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variable_script.r
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variable_script.r
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### NONMEM vs. NCA in Bioequivalence Studies
# All values needing definition occur before simulation code
# Remove any previous objects in the workspace
rm(list=ls(all=TRUE))
graphics.off()
# Source functions file and NONMEM .ctl reference txt
master.dir <- "E:/hscpw-df1/Data1/Jim Hughes/DDPLY/PARAM_VARY2" ### Directory containing source files
setwd(master.dir)
source("functions_NCAvNLME_2016.r")
ctlm1 <- readLines("NONMEM_ref_M1.ctl") #No BSV or BOV on Q & V3
ctlm3 <- readLines("NONMEM_ref_M3.ctl")
rscript <- readLines("Rscript.r")
wfn.dir <- "c:/nm73ifort/wfn7/bin/wfn.bat"
# Load libraries
library(doBy)
library(plyr)
library(MASS)
library(MBESS)
library(stringr)
library(reshape2)
set.seed(1234)
# Specify run characteristics
#set up values that are variable between runs
rundf <- data.frame(
RUN = rep(1:7, each = 15), #rep(number.of.runs, each=number.of.scenarios)
SCEN = rep(1:15, times = 7), #opposite of above
RUV.TYPE = rep(c(rep(1, 3),rep(2, 2)),21), #changing calculation of RUV for scenarios 10:15
RUV.BLQ = rep(c(0.2, 0.15, 0.1, 0.1, 0.5), 21),
F1.POP = rep(c(rep(1.0, 5), rep(0.9, 5), rep(1.11, 5)), 7), #changing Frel of generic
F1.BSV = rep(c(rep(0.1225, 5), rep(0.0484, 5), rep(0.0529, 5)), 7), #changing BSV on frel
BLQ = c(rep(0.01, 15), rep(0.1, 15),rep(0.01, 75)), #Run 2 - raised LLOQ
RUV.PROP = c(rep(0.05, 30), rep(0.09, 15), rep(0.05, 60)), #Run 3 - increased proportional RUV
SS.TYPE = c(rep(1, 45), rep(2, 15), rep(1, 45)), #Run 4 - reduced sampling schedule
KA.TYPE = c(rep(1, 60), rep(2, 15), rep(1, 30)), #Run 5 - 20% lower generic KA
BOV.TYPE = c(rep(1, 75), rep(2, 15), rep(3, 15))) #Run 6 & 7 - BOV testing
#set up values that are constant between runs
runvec <- c(
NID = 24,
NSIM = 20,
LIMITLO = 0.8,
LIMITHI = 1.25,
NCORE.M1 = 5,
NCORE.M3 = 20,
AMT = 125,
CL.POP = 20,
V2.POP = 100,
Q.POP = 35,
V3.POP = 400,
KA.POP = 0.5,
KA.GEN = 0.3,
CL.BSV = 0.045,
V2.BSV = 0.045,
V3.BSV = 0.045,
Q.BSV = 0.045,
KA.BSV = 0.01,
CL.BOV = 0.045,
V2.BOV = 0.045,
V3.BOV = 0.045,
Q.BOV = 0.045,
ALT.BOV = 0.0001)
#set up time vector for simulation
timevec <- c(
seq(0, 3, 0.05),
seq(3.25, 6, 0.25),
seq(6.5, 12, 0.5),
seq(13, 96, 1))
TIME <- timevec
#set up correlation vector for simulation
corvec <- c(
1, 0.3, 0.3, 0.3,
0.3, 1, 0.3, 0.3,
0.3, 0.3, 1, 0.3,
0.3, 0.3 ,0.3 , 1)
varydf <- data.frame(
CL = rnorm(runvec["NSIM"],mean=0,sd=0.1*runvec["CL.POP"]),
V2 = rnorm(runvec["NSIM"],mean=0,sd=0.1*runvec["V2.POP"]),
Q = rnorm(runvec["NSIM"],mean=0,sd=0.1*runvec["Q.POP"]),
V3 = rnorm(runvec["NSIM"],mean=0,sd=0.1*runvec["V3.POP"]),
KA = rnorm(runvec["NSIM"],mean=0,sd=0.1*runvec["KA.POP"])
)
### FIRST HALF -----------------------------------------------------------------
# First loop includes
# 1. Simulation of data
# 2. Initial analysis of simulated data
# 3. Non-Compartmental Analysis
# 4. Initial analysis of NCA data
# 5. Non-Linear Mixed Effects
# 6. Create .r script for loading
ddply(rundf, .(RUN, SCEN), function(df, vec, time, cor, vary) {
### 1. Simulation of data
#Set working directory and file names
SIM.name.out <- paste0("Run", df$RUN, "_Scen", df$SCEN)
SIM.dir <- paste(master.dir,SIM.name.out,sep="/")
SIM.file <- paste(SIM.dir,SIM.name.out,sep="/")
EST.dir <- paste(SIM.dir,"ctl",sep="/")
EST.file <- paste(EST.dir,SIM.name.out,sep="/")
FIT.dir <- paste(SIM.dir,"fit",sep="/")
# Define study design
nid <- vec["NID"]
nsim <- vec["NSIM"]
nsub <- nid*nsim
nobs <- length(time)
if (df$SS.TYPE == 1) {
