Ok. I tried copying the original program, changing all _ to <- and running in 3.0.1. Still no error message or output. It's a mystery to me.
----- Original Message ----- From: William Dunlap <wdun...@tibco.com> To: Scott Raynaud <scott.rayn...@yahoo.com>; "r-help@r-project.org" <r-help@r-project.org> Cc: Sent: Wednesday, June 5, 2013 2:17 PM Subject: RE: [R] SPlus script Both the R and S+ versions (which seem to differ only in the use of _ for assignment in the S+ version) do nothing but define some functions. You would not expect any printed output unless you used those functions on some data. Is there another script that does that? 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: Wednesday, June 05, 2013 6:21 AM > To: r-help@r-project.org > Subject: [R] SPlus script > > This originally was an SPlus script that I modifeid about a year-and-a-half > ago. It worked > perfectly then. Now I can't get any output despite not receiving an error > message. I'm > providing the SPLUS script as a reference. I'm running R15.2.2. Any help > appreciated. > > ************************************MY > MODIFICATION*********************************************************** > ********** > ## 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 $Ep contains the expected power corresponding to > # the true pc = (0, 1, 2, ..., nc*(1-d)) / nc > # > #------------------------------------------------------------------ > sshc<-function(rc, nc=1092, d=.085779816, method=3, alpha=0.05, power=0.8, > tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5) > { > ### for method 1 > if (method==1) { > ne1<-ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01) > return(ne=ne1) > } > ### for method 2 > if (method==2) { > ne<-nc > ne1<-nc+50 > while(abs(ne-ne1)>tol & ne1<100000){ > ne<-ne1 > pe<-d+rc/nc > ne1<-nef(rc,nc,pe*ne,ne,alpha,power) > ## if(is.na(ne1)) print(paste('rc=',rc,',nc=',nc,',pe=',pe,',ne=',ne)) > } > if (ne1>100000) return(NA) > else return(ne=ne1) > } > ### for method 3 > if (method==3) { > if (tol1 > tol2/10) tol1<-tol2/10 > ncstar<-(1-d)*nc > pc<-(0:ncstar)/nc > ne<-rep(NA,ncstar + 1) > for (i in (0:ncstar)) > { ne[i+1]<-ss.rand(i,nc,d,alpha=.05,power=.8,tol=.01) > } > plot(pc,ne,type='l',ylim=c(0,max(ne)*1.5)) > ans<-c.searchd(nc, d, ne, alpha, power, cc, tol1) > ### check overall absolute deviance > old.abs.dev<-sum(abs(ans$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(list(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(list(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(list(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(list(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){ > #------------------------------------- > # rc number of response in historical control > # nc sample size in historical control > # ne sample size in experitment group > # pie true response rate for experiment group > # alpha size 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)) > } > > > > *************************************ORIGINAL SPLUS > SCRIPT************************************************************ > ## 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 $Ep contains the expected power corresponding to > # the true pc = (0, 1, 2, ..., nc*(1-d)) / nc > # > > #------------------------------------------------------------------ > sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8, > tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5) > { > ### for method 1 > if (method==1) { > ne1 _ ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01) > return(ne=ne1) > } > ### for method 2 > if (method==2) { > ne_nc > ne1_nc+50 > while(abs(ne-ne1)>tol & ne1<100000){ > ne_ne1 > pe_d+rc/nc > ne1_nef(rc,nc,pe*ne,ne,alpha,power) > ## if(is.na(ne1)) print(paste('rc=',rc,',nc=',nc,',pe=',pe,',ne=',ne)) > } > if (ne1>100000) return(NA) > else return(ne=ne1) > } > ### for method 3 > if (method==3) { > if (tol1 > tol2/10) tol1_tol2/10 > ncstar _ (1-d)*nc > pc_(0:ncstar)/nc > ne _ rep(NA,ncstar + 1) > for (i in (0:ncstar)) > { ne[i+1] _ ss.rand(i,nc,d,alpha=.05,power=.8,tol=.01) > } > plot(pc,ne,type='l',ylim=c(0,max(ne)*1.5)) > ans_c.searchd(nc, d, ne, alpha, power, cc, tol1) > ### check overall absolute deviance > old.abs.dev _ sum(abs(ans$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){ > #------------------------------------- > # rc number of response in historical control > # nc sample size in historical control > # ne sample size in experitment group > # pie true response rate for experiment group > # alpha size 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)) > } > > ______________________________________________ > 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. ______________________________________________ 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.
