[R] SPlus to R
William Dunlap
wdunlap at tibco.com
Wed Oct 5 18:25:03 CEST 2011
I think you only have to change the multi-argument
returns to call list. You can remove the name from
the single argument return, as it will be ignore
return(name=value) -> return(value)
return(n1=v1, n2=v2) -> return(list(n1=v1, n2=v2))
(I say "I think" because I don't have easy access to
S+ 4.5, from 1999 or so, to check this out.)
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 William Dunlap
> Sent: Wednesday, October 05, 2011 9:09 AM
> To: Scott Raynaud; r-help at r-project.org
> Subject: Re: [R] SPlus to R
>
> 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))
> > + }
> >
> > [[alternative HTML version deleted]]
>
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