[R] SPlus to R

[William Dunlap]

It looks like this code was written for S+ 4.5 (aka '2000')
or before, which was based on S version 3.  Try changing
   return(name1=value1, name2=value2)
to
   return(list(name1=value1, name2=value2))
In S+ from 5.0 onwards return(name=value) or return(name1=value1,
name2=value2) throws away the names and in R return only takes
a single object (and also ignores the name).

The c.search function in your code ends with
   return(ne=ne, Ep=Ep1)
and the code calling c.search() acts as though the writer
expects that function to return list(ne=ne, Ep=Ep1)
   ans <- c.searchd(nc, d, ne, alpha, power, cc, tol1)
   ...
   old.ne <- ans$ne

Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com 

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Scott Raynaud
> Sent: Tuesday, October 04, 2011 6:53 PM
> To: r-help at r-project.org
> Subject: [R] SPlus to R
> 
> I'm trying to convert an S-Plus program to R.  Since I'm a SAS programmer I'm not facile is either S-
> Plus or R, so I need some help.  All I did was convert the underscores in S-Plus to the assignment
> operator <-.  Here are the first few lines of the S-Plus file:
> 
> sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8,
>              tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5)
> {
> ### for method 1
> if (method==1) {
> ne1 _ ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01)
> return(ne=ne1)
>                }
> 
> 
> My translation looks like this:
> 
> sshc<-function(rc, nc=500, d=.5, method=3, alpha=0.05, power=0.8,
>               tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5)
> {
> ### for method 1
> if (method==1) {
>  ne1<-ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01)
>  return(ne=ne1)
>                }
> 
> The program runs without throwing errors, but I'm not getting any ourput in the console.  This is
> where it should be, right?  I think I have this set up correctly.  I'm using method=3 which only
> requires nc and d to be specified.  Any ideas why I'm not seeing output?
> 
> Here is the entire output:
> 
> > ## sshc.ssc: sample size calculation for historical control studies
> > ## J. Jack Lee (jjlee at mdanderson.org) and Chi-hong Tseng
> > ## Department of Biostatistics, Univ. of Texas M.D. Anderson Cancer Center
> > ##
> > ## 3/1/99
> > ## updated 6/7/00: add loess
> > ##------------------------------------------------------------------
> > ######## Required Input:
> > #
> > # rc     number of response in historical control group
> > # nc     sample size in historical control
> > # d      target improvement = Pe - Pc
> > # method 1=method based on the randomized design
> > #        2=Makuch & Simon method (Makuch RW, Simon RM. Sample size considerations
> > #          for non-randomized comparative studies. J of Chron Dis 1980; 3:175-181.
> > #        3=uniform power method
> > ######## optional Input:
> > #
> > # alpha  size of the test
> > # power  desired power of the test
> > # tol    convergence criterion for methods 1 & 2 in terms of sample size
> > # tol1   convergence criterion for method 3 at any given obs Rc in terms of difference
> > #          of expected power from target
> > # tol2   overall convergence criterion for method 3 as the max absolute deviation
> > #          of expected power from target for all Rc
> > # cc     range of multiplicative constant applied to the initial values ne
> > # l.span smoothing constant for loess
> > #
> > # Note:  rc is required for methods 1 and 2 but not 3
> > #        method 3 return the sample size need for rc=0 to (1-d)*nc
> > #
> > ######## Output
> > # for methdos 1 & 2: return the sample size needed for the experimental group (1 number)
> > #                    for given rc, nc, d, alpha, and power
