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TADPOLE_Time_ADAS13__NL.Rmd
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---
title: "TADPOLE"
author: "José Tamez-Peña"
date: "October 20, 2017"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, warning = FALSE, message = FALSE,comment = "#>")
```
```{r Libraries, echo = FALSE}
library("epiR")
library("FRESA.CAD")
library(network)
library(GGally)
library("e1071")
TADPOLE_predictors <- read.delim("PredictorsList.txt")
load(file="D3Imputed.RDATA")
load(file="TEST_TADPOLEMRI.RDATA")
load(file="TRAIN_TADPOLEMRI.RDATA")
load(file="trainTadploe.norm.RDATA")
load(file="testTadploe.norm.RDATA")
load(file="D3.norm.RDATA")
```
```{r spliting by visit}
months <- c(0,3,6,12,18,24,30,36,42,48,54,60,66,72,78,84,90,96,102,108,114,120)
VISIT_TRAINCROSSMRI <- list()
VISIT_TESTCROSSMRI <- list()
i = 1;
for (j in months )
{
VISIT_TRAINCROSSMRI[[i]] <- subset(trainTadploe.norm,Month==j)
VISIT_TESTCROSSMRI[[i]] <- subset(testTadploe.norm,Month==j)
i = i + 1
}
sampledcolumns <- c("RID",as.character(TADPOLE_predictors[,1]));
testLastTimePointWithNL <- subset(testTadploe.norm,LastCompleteObs==1 & LastDX2=="NL")[,sampledcolumns]
testLastTimePointWithMCI <- subset(testTadploe.norm,LastCompleteObs==1 & LastDX2=="MCI")[,sampledcolumns]
testLastTimePointWithAD <- subset(testTadploe.norm,LastCompleteObs==1 & LastDX2=="Dementia")[,sampledcolumns]
trainLastTimePointWithNL <- subset(trainTadploe.norm,LastCompleteObs==1 & LastDX2=="NL")
trainLastTimePointWithMCI <- subset(trainTadploe.norm,LastCompleteObs==1 & LastDX2=="MCI")
trainLastTimePointWithAD <- subset(trainTadploe.norm,LastCompleteObs==1 & LastDX2=="Dementia")
rownames(trainLastTimePointWithAD) <- trainLastTimePointWithAD$RID
testWithNL <- subset(testTadploe.norm,LastDX2=="NL")[,sampledcolumns]
testWithMCI <- subset(testTadploe.norm,LastDX2=="MCI")[,sampledcolumns]
testWithAD <- subset(testTadploe.norm,LastDX2=="Dementia")[,sampledcolumns]
```
```{r univariate ADAS13 to Dementia}
CASESNL <- NULL
j=1
lotmpss <- subset(trainTadploe.norm,LastCompleteObs==1)
for (j in 1:length(VISIT_TRAINCROSSMRI) )
{
tmpss <- subset(VISIT_TRAINCROSSMRI[[j]],BDX==1 & LastCompleteObs==0)
rids <- as.character(tmpss$RID)
DADAS13 <- lotmpss[rids,"ADAS13"]-tmpss$ADAS13
timetolast <- lotmpss[rids,"Years_bl"]-tmpss$Years_bl;
CASESNL <- rbind(CASESNL,cbind(DADAS13,timetolast,tmpss[,sampledcolumns]))
}
hist(CASESNL$DADAS13)
CASESNL <- CASESNL[complete.cases(CASESNL),]
CASESNL$ICV <- NULL
#CASESNL$ADAS13 <- NULL
CASESNL$timetolast2 <- CASESNL$timetolast*CASESNL$timetolast
CASESNL$timetolast3 <- log(CASESNL$timetolast)
CASESNL$timetolast_ST34CMVO <- CASESNL$ST34CMVO*CASESNL$timetolast
TADPOLE_predictors <- read.delim("PredictorsList.txt")
rownames(TADPOLE_predictors) <- TADPOLE_predictors$Var
#TADPOLE_predictors <- subset(TADPOLE_predictors,Var != "ADAS13")
TADPOLE_predictors <- subset(TADPOLE_predictors,Var != "ICV")
timename <- c("timetolast","timetolast")
names(timename) <- colnames(TADPOLE_predictors)
TADPOLE_predictors <- rbind(t(timename),TADPOLE_predictors)
timename <- c("timetolast2","timetolast2")
names(timename) <- colnames(TADPOLE_predictors)
TADPOLE_predictors <- rbind(t(timename),TADPOLE_predictors)
timename <- c("timetolast3","timetolast3")
names(timename) <- colnames(TADPOLE_predictors)
