glpls1a.mlogit {gpls}R Documentation

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

Details

Value

coefficients regression coefficient matrix
convergence whether convergence is achieved
niter total number of iterations
bias.reduction whether Firth's procedure is used

Note

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)
    

    [Package gpls version 1.0.6 Index]