glpls1a.mlogit: Fit MIRWPLS and MIRWPLSF model

In gpls: Classification using generalized partial least squares

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

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,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)

gpls documentation built on Nov. 8, 2020, 6:50 p.m.