glpls1a: Fit IRWPLS and IRWPLSF model

In gpls: Classification using generalized partial least squares

Description

Fit Iteratively ReWeighted Least Squares (IRWPLS) with an option of Firth's bias reduction procedure (IRWPLSF) for two-group classification

Usage

glpls1a(X, y, K.prov = NULL, eps = 0.001, lmax = 100, b.ini = NULL, 
      denom.eps = 1e-20, family = "binomial", link = NULL, br = TRUE)

Arguments

X	n by p design matrix (with no intercept term)
y	response vector 0 or 1
K.prov	number of PLS components, default is the rank of X
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 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 coefficients
convergence	whether convergence is achieved
niter	total number of iterations
bias.reduction	whether Firth's procedure is used
loading.matrix	the matrix of loadings

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

Examples

 x <- matrix(rnorm(20),ncol=2)
 y <- sample(0:1,10,TRUE)
 ## no bias reduction
 glpls1a(x,y,br=FALSE)
  
 ## no bias reduction and 1 PLS component
 glpls1a(x,y,K.prov=1,br=FALSE)

 ## bias reduction
 glpls1a(x,y,br=TRUE)

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