Nothing
elnet=function(x,is.sparse,y,weights,offset,type.gaussian=c("covariance","naive"),alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit,pb){
maxit=as.integer(maxit)
weights=as.double(weights)
type.gaussian=match.arg(type.gaussian)
ka=as.integer(switch(type.gaussian,
covariance=1,
naive=2,
))
storage.mode(y)="double"
if(is.null(offset)){
is.offset=FALSE}
else{
storage.mode(offset)="double"
is.offset=TRUE
y=y-offset
}
### compute the null deviance
ybar=if(intr)weighted.mean(y,weights)else 0
nulldev=sum(weights* (y-ybar)^2)
if(nulldev==0)stop("y is constant; gaussian glmnet fails at standardization step")
fit=if(is.sparse) spelnet_exp(
ka,parm=alpha,x,y,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,pb,
lmu=integer(1),
a0=double(nlam),
ca=matrix(0.0, nrow=nx, ncol=nlam),
ia=integer(nx),
nin=integer(nlam),
rsq=double(nlam),
alm=double(nlam),
nlp=integer(1),
jerr=integer(1)
)
else elnet_exp(
ka,parm=alpha,x,y,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,pb,
lmu=integer(1),
a0=double(nlam),
ca=matrix(0.0, nrow=nx, ncol=nlam),
ia=integer(nx),
nin=integer(nlam),
rsq=double(nlam),
alm=double(nlam),
nlp=integer(1),
jerr=integer(1)
)
if(fit$jerr!=0){
errmsg=jerr(fit$jerr,maxit,pmax=nx,family="gaussian")
if(errmsg$fatal)stop(errmsg$msg,call.=FALSE)
else warning(errmsg$msg,call.=FALSE)
}
outlist=getcoef(fit,nvars,nx,vnames)
dev=fit$rsq[seq(fit$lmu)]
outlist=c(outlist,list(dev.ratio=dev,nulldev=nulldev,npasses=fit$nlp,jerr=fit$jerr,offset=is.offset))
class(outlist)="elnet"
outlist
}
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