gpls (version 1.44.0)

glpls1a.mlogit: Fit MIRWPLS and MIRWPLSF model

Description

Fit multi-logit Iteratively ReWeighted Least Squares (MIRWPLS) with an option of Firth's bias reduction procedure (MIRWPLSF) for multi-group classification

Usage

glpls1a.mlogit(x, y, K.prov = NULL, eps = 0.001, lmax = 100, b.ini = NULL, denom.eps = 1e-20, family = "binomial", link = "logit", br = T)

Arguments

x

n by p design matrix (with intercept term)

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

b.ini

initial value of regression coefficients

denom.eps

small quanitity to guarantee nonzero denominator in deciding convergence

family

glm family, binomial (i.e. multinomial here) is the only relevant one here

link

link function, logit is the only one practically implemented now

br

TRUE if Firth's bias reduction procedure is used

Value

coefficients

regression coefficient matrix

convergence

whether convergence is achieved

niter

total number of iterations

bias.reduction

whether Firth's procedure is used

Details

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

Examples

 x <- matrix(rnorm(20),ncol=2)
 y <- sample(1:3,10,TRUE)
 ## no bias reduction and 1 PLS component
 glpls1a.mlogit(cbind(rep(1,10),x),y,K.prov=1,br=FALSE)
 ## bias reduction
 glpls1a.mlogit(cbind(rep(1,10),x),y,br=TRUE)