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883 lines (775 loc) · 29.2 KB
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################################################
################################################
## ##
## PROGRAM NAME: 24_create_table_S4 ##
## AUTHOR: GW ##
## DESCRIPTION: ##
## ##
## create table of nh effect estimates ##
## based on alternative Tx contrasts ##
## ##
################################################
################################################
rm(list=ls())
list.of.packages<-c(
"haven",
"foreign",
"dplyr",
"tidyr",
"ggplot2",
"ranger",
"caret",
"e1071",
"SuperLearner",
"dotwhisker",
"tidyverse",
"kableExtra",
"sys",
"foreach",
"doParallel",
"doRNG",
"Hmisc")
for(package.i in list.of.packages) {
suppressPackageStartupMessages(library(package.i, character.only = TRUE))
}
startTime<-Sys.time()
set.seed(8675309)
##### LOAD ECLS-B #####
eclsb.mi<-read.dta("C:\\Users\\wodtke\\Desktop\\projects\\nhood_mediation_toxins\\data\\eclsb\\v04_eclsb_mi.dta")
eclsb.mi<-as.data.frame(eclsb.mi[which(eclsb.mi$minum!=0),])
eclsb.mi<-eclsb.mi[order(eclsb.mi$caseid,eclsb.mi$minum),]
nmi<-5
nboot<-1000
ntrees<-200
n.cores<-parallel::detectCores()-1
a5<-0.05
a10<-0.10
a15<-0.15
a20<-0.20
a30<-0.30
##### DEFINE VARIABLE SETS #####
vars.base<-c(
"gender",
"race",
"twinid",
"birthwt",
"momage01",
"dadage01",
"wic01",
"foodst01",
"medicd01",
"tanf01")
vars.covw1<-c(
"nhpovrt01",
"faminc01",
"pared01",
"parocc01",
"momemp01",
"dademp01",
"hhtotal01",
"biodad01",
"married01",
"house01",
"rbooks01",
"prmlang01",
"nhpopden01",
"urban01",
"region01",
"age01",
"age05")
vars.ntxw1<-c(
"co_2001",
"no2_2001",
"o3_2001",
"pm10_2001",
"pm25_2001",
"so2_2001",
"chem6_2001",
"chem10_2001",
"chem12_2001",
"chem17_2001",
"chem36_2001",
"chem39_2001",
"chem40_2001",
"chem70_2001",
"chem75_2001",
"chem108_2001",
"chem109_2001",
"chem114_2001",
"chem141_2001",
"chem163_2001",
"chem180_2001",
"chem217_2001",
"chem225_2001",
"chem236_2001",
"chem290_2001",
"chem293_2001",
"chem323_2001",
"chem332_2001",
"chem346_2001",
"chem347_2001",
"chem351_2001",
"chem355_2001",
"chem356_2001",
"chem359_2001",
"chem360_2001",
"chem362_2001",
"chem364_2001",
"chem381_2001",
"chem447_2001",
"chem454_2001",
"chem474_2001",
"chem519_2001",
"chem522_2001",
"chem531_2001",
"chem549_2001",
"chem565_2001",
"chem566_2001",
"chem567_2001",
"chem572_2001",
"chem586_2001",
"chem592_2001",
"chem599_2001")
##### STANDARDIZE VARS ######
for (v in 1:length(vars.ntxw1)) {
eclsb.mi[,vars.ntxw1[v]]<-(eclsb.mi[,vars.ntxw1[v]]-weighted.mean(eclsb.mi[,vars.ntxw1[v]],eclsb.mi$sampwt))/sqrt(wtd.var(eclsb.mi[,vars.ntxw1[v]],eclsb.mi$sampwt))
}
eclsb.mi$readtheta05<-(eclsb.mi$readtheta05-weighted.mean(eclsb.mi$readtheta05,eclsb.mi$sampwt))/sqrt(wtd.var(eclsb.mi$readtheta05,eclsb.mi$sampwt))
eclsb.mi$maththeta05<-(eclsb.mi$maththeta05-weighted.mean(eclsb.mi$maththeta05,eclsb.mi$sampwt))/sqrt(wtd.var(eclsb.mi$maththeta05,eclsb.mi$sampwt))
##### ESTIMATE MARGINAL EFFECTS #####
miest.rd.10v5<-miest.mt.10v5<-matrix(data=NA,nrow=nmi,ncol=3)
