glpls1a.logit.all

Fit MIRWPLS and MIRWPLSF model separately for logits

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.

Keywords
regression
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
Details

Value

coefficients
regression coefficient matrix

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

Aliases
  • glpls1a.logit.all
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)
Documentation reproduced from package gpls, version 1.44.0, License: Artistic-2.0

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