glpls1a.logit.all: 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
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.
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)