gfmR (version 1.1-0)

print.gfmR.cv: print method for group fused multinomial logistic regression validation likelihood tuning parameter selection

Description

This routine fits the group fused multinomial logistic regression model, which uses fusion shrinkage to automatically combine response categories. This specifically focuses on tuning parameter selection with validation likelihood.

Usage

# S3 method for gfmR.cv
print(x,...)

Arguments

x

A gfmr.cv object which specifically is the output from the GroupFusedMulti function.

...

Other arguments

Value

A readable printout of cross validation

Details

print method for gfmR.cv objects.

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 {
# }
# 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))

set.seed(1010)
n=dim(Response)[1]
sampID=rep(5,n)
samps=sample(1:n)
mine=floor(n/5)
for(j in 1:4){
  sampID[samps[((j-1)*mine+1):(j*mine)]]=j
}

o1<-GFMR.cv(Response,X,lamb = 2^seq(4.2,4.3,.1),H=Hmat2,sampID = sampID,n.cores =5)
o1
# }
# NOT RUN {
# }