miest.rd.15v5<-miest.mt.15v5<-matrix(data=NA,nrow=nmi,ncol=3)
miest.rd.20v5<-miest.mt.20v5<-matrix(data=NA,nrow=nmi,ncol=3)
miest.rd.30v5<-miest.mt.30v5<-matrix(data=NA,nrow=nmi,ncol=3)
boot.ci.rd.10v5<-boot.ci.mt.10v5<-NULL
boot.ci.rd.15v5<-boot.ci.mt.15v5<-NULL
boot.ci.rd.20v5<-boot.ci.mt.20v5<-NULL
boot.ci.rd.30v5<-boot.ci.mt.30v5<-NULL
for (i in 1:nmi) {
### LOAD MI DATA ###
print(i)
eclsb<-eclsb.mi[which(eclsb.mi$minum==i),]
### TUNE HYPERPARAMETERS ###
registerDoSEQ()
cntrl<-trainControl(method="CV",number=5)
rf.m1.grid<-expand.grid(
min.node.size=c(5,10,15,20),
mtry=floor(length(c(vars.base,vars.covw1))*c(0.3,0.4,0.5,0.6,0.7)),
splitrule="variance")
rf.m2.grid<-expand.grid(
min.node.size=c(5,10,15,20),
mtry=floor(length(c(vars.base,vars.covw1,vars.ntxw1))*c(0.3,0.4,0.5,0.6,0.7)),
splitrule="variance")
rf.m3.grid<-expand.grid(
min.node.size=c(5,10,15,20),
mtry=floor(length(c(vars.base,vars.covw1))*c(0.3,0.4,0.5,0.6,0.7)),
splitrule="variance")
rf.rd.m1<-train(readtheta05~.,
data=eclsb[,c("readtheta05",vars.base,vars.covw1)],
method="ranger",
tuneGrid=rf.m1.grid,
trControl=cntrl,
metric="RMSE",
respect.unordered.factors=TRUE,
num.trees=ntrees,
weights=eclsb$sampwt,
seed=8675309)
rf.mt.m1<-train(maththeta05~.,
data=eclsb[,c("maththeta05",vars.base,vars.covw1)],
method="ranger",
tuneGrid=rf.m1.grid,
trControl=cntrl,
metric="RMSE",
respect.unordered.factors=TRUE,
num.trees=ntrees,
weights=eclsb$sampwt,
seed=8675309)
rf.rd.m2<-train(readtheta05~.,
data=eclsb[,c("readtheta05",vars.base,vars.covw1,vars.ntxw1)],
method="ranger",
tuneGrid=rf.m2.grid,
trControl=cntrl,
metric="RMSE",
respect.unordered.factors=TRUE,
num.trees=ntrees,
weights=eclsb$sampwt,
seed=8675309)
rf.mt.m2<-train(maththeta05~.,
data=eclsb[,c("maththeta05",vars.base,vars.covw1,vars.ntxw1)],
method="ranger",
tuneGrid=rf.m2.grid,
trControl=cntrl,
metric="RMSE",
respect.unordered.factors=TRUE,
num.trees=ntrees,
weights=eclsb$sampwt,
seed=8675309)
rd.m2.tune<-ranger(readtheta05~.,
data=eclsb[,c("readtheta05",vars.base,vars.covw1,vars.ntxw1)],
num.trees=ntrees,
min.node.size=rf.rd.m2$bestTune[,"min.node.size"],
mtry=rf.rd.m2$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=eclsb$sampwt,
seed=8675309)
mt.m2.tune<-ranger(maththeta05~.,
data=eclsb[,c("maththeta05",vars.base,vars.covw1,vars.ntxw1)],
num.trees=ntrees,
min.node.size=rf.mt.m2$bestTune[,"min.node.size"],
mtry=rf.mt.m2$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=eclsb$sampwt,
seed=8675309)
eclsb.tune.imp<-eclsb[,c(vars.base,vars.covw1,vars.ntxw1)]
eclsb.tune.imp$nhpovrt01<-a20
read.astar.m.tune<-predict(rd.m2.tune,eclsb.tune.imp)$pred
math.astar.m.tune<-predict(mt.m2.tune,eclsb.tune.imp)$pred
rf.rd.m3<-train(
y=read.astar.m.tune,
x=eclsb[,c(vars.base,vars.covw1)],
method="ranger",
tuneGrid=rf.m3.grid,
trControl=cntrl,
metric="RMSE",
respect.unordered.factors=TRUE,
num.trees=ntrees,
weights=eclsb$sampwt,
seed=8675309)
rf.mt.m3<-train(
y=math.astar.m.tune,
x=eclsb[,c(vars.base,vars.covw1)],
method="ranger",
tuneGrid=rf.m3.grid,