sstime <- c(0,0.25,0.5,1,2,4,6,8,12,16,24,36,48,72,96)
} else {
sstime <- c(0,0.25,0.5,1,2,4,8,16,36,96)
}
# Define random unexplained variability (SIGMA) values
ruv.prop <- df$RUV.PROP
ruv.blq <- df$RUV.BLQ
blq <- df$BLQ
if (df$RUV.TYPE == 1) { #Scenarios 1-9
ruv.add <- (ruv.blq - ruv.prop) * blq
trunc.blq <- blq
}
if (df$RUV.TYPE == 2) { #Scenarios 10-15
ruv.add <- (0.2 - ruv.prop) * blq
trunc.blq <- ruv.add/(ruv.blq - ruv.prop)
}
ruv.add.nm <- ifelse(blq == 0 || ruv.add == 0,
paste(ruv.add, "FIX"),
ruv.add
)
# Set cores
ncore.m1 <- vec["NCORE.M1"]
ncore.m3 <- vec["NCORE.M3"]
# Correlation between parameters
cormat <- matrix(cor, nrow = 4, ncol = 4)
std.bsv <- c(sqrt(vec["CL.BSV"]),sqrt(vec["V2.BSV"]),sqrt(vec["Q.BSV"]),sqrt(vec["V3.BSV"]))
omega <- cor2cov(cormat, std.bsv)
# Account for random BSV
etamat <- mvrnorm(n = nsub, mu = c(0, 0, 0, 0), omega)
ETA1 <- rep(etamat[,1],each=2) # CLbsv
ETA2 <- rep(etamat[,2],each=2) # V2bsv
ETA3 <- rep(etamat[,3],each=2) # Qbsv
ETA4 <- rep(etamat[,4],each=2) # V3bsv
if (vec["KA.BSV"]==0){
ETA5 <- rep(0, times=nsub) # KA (no bsv)
} else {
ETA5 <- rnorm(nsub,mean=0,sd=sqrt(vec["KA.BSV"])) # KA (bsv)
}
if (df$F1.BSV==0){
ETA6 <- rep(0, times=nsub) # F1 (no bsv)
} else {
ETA6 <- rnorm(nsub,mean=0,sd=sqrt(df$F1.BSV)) # F1 (bsv)
}
if (df$BOV.TYPE != 2) {
ETA7 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["CL.BOV"])) # CLbov
} else {
ETA7 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["ALT.BOV"])) # CLbov
}
if (df$BOV.TYPE == 1) {
ETA8 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["V2.BOV"])) # V2bov
ETA9 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["Q.BOV"])) # Qbov
ETA10 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["V3.BOV"])) # V3bov
} else {
ETA8 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["ALT.BOV"])) # V2bov
ETA9 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["ALT.BOV"])) # Qbov
ETA10 <- rnorm(nsub*2,mean=0,sd=sqrt(vec["ALT.BOV"])) # V3bov
}
if (vec["KA.BSV"]==0){
ETA11 <- rep(0, times=nsub) # KA (no bsv)
} else {
ETA11 <- rnorm(nsub,mean=0,sd=sqrt(vec["KA.BSV"])) # KA (bsv)
}
ka.param.pop <- vec["KA.POP"] + vary$KA
if (df$KA.TYPE == 1) {
ka.theta <- rep(ka.param.pop * exp(ETA5), each = 2)
} else {
ka.param.gen <- vec["KA.GEN"] + vary$KA
ka.theta <- as.vector(rbind(
ka.param.pop * exp(ETA5), ka.param.gen * exp(ETA11)
))
}
# Create/clear directories before run
dir.setup(SIM.dir)
dir.setup(EST.dir)
dir.setup(FIT.dir)
nm.clear(EST.dir, nsim*2)
# Define theta table
paramdf <- data.frame(
SIM = 1:nsim,
AMT = vec["AMT"],
CL = vec["CL.POP"] + vary$CL,
V2 = vec["V2.POP"] + vary$V2,
Q = vec["Q.POP"] + vary$Q,
V3 = vec["V3.POP"] + vary$V3,
KA = ka.param.pop)
write.csv(paramdf, file = paste(SIM.file, "PARAM_VARY.csv", sep = "_"), row.names = F)
names(paramdf)[1] <- "ID"
thetadf <- data.frame(
SIM = rep(1:nsim, each = nid*2),
ID = rep(1:nid, times = nsim, each = 2),
AMT = rep(vec["AMT"], times = nsub * 2),
FORM = rep(1:2, times = nsub),
CL = paramdf$CL * exp(ETA1 + ETA7),
V2 = paramdf$V2 + vary$V2 * exp(ETA2 + ETA8),
Q = paramdf$Q + vary$Q * exp(ETA3 + ETA9),
V3 = paramdf$V3 + vary$V3 * exp(ETA4 + ETA10),
KA = ka.theta,
F1 = as.vector(rbind(df$F1.POP * exp(ETA6), rep(1, nsim))))
write.csv(thetadf, file = paste(SIM.file, "THETAS.csv", sep = "_"), row.names = F)
thetadf$FORM <- NULL
thetadf$SIM <- NULL
# Population simulation
paramdf.inn <- paramdf
paramdf.inn$F1 <- 1
simdataPRED1 <- mdply(paramdf.inn, simulate.2comp.abs)[c(1,9:10)]
names(simdataPRED1)[c(1,3)] <- c("SIM","PRED")
simdataPRED1$FORM <- rep(1, times = nobs*nsim)
paramdf.gen <- paramdf
paramdf.gen$F1 <- df$F1.POP
