Description Usage Arguments Details Value Author(s) See Also Examples
Finds the optimal number of component for one of the three extesions of LS-PLS. Moreover it finds the lambda
optimal for the R-LS-PLS method.
1 2 3 |
Y |
a vector of length |
X |
a data matrix ( |
D |
a data matrix ( |
ncompmax |
a positive integer. |
folds |
a positive integer indicating the number of folds in K-folds cross-validation procedure. |
proportion |
proportion of the dataset in the learning sample. |
method |
one of the three extensions of LS-PLS for logistic regression models (LS-PLS-IRLS, R-LS-PLS, IR-LS-PLS). |
lambda.grid |
vector of positif real (grid for ridge parameter). To use only if |
penalized |
if TRUE the parameter associated with D is ridge penalized. To use only if |
nbrIterMax |
maximal number of iterations. To use only if |
threshold |
used for the stopping rule. To use only if |
This function finds the optimal number of component and the optimal lambda for a LS-PLS regression. At each cross validation run, X
, D
and Y
are split into one training set and one test set (of proportion proportion
and 1-proportion
). Then for each component between 1 and ncompmax
(and for each value of lambda.grid
if method
equals to R-LS-PLS) classification error rate is determined. At the end we choose the lambda
and the ncomp
for which the classification error rate is minimal. This function returns also p.cvg
. It's a vector of size ncompmax
which contains convergence proportion for each number of component between 1 and ncompmax
. For the method R-LS-PLS, p.cvg is a matrix of size ncompmax x length(lambda.grid
).
ncompopt |
the optimal number of component. |
lambdaopt |
lambda optimal. |
p.cvg |
convergence proportion. |
Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | #data
data(BreastCancer)
#vector of responses
Y<-BreastCancer$Y
#Genetic data
X<-BreastCancer$X
#Clinical data
D<-BreastCancer$D
#SIS selection
X<-scale(X)
X<-SIS.selection(X=X,Y=Y,pred=50)
#Cross validation, 90% of our datasets is used to compose learning samples
#method LS-PLS-IRLS
ncompopt.lsplsirls<-cv.lspls.glm(Y=Y,X=X,D=D,folds=5,ncompmax=5,proportion=0.9,
method="LS-PLS-IRLS")$ncompopt
#method R-LS-PLS
cv<-cv.lspls.glm(Y=Y,X=X,D=D,ncompmax=5,proportion=0.9,method="R-LS-PLS",
lambda.grid=exp(log(10^seq(-3,2,0.7))),
penalized=TRUE,nbrIterMax=15,
threshold=10^(-12))
ncompopt.rlspls<-cv$ncompopt
lambdaopt.rlspls<-cv$lambdaopt
#method IR-LS-PLS
ncompopt.irlspls<-cv.lspls.glm(Y=Y,X=X,D=D,ncompmax=5,proportion=0.9,method="IR-LS-PLS",
nbrIterMax=15,threshold=10^(-12))$ncompopt
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.