glpls1a.logit.all {gpls}R Documentation

Fit MIRWPLS and MIRWPLSF model separately for logits

Description

Apply Iteratively ReWeighted Least Squares (MIRWPLS) with an option of Firth's bias reduction procedure (MIRWPLSF) for multi-group (say C+1 classes) classification by fitting logit models for all C classes vs baseline class separately.

Usage

glpls1a.logit.all(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 no 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

Author(s)

Beiying Ding, Robert Gentleman

References

See Also

glpls1a.mlogit,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
 glpls1a.logit.all(x,y,br=FALSE)
 ## bias reduction
 glpls1a.logit.all(x,y,br=TRUE)

[Package gpls version 1.16.0 Index]