predict.gfmR: Predict method for group fused multinomial logistic...

In gfmR: Implements Group Fused Multinomial Regression

Description

This routine fits the group fused multinomial logistic regression model, which uses fusion shrinkage to automatically combine response categories.

Usage

## S3 method for class 'gfmR'
predict(object,newdata,type="probs",...)

Arguments

object	A gfmr object which specifically is the output from the GroupFusedMulti function.
newdata	value to be predicted in model matrix form
type	Type of prediction, "probs" returns probabilities, while response returns log of the ratio of probabilities for baseline category

.

...	Other arguments

Details

Prediction function for GFMR

Value

A vector or a matrix corresponding to type return.

Author(s)

Brad Price, [e-mail].

References

Price, B.S, Geyer, C.J. and Rothman, A.J. "Automatic Response Category Combination in Multinomial Logistic Regression." https://arxiv.org/abs/1705.03594.

Examples

## Not run: data(nes96)
attach(nes96)
Response=matrix(0,944,7)
for(i in 1:944){
  if(PID[i]=="strRep"){Response[i,1]=1}
  if(PID[i]=="weakRep"){Response[i,2]=1}
  if(PID[i]=="indRep"){Response[i,3]=1}
  if(PID[i]=="indind"){Response[i,4]=1}
  if(PID[i]=="indDem"){Response[i,5]=1}
  if(PID[i]=="weakDem"){Response[i,6]=1}
  if(PID[i]=="strDem"){Response[i,7]=1}
}

Hmat=matrix(1,dim(Response)[2],dim(Response)[2])
diag(Hmat)=0
ModMat<-lm(popul~age,x=TRUE)$x

X=cbind(ModMat[,1],apply(ModMat[,-1],2,scale))
mod<-GroupFusedMulti(Response,X,lambda=2^4.3,H=Hmat2,rho=10^2,iter=50,tol1=10^-4,tol2=10^-4)
predict(mod,X[1,])

## End(Not run)

gfmR documentation built on May 1, 2019, 8:41 p.m.