Nothing
library(clinDR)
if(file.exists("./clinDR/inst/tests/extraGraphics/pdfoutput")){
pvar<-"./clinDR/inst/tests/extraGraphics/pdfoutput"
} else pvar<-NULL
if(file.exists(file.path(pvar,"output.fitEmax_new.pdf"))) file.rename(file.path(pvar,"output.fitEmax_new.pdf"),
file.path(pvar,"output.fitEmax_old.pdf"))
pdf(file=paste(file.path(pvar,"output.fitEmax_new.pdf")))
set.seed(12357)
doselev<-c(0,5,25,50,100,350)
n<-c(78,81,81,81,77,80)
### population parameters for simulation
e0<-2.465375
ed50<-67.481113
emax<-15.127726
sdy<-8.0
pop.parm<-c(log(ed50),emax,e0)
dose<-rep(doselev,n)
meanlev<-emaxfun(dose,pop.parm)
y<-rnorm(sum(n),meanlev,sdy)
testout<-fitEmax(y,dose,modType=4)
### basic test
plot(testout)
plot(testout,ncol=1)
#plot(testout, xat=c(-100, 0, 5, 100, 350)) #fail planned
#plot(testout, xat=c(0, 5, 100, 350, 400)) #fail planned
plot(testout, xat=c(0, 100, 350))
plot(testout, log=TRUE)
#plot(testout, log=TRUE, xat=c(-100, 0, 5, 100, 350)) #fail planned
#plot(testout, log=TRUE, xat=c(0, 5, 100, 350, 400)) #fail planned
plot(testout, log=TRUE, xat=c(0, 5, 100, 350))
plot(testout,plotDif=TRUE)
plot(testout,plotDif=TRUE, log=TRUE)
### with symbol
symbol<-1+1*(dose==100)
plot(testout,symbol=symbol)
plot(testout,symbol=symbol, log=TRUE)
plot(testout,symbol=symbol,
symbolLabel='TESTGROUP',
symbolShape=c(8,10),symbolColor=c('blue','red'))
plot(testout,symbol=symbol, log=TRUE,
symbolLabel='TESTGROUP',
symbolShape=c(8,10),symbolColor=c('blue','red'))
plot(testout,symbol=symbol,
symbolLabel='TESTGROUP',
symbolShape=c(8,10),symbolColor=c('blue','red'),plotDif=TRUE)
plot(testout,symbol=symbol, log=TRUE,
symbolLabel='TESTGROUP',
symbolShape=c(8,10),symbolColor=c('blue','red'),plotDif=TRUE)
plot(testout,symbol=symbol, log=TRUE,
symbolLabel='TESTGROUP',
symbolShape=c(8,10),symbolColor=c('blue','red'),plotDif=TRUE,
ncol=1)
### residual plot
plot(testout,plotResid=TRUE)
plot(testout,plotResid=TRUE, log=TRUE)
#####################################################################
##### tests with multiple protocols and 3-parm emax
##### one protocol much larger than other
set.seed(12357)
doselev<-c(0,5,25,50,100,350)
n<-80
### population parameters for simulation
e0<-2.465375
ed50<-67.481113
emax<-15.127726
sdy<-8.0
pop.parm<-c(log(ed50),emax,e0)
dose<-rep(doselev,n)
meanlev<-emaxfun(dose,pop.parm)
y<-rnorm(n*length(doselev),meanlev,sdy)
prot<-rep('a',length(y))
prot[1:(5*length(doselev))]<-'b'
symbol=1*(dose<300)+2*(dose>=300)
symbolLabel='TESTNAME'
symbolColor=c('red','blue','green')
symbolShape=c(8,10)
testout2<-fitEmax(y,dose,modType=3,prot=prot)
plot(testout2,bwidth=10,symbol=symbol,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor)
plot(testout2,bwidth=10,symbol=symbol,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor,ncol=1)
plot(testout2,bwidth=10,symbol=symbol,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor, log=TRUE)
plot(testout2,bwidth=10,symbol=symbol,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor,plotDif=TRUE)
plot(testout2,bwidth=10,symbol=symbol,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor,plotDif=TRUE, log=TRUE)
testout3<-fitEmax(y,dose,modType=3)
symbmod<-symbol
symbmod[prot==1 & dose==350]<-1
plot(testout3,bwidth=10,symbol=symbmod,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor)
plot(testout3,bwidth=10,symbol=symbmod,symbolLabel=symbolLabel,
symbolShape=symbolShape,symbolColor=symbolColor, log=TRUE)
###############################################################
#### 3 parm, int=2, starting value given
#### no intercept
####
set.seed(12357)
