[R] SPlus script
Ista Zahn
istazahn at gmail.com
Thu Jun 6 16:15:45 CEST 2013
Presumably something like
r <- sshc(50)
print(r)
But if you were getting output before than you already have a script
that does something like this. It would be better to find it...
Best,
Ista
On Thu, Jun 6, 2013 at 9:02 AM, Scott Raynaud <scott.raynaud at yahoo.com> wrote:
> Ok. Now I see that the sshc function is not being called. Thanks for pointing that out.
> I'm not certain about the solution, however. I tried putting call("sshc") at the end of the
> program, but nothing happened. My memory about all of this is fuzzy. Suggestions
> on how to call the function appreciated.
>
> ----- Original Message -----
> From: William Dunlap <wdunlap at tibco.com>
> To: Scott Raynaud <scott.raynaud at yahoo.com>; "r-help at r-project.org" <r-help at 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-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf
>> Of Scott Raynaud
>> Sent: Wednesday, June 05, 2013 6:21 AM
>> To: r-help at 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 (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))
>> }
>>
>> ______________________________________________
>> R-help at 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 at 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.
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