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TADPOLE_Time_to_AD_V2.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]
testWithNL <- subset(testTadploe.norm,LastDX2=="NL")[,sampledcolumns]
testWithMCI <- subset(testTadploe.norm,LastDX2=="MCI")[,sampledcolumns]
testWithAD <- subset(testTadploe.norm,LastDX2=="Dementia")[,sampledcolumns]
```
```{r univariate MCI to AD}
CASESMCItoAD <- NULL
YearMCItoAD <- NULL
controlYearMCItoAD <- NULL;
CONTROLMCItoAD <- NULL;
TestSetMCItoAD <- NULL;
j=1
for (j in 1:length(VISIT_TRAINCROSSMRI) )
{
tmpss <- subset(VISIT_TRAINCROSSMRI[[j]],MCI_AD==1 & BDX==2 & LastDX2=="Dementia")
YearMCItoAD <- c(YearMCItoAD,as.numeric(tmpss$YearMCItoAD))
CASESMCItoAD <- rbind(CASESMCItoAD,tmpss[,sampledcolumns])
tmpss <- subset(VISIT_TRAINCROSSMRI[[j]],BXBL=="MCI" & LastDX2=="MCI" & LastVisit>4.0)
controlYearMCItoAD <- c(controlYearMCItoAD,(tmpss$YearMCItoAD))
CONTROLMCItoAD <- rbind(CONTROLMCItoAD,tmpss[,sampledcolumns])
}
CASESMCItoAD <- cbind(YearMCItoAD,CASESMCItoAD)
CASESMCItoAD <- CASESMCItoAD[complete.cases(CASESMCItoAD),]
CONTROLMCItoAD <- cbind(controlYearMCItoAD,CONTROLMCItoAD)
CONTROLMCItoAD <- CONTROLMCItoAD[complete.cases(CONTROLMCItoAD),]
colnames(CONTROLMCItoAD) <- colnames(CASESMCItoAD)
MCIADUniRankFeaturesRaw <- univariateRankVariables(variableList = TADPOLE_predictors,
formula = "YearMCItoAD ~ 1",
Outcome = "YearMCItoAD",
data = CASESMCItoAD,
categorizationType = "Raw",
type = "LM",
rankingTest = "Ztest",
description = "Description",
uniType="Regression")
CASESNLtoAD <- NULL
YearNLtoAD <- NULL
for (j in 1:length(VISIT_TRAINCROSSMRI) )
{
tmpss <- subset(VISIT_TRAINCROSSMRI[[j]],NL_AD==1 & BDX==1 & LastDX2=="Dementia")
YearNLtoAD <- c(YearNLtoAD,(tmpss$YearNLtoAD))
CASESNLtoAD <- rbind(CASESNLtoAD,tmpss[,sampledcolumns])
}
CasesTimetoAD <- cbind(YearNLtoAD,CASESNLtoAD)
colnames(CasesTimetoAD) <- c("theTimeToAD",colnames(CasesTimetoAD)[-1])
colnames(CASESMCItoAD) <- colnames(CasesTimetoAD)
CasesTimetoAD <- rbind(CasesTimetoAD,CASESMCItoAD)
CasesTimetoAD <- CasesTimetoAD[complete.cases(CasesTimetoAD),]
CasesTimetoAD$theTimeToAD <- sqrt(abs(CasesTimetoAD$theTimeToAD))
ToADTIme <- univariateRankVariables(variableList = TADPOLE_predictors,
formula = "theTimeToAD ~ 1",
Outcome = "theTimeToAD",
data = CasesTimetoAD,
categorizationType = "Raw",
type = "LM",
rankingTest = "Ztest",
description = "Description",
uniType="Regression")
```
```{r Modeling time to AD}
compute = FALSE
numberofRepp = 5
numberofFolds = 5
numberOfTrials = 5
allformulas <- character();
logitcor <- numeric();
logitMAD <- numeric();
testWithMCI$theTimeToAD <- numeric(nrow(testWithMCI))
alltestpredicts <- NULL
allD3predicts <- NULL
for ( repp in 1:numberofRepp)
{
ids_ca <- unique(as.character(CasesTimetoAD$RID))
casefolds <- cvTools::cvFolds(length(ids_ca), numberofFolds,1, "random");
j=1
for (j in 1:numberofFolds)