simdataPRED2 <- mdply(paramdf.gen, simulate.2comp.abs)[c(1,9:10)]
names(simdataPRED2)[c(1,3)] <- c("SIM","PRED")
simdataPRED2$FORM <- rep(2, times = nobs*nsim)
simdataPRED <- rbind(simdataPRED1, simdataPRED2)
# Individual simulation
simdataIPRED <- mdply(thetadf,simulate.2comp.abs)
names(simdataIPRED)[10] <- "IPRED"
simdataIPRED$UID <- rep(1:nsub, each = nobs * 2)
simdataIPRED$SIM <- rep(1:nsim, each = nobs * nid * 2)
simdataIPRED$FORM <- ifelse(simdataIPRED$F1 == 1, 1, 2)
# Combine PRED and IPRED
simdata <- orderBy(~UID + FORM + TIME,
merge(simdataIPRED, simdataPRED, all = T)
)[
c("UID", "ID", "SIM", "TIME", "AMT", "FORM",
"CL", "V2", "Q", "V3", "KA", "F1", "PRED", "IPRED")
]
# Add residual error
CP <- simdata$IPRED
tnobs <- length(CP)
EPS1 <- rnorm(tnobs, mean = 0, sd = ruv.prop)
EPS2 <- rnorm(tnobs, mean = 0, sd = ruv.add)
simdata$DV <- CP * (1 + EPS1) + EPS2
simdata$DV <- ifelse(simdata$TIME == 0, 0, simdata$DV)
# Save simulation file
write.csv(simdata, file = paste(SIM.file, "_RAW.csv", sep = ""), row.names = F)
### 2. Initial analysis of simulated data
# Process results for IPRED (see functions utility)
data.process(simdata,TIME,nid,nsim,SIM.file,trunc.blq,mode=1)
### 3. Non-Compartmental Analysis
### 4. Initial analysis of NCA data
# Process results for NCA with truncation (see functions utility)
data.process(simdata,sstime,nid,nsim,SIM.file,trunc.blq,mode=2)
### 5. Non-Linear Mixed Effects
# Source truncated dataset from NCA data.process function
trunc.file <- paste(SIM.name.out,"TRUNCATED.csv",sep="_")
limdata <- read.csv(paste(SIM.dir,trunc.file,sep="/"))
limobs <- length(sstime)
per.bloq <- percent.blq(limdata$DV,limdata$TIME,trunc.blq)
# Create .ctl for NONMEM to run for each SIM (and a .bat placeholder)
# Replaces phrases in the .ctl reference file to values stated at the
# beginning of the script
ctlm1 <- ctl.prep(ctlm1, df, vec, omega, ruv.prop, ruv.add.nm, trunc.blq)
ctlm3 <- ctl.prep(ctlm3, df, vec, omega, ruv.prop, ruv.add.nm, trunc.blq)
# Create .bat files for NONMEM shell and run in NONMEM (number of .bats
# determined by ncore value)
nmbat1 <- rep("", times =nsim)
nmbat1 <- nm.prep(
nsim, ncore.m1, nid, EST.file, 1, SIM.name.out,
limdata, limobs, trunc.blq, ctlm1, nmbat1, vec["AMT"])
nmbat2 <- rep("", times = nsim)
nmbat2 <- nm.prep(
nsim, ncore.m3, nid, EST.file, 3, SIM.name.out,
limdata, limobs, trunc.blq, ctlm3, nmbat2, vec["AMT"])
setwd(EST.dir)
cd.EST.dir <- paste("cd", EST.dir)
# Run batch files in multiple instances of NONMEM
for(i in 1:ncore.m1) {
nmbat.split1 <- nmbat1[1:(nsim/ncore.m1)+(nsim/ncore.m1)*(i-1)]
nmbat.split1 <- c(paste0("call ",wfn.dir), "E:", cd.EST.dir, nmbat.split1)
bat.name1 <- paste(EST.file,"_M1",i,".bat",sep="")
cmd <- paste(SIM.name.out,"_M1",i,".bat",sep="")
writeLines(nmbat.split1,bat.name1)
system(cmd, input=nmbat1, invisible=F, show.output.on.console=F, wait=F)
}
for (i in 1:ncore.m3) {
nmbat.split2 <- nmbat2[1:(nsim/ncore.m3)+(nsim/ncore.m3)*(i-1)]
nmbat.split2 <- c(paste("call ",wfn.dir,sep=""),"E:",cd.EST.dir,nmbat.split2)
bat.name2 <- paste(EST.file,"_M3",i,".bat",sep="")
cmd <- paste(SIM.name.out,"_M3",i,".bat",sep="")
writeLines(nmbat.split2,bat.name2)
if (i!=ncore.m3) {
system(cmd, input=nmbat2, invisible=F, show.output.on.console=F, wait=F)
# If final batch file wait to prevent other NONMEM instances finishing first
} else {
Sys.sleep(300)
system(cmd, input=nmbat2, invisible=F, show.output.on.console=F, wait=F)
wait.file <- paste0(SIM.name.out,"3_model",nsim)
start.time <- Sys.time()
iter <- 0
# Wait for final batch file to produce .fit file
while(!file.exists(paste0(
EST.dir,"/",wait.file,".nm7/",tolower(wait.file),".fit")) |
