SPlus script

Hello,

1) It is always nice to say something as "Hello", Tried, but could make it come out in anything other than four letters.
2) What do you want us to do with that script, without the required "commented, minimal, self-contained, reproducible code"? I want it to run as it did before.? I cannot explain why the exact same code used to run and now doesn't.? I've tried everything I can think of, so maybe brighter minds than mine can shed some light.
3) The lastest version of R is 3.0.1. Yes, I know.? I was trying to keep things as constant as possible.? Besides, do you really think the version wouold make a difference?? The code runs without error.? It just doesn't produce output.? 

Regards,
Pascal



2013/6/5 Scott Raynaud <scott.raynaud at yahoo.com>

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 (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 $Ep contains the expected power corresponding to
>#????????????????????? the true pc = (0, 1, 2, ..., nc*(1-d)) / nc
>#?????????????????????????????????????????????????
>#------------------------------------------------------------------
>sshc<-function(rc, nc=1092, d=.085779816, method=3, alpha=0.05, power=0.8,
>????????????? tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5)
>{
>### for method 1
>if (method==1) {
>?ne1<-ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01)
>?return(ne=ne1)?
>?????????????? }
>### for method 2
>if (method==2) {
>ne<-nc
>ne1<-nc+50
>while(abs(ne-ne1)>tol & ne1<100000){
>ne<-ne1
>pe<-d+rc/nc
>ne1<-nef(rc,nc,pe*ne,ne,alpha,power)
>## if(is.na(ne1)) print(paste('rc=',rc,',nc=',nc,',pe=',pe,',ne=',ne))
>}
>if (ne1>100000) return(NA)
>else return(ne=ne1)
>}
>### for method 3
>if (method==3) {
>if (tol1 > tol2/10) tol1<-tol2/10
>ncstar<-(1-d)*nc
>pc<-(0:ncstar)/nc
>ne<-rep(NA,ncstar + 1)
>for (i in (0:ncstar))
>{ ne[i+1]<-ss.rand(i,nc,d,alpha=.05,power=.8,tol=.01)
>}
>plot(pc,ne,type='l',ylim=c(0,max(ne)*1.5))
>ans<-c.searchd(nc, d, ne, alpha, power, cc, tol1)
>### check overall absolute deviance
>old.abs.dev<-sum(abs(ans$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 (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 $Ep contains the expected power corresponding to
># ? ? ? ? ? ? ? ? ? ? ?the true pc = (0, 1, 2, ..., nc*(1-d)) / nc
>#
>
>#------------------------------------------------------------------
>sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8,
>? ? ? ? ? ? ? ? ? ? ?tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2), l.span=.5)
>{
>### for method 1
>if (method==1) {
>? ? ? ? ne1 _ ss.rand(rc,nc,d,alpha=.05,power=.8,tol=.01)
>? ? ? ? return(ne=ne1)
>? ? ? ? ? ? ? ?}
>### for method 2
>if (method==2) {
>ne_nc
>ne1_nc+50
>while(abs(ne-ne1)>tol & ne1<100000){
>ne_ne1
>pe_d+rc/nc
>ne1_nef(rc,nc,pe*ne,ne,alpha,power)
>## if(is.na(ne1)) print(paste('rc=',rc,',nc=',nc,',pe=',pe,',ne=',ne))
>}
>if (ne1>100000) return(NA)
>else return(ne=ne1)
>}
>### for method 3
>if (method==3) {
>if (tol1 > tol2/10) tol1_tol2/10
>ncstar _ (1-d)*nc
>pc_(0:ncstar)/nc
>ne _ rep(NA,ncstar + 1)
>for (i in (0:ncstar))
>{ ne[i+1] _ ss.rand(i,nc,d,alpha=.05,power=.8,tol=.01)
>}
>plot(pc,ne,type='l',ylim=c(0,max(ne)*1.5))
>ans_c.searchd(nc, d, ne, alpha, power, cc, tol1)
>### check overall absolute deviance
>old.abs.dev _ sum(abs(ans$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))
>}
>
>______________________________________________
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>and provide commented, minimal, self-contained, reproducible code.
>