trControl=cntrl,
metric="RMSE",
respect.unordered.factors=TRUE,
num.trees=ntrees,
weights=eclsb$sampwt,
seed=8675309)
### COMPUTE ESTIMATES ###
read.train<-eclsb[,"readtheta05"]
math.train<-eclsb[,"maththeta05"]
c.a.train<-eclsb[,c(vars.base,vars.covw1)]
c.a.m.train<-eclsb[,c(vars.base,vars.covw1,vars.ntxw1)]
wts.train<-eclsb[,"sampwt"]
# STEP 1: ESTIMATE E(Y(astar)) and E(Y(a)) #
read.giv.c.a<-ranger(
y=read.train,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m1$bestTune[,"min.node.size"],
mtry=rf.rd.m1$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
math.giv.c.a<-ranger(
y=math.train,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m1$bestTune[,"min.node.size"],
mtry=rf.mt.m1$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
c.a.imp<-eclsb[,c(vars.base,vars.covw1)]
c.a.imp$nhpovrt01<-a5
read.a5<-predict(read.giv.c.a,c.a.imp)$pred
math.a5<-predict(math.giv.c.a,c.a.imp)$pred
c.a.imp$nhpovrt01<-a10
read.a10<-predict(read.giv.c.a,c.a.imp)$pred
math.a10<-predict(math.giv.c.a,c.a.imp)$pred
c.a.imp$nhpovrt01<-a15
read.a15<-predict(read.giv.c.a,c.a.imp)$pred
math.a15<-predict(math.giv.c.a,c.a.imp)$pred
c.a.imp$nhpovrt01<-a20
read.a20<-predict(read.giv.c.a,c.a.imp)$pred
math.a20<-predict(math.giv.c.a,c.a.imp)$pred
c.a.imp$nhpovrt01<-a30
read.a30<-predict(read.giv.c.a,c.a.imp)$pred
math.a30<-predict(math.giv.c.a,c.a.imp)$pred
# STEP 2: ESTIMATE E(Y(astar,m(a)) #
read.giv.c.a.m<-ranger(
y=read.train,
x=c.a.m.train,
num.trees=ntrees,
min.node.size=rf.rd.m2$bestTune[,"min.node.size"],
mtry=rf.rd.m2$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
math.giv.c.a.m<-ranger(
y=math.train,
x=c.a.m.train,
num.trees=ntrees,
min.node.size=rf.mt.m2$bestTune[,"min.node.size"],
mtry=rf.mt.m2$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
c.a.m.imp<-eclsb[,c(vars.base,vars.covw1,vars.ntxw1)]
c.a.m.imp$nhpovrt01<-a10
read.a10.m<-predict(read.giv.c.a.m,c.a.m.imp)$pred
math.a10.m<-predict(math.giv.c.a.m,c.a.m.imp)$pred
c.a.m.imp$nhpovrt01<-a15
read.a15.m<-predict(read.giv.c.a.m,c.a.m.imp)$pred
math.a15.m<-predict(math.giv.c.a.m,c.a.m.imp)$pred
c.a.m.imp$nhpovrt01<-a20
read.a20.m<-predict(read.giv.c.a.m,c.a.m.imp)$pred
math.a20.m<-predict(math.giv.c.a.m,c.a.m.imp)$pred
c.a.m.imp$nhpovrt01<-a30
read.a30.m<-predict(read.giv.c.a.m,c.a.m.imp)$pred
math.a30.m<-predict(math.giv.c.a.m,c.a.m.imp)$pred
read.uhat.a10.m<-ranger(
y=read.a10.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
math.uhat.a10.m<-ranger(
y=math.a10.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
read.uhat.a15.m<-ranger(
y=read.a15.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
math.uhat.a15.m<-ranger(
y=math.a15.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
read.uhat.a20.m<-ranger(
y=read.a20.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
math.uhat.a20.m<-ranger(
y=math.a20.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
read.uhat.a30.m<-ranger(
y=read.a30.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
math.uhat.a30.m<-ranger(
y=math.a30.m,
x=c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=wts.train,