iter > 60) {
Sys.sleep(60)
print(Sys.time() - start.time)
iter <- iter + 1
}
}
}
setwd(master.dir)
print(paste(SIM.name.out,"complete"))
}, vec = runvec, time = timevec, cor = corvec, vary = varydf)
### SECOND HALF ----------------------------------------------------------------
bioqtable <- ddply(rundf, .(RUN, SCEN), function(df, vec, time) {
#Set working directory
SIM.name.out <- paste0("Run", df$RUN, "_Scen", df$SCEN)
SIM.dir <- paste(master.dir,SIM.name.out,sep="/")
SIM.file <- paste(SIM.dir,SIM.name.out,sep="/")
EST.dir <- paste(SIM.dir,"ctl",sep="/")
EST.file <- paste(EST.dir,SIM.name.out,sep="/")
FIT.dir <- paste(SIM.dir,"fit",sep="/")
# Define study design
nid <- vec["NID"]
nsim <- vec["NSIM"]
nsub <- nid*nsim
nobs <- length(time)
if (df$SS.TYPE == 1) {
sstime <- c(0,0.25,0.5,1,2,4,6,8,12,16,24,36,48,72,96)
} else {
sstime <- c(0,0.25,0.5,1,2,4,8,16,36,96)
}
# Define random unexplained variability (SIGMA) values
ruv.prop <- df$RUV.PROP
ruv.blq <- df$RUV.BLQ
blq <- df$BLQ
if (df$RUV.TYPE == 1) {
ruv.add <- (ruv.blq - ruv.prop) * blq
trunc.blq <- blq
}
if (df$RUV.TYPE == 2) {
ruv.add <- (0.2 - ruv.prop) * blq
trunc.blq <- ruv.add/(ruv.blq - ruv.prop)
}
ruv.add.nm <- ifelse(blq == 0 || ruv.add == 0,
paste(ruv.add, "FIX"),
ruv.add)
# Load data files for processing
# Only load RAW if you need it for troubleshooting, RAW.csv can be 500MB+
#simdata <- read.csv(paste(SIM.file,"_RAW.csv", sep=""))
ipredresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_IPREDresult.csv"))
ipredfresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_IPREDFresult.csv"))
ipredcresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_IPREDCresult.csv"))
ncaresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_NCAresult.csv"))
ncafresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_NCAFresult.csv"))
ncacresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_NCACresult.csv"))
trunc.file <- paste(SIM.name.out,"TRUNCATED.csv",sep="_")
limdata <- read.csv(paste(SIM.dir,trunc.file,sep="/"))
limobs <- length(sstime)
per.bloq <- percent.blq(limdata$DV,limdata$TIME,trunc.blq)
# Setup mbt .bat file (is run manually)
setwd(EST.dir)
cd.EST.dir <- paste("cd", EST.dir)
mbtcall <- c(paste("call", wfn.dir), "E:", cd.EST.dir, "call nmmbt")
mbtbat <- "nmmbtrun.bat"
mbtbat.dir <- paste(EST.dir, mbtbat, sep="/")
writeLines(mbtcall, mbtbat.dir)
system(mbtbat, show.output.on.console=F)
setwd(master.dir)
# Process fit files into results table (see functions utility)
m1nlme.fitout <- nlme.fit(SIM.name.out,FIT.dir,EST.dir,"M1",nsim,nid,limobs)
m1.nsim <- m1nlme.fitout[1]
m1.nsub <- m1.nsim*nid
m1.fitfail <- m1nlme.fitout[-1]
m1sim.in <- read.csv(paste(SIM.file,"M1_NMTHETAS.csv", sep="_"))
m1sim.time <- nlme.simtime(m1sim.in)
m1sim.out <- nlme.sim(m1sim.in,m1sim.time,nid,m1.nsim)
data.process(m1sim.out,m1sim.time,nid,m1.nsim,SIM.file,trunc.blq,mode=3)
m1result <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M1result.csv"))
m1fresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M1Fresult.csv"))
m1cresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M1Cresult.csv"))
m1phfresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M1PHFresult.csv"))
m3nlme.fitout <- nlme.fit(SIM.name.out,FIT.dir,EST.dir,"M3",nsim,nid,limobs)
m3.nsim <- m3nlme.fitout[1]
m3.nsub <- m3.nsim*nid
m3.fitfail <- m3nlme.fitout[-1]
m3sim.in <- read.csv(paste(SIM.file, "M3_NMTHETAS.csv", sep="_"))
m3sim.time <- nlme.simtime(m3sim.in)
m3sim.out <- nlme.sim(m3sim.in,m3sim.time,nid,m3.nsim)
data.process(m3sim.out,m3sim.time,nid,m3.nsim,SIM.file,trunc.blq,mode=4)
m3result <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M3result.csv"))