{
sampleIDS_TimeToAD <- ids_ca[casefolds$subsets[casefolds$which != j,]];
trainTimeToAD <- subset(CasesTimetoAD,RID %in% sampleIDS_TimeToAD)
testTimeToAD <- subset(CasesTimetoAD,!(RID %in% sampleIDS_TimeToAD))
testD3 <- subset(D3.norm,!(RID %in% sampleIDS_TimeToAD))
testD3$theTimeToAD <- rep(0,nrow(testD3))
print(length(unique(as.character(trainTimeToAD$RID))))
print(length(unique(as.character(testTimeToAD$RID))))
ids <- unique(as.character(trainTimeToAD$RID))
sys_times <- NULL;
TimeToAD <- list();
n=1
if (compute)
{
for (n in 1:numberOfTrials)
{
singlecaseTimeToAD <- NULL;
for (i in ids)
{
case1 <- subset(trainTimeToAD,RID==i)
caserows <- nrow(case1)
if (caserows>1)
{
singlecaseTimeToAD <- rbind(singlecaseTimeToAD,case1[sample(caserows, 1),])
}
else
{
singlecaseTimeToAD <- rbind(singlecaseTimeToAD,case1)
}
}
singlecaseTimeToAD$RID <- NULL;
singlecaseTimeToAD$BDX <- NULL;
print(nrow(singlecaseTimeToAD))
sys_times <- rbind(sys_times,system.time(TimeToAD[[n]] <- FRESA.Model(theTimeToAD ~ 1,singlecaseTimeToAD)))
}
save(sys_times,file=paste("sys_times_TimeToAD3",numberOfTrials,j,repp,".RDATA",sep="_"))
save(TimeToAD,file=paste("TimeToAD",numberOfTrials,j,repp,".RDATA",sep="_"))
}
else
{
load(file=paste("TimeToAD",numberOfTrials,j,repp,".RDATA",sep="_"))
sampleIDS_TimeToAD <- NULL;
}
baggedformula <- character();
for (n in 1:numberOfTrials)
{
if (length(TimeToAD[[n]]$BSWiMS.model$coefficients)>1)
{
baggedformula <- append(baggedformula,TimeToAD[[n]]$BSWiMS.model$formula)
}
if (!compute)
{
idst <- unlist(str_split(rownames(TimeToAD[[n]]$bootstrappedModel$data),"_"))
sampleIDS_TimeToAD <- append(sampleIDS_TimeToAD,idst[2*(0:(length(idst)-1))+1])
}
}
if (!compute)
{
sampleIDS_TimeToAD <- unique(sampleIDS_TimeToAD)
trainTimeToAD <- subset(CasesTimetoAD,(RID %in% sampleIDS_TimeToAD))
testTimeToAD <- subset(CasesTimetoAD,!(RID %in% sampleIDS_TimeToAD))
testD3 <- subset(D3.norm,!(RID %in% sampleIDS_TimeToAD))
testD3$theTimeToAD <- rep(0,nrow(testD3))
}
mp <- medianPredict(as.character(baggedformula),trainTimeToAD,testTimeToAD,predictType ="linear",type="LM")
alltestpredicts <- rbind(alltestpredicts,cbind(testTimeToAD$theTimeToAD,mp$medianPredict))
plot(testTimeToAD$theTimeToAD~mp$medianPredict)
ct <- cor.test(testTimeToAD$theTimeToAD,mp$medianPredict)
logitcor <- append(logitcor,ct$estimate)
res <- testTimeToAD$theTimeToAD^2-mp$medianPredict^2
res <- res[testTimeToAD$theTimeToAD<2]
logitMAD <- append(logitMAD,mean(abs(res)))
print(logitMAD)
allformulas <- append(allformulas,as.character(baggedformula))
mpD3 <- medianPredict(as.character(baggedformula),trainTimeToAD,testD3,predictType ="linear",type="LM")
allD3predicts <- rbind(allD3predicts,cbind(testD3$RID,mpD3$medianPredict))
}
}
plot(alltestpredicts[,1]~alltestpredicts[,2],main="All Tests")
tsamples <- length(allformulas)