seed=8675309)
c.a.imp<-eclsb[,c(vars.base,vars.covw1)]
c.a.imp$nhpovrt01<-a5
read.a10.mofa<-predict(read.uhat.a10.m,c.a.imp)$pred
math.a10.mofa<-predict(math.uhat.a10.m,c.a.imp)$pred
read.a15.mofa<-predict(read.uhat.a15.m,c.a.imp)$pred
math.a15.mofa<-predict(math.uhat.a15.m,c.a.imp)$pred
read.a20.mofa<-predict(read.uhat.a20.m,c.a.imp)$pred
math.a20.mofa<-predict(math.uhat.a20.m,c.a.imp)$pred
read.a30.mofa<-predict(read.uhat.a30.m,c.a.imp)$pred
math.a30.mofa<-predict(math.uhat.a30.m,c.a.imp)$pred
# STEP 3: COMPUTE ATE, NDE, NIE #
miest.rd.10v5[i,1]<-weighted.mean(read.a10,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.10v5[i,2]<-weighted.mean(read.a10.mofa,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.10v5[i,3]<-weighted.mean(read.a10,wts.train)-weighted.mean(read.a10.mofa,wts.train)
miest.mt.10v5[i,1]<-weighted.mean(math.a10,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.10v5[i,2]<-weighted.mean(math.a10.mofa,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.10v5[i,3]<-weighted.mean(math.a10,wts.train)-weighted.mean(math.a10.mofa,wts.train)
miest.rd.15v5[i,1]<-weighted.mean(read.a15,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.15v5[i,2]<-weighted.mean(read.a15.mofa,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.15v5[i,3]<-weighted.mean(read.a15,wts.train)-weighted.mean(read.a15.mofa,wts.train)
miest.mt.15v5[i,1]<-weighted.mean(math.a15,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.15v5[i,2]<-weighted.mean(math.a15.mofa,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.15v5[i,3]<-weighted.mean(math.a15,wts.train)-weighted.mean(math.a15.mofa,wts.train)
miest.rd.20v5[i,1]<-weighted.mean(read.a20,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.20v5[i,2]<-weighted.mean(read.a20.mofa,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.20v5[i,3]<-weighted.mean(read.a20,wts.train)-weighted.mean(read.a20.mofa,wts.train)
miest.mt.20v5[i,1]<-weighted.mean(math.a20,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.20v5[i,2]<-weighted.mean(math.a20.mofa,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.20v5[i,3]<-weighted.mean(math.a20,wts.train)-weighted.mean(math.a20.mofa,wts.train)
miest.rd.30v5[i,1]<-weighted.mean(read.a30,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.30v5[i,2]<-weighted.mean(read.a30.mofa,wts.train)-weighted.mean(read.a5,wts.train)
miest.rd.30v5[i,3]<-weighted.mean(read.a30,wts.train)-weighted.mean(read.a30.mofa,wts.train)
miest.mt.30v5[i,1]<-weighted.mean(math.a30,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.30v5[i,2]<-weighted.mean(math.a30.mofa,wts.train)-weighted.mean(math.a5,wts.train)
miest.mt.30v5[i,3]<-weighted.mean(math.a30,wts.train)-weighted.mean(math.a30.mofa,wts.train)
### COMPUTE BOOTSTRAP CIs ###
my.cluster<-parallel::makeCluster(n.cores,type="PSOCK")
doParallel::registerDoParallel(cl=my.cluster)
foreach::getDoParRegistered()
clusterEvalQ(cl=my.cluster, library(ranger))
registerDoRNG(8675309)
clusterExport(cl=my.cluster,
list(
"vars.ntxw1",
