glpls1a.mlogit.cv.error

Leave-one-out cross-validation error using MIRWPLS and MIRWPLSF model

Leave-one-out cross-validation training set error for fitting MIRWPLS or MIRWPLSF model for multi-group classification

Keywords
regression
Usage
glpls1a.mlogit.cv.error(train.X, train.y, K.prov = NULL, eps = 0.001,lmax = 100, mlogit = T, br = T)
Arguments
train.X
n by p design matrix (with no intercept term) for training set
train.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
mlogit
if TRUE use the multinomial logit model, otherwise fit all C-1 logistic models (vs baseline class 1) separately
br
TRUE if Firth's bias reduction procedure is used
Value

error
LOOCV training error
error.obs
the misclassified error observation indices

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

Aliases
  • glpls1a.mlogit.cv.error
Examples
 x <- matrix(rnorm(20),ncol=2)
 y <- sample(1:3,10,TRUE)

 ## no bias reduction
 glpls1a.mlogit.cv.error(x,y,br=FALSE)
 glpls1a.mlogit.cv.error(x,y,mlogit=FALSE,br=FALSE)
 ## bias reduction
 glpls1a.mlogit.cv.error(x,y,br=TRUE)
 glpls1a.mlogit.cv.error(x,y,mlogit=FALSE,br=TRUE)

Documentation reproduced from package gpls, version 1.44.0, License: Artistic-2.0

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