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run_1114_script.r
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run_1114_script.r
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### ---------------------------- NLMEvNCA BEAST ---------------------------- ###
# (NLME vs. NCA Bioequivalence Analysis Stimulation Tool) #
# As used in:
# Hughes JH, Upton RU, Foster DJ (2016)
# Comparison of Non-Compartmental and Mixed Effect Modelling Methods for
# Establishing Bioequivalence for the Case of Two Compartment Kinetics and
# Censored Concentrations
# The code below enables the user to compare the ability of NLME program NONMEM and
# the automated NCA methods used by WinNonlin to determine the bioequivalence of a
# drug. To run the code you require the five files provided in the supplementary
# 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/2016/07_noBOV" ### 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:/nm72/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
run.order <- c(rep(1, times = 9), rep(2, times = 6))
f1.order <- c(1, 0.9, 1.11)
bsv.order <- c(0.1225, 0.0484, 0.0529)
rundf <- data.frame(
RUN = unlist(llply(5:6, function(i) run.order + i*2)), #rep(number.of.runs, each=number.of.scenarios)
SCEN = rep(c(1:9,1:6), times = 2), #opposite of above
RUV.TYPE = rep(c(rep(1, 9),rep(2, 6)), 2),
RUV.BLQ = rep(c(rep(c(0.2, 0.15, 0.1), 3),rep(c(0.1, 0.5), 3)), 2),
F1.POP = rep(c(rep(f1.order, each = 3), rep(f1.order, each = 2)), 2), #changing Frel of generic
F1.BSV = rep(c(rep(bsv.order, each = 3), rep(bsv.order, each = 2)), 2), #changing BSV on frel
BLQ = rep(0.01, 30), #Run 2 - raised LLOQ
RUV.PROP = rep(0.05, 30), #Run 3 - increased proportional RUV
SS.TYPE = rep(1, 30), #Run 4 - reduced sampling schedule
KA.TYPE = rep(1, 30), #Run 5 - 20% lower generic KA
BOV.TYPE = c(rep(2, 15), rep(3, 15))) #Run 6 & 7 - BOV testing
#set up values that are constant between runs
runvec <- c(
NID = 24,
NSIM = 500,
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)
### 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
#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
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
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 = c(m1.nsim, m1.nsim - 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 = c(m3.nsim, m3.nsim - 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$SCEN, ]
l.shk <- dim(sub.shk)[2]
if(df$BOV.TYPE == 2) {
l.shk <- 8
names(sub.shk)[5:l.shk] <- c(
"BSVCL", "BSVV2", "BSVKA", "BSVF1")
} else if(df$BOV.TYPE == 3) {
l.shk <- 10
names(sub.shk)[5:l.shk] <- c(
"BSVCL", "BSVV2", "BSVKA", "BSVF1",
"BOVCL1", "BOVCL2")
} else {
l.shk <- dim(sub.shk)[2]
names(sub.shk)[5:l.shk] <- c(
"BSVCL", "BSVV2", "BSVKA", "BSVF1",
"BOVCL1", "BOVCL2", "BOVV21", "BOVV22")
}
#Load data for processing
#simdata <- read.csv(paste(SIM.file,"_RAW.csv", sep="")) #Only do this if you need it for troubleshooting, RAW.csv can be 500MB+
#ipredresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_IPREDresult.csv"))
#ncaresult <- read.csv(paste0(SIM.dir,"/",SIM.name.out,"_NCAresult.csv"))
trunc.file <- paste(SIM.name.out,"TRUNCATED.csv",sep="_")
limdata <- read.csv(paste(SIM.dir,trunc.file,sep="/"))
limobs <- length(sstime)
per.bloq <- percent.blq(limdata$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$Term <- "Success"
comb.shk <- rbind(sub.shk, term.shk)
sub.shk$Term <- "All"
term.shk$Term <- "Success"
comb.shk <- rbind(sub.shk, term.shk)
#insert alternate ending here
}, vec = runvec, time = timevec, shk = shrink.data)
shrink.sum <- arrange(shrink.sum, RUN, SCEN, Method, variable)
shk.filename <- paste(master.dir, "collatedterm_shrinkage_data.csv", sep = "/")
write.csv(shrink.sum,
file = shk.filename,
row.names = F)
#removing everything between
collate.ERR <- function(dir.name, work.dir) {
nm.dir <- "nm7"
lst.file.name <- gsub(nm.dir, "lst", dir.name)
lst.file.path <- paste(work.dir, dir.name, lst.file.name, sep = "/")
#Scrape data from the *.lst file
if (file.exists(lst.file.path) == T) { #screen for missing file
lst.lines <- readLines(lst.file.path)
first.line <- grep("#TERM:", lst.lines)
last.line <- grep(" NO. OF SIG. DIGITS", lst.lines)
if (length(last.line) == 0 | length(first.line) == 0) {
info <- c("Function has not detected", "a termination status in the",
".lst file. Please check", "models.lst file manually.", dir.name)
browser("Unknown error, type info for more information, type Q to quit.")