m3fresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M3Fresult.csv"))
m3cresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M3Cresult.csv"))
m3phfresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M3PHFresult.csv"))
# Find percentage of successful runs
mbt <- read.table(file=paste(EST.dir,"nmmbt.nm7.txt",sep="/"),header=TRUE)
mbt <- orderBy(~Run,mbt)
mbt$Method <- rep(1:2,each=nsim)
mbt$ModelNum <- rep(order(as.character(1:nsim)), times = 2)
mbt$Success <- gsub("SUCCESSFUL",1,mbt$Min)
mbt$Success <- gsub("TERMINATED",0,mbt$Min)
mbt <- orderBy(~Method+ModelNum,mbt)
m1.term <- which(mbt$Success == 0 & mbt$Method == 1)
m3.term <- which(mbt$Success == 0 & mbt$Method == 2) - 20
m1mbt <- mbt[mbt$Method == 1, c(1,5,6)] #m1
m1mbt$Min <- gsub("SUCCESSFUL",1,m1mbt$Min)
m1mbt$Min <- gsub("TERMINATED",0,m1mbt$Min)
m1mbt$Cov <- gsub("NONE",0,m1mbt$Cov)
m1mbt$Cov <- gsub("OK",1,m1mbt$Cov)
m1mbt$Cov <- gsub("ABORTED",0,m1mbt$Cov)
m1mbt$Cov <- gsub("UNOBTAINABLE",0,m1mbt$Cov)
m3mbt <- mbt[mbt$Method == 2, c(1,5,6)] #m3
m3mbt$Min <- gsub("SUCCESSFUL",1,m3mbt$Min)
m3mbt$Min <- gsub("TERMINATED",0,m3mbt$Min)
m3mbt$Cov <- gsub("NONE",0,m3mbt$Cov)
m3mbt$Cov <- gsub("OK",1,m3mbt$Cov)
m3mbt$Cov <- gsub("ABORTED",0,m3mbt$Cov)
m3mbt$Cov <- gsub("UNOBTAINABLE",0,m3mbt$Cov)
m1min <- mean(as.numeric(m1mbt$Min))*100
m1cov <- mean(as.numeric(m1mbt$Cov))*100
m3min <- mean(as.numeric(m3mbt$Min))*100
m3cov <- mean(as.numeric(m3mbt$Cov))*100
### Determine for bioequvalence
# FREL
#should do all this in ddply, doing the same things for each dataset
aovprep <- data.frame(matrix(NA, nrow=nsub*4+m1.nsub*4+m3.nsub*4, ncol=8))
colnames(aovprep) <- c("METH","ID","SIM","FORM","AUC","CMAX","IDf","FORMf")
aovprep$METH <- as.factor(c(
rep("IPRED", nsub*2), #IPRED
rep("NCA", nsub*2), #NCA
rep("M1PH", m1.nsub*2), #M1PH
rep("M1F1", m1.nsub*2), #M1F1
rep("M3PH", m3.nsub*2), #M3PH
rep("M3F1", m3.nsub*2))) #M3F1
aovprep$ID <- c(
limdata[limdata$TIME == 0, 2], #IPRED
rep(ncaresult$STUD_ID, each = 2), #NCA
rep(m1sim.in$STUD_ID,2), #M1PH & M1F1
rep(m3sim.in$STUD_ID,2)) #M3PH & M3F1
aovprep$SIM <- c(
limdata[limdata$TIME == 0, 3], #IPRED
rep(ncaresult$SIM_ID, each = 2), #NCA
if (m1.fitfail == 0) { #M1PH & M1F1
rep(m1sim.in$SIM_ID,2)
} else {
rep((1:nsim)[-m1.fitfail], each = nid*2, times = 2)
},
if (m3.fitfail == 0) { #M3PH & M1F3
rep(m3sim.in$SIM_ID,2)
} else {
rep((1:nsim)[-m3.fitfail], each = nid*2, times = 2)
})
aovprep$FORM <- c(
limdata[limdata$TIME == 0, 6], #IPRED
rep(1:2, times = nsub), #NCA
rep(1:2, times = m1.nsub*2), #M1PH & M1F1
rep(1:2, times = m3.nsub*2)) #M3PH &M3F1
aovprep$AUC <- c(
limdata[limdata$TIME == 0, 12], #IPRED
as.vector(rbind(ncaresult$INN_AUC, ncaresult$GEN_AUC)), #NCA
as.vector(rbind(m1result$INN_AUC, m1result$GEN_AUC)), #M1PH
m1sim.in$F1, #M1F1
as.vector(rbind(m3result$INN_AUC, m3result$GEN_AUC)), #M3PH
m3sim.in$F1) #M3F1
aovprep$CMAX <- c(
as.vector(rbind(ipredresult$INN_CMAX,ipredresult$GEN_CMAX)), #IPRED
as.vector(rbind(ncaresult$INN_CMAX, ncaresult$GEN_CMAX)), #NCA
rep(as.vector(rbind(m1result$INN_CMAX, m1result$GEN_CMAX)),2), #M1
rep(as.vector(rbind(m3result$INN_CMAX, m3result$GEN_CMAX)),2)) #M3
aovprep$IDf <- as.factor(aovprep$ID)
aovprep$FORMf <- as.factor(aovprep$FORM)
faov <- ddply(aovprep, .(METH, SIM), function(df) runaov2(df, USE = "AUC"))
caov <- ddply(aovprep, .(METH, SIM), function(df) runaov2(df, USE = "CMAX"))
meth.faov <- faov[faov$METH != "IPRED", ]
meth.faov$IPRED.BE <- c(
rep(faov$BE[faov$METH == "IPRED" & !faov$SIM %in% m1.fitfail], 2),
rep(faov$BE[faov$METH == "IPRED" & !faov$SIM %in% m3.fitfail], 2),
faov$BE[faov$METH == "IPRED"])
