Description Usage Arguments Details Value Author(s) References Examples
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.
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x |
A gfmr.cv object which specifically is the output from the GroupFusedMulti function. |
... |
Other arguments |
print method for gfmR.cv objects.
A readable printout of cross validation
Brad Price, brad.price@mail.wvu.edu.
Price, B.S, Geyer, C.J. and Rothman, A.J. "Automatic Response Category Combination in Multinomial Logistic Regression." https://arxiv.org/abs/1705.03594.
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 28 29 30 31 32 | ## 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)
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