> > # for method 3:      return the profile of sample size needed for given nc, d, alpha, and power
> > #                    vector $ne contains the sample size corresponding to rc=0, 1, 2, ... nc*(1-d)
> > #                    vector $Ep contains the expected power corresponding to
> > #                      the true pc = (0, 1, 2, ..., nc*(1-d)) / nc
> > #
> > #------------------------------------------------------------------
> > sshc<-function(rc, nc=500, d=.5, method=3, alpha=0.05, power=0.8,
> +              tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5)
> + {
> + ### for method 1
> + if (method==1) {
> + ne1<-ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01)
> + return(ne=ne1)
> +                }
> + ### for method 2
> + if (method==2) {
> + ne<-nc
> + ne1<-nc+50
> + while(abs(ne-ne1)>tol & ne1<100000){
> + ne<-ne1
> + pe<-d+rc/nc
> + ne1<-nef(rc,nc,pe*ne,ne,alpha,power)
> + ## if(is.na(ne1)) print(paste('rc=',rc,',nc=',nc,',pe=',pe,',ne=',ne))
> + }
> + if (ne1>100000) return(NA)
> + else return(ne=ne1)
> + }
> + ### for method 3
> + if (method==3) {
> + if (tol1 > tol2/10) tol1<-tol2/10
> + ncstar<-(1-d)*nc
> + pc<-(0:ncstar)/nc
> + ne<-rep(NA,ncstar + 1)
> + for (i in (0:ncstar))
> + { ne[i+1]<-ss.rand(i,nc,d,alpha=.05,power=.8,tol=.01)
> + }
> + plot(pc,ne,type='l',ylim=c(0,max(ne)*1.5))
> + ans<-c.searchd(nc, d, ne, alpha, power, cc, tol1)
> + ### check overall absolute deviance
> + old.abs.dev<-sum(abs(ans$Ep-power))
> + ##bad<-0
> + print(round(ans$Ep,4))
> + print(round(ans$ne,2))
> + lines(pc,ans$ne,lty=1,col=8)
> + old.ne<-ans$ne
> + ##while(max(abs(ans$Ep-power))>tol2 & bad==0){  #### unnecessary ##
> + while(max(abs(ans$Ep-power))>tol2){
> + ans<-c.searchd(nc, d, ans$ne, alpha, power, cc, tol1)
> + abs.dev<-sum(abs(ans$Ep-power))
> + print(paste(" old.abs.dev=",old.abs.dev))
> + print(paste("     abs.dev=",abs.dev))
> + ##if (abs.dev > old.abs.dev) { bad<-1}
> + old.abs.dev<-abs.dev
> + print(round(ans$Ep,4))
> + print(round(ans$ne,2))
> + lines(pc,old.ne,lty=1,col=1)
> + lines(pc,ans$ne,lty=1,col=8)
> + ### add convex
> + ans$ne<-convex(pc,ans$ne)$wy
> + ### add loess
> + ###old.ne<-ans$ne
> + loess.ne<-loess(ans$ne ~ pc, span=l.span)
> + lines(pc,loess.ne$fit,lty=1,col=4)
> + old.ne<-loess.ne$fit
> + ###readline()
> + }
> + return(ne=ans$ne, Ep=ans$Ep)
> +                }
> + }
> >
> > ## needed for method 1
> > nef2<-function(rc,nc,re,ne,alpha,power){
> + za<-qnorm(1-alpha)
> + zb<-qnorm(power)
> + xe<-asin(sqrt((re+0.375)/(ne+0.75)))
> + xc<-asin(sqrt((rc+0.375)/(nc+0.75)))
> + ans<- 1/(4*(xc-xe)^2/(za+zb)^2-1/(nc+0.5)) - 0.5
> + return(ans)
> + }
> > ## needed for method 2
> > nef<-function(rc,nc,re,ne,alpha,power){
> + za<-qnorm(1-alpha)
> + zb<-qnorm(power)
> + xe<-asin(sqrt((re+0.375)/(ne+0.75)))
> + xc<-asin(sqrt((rc+0.375)/(nc+0.75)))
> + ans<-(za*sqrt(1+(ne+0.5)/(nc+0.5))+zb)^2/(2*(xe-xc))^2-0.5
> + return(ans)
> + }
> > ## needed for method 3
> > c.searchd<-function(nc, d, ne, alpha=0.05, power=0.8, cc=c(0.1,2),tol1=0.0001){
> + #---------------------------
> + # nc     sample size of control group
> + # d      the differece to detect between control and experiment
> + # ne     vector of starting sample size of experiment group
> + #    corresonding to rc of 0 to nc*(1-d)
> + # alpha  size of test
> + # power  target power
> + # cc  pre-screen vector of constant c, the range should cover the
> + #    the value of cc that has expected power
> + # tol1   the allowance between the expceted power and target power
> + #---------------------------
> + pc<-(0:((1-d)*nc))/nc
> + ncl<-length(pc)
> + ne.old<-ne
> + ne.old1<-ne.old
> + ### sweeping forward
> + for(i in 1:ncl){
> + cmin<-cc[1]
> + cmax<-cc[2]
> + ### fixed cci<-cmax bug
> + cci <-1
> + lhood<-dbinom((i:ncl)-1,nc,pc[i])
