print.gfmR.cv: print method for group fused multinomial logistic regression...

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. This specifically focuses on tuning parameter selection with validation likelihood.

Usage

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

Arguments

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

Details

print method for gfmR.cv objects.

Value

A readable printout of cross validation

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))

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

## End(Not run)

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