glpls1a

Fit IRWPLS and IRWPLSF model

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

Keywords
regression
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
Details

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

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

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

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