TADPOLE_predictors <- rbind(t(timename),TADPOLE_predictors)
timename <- c("timetolast_ST34CMVO","timetolast_ST34CMVO")
names(timename) <- colnames(TADPOLE_predictors)
TADPOLE_predictors <- rbind(t(timename),TADPOLE_predictors)
ADAS13_RankFeaturesRaw <- univariateRankVariables(variableList = TADPOLE_predictors,
formula = "DADAS13 ~ 1",
Outcome = "DADAS13",
data = CASESNL,
categorizationType = "Raw",
type = "LM",
rankingTest = "Ztest",
description = "Description",
uniType="Regression")
ADAS13_RankFeaturesRaw["timetolast",]
ADAS13_RankFeaturesRaw["timetolast2",]
ADAS13_RankFeaturesRaw["timetolast3",]
ADAS13_RankFeaturesRaw["ADAS13",]
```
```{r Modeling ADAS13 time in NL}
compute = TRUE
numberofRepp = 10
numberofFolds = 5
numberOfTrials = 5
allformulas <- character();
logitcor <- numeric();
logitMAD <- numeric();
testWithMCI$ADAS13 <- numeric(nrow(testWithMCI))
alltestpredicts <- NULL
allD3predicts <- NULL
allforcasttestPredicts <- NULL
repp = 1
for ( repp in 1:numberofRepp)
{
ids_ca <- unique(as.character(CASESNL$RID))
casefolds <- cvTools::cvFolds(length(ids_ca), numberofFolds,1, "random");
j=1
for (j in 1:numberofFolds)
{
sampleIDS_TimeADAS13 <- ids_ca[casefolds$subsets[casefolds$which != j,]];
trainTime_ADAS13 <- subset(CASESNL,RID %in% sampleIDS_TimeADAS13)
testTime_ADAS13 <- subset(CASESNL,!(RID %in% sampleIDS_TimeADAS13))
testD3 <- subset(D3.norm,!(RID %in% sampleIDS_TimeADAS13))
testD3$ADAS13 <- rep(0,nrow(testD3))
print(length(unique(as.character(trainTime_ADAS13$RID))))
print(length(unique(as.character(testTime_ADAS13$RID))))
ids <- unique(as.character(trainTime_ADAS13$RID))
sys_times <- NULL;
Time_ADAS13 <- list();
n=1
hist(CASESNL$timetolast)
if (compute)
{
for (n in 1:numberOfTrials)
{
singlecaseTime_ADAS13 <- NULL;
for (i in ids)
{
case1 <- subset(trainTime_ADAS13,RID==i)
caserows <- nrow(case1)
if (caserows>1)
{
singlecaseTime_ADAS13 <- rbind(singlecaseTime_ADAS13,case1[sample(caserows, 1),])
}
else
{
singlecaseTime_ADAS13 <- rbind(singlecaseTime_ADAS13,case1)
}
}
singlecaseTime_ADAS13$RID <- NULL;
singlecaseTime_ADAS13$BDX <- NULL;
print(nrow(singlecaseTime_ADAS13))
sys_times <- rbind(sys_times,system.time(Time_ADAS13[[n]] <- FRESA.Model(DADAS13 ~ 1,singlecaseTime_ADAS13)))
pr <- predict(Time_ADAS13[[n]]$BSWiMS.model,testTime_ADAS13)
cat("MAD:",mean(abs(pr-testTime_ADAS13$DADAS13)))
plot(pr~testTime_ADAS13$DADAS13)
}
save(sys_times,file=paste("sys_times_Time_ADAS13_NL",numberOfTrials,j,repp,".RDATA",sep="_"))
save(Time_ADAS13,file=paste("Time_ADAS13_NL",numberOfTrials,j,repp,".RDATA",sep="_"))
}
else
{
load(file=paste("Time_ADAS13_NL",numberOfTrials,j,repp,".RDATA",sep="_"))
sampleIDS_TimeADAS13 <- NULL;
}
baggedformula <- character();
for (n in 1:numberOfTrials)
{
if (length(Time_ADAS13[[n]]$BSWiMS.model$coefficients)>1)
{
baggedformula <- append(baggedformula,Time_ADAS13[[n]]$BSWiMS.model$formula)
}
if (!compute)
{
idst <- unlist(str_split(rownames(Time_ADAS13[[n]]$bootstrappedModel$data),"_"))
sampleIDS_TimeADAS13 <- append(sampleIDS_TimeADAS13,idst[2*(0:(length(idst)-1))+1])
}
}
if (!compute)
{
sampleIDS_TimeADAS13 <- unique(sampleIDS_TimeADAS13)
trainTime_ADAS13 <- subset(CASESNL,(RID %in% sampleIDS_TimeADAS13))
testTime_ADAS13 <- subset(CASESNL,!(RID %in% sampleIDS_TimeADAS13))
testD3 <- subset(D3.norm,!(RID %in% sampleIDS_TimeADAS13))