meth.caov <- caov[caov$METH != "IPRED", ]
meth.caov$IPRED.BE <- c(
rep(caov$BE[caov$METH == "IPRED" & !caov$SIM %in% m1.fitfail], 2),
rep(caov$BE[caov$METH == "IPRED" & !caov$SIM %in% m3.fitfail], 2),
caov$BE[caov$METH == "IPRED"])
fbioq <- ddply(faov, .(METH), function(df) mean(df$BE))
cbioq <- ddply(caov, .(METH), function(df) mean(df$BE))
ipred.fbioq <- ddply(meth.faov, .(METH), function(df) mean(df$IPRED.BE))
ipred.cbioq <- ddply(meth.caov, .(METH), function(df) mean(df$IPRED.BE))
ferror <- ddply(meth.faov, .(METH), function(df)
errortype.func2(df$IPRED.BE, df$BE))
ferror.t1 <- ddply(ferror, .(METH), function(df)
mean(as.numeric(as.vector(df$pT1))))
ferror.t2 <- ddply(ferror, .(METH), function(df)
mean(as.numeric(as.vector(df$pT2))))
cerror <- ddply(meth.caov, .(METH), function(df)
errortype.func2(df$IPRED.BE, df$BE))
cerror.t1 <- ddply(cerror, .(METH), function(df)
mean(as.numeric(as.vector(df$pT1))))
cerror.t2 <- ddply(cerror, .(METH), function(df)
mean(as.numeric(as.vector(df$pT2))))
faov.termstat <- faov[!(
faov$METH == "M1F1" & faov$SIM %in% m1.term |
faov$METH == "M1PH" & faov$SIM %in% m1.term |
faov$METH == "M3F1" & faov$SIM %in% m3.term |
faov$METH == "M3PH" & faov$SIM %in% m3.term), ]
caov.termstat <- caov[!(
caov$METH == "M1F1" & caov$SIM %in% m1.term |
caov$METH == "M3F1" & caov$SIM %in% m3.term |
caov$METH == "M1PH" | caov$METH == "M3PH"), ]
meth.faov.termstat <- faov.termstat[faov.termstat$METH != "IPRED", ]
meth.faov.termstat$IPRED.BE <- c(
rep(
faov.termstat$BE[faov.termstat$METH == "IPRED" &
!faov.termstat$SIM %in% m1.term &
!faov.termstat$SIM %in% m1.fitfail], 2),
rep(
faov.termstat$BE[faov.termstat$METH == "IPRED" &
!faov.termstat$SIM %in% m3.term &
!faov.termstat$SIM %in% m3.fitfail], 2),
faov.termstat$BE[faov.termstat$METH == "IPRED"])
meth.caov.termstat <- caov.termstat[caov.termstat$METH != "IPRED", ]
meth.caov.termstat$IPRED.BE <- c(
caov.termstat$BE[caov.termstat$METH == "IPRED" &
!caov.termstat$SIM %in% m1.term &
!caov.termstat$SIM %in% m1.fitfail],
caov.termstat$BE[caov.termstat$METH == "IPRED" &
!caov.termstat$SIM %in% m3.term &
!caov.termstat$SIM %in% m3.fitfail],
caov.termstat$BE[caov.termstat$METH == "IPRED"])
fbioq.termstat <- ddply(faov.termstat, .(METH),
function(df) mean(df$BE))
cbioq.termstat <- ddply(caov.termstat, .(METH),
function(df) mean(df$BE))
ipred.fbioq.termstat <- ddply(meth.faov.termstat, .(METH),
function(df) mean(df$IPRED.BE))
ipred.cbioq.termstat <- ddply(meth.caov.termstat, .(METH),
function(df) mean(df$IPRED.BE))
ferror.termstat <- ddply(meth.faov.termstat, .(METH),
function(df) errortype.func2(df$IPRED.BE, df$BE))
ferror.termstat.t1 <- ddply(ferror.termstat, .(METH),
function(df) mean(as.numeric(as.vector(df$pT1))))
ferror.termstat.t2 <- ddply(ferror.termstat, .(METH),
function(df) mean(as.numeric(as.vector(df$pT2))))
cerror.termstat <- ddply(meth.caov.termstat, .(METH),
function(df) errortype.func2(df$IPRED.BE, df$BE))
cerror.termstat.t1 <- ddply(cerror.termstat, .(METH),
function(df) mean(as.numeric(as.vector(df$pT1))))
cerror.termstat.t2 <- ddply(cerror.termstat, .(METH),
function(df) mean(as.numeric(as.vector(df$pT2))))
print(paste(SIM.name.out,"processed"))
aovbioqtable <- data.frame(
TERMSTAT = c("All","Only Success"),
IPREDBE = c(fbioq$V1[1]*100,fbioq.termstat$V1[1]*100),
NCABE = c(fbioq$V1[6]*100,fbioq.termstat$V1[6]*100),
M1F1BE = c(fbioq$V1[2]*100,fbioq.termstat$V1[2]*100),
M1PHBE = c(fbioq$V1[3]*100,fbioq.termstat$V1[3]*100),
M3F1BE = c(fbioq$V1[4]*100,fbioq.termstat$V1[4]*100),
M3PHBE = c(fbioq$V1[5]*100,fbioq.termstat$V1[5]*100),
IPREDCM = c(cbioq$V1[1]*100,cbioq.termstat$V1[1]*100),
NCACM = c(cbioq$V1[6]*100,cbioq.termstat$V1[4]*100),
