductor/gpls: Classification using generalized partial least squares

Leave-one-out cross-validation error using MIRWPLS and MIRWPLSF model

Description

Leave-one-out cross-validation training set error for fitting MIRWPLS or MIRWPLSF model for multi-group classification

Usage

glpls1a.mlogit.cv.error(train.X, train.y, K.prov = NULL, eps = 0.001,lmax = 100, mlogit = T, br = T)

Arguments

train.X	n by p design matrix (with no intercept term) for training set
train.y	response vector with class lables 1 to C+1 for C+1 group classification, baseline class should be 1
K.prov	number of PLS components
eps	tolerance for convergence
lmax	maximum number of iteration allowed
mlogit	if TRUE use the multinomial logit model, otherwise fit all C-1 logistic models (vs baseline class 1) separately
br	TRUE if Firth's bias reduction procedure is used

Value

error	LOOCV training error
error.obs	the misclassified error observation indices

Author(s)

Beiying Ding, Robert Gentleman

References

• Ding, B.Y. and Gentleman, R. (2003) Classification using generalized partial least squares.

• Marx, B.D (1996) Iteratively reweighted partial least squares estimation for generalized linear regression. Technometrics 38(4): 374-381.

See Also

glpls1a.cv.error, glpls1a.train.test.error,glpls1a, glpls1a.mlogit,glpls1a.logit.all

Examples

 x <- matrix(rnorm(20),ncol=2)
 y <- sample(1:3,10,TRUE)

 ## no bias reduction
 glpls1a.mlogit.cv.error(x,y,br=FALSE)
 glpls1a.mlogit.cv.error(x,y,mlogit=FALSE,br=FALSE)
 ## bias reduction
 glpls1a.mlogit.cv.error(x,y,br=TRUE)
 glpls1a.mlogit.cv.error(x,y,mlogit=FALSE,br=TRUE)

Bioconductor/gpls documentation built on Oct. 29, 2023, 5:06 p.m.