doselev<-c(0,5,25,50,100,350)
n<-10*c(78,81,81,81,77,80)
n1<-sum(n)
n2<-sum(n[1:4])
doselev<-c(doselev,doselev[1:4])
n<-c(n,n[1:4])
### population parameters for simulation
e0<-0
ed50<-67.481113
emax<-15.127726
sdy<-8.0
pop.parm<-c(log(ed50),emax,e0)
dose<-rep(doselev,n)
meanlev<-emaxfun(dose,pop.parm)
y<-rnorm(n1+n2,meanlev,sdy)
prots<-c(rep('d',n1),rep('ee',n2))
ysub<-y[dose!=0]
dsub<-dose[dose!=0]
protsub<-prots[dose!=0]
testout<-fitEmax(ysub,dsub,iparm=pop.parm,modType=3,prot=protsub,pboAdj=c(TRUE,TRUE))
plot(testout)
plot(testout,ncol=1)
plot(testout, log=TRUE)
testout4<-fitEmax(ysub,dsub,iparm=c(pop.parm[1],1,pop.parm[2],0),
modType=4,prot=protsub,pboAdj=c(TRUE,TRUE))
plot(testout4)
plot(testout4, log=TRUE)
#########################################################################
##### binary
set.seed(12357)
dvec1<-c(0,.1,.3,.6,1)
dvec2<-c(0,.1,.2,.4,.6,1)
nd1<-length(dvec1)
nd2<-length(dvec2)
n1<-rep(10000,nd1)
n2<-rep(10000,nd2)
parms<-c(log(0.25),1,1.4,-0.5,-0.85)
mlev1<-plogis(emaxfun(dvec1,parms[1:4]))
mlev2<-plogis(emaxfun(dvec2,parms[c(1:3,5)]))
y1<-rbinom(nd1,n1,mlev1)
y2<-rbinom(nd2,n2,mlev2)
### fitEmax inputs
y<-c(rep(1,nd1),rep(0,nd1),rep(1,nd2),rep(0,nd2))
counts<-c(y1,n1-y1,y2,n2-y2)
prots<-c(rep('eg',2*nd1),rep('fg',2*nd2))
dvec<-c(dvec1,dvec1,dvec2,dvec2)
testout<-fitEmax(y,dvec,modType=4,parms[1:4],
prot=prots,
count=counts,binary=TRUE,diagnostics=FALSE)
plot(testout)
plot(testout, log=TRUE)
plot(testout, log=TRUE, xat=c(0.1, 0.2, 0.4, 0.6, 1.0))
plot(testout, log=TRUE, xat=c(0.1, 0.2, 0.4, 0.6, 1.0),ncol=1)
plot(testout,plotDif=TRUE)
plot(testout,plotDif=TRUE, log=TRUE)
plot(testout,plotDif=TRUE, log=TRUE, xat=c(0.1, 0.2, 0.4, 0.6, 1.0))
testout2<-fitEmax(y[prots=='fg'],dvec[prots=='fg'],modType=4,parms[1:4],
count=counts[prots=='fg'],binary=TRUE,diagnostics=FALSE)
plot(testout2)
plot(testout2, log=TRUE)
plot(testout2,plotDif=TRUE)
plot(testout2,plotDif=TRUE, log=TRUE)
##################
### covariates
## 3 covariates, 2 protocols
set.seed(12357)
doselev<-c(0,5,25,50,100,350)
n<-c(78,81,81,81,77,80)
n1<-sum(n)
n2<-sum(n[1:4])
doselev<-c(doselev,doselev[1:4])
n<-c(n,n[1:4])
### population parameters for simulation
e0<-2.465375
ed50<-67.481113
emax<-15.127726
sdy<-8.0
x1<-matrix(rnorm(3*n1),ncol=3)
x1<-scale(x1,center=TRUE,scale=FALSE)
x2<-matrix(rnorm(3*n2),ncol=3)
x2<-scale(x2,center=TRUE,scale=FALSE)
x<-rbind(x1,x2)
pop<-c(log(ed50),emax,e0)
dose<-rep(doselev,n)
bparm<-c(2,-1,0.5)
meanlev<-emaxfun(dose,pop) + x%*%bparm
y<-rnorm(n1+n2,meanlev,sdy)
prots<-c(rep(1,n1),rep(2,n2))
testout2<-fitEmax(y,dose,modType=4,prot=prots,xbase=x,diagnostics = FALSE)
plot(testout2)
plot(testout2,ncol=1)
plot(testout2, log=TRUE)
plot(testout2,plotDif=TRUE)
plot(testout2,plotDif=TRUE, log=TRUE)
### binary, covariates
### check with larger n for better asymptotics
### 4-parm model, 2 covariates, 1 protocol
### starting values specified
###
set.seed(12357)
doselev<-c(0,5,25,50,100,350)
n<-5*c(78,81,81,81,77,80)
ntot<-sum(n)
### population parameters for simulation
e0<- -1.5
ed50<-67.481113
emax<-4.0
pop<-c(log(ed50),emax,e0)
dose<-rep(doselev,n)
meanlev<-emaxfun(dose,pop)
iparm<-c(log(ed50),1,emax,e0)
x<-matrix(rnorm(2*ntot),ncol=2)
x<-scale(x,center=TRUE,scale=FALSE)
bparm<-c(2,-1)
meanlev<-plogis(meanlev + x%*%bparm)
y<-rbinom(ntot,1,meanlev)
testout4b<-fitEmax(y,dose,modType=4,iparm=iparm,xparm=bparm,xbase=x,
binary=TRUE,diagnostics=FALSE)
plot(testout4b)
plot(testout4b,plotDif=TRUE)
dev.off()
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