M1CM = c(cbioq$V1[2]*100,cbioq.termstat$V1[2]*100),
M3CM = c(cbioq$V1[4]*100,cbioq.termstat$V1[3]*100),
NCAFT1 = c(ferror.t1$V1[5]*100,ferror.termstat.t1$V1[5]*100),
M1F1FT1 = c(ferror.t1$V1[1]*100,ferror.termstat.t1$V1[1]*100),
M1PHFT1 = c(ferror.t1$V1[2]*100,ferror.termstat.t1$V1[2]*100),
M3F1FT1 = c(ferror.t1$V1[3]*100,ferror.termstat.t1$V1[3]*100),
M3PHFT1 = c(ferror.t1$V1[4]*100,ferror.termstat.t1$V1[4]*100),
NCAFT2 = c(ferror.t2$V1[5]*100,ferror.termstat.t2$V1[5]*100),
M1F1FT2 = c(ferror.t2$V1[1]*100,ferror.termstat.t2$V1[1]*100),
M1PHFT2 = c(ferror.t2$V1[2]*100,ferror.termstat.t2$V1[2]*100),
M3F1FT2 = c(ferror.t2$V1[3]*100,ferror.termstat.t2$V1[3]*100),
M3PHFT2 = c(ferror.t2$V1[4]*100,ferror.termstat.t2$V1[4]*100),
NCACT1 = c(cerror.t1$V1[5]*100,cerror.termstat.t1$V1[3]*100),
M1CT1 = c(cerror.t1$V1[1]*100,cerror.termstat.t1$V1[1]*100),
M3CT1 = c(cerror.t1$V1[3]*100,cerror.termstat.t1$V1[2]*100),
NCACT2 = c(cerror.t2$V1[5]*100,cerror.termstat.t2$V1[3]*100),
M1CT2 = c(cerror.t2$V1[1]*100,cerror.termstat.t2$V1[1]*100),
M3CT2 = c(cerror.t2$V1[3]*100,cerror.termstat.t2$V1[2]*100),
PERBLOQ = per.bloq,
TRUNCBLQ = trunc.blq,
RUVPROP = ruv.prop,
RUVADD = ruv.add,
M1MIN = m1min,
M3MIN = m3min,
M1COV = m1cov,
M3COV = m3cov,
M1NSIM = m1.nsim,
M1TERM = length(m1.term),
M1IPREDBE = c(ipred.fbioq$V1[1]*100,ipred.fbioq.termstat$V1[1]*100),
M1IPREDCM = c(ipred.cbioq$V1[1]*100,ipred.cbioq.termstat$V1[1]*100),
M3NSIM = m3.nsim,
M3TERM = length(m3.term),
M3IPREDBE = c(ipred.fbioq$V1[3]*100,ipred.fbioq.termstat$V1[3]*100),
M3IPREDCM = c(ipred.cbioq$V1[3]*100,ipred.cbioq.termstat$V1[3]*100))
aovbioqtable
}, vec = runvec, time = timevec)
write.csv(bioqtable,
file = paste(master.dir, "collated_bioq_table.csv", sep = "/"),
row.names = F)
collate.SHK <- function(dir.name, work.dir) {
result1 <- NA
nm.dir <- "nm7"
shk.file.name <- gsub(nm.dir,"shk",dir.name)
shk.file.path <- paste(work.dir,dir.name,shk.file.name, sep="/")
#Scrape data from the *.smr file
if (file.exists(shk.file.path)==T) { #to screen for missing file
shk.data <- read.table(shk.file.path, skip=1, header=T) #read all the lines of the shk file
shk.data <- subset(shk.data, SUBPOP==1) #Only take SUBPOP 1 for mixture models
shrink.vals <- subset(shk.data, TYPE==4)
} else {
shrink.vals <- rep(0, 10)
}
data.frame(
Sim = if(!is.na(str_extract(dir.name, "l[0-9]{3}"))) {
str_extract(str_extract(dir.name, "l[0-9]{3}"), "[0-9]{3}")
} else if (!is.na(str_extract(dir.name, "l[0-9]{2}"))) {
str_extract(str_extract(dir.name, "l[0-9]{2}"), "[0-9]{2}")
} else if (!is.na(str_extract(dir.name, "l[0-9]"))) {
str_extract(str_extract(dir.name, "l[0-9]"), "[0-9]")
} else {
NA
},
Method = str_extract(str_extract(dir.name, "[0-9]_m"), "[0-9]"),
shrink.vals[-(1:2)])
}
nm.dir <- "nm7"
search.term <- paste("*",nm.dir, sep="")
shrink.data <- ddply(rundf, .(RUN, SCEN), function(df) {
#Set working directory
SIM.name.out <- paste0("Run", df$RUN, "_Scen", df$SCEN)
SIM.dir <- paste(master.dir,SIM.name.out,sep="/")
SIM.file <- paste(SIM.dir,SIM.name.out,sep="/")
EST.dir <- paste(SIM.dir,"ctl",sep="/")
EST.file <- paste(EST.dir,SIM.name.out,sep="/")
FIT.dir <- paste(SIM.dir,"fit",sep="/")
dir.names <- dir(path=EST.dir,pattern=glob2rx(search.term))
shk.out <- mdply(dir.names, collate.SHK, work.dir = EST.dir)
print(paste(SIM.name.out,"processed"))
shk.out
})
shk.filename <- paste(master.dir, "collated_shrinkage_data.csv", sep = "/")
write.csv(shrink.data,
file = shk.filename,
row.names = F)
shrink.data <- arrange(read.csv(shk.filename), RUN, SCEN, Method, Sim)
shrink.data$X1 <- NULL
shrink.sum <- ddply(rundf, .(RUN, SCEN), function(df, vec, time, shk) {
