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### Using an IPF-lasso model for prediction of new observations
###
### Copyright 2019-05 Anne-Laure Boulesteix
###
### Using an IPF-lasso model for prediction of new observations
###
###
### This file is part of the `ipflasso' library for R and related languages.
### It is made available under the terms of the GNU General Public
### License, version 2, or at your option, any later version,
### incorporated herein by reference.
###
### This program is distributed in the hope that it will be
### useful, but WITHOUT ANY WARRANTY; without even the implied
### warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
### PURPOSE. See the GNU General Public License for more
### details.
###
### You should have received a copy of the GNU General Public
### License along with this program; if not, write to the Free
### Software Foundation, Inc., 59 Temple Place - Suite 330, Boston,
### MA 02111-1307, USA
ipflasso.predict<-function(object,Xtest)
{
coeff<-object$coeff[,object$ind.bestlambda]
if (object$family=="gaussian"|object$family=="binomial")
{
linpredtest<-coeff[1]+Xtest%*%coeff[-1]
}
if (object$family=="cox")
{
linpredtest<-Xtest%*%coeff[-1]
}
if (object$family=="gaussian"|object$family=="cox")
{
classtest<-NULL
probabilitiestest<-NULL
}
if (object$family=="binomial")
{
probabilitiestest<-as.numeric(plogis(linpredtest))
classtest<-as.numeric(linpredtest>0)
}
return(list(linpredtest=linpredtest,classtest=classtest,probabilitiestest=probabilitiestest))
}
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