mpl <- medianPredict(allformulas,CasesTimetoAD,testWithMCI,predictType ="linear",type="LM")
mpl$predictions[,-1] <- mpl$predictions[,-1]^2
cdft <- NULL
sd(mpl$predictions[30,-1])
nelem <- nrow(mpl$predictions[,-1])*ncol(mpl$predictions[,-1])
mpl$predictions[,-1] <- mpl$predictions[,-1]+as.data.frame(matrix(rnorm(nelem,0,mean(logitMAD)),nrow=nrow(mpl$predictions)));
sd(mpl$predictions[30,-1])
to2018 <- testTadploe.norm[rownames(mpl$predictions),"to2018"]
names(to2018) <- rownames(mpl$predictions)
for (i in 1:nrow(mpl$predictions))
{
nd <- mpl$predictions[i,-1]
if ((sna <- sum(is.na(nd))) > 0)
{
if (sna > tsamples/2)
{
nd <- numeric(tsamples)
}
else
{
nd <- nd[!is.na(nd)];
}
}
fn <- ecdf(as.numeric(nd))
x <- (1:60)/12+to2018[i]
cdft <- rbind(cdft,fn(x))
}
rownames(cdft) <- rownames(mpl$predictions)
write.csv(cdft,file=paste(repp,"TimeToAD_CDF.csv",sep="_"))
write.csv(mpl$predictions,file=paste(repp,"TimeToAD.csv",sep="_"))
write.csv(allformulas,file=paste(repp,"AllFormulasTimeToAD.csv",sep="_"))
MCIandNL <- rbind(testLastTimePointWithMCI,testLastTimePointWithNL)
MCIandNL$theTimeToAD <- numeric(nrow(MCIandNL))
mpl <- medianPredict(allformulas,CasesTimetoAD,MCIandNL,predictType ="linear",type="LM")
mpl$predictions[,-1] <- mpl$predictions[,-1]^2
cdft <- NULL
sd(mpl$predictions[10,-1])
nelem <- nrow(mpl$predictions[,-1])*ncol(mpl$predictions[,-1])
mpl$predictions[,-1] <- mpl$predictions[,-1]+as.data.frame(matrix(rnorm(nelem,0,mean(logitMAD)),nrow=nrow(mpl$predictions)));
sd(mpl$predictions[10,-1])
sum(is.na(mpl$predictions[,-1]))
to2018 <- testTadploe.norm[rownames(mpl$predictions),"to2018"]
names(to2018) <- rownames(mpl$predictions)
for (i in 1:nrow(mpl$predictions))
{
nd <- mpl$predictions[i,-1]
if ((sna <- sum(is.na(nd))) > 0)
{
if (sna > tsamples/2)
{
nd <- numeric(tsamples)
}
else
{
nd <- nd[!is.na(nd)];
}
}
fn <- ecdf(as.numeric(nd))
x <- (1:60)/12+to2018[i]
fn <- cbind(rep(names(to2018[i]),60),fn(x))
cdft <- rbind(cdft,fn)
}
#rownames(cdft) <- rownames(mpl$predictions)
write.csv(cdft,file=paste(repp,"LastObservation_TimeToAD_CDF_C.csv",sep="_"))
write.csv(mpl$predictions,file=paste(repp,"LastObservation_TimeToAD.csv",sep="_"))
barplot(logitcor)
barplot(logitMAD)
bp <- boxplot(allD3predicts[,2]~allD3predicts[,1])
cdft <- NULL
to2018 <- D3.norm$to2018
names(to2018) <- rownames(D3.norm)
allD3predicts <- as.data.frame(allD3predicts)
i=1
tsamples <-5
for (i in 1:nrow(D3.norm))
{
nd <- subset(allD3predicts,V1==D3.norm$RID[i])[,2];
nd <- nd + rnorm(length(nd),0,mean(logitMAD))
if ((sna <- sum(is.na(nd))) > 0)
{
if (sna > tsamples/2)
{
nd <- numeric(tsamples)
}
else
{
nd <- nd[!is.na(nd)];
}
}
fn <- ecdf(as.numeric(nd))
x <- (1:60)/12+to2018[i]
fn <- cbind(rep(names(to2018[i]),60),fn(x))
cdft <- rbind(cdft,fn)
# cdft <- rbind(cdft,fn(x))
}
#rownames(cdft) <- rownames(D3.norm)
write.csv(cdft,file=paste(repp,"D3_TimeToAD_CDF.csv",sep="_"))
```