glpls1a.mlogit {gpls} | R Documentation |
Fit multi-logit Iteratively ReWeighted Least Squares (MIRWPLS) with an option of Firth's bias reduction procedure (MIRWPLSF) for multi-group classification
glpls1a.mlogit(x, y, K.prov = NULL, eps = 0.001, lmax = 100, b.ini = NULL, denom.eps = 1e-20, family = "binomial", link = NULL, br = T)
x |
n by p design matrix (with 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 |
coefficients |
regression coefficient matrix |
convergence |
whether convergence is achieved |
niter |
total number of iterations |
bias.reduction |
whether Firth's procedure is used |
Beiying Ding, Robert Gentleman
glpls1a
,glpls1a.mlogit.cv.error
,
glpls1a.train.test.error
,
glpls1a.cv.error
x <- matrix(rnorm(20),ncol=2) y <- sample(1:3,10,TRUE) ## no bias reduction and 1 PLS component glpls1a.mlogit(cbind(rep(1,10),x),y,K.prov=1,br=FALSE) ## bias reduction glpls1a.mlogit(cbind(rep(1,10),x),y,br=TRUE)