R/plotCoef.R

In c060: Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

# this function was copied from the glmnet package 
# https://cran.r-project.org/web/packages/glmnet/index.html

plotCoef=function(beta,norm,lambda,df,dev,label=FALSE,xvar=c("norm","lambda","dev"),xlab=iname,ylab="Coefficients",...){
  ##beta should be in "dgCMatrix" format
  which=nonzeroCoef(beta)
  nwhich=length(which)
  switch(nwhich+1,#we add one to make switch work
         "0"={
           warning("No plot produced since all coefficients zero")
           return()
         },
         "1"=warning("1 or less nonzero coefficients; glmnet plot is not meaningful")
         )
  beta=as.matrix(beta[which,,drop=FALSE])
  xvar=match.arg(xvar)
  switch(xvar,
    "norm"={
      index=if(missing(norm))apply(abs(beta),2,sum)else norm
      iname="L1 Norm"
      approx.f=1
    },
    "lambda"={
      index=log(lambda)
      iname="Log Lambda"
      approx.f=0
    },
    "dev"= {
      index=dev
      iname="Fraction Deviance Explained"
      approx.f=1
    }
         )
  dotlist=list(...)
  type=dotlist$type
  if(is.null(type))
    matplot(index,t(beta),lty=1,xlab=xlab,ylab=ylab,type="l",...)
  else matplot(index,t(beta),lty=1,xlab=xlab,ylab=ylab,...)
  atdf=pretty(index)
  ### compute df by interpolating to df at next smaller lambda
  ### thanks to Yunyang Qian
 prettydf=approx(x=index,y=df,xout=atdf,rule=2,method="constant",f=approx.f)$y
# prettydf=ceiling(approx(x=index,y=df,xout=atdf,rule=2)$y)
 axis(3,at=atdf,labels=prettydf,tcl=NA)
 if(label){
   nnz=length(which)
   xpos=max(index)
   pos=4
   if(xvar=="lambda"){
     xpos=min(index)
     pos=2
   }
   xpos=rep(xpos,nnz)
   ypos=beta[,ncol(beta)]
   text(xpos,ypos,paste(which),cex=.5,pos=pos)
 }

}