testD3$ADAS13 <- rep(0,nrow(testD3))
}
mp <- medianPredict(as.character(baggedformula),trainTime_ADAS13,testTime_ADAS13,predictType ="linear",type="LM")
alltestpredicts <- rbind(alltestpredicts,cbind(testTime_ADAS13$DADAS13,mp$medianPredict))
plot(testTime_ADAS13$DADAS13~mp$medianPredict)
ct <- cor.test(testTime_ADAS13$DADAS13,mp$medianPredict)
logitcor <- append(logitcor,ct$estimate)
res <- testTime_ADAS13$DADAS13-mp$medianPredict
logitMAD <- append(logitMAD,mean(abs(res)))
print(logitMAD)
print(logitcor)
allformulas <- append(allformulas,as.character(baggedformula))
testWithAD_s <- subset(testTadploe.norm,LastCompleteObs==1 & BDX==1)
testWithAD_s$DADAS13 <- numeric(nrow(testWithAD_s))
n=1
forcasttestPredicts <- testWithAD_s$RID
for (n in 1:60)
{
timetolast <- testWithAD_s$to2018+(n-0.5)/12
testWithAD_s$timetolast <- timetolast
testWithAD_s$timetolast2 <- timetolast*timetolast
testWithAD_s$timetolast3 <- log(timetolast)
testWithAD_s$timetolast_ST34CMVO <- testWithAD_s$ST34CMVO*log(timetolast)
mp <- medianPredict(as.character(baggedformula),trainTime_ADAS13,testWithAD_s,predictType ="linear",type="LM")
forcasttestPredicts <- cbind(forcasttestPredicts,mp$medianPredict+testWithAD_s$ADAS13)
}
allforcasttestPredicts <- rbind(allforcasttestPredicts,forcasttestPredicts)
testD3 <- subset(D3.norm,!(RID %in% sampleIDS_TimeADAS13))
testD3$DADAS13 <- rep(0,nrow(testD3))
n=1
forcasttestPredicts <- testD3$RID
for (n in 1:60)
{
timetolast <- testD3$to2018+(n-0.5)/12
testD3$timetolast <- timetolast
testD3$timetolast2 <- timetolast*timetolast
testD3$timetolast3 <- log(timetolast)
testD3$timetolast_ST34CMVO <- testD3$ST34CMVO*log(timetolast)
mp <- medianPredict(as.character(baggedformula),trainTime_ADAS13,testD3,predictType ="linear",type="LM")
tmp <- mp$medianPredict+testD3$ADAS13
forcasttestPredicts <- cbind(forcasttestPredicts,tmp)
}
allD3predicts <- rbind(allD3predicts,forcasttestPredicts)
}
}
write.csv(allforcasttestPredicts,file=paste(repp,"D2_ADAS13_NL.csv",sep="_"))
write.csv(allD3predicts,file=paste(repp,"D3_ADAS13_NL.csv",sep="_"))
rowss <- nrow(testWithAD_s)
totrep <- numberofRepp*numberofFolds
n=1
mean50CI <- NULL;
id = 1
for (id in 1:rowss)
{
ids <- allforcasttestPredicts[id,1]
for (n in 2:61)
{
dta <- allforcasttestPredicts[rowss*(0:(totrep-1))+id,n]
mean50CI <- rbind(mean50CI,c(ids,mean(dta),as.vector(quantile(dta, probs = c(0.25, 0.75), na.rm = TRUE,names = FALSE, type = 7))))
}
}
write.csv(mean50CI,file=paste(repp,"D2_ADAS13_NL_mean50CI.csv",sep="_"))
rowss <- nrow(D3.norm)
totrep <- numberofRepp*numberofFolds
n=1
mean50CI <- NULL;
ids <- D3.norm$RID;
allD3predicts <- as.data.frame(allD3predicts)
colnames(allD3predicts) <- c("RID",paste("V",1:60,sep="_"))
for (id in ids)
{
for (n in 2:61)
{
dta <- subset(allD3predicts,RID==id)[,n]
mean50CI <- rbind(mean50CI,c(id,mean(dta),as.vector(quantile(dta, probs = c(0.25, 0.75), na.rm = TRUE,names = FALSE, type = 7))))
}
}
write.csv(mean50CI,file=paste(repp,"D3_ADAS13_NL_mean50CI.csv",sep="_"))
bp <- boxplot(alltestpredicts[,2]~rownames(alltestpredicts),plot=FALSE)
bpstatx <- bp$stats[3,]
bp <- boxplot(alltestpredicts[,1]~rownames(alltestpredicts),plot=FALSE)
bpstaty <- bp$stats[3,]
plot(bpstaty~bpstatx,main="All Tests",xlim=c(-5,15),ylim=c(-5,15))
ct <- cor.test(bpstaty,bpstatx)
cat("MAD:",mean(abs(bpstaty-bpstatx)))
```