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Added univariate functionality #1

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df0db73
extend glmnet to univariate cases (one variable)
Nov 9, 2017
3169f1b
Univariate Glmnet
Nov 9, 2017
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25 changes: 12 additions & 13 deletions R/glmnet.R
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Original file line number Diff line number Diff line change
Expand Up @@ -17,27 +17,26 @@ glmnet=function(x,y,family=c("gaussian","binomial","poisson","multinomial","cox"
y=drop(y) # we dont like matrix responses unless we need them
np=dim(x)
###check dims
if(is.null(np)|(np[2]<=1))stop("x should be a matrix with 2 or more columns")
nobs=as.integer(np[1])
if(is.null(np)){
nobs = as.integer(length(x))
nvars = as.integer(1)
}
else {
nobs = as.integer(np[1])
nvars = as.integer(np[2])
}
if(missing(weights))weights=rep(1,nobs)
else if(length(weights)!=nobs)stop(paste("number of elements in weights (",length(weights),") not equal to the number of rows of x (",nobs,")",sep=""))
nvars=as.integer(np[2])
dimy=dim(y)
nrowy=ifelse(is.null(dimy),length(y),dimy[1])
if(nrowy!=nobs)stop(paste("number of observations in y (",nrowy,") not equal to the number of rows of x (",nobs,")",sep=""))
vnames=colnames(x)
if(is.null(vnames))vnames=paste("V",seq(nvars),sep="")
ne=as.integer(dfmax)
nx=as.integer(pmax)
if(missing(exclude))exclude=integer(0)
if(any(penalty.factor==Inf)){
exclude=c(exclude,seq(nvars)[penalty.factor==Inf])
exclude=sort(unique(exclude))
}
if(length(exclude)>0){
nx=as.integer(pmax)
if(!missing(exclude)){
jd=match(exclude,seq(nvars),0)
if(!all(jd>0))stop("Some excluded variables out of range")
penalty.factor[jd]=1 #ow can change lambda sequence
jd=as.integer(c(length(jd),jd))
}else jd=as.integer(0)
vp=as.double(penalty.factor)
Expand Down Expand Up @@ -67,7 +66,7 @@ glmnet=function(x,y,family=c("gaussian","binomial","poisson","multinomial","cox"
}
storage.mode(cl)="double"
### end check on limits

isd=as.integer(standardize)
intr=as.integer(intercept)
if(!missing(intercept)&&family=="cox")warning("Cox model has no intercept")
Expand All @@ -79,7 +78,7 @@ glmnet=function(x,y,family=c("gaussian","binomial","poisson","multinomial","cox"
ulam=double(1)
}
else{
flmin=as.double(1)
flmin=as.double(1)
if(any(lambda<0))stop("lambdas should be non-negative")
ulam=as.double(rev(sort(lambda)))
nlam=as.integer(length(lambda))
Expand Down