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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Scott Raynaud > Sent: Tuesday, October 04, 2011 6:53 PM > To: r-help@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 (jj...@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)) > + } > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