> + ne[i:ncl]<-(1+(cci-1)*(lhood/lhood[1])) * ne.old1[i:ncl]
> + Ep0 <-Epower(nc, d, ne, pc, alpha)
> + while(abs(Ep0[i]-power)>tol1){
> + if(Ep0[i]<power) cmin<-cci
> + else cmax<-cci
> + cci<-(cmax+cmin)/2
> + ne[i:ncl]<-(1+(cci-1)*(lhood/lhood[1])) * ne.old1[i:ncl]
> + Ep0<-Epower(nc, d, ne, pc, alpha)
> + }
> +  ne.old1<-ne
> + }
> + ne1<-ne
> + ### sweeping backward -- ncl:i
> + ne.old2<-ne.old
> + ne     <-ne.old
> + for(i in ncl:1){
> + cmin<-cc[1]
> + cmax<-cc[2]
> + ### fixed cci<-cmax bug
> + cci <-1
> + lhood<-dbinom((ncl:i)-1,nc,pc[i])
> + lenl <-length(lhood)
> + ne[ncl:i]<-(1+(cci-1)*(lhood/lhood[lenl]))*ne.old2[ncl:i]
> + Ep0 <-Epower(nc, d, cci*ne, pc, alpha)
> + while(abs(Ep0[i]-power)>tol1){
> + if(Ep0[i]<power) cmin<-cci
> + else cmax<-cci
> + cci<-(cmax+cmin)/2
> + ne[ncl:i]<-(1+(cci-1)*(lhood/lhood[lenl]))*ne.old2[ncl:i]
> + Ep0<-Epower(nc, d, ne, pc, alpha)
> + }
> +  ne.old2<-ne
> + }
> + ne2<-ne
> + ne<-(ne1+ne2)/2
> + #cat(ccc*ne)
> + Ep1<-Epower(nc, d, ne, pc, alpha)
> + return(ne=ne, Ep=Ep1)
> + }
> > ###
> > vertex<-function(x,y)
> + { n<-length(x)
> + vx<-x[1]
> + vy<-y[1]
> + vp<-1
> + up<-T
> + for (i in (2:n))
> + { if (up)
> + { if (y[i-1] > y[i])
> + {vx<-c(vx,x[i-1])
> +  vy<-c(vy,y[i-1])
> +  vp<-c(vp,i-1)
> +  up<-F
> + }
> + }
> + else
> + { if (y[i-1] < y[i]) up<-T
> + }
> + }
> + vx<-c(vx,x[n])
> + vy<-c(vy,y[n])
> + vp<-c(vp,n)
> + return(vx=vx,vy=vy,vp=vp)
> + }
> > ###
> > convex<-function(x,y)
> + {
> + n<-length(x)
> + ans<-vertex(x,y)
> + len<-length(ans$vx)
> + while (len>3)
> + {
> + #cat("x=",x,"\n")
> + #cat("y=",y,"\n")
> + newx<-x[1:(ans$vp[2]-1)]
> + newy<-y[1:(ans$vp[2]-1)]
> + for (i in (2:(len-1)))
> + {
> +  newx<-c(newx,x[ans$vp[i]])
> + newy<-c(newy,y[ans$vp[i]])
> + }
> + newx<-c(newx,x[(ans$vp[len-1]+1):n])
> + newy<-c(newy,y[(ans$vp[len-1]+1):n])
> + y<-approx(newx,newy,xout=x)$y
> + #cat("new y=",y,"\n")
> + ans<-vertex(x,y)
> + len<-length(ans$vx)
> + #cat("vx=",ans$vx,"\n")
> + #cat("vy=",ans$vy,"\n")
> + }
> + return(wx=x,wy=y)}
> > ###
> > Epower<-function(nc, d, ne, pc = (0:((1 - d) * nc))/nc, alpha = 0.05)
> + {
> + #-------------------------------------
> + # nc     sample size in historical control
> + # d      the increase of response rate between historical and experiment
> + # ne     sample size of corresonding rc of 0 to nc*(1-d)
> + # pc     the response rate of control group, where we compute the
> + #        expected power
> + # alpha  the size of test
> + #-------------------------------------
> + kk <- length(pc)
> + rc <- 0:(nc * (1 - d))
> + pp <- rep(NA, kk)
> + ppp <- rep(NA, kk)
> + for(i in 1:(kk)) {
> + pe <- pc[i] + d
> + lhood <- dbinom(rc, nc, pc[i])
> + pp <- power1.f(rc, nc, ne, pe, alpha)
> + ppp[i] <- sum(pp * lhood)/sum(lhood)
> + }
> + return(ppp)
> + }
> >
> > # adapted from the old biss2
> > ss.rand<-function(rc,nc,d,alpha=.05,power=.8,tol=.01)
> + {
> + ne<-nc
> + ne1<-nc+50
> + while(abs(ne-ne1)>tol & ne1<100000){
> + ne<-ne1
> + pe<-d+rc/nc
> + ne1<-nef2(rc,nc,pe*ne,ne,alpha,power)
> +
> + ## if(is.na(ne1)) print(paste('rc=',rc,',nc=',nc,',pe=',pe,',ne=',ne))
> + }
> + if (ne1>100000) return(NA)
> + else return(ne1)
> + }
> > ###
> > power1.f<-function(rc,nc,ne,pie,alpha=0.05){
> + #-------------------------------------
> + # rcnumber of response in historical control
> + # ncsample size in historical control
> + # ne    sample size in experitment group
> + # pietrue response rate for experiment group
> + # alphasize of the test
> + #-------------------------------------
> +
> + za<-qnorm(1-alpha)
> + re<-ne*pie
> + xe<-asin(sqrt((re+0.375)/(ne+0.75)))
> + xc<-asin(sqrt((rc+0.375)/(nc+0.75)))
> + ans<-za*sqrt(1+(ne+0.5)/(nc+0.5))-(xe-xc)/sqrt(1/(4*(ne+0.5)))
> + return(1-pnorm(ans))
> + }
> 
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