SPlus script
Hi Scott, As others have pointed out, all your script does is define functions. It doesn't generate "output" because those functions are never called. Basically you are missing part of the program--possibly in another file--that actually calls the sshc function. Best, Ista
On Thu, Jun 6, 2013 at 7:24 AM, Scott Raynaud <scott.raynaud at yahoo.com> wrote:
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 <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.