} else {
error.lines <- lst.lines[first.line:last.line[1]]
}
err.code <- "None"
desc.code <- "None"
#Determine termination status and error status
if (length(grep("TERMINATED", error.lines[2])) != 0) {
term.code <- "Unsuccessful"
if (length(grep("ROUNDING", error.lines)) != 0) {
err.code <- "Rounding errors"
if (length(grep("0PARAMETER ESTIMATE IS NEAR", lst.lines[last.line[1] + 1])) != 0) {
desc.code <- "Parameter estimate near boundary"
}
} else if (length(grep("ZERO GRADIENT", error.lines)) != 0) {
err.code <- "Zero gradient"
} else if (length(grep("OBJ. FUNC. IS INFINITE", error.lines)) != 0) {
err.code <- "Obj close to infinity"
if (length(grep("LAST ITERATION", error.lines)) != 0) {
desc.code <- "Due to last iteration"
} else if (length(grep("NEXT ITERATION", error.lines)) != 0) {
desc.code <- "Due to next iteration"
} else {
desc.code <- error.lines[3]
}
} else if (length(grep("MAX. NO. OF FUNCTION", error.lines)) != 0) {
err.code <- "Reached max evaluations"
if (length(grep("0PARAMETER ESTIMATE IS NEAR", lst.lines[last.line[1] + 1])) != 0) {
desc.code <- "Parameter estimate near boundary"
}
} else {
err.code <- error.lines[3]
}
} else if (length(grep("SUCCESSFUL", error.lines[2])) != 0) {
term.code <- "Successful"
if (length(grep("HOWEVER, PROBLEMS", error.lines[3])) != 0) {
desc.code <- "Problems occurred during minimisation"
}
if (length(grep("0PARAMETER ESTIMATE IS NEAR", lst.lines[last.line[1] + 1])) != 0) {
desc.code <- "Parameter estimate near boundary"
}
if (length(grep("0R MATRIX", lst.lines)) != 0) {
desc.code <- "R matrix algorithmically singular"
}
if (length(grep("0S MATRIX", lst.lines)) != 0) {
desc.code <- "S matrix unobtainable"
}
if (length(grep("0PRED EXIT CODE = 1", lst.lines)) != 0) {
desc.code <- "Problems with individual"
}
} else {
term.code <- error.lines[2]
}
#Determine covariance step status
cov.line <- grep("STANDARD ERROR OF ESTIMATE", lst.lines)
if (length(cov.line) > 0) {
cov.code <- "Passed"
} else if (length(cov.line) == 0) {
cov.code <- "Failed"
}
}
data.frame(
Model = dir.name,
TermStat = term.code,
CovCode = cov.code,
ErrCode = err.code,
TermDesc = desc.code)
}
nm.dir <- "nm7"
search.term <- paste("*",nm.dir, sep="")
error.data <- ddply(rundf, .(RUN, SCEN), function(df) {
#Set working directory
SIM.name.out <- paste0("Run", df$RUN, "_Scen", df$SCEN)
SIM.dir <- paste(master.dir,SIM.name.out,sep="/")
SIM.file <- paste(SIM.dir,SIM.name.out,sep="/")
EST.dir <- paste(SIM.dir,"ctl",sep="/")
EST.file <- paste(EST.dir,SIM.name.out,sep="/")
FIT.dir <- paste(SIM.dir,"fit",sep="/")
dir.names <- dir(path=EST.dir,pattern=glob2rx(search.term))
err.out <- mdply(dir.names, collate.ERR, work.dir = EST.dir)
print(paste(SIM.name.out,"processed"))
err.out
})
err.filename <- paste(master.dir, "collated_error_data.csv", sep = "/")
write.csv(error.data,
file = err.filename,
row.names = F)
# Alternate ending
# ddply(comb.shk, .(Term, Method), function(x) {
# y <- data.frame(
# Stat = c("Min", "Q1", "Med", "Mean", "Q3", "Max"),
# colwise(summary)(x[5:l.shk]))
# z <- dcast(melt(y, "Stat"), variable~Stat)[c(1,5,6,4,3,7,2)]
# })
# }, vec = runvec, time = timevec, shk = shrink.data)
# shrink.sum <- arrange(shrink.sum, RUN, SCEN, Method, variable)
# shk.filename <- paste(master.dir, "summaryall_shrinkage_data.csv", sep = "/")
# write.csv(shrink.sum,
# file = shk.filename,
# row.names = F)
#
# shrink.sum <- arrange(shrink.sum, RUN, Method, variable)
# shk.filename <- paste(master.dir, "summarysub_shrinkage_data.csv", sep = "/")
# write.csv(shrink.sum[shrink.sum$Term == "Success", ],
# file = shk.filename,
# row.names = F)