glpls1a.logit.all: Fit MIRWPLS and MIRWPLSF model separately for logits

In gpls: Classification using generalized partial least squares

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

• 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

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)

Example output

$coefficients
          [,1]      [,2]
[1,] -1.540271 -15.33824
[2,] 12.986848  36.83434
[3,] 34.194429  10.00955

$coefficients
           [,1]      [,2]
[1,] -0.1548631 -1.372105
[2,]  0.7728681  3.527145
[3,]  2.7264810  1.122339

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