#Set working directory
SIM.name.out <- paste0("Run", df$RUN, "_Scen", df$SCEN)
SIM.dir <- paste(master.dir,SIM.name.out,sep="/")
SIM.file <- paste(SIM.dir,SIM.name.out,sep="/")
EST.dir <- paste(SIM.dir,"ctl",sep="/")
EST.file <- paste(EST.dir,SIM.name.out,sep="/")
FIT.dir <- paste(SIM.dir,"fit",sep="/")
nid <- vec["NID"]
nsim <- vec["NSIM"]
nsub <- nid*nsim
nobs <- length(time)
if (df$SS.TYPE == 1) {
sstime <- c(0,0.25,0.5,1,2,4,6,8,12,16,24,36,48,72,96)
} else {
sstime <- c(0,0.25,0.5,1,2,4,8,16,36,96)
}
ruv.prop <- df$RUV.PROP
ruv.blq <- df$RUV.BLQ
blq <- df$BLQ
if (df$RUV.TYPE == 1) {
ruv.add <- (ruv.blq - ruv.prop) * blq
trunc.blq <- blq
}
if (df$RUV.TYPE == 2) {
ruv.add <- (0.2 - ruv.prop) * blq
trunc.blq <- ruv.add/(ruv.blq - ruv.prop)
}
ruv.add.nm <- ifelse(blq == 0 || ruv.add == 0,
paste(ruv.add, "FIX"),
ruv.add)
sub.shk <- shk[shk$RUN == df$RUN & shk$SCEN == df$SCEN, ]
l.shk <- dim(sub.shk)[2]
names(sub.shk)[5:l.shk] <- c(
"BSVCL", "BSVV2", "BSVKA", "BSVF1",
"BOVCL1", "BOVCL2", "BOVV21", "BOVV22")
#Load data for processing
#simdata <- read.csv(paste(SIM.file,"_RAW.csv", sep="")) #Only do this if you need it for troubleshooting, RAW.csv can be 500MB+
#ipredresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_IPREDresult.csv"))
#ncaresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_NCAresult.csv"))
trunc.file <- paste(SIM.name.out,"TRUNCATED.csv",sep="_")
limdata <- read.csv(paste(SIM.dir,trunc.file,sep="/"))
limobs <- length(sstime)
per.bloq <- percent.blq(limdata$DV,limdata$TIME,trunc.blq)
# Process fit files into results table (see functions utility)
m1nlme.fitout <- nlme.fit(SIM.name.out,FIT.dir,EST.dir,"M1",nsim,nid,limobs)
m1.nsim <- m1nlme.fitout[1]
m1.nsub <- m1.nsim*nid
m1.fitfail <- m1nlme.fitout[-1]
#m1sim.in <- read.csv(paste(SIM.file,"M1_NMTHETAS.csv", sep="_"))
#m1result <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M1result.csv"))
m3nlme.fitout <- nlme.fit(SIM.name.out,FIT.dir,EST.dir,"M3",nsim,nid,limobs)
m3.nsim <- m3nlme.fitout[1]
m3.nsub <- m3.nsim*nid
m3.fitfail <- m3nlme.fitout[-1]
#m3sim.in <- read.csv(paste(SIM.file,"M3_NMTHETAS.csv", sep="_"))
#m3result <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_M3result.csv"))
# Find percentage of successful runs
mbt <- read.table(file=paste(EST.dir,"nmmbt.nm7.txt",sep="/"),header=TRUE)
mbt <- orderBy(~Run,mbt)
mbt$Method <- rep(1:2,each=nsim)
mbt$ModelNum <- rep(order(as.character(1:nsim)), times = 2)
mbt$Success <- gsub("SUCCESSFUL",1,mbt$Min)
mbt$Success <- gsub("TERMINATED",0,mbt$Min)
mbt <- orderBy(~Method+ModelNum,mbt)
m1.term <- which(mbt$Success == 0 & mbt$Method == 1)
m3.term <- which(mbt$Success == 0 & mbt$Method == 2) - nsim
term.shk <- sub.shk[
sub.shk$Method == 1 &
!sub.shk$Sim %in% m1.fitfail &
!sub.shk$Sim %in% m1.term |
sub.shk$Method == 3 &
!sub.shk$Sim %in% m3.fitfail &
!sub.shk$Sim %in% m3.term, ]
sub.shk$Term <- "All"
term.shk$Term <- "Success"
comb.shk <- rbind(sub.shk, term.shk)
ddply(comb.shk, .(Term, Method), function(x) {
y <- data.frame(
Stat = c("Min", "Q1", "Med", "Mean", "Q3", "Max"),
colwise(summary)(x[5:l.shk]))
z <- dcast(melt(y, "Stat"), variable~Stat)[c(1,5,6,4,3,7,2)]
})
}, vec = runvec, time = timevec, shk = shrink.data)
shrink.sum <- arrange(shrink.sum, RUN, SCEN, Method, variable)
shk.filename <- paste(master.dir, "summaryall_shrinkage_data.csv", sep = "/")
write.csv(shrink.sum,
file = shk.filename,
row.names = F)
shrink.sum <- arrange(shrink.sum, RUN, Method, variable)
shk.filename <- paste(master.dir, "summarysub_shrinkage_data.csv", sep = "/")
write.csv(shrink.sum[shrink.sum$Term == "Success", ],
file = shk.filename,
row.names = F)