The function mgsim
performs prediction using LambertLacroix and Peyre's MGSIM algorithm.
1 
Xtrain 
a (ntrain x p) data matrix of predictors. 
Ytrain 
a ntrain vector of responses. 
Xtest 
a (ntest x p) matrix containing the predictors for the test data
set. 
Lambda 
a positive real value. 
h 
a strictly positive real value. 
NbIterMax 
a positive integer. 
The columns of the data matrices Xtrain
and Xtest
may not be standardized,
since standardizing is performed by the function mgsim
as a preliminary step
before the algorithm is run.
The procedure described in LambertLacroix and Peyre (2005) is used to estimate
the c projection directions and the coefficients of the parametric fit obtained
after projecting predictor variables onto the estimated directions. When Xtest
is not equal to NULL, the procedure predicts the labels for these new predictor variables.
A list with the following components:
Ytest 
the ntest vector containing the predicted labels for the observations from

beta 
the (p x c) matrix containing the c estimated projection directions. 
Coefficients 
the (2 x c) matrix containing the coefficients of the parametric fit obtained after projecting predictor variables onto these estimated directions. 
DeletedCol 
the vector containing the column number of 
Cvg 
the 01 value indicating convergence of the algorithm (1 for convergence, 0 otherwise). 
Sophie LambertLacroix (http://membrestimc.imag.fr/Sophie.Lambert/) and Julie Peyre (http://wwwlmc.imag.fr/lmcsms/Julie.Peyre/).
S. LambertLacroix, J. Peyre . (2006) Local likelyhood regression in generalized linear singleindex models with applications to microarrays data. Computational Statistics and Data Analysis, vol 51, n 3, 20912113.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  # load plsgenomics library
library(plsgenomics)
# load SRBCT data
data(SRBCT)
IndexLearn < c(sample(which(SRBCT$Y==1),10),sample(which(SRBCT$Y==2),4),
sample(which(SRBCT$Y==3),7),sample(which(SRBCT$Y==4),9))
# perform prediction by MGSIM
res < mgsim(Ytrain=SRBCT$Y[IndexLearn],Xtrain=SRBCT$X[IndexLearn,],Lambda=0.001,h=19,
Xtest=SRBCT$X[IndexLearn,])
res$Cvg
sum(res$Ytest!=SRBCT$Y[IndexLearn])
# prediction for another sample
Xnew < SRBCT$X[83,]
# projection of Xnew onto the c estimated direction
Xproj < Xnew %*% res$beta
# Compute the linear predictor for each classes expect class 1
eta < diag(cbind(rep(1,3),t(Xproj)) %*% res$Coefficients)
Ypred < which.max(c(0,eta))
Ypred
SRBCT$Y[83]

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