"vars.covw1",
"vars.base",
"nmi",
"nboot",
"a5",
"a10",
"a15",
"a20",
"a30",
"eclsb",
"ntrees",
"rf.rd.m1",
"rf.mt.m1",
"rf.rd.m2",
"rf.mt.m2",
"rf.rd.m3",
"rf.mt.m3"),
envir=environment())
boot.est.w1<-foreach(h=1:nboot, .combine=cbind) %dopar% {
boot.eclsb<-NULL
for (s in 1:length(unique(eclsb$strat))) {
eclsb.strata<-eclsb[which(eclsb$strat==s),]
idboot.1<-sample(unique(eclsb.strata$psu),length(unique(eclsb.strata$psu))-1,replace=T)
idboot.2<-table(idboot.1)
boot.eclsb.strata<-NULL
for (g in 1:max(idboot.2)) {
boot.data<-eclsb.strata[eclsb.strata$psu %in% names(idboot.2[idboot.2 %in% g]),]
for (l in 1:g) {
boot.eclsb.strata<-rbind(boot.eclsb.strata,boot.data)
}
}
boot.eclsb<-rbind(boot.eclsb,boot.eclsb.strata,boot.eclsb.strata)
}
boot.read.train<-boot.eclsb[,"readtheta05"]
boot.math.train<-boot.eclsb[,"maththeta05"]
boot.c.a.train<-boot.eclsb[,c(vars.base,vars.covw1)]
boot.c.a.m.train<-boot.eclsb[,c(vars.base,vars.covw1,vars.ntxw1)]
boot.wts.train<-boot.eclsb[,"sampwt"]
boot.read.giv.c.a<-ranger(
y=boot.read.train,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m1$bestTune[,"min.node.size"],
mtry=rf.rd.m1$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.math.giv.c.a<-ranger(
y=boot.math.train,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m1$bestTune[,"min.node.size"],
mtry=rf.mt.m1$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.c.a.imp<-boot.eclsb[,c(vars.base,vars.covw1)]
boot.c.a.imp$nhpovrt01<-a5
boot.read.a5<-predict(boot.read.giv.c.a,boot.c.a.imp)$pred
boot.math.a5<-predict(boot.math.giv.c.a,boot.c.a.imp)$pred
boot.c.a.imp$nhpovrt01<-a10
boot.read.a10<-predict(boot.read.giv.c.a,boot.c.a.imp)$pred
boot.math.a10<-predict(boot.math.giv.c.a,boot.c.a.imp)$pred
boot.c.a.imp$nhpovrt01<-a15
boot.read.a15<-predict(boot.read.giv.c.a,boot.c.a.imp)$pred
boot.math.a15<-predict(boot.math.giv.c.a,boot.c.a.imp)$pred
boot.c.a.imp$nhpovrt01<-a20
boot.read.a20<-predict(boot.read.giv.c.a,boot.c.a.imp)$pred
boot.math.a20<-predict(boot.math.giv.c.a,boot.c.a.imp)$pred
boot.c.a.imp$nhpovrt01<-a30
boot.read.a30<-predict(boot.read.giv.c.a,boot.c.a.imp)$pred
boot.math.a30<-predict(boot.math.giv.c.a,boot.c.a.imp)$pred
boot.read.giv.c.a.m<-ranger(
y=boot.read.train,
x=boot.c.a.m.train,
num.trees=ntrees,
min.node.size=rf.rd.m2$bestTune[,"min.node.size"],
mtry=rf.rd.m2$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.math.giv.c.a.m<-ranger(
y=boot.math.train,
x=boot.c.a.m.train,
num.trees=ntrees,
min.node.size=rf.mt.m2$bestTune[,"min.node.size"],
mtry=rf.mt.m2$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.c.a.m.imp<-boot.eclsb[,c(vars.base,vars.covw1,vars.ntxw1)]
boot.c.a.m.imp$nhpovrt01<-a10
boot.read.a10.m<-predict(boot.read.giv.c.a.m,boot.c.a.m.imp)$pred
boot.math.a10.m<-predict(boot.math.giv.c.a.m,boot.c.a.m.imp)$pred
boot.c.a.m.imp$nhpovrt01<-a15
boot.read.a15.m<-predict(boot.read.giv.c.a.m,boot.c.a.m.imp)$pred
boot.math.a15.m<-predict(boot.math.giv.c.a.m,boot.c.a.m.imp)$pred
boot.c.a.m.imp$nhpovrt01<-a20
boot.read.a20.m<-predict(boot.read.giv.c.a.m,boot.c.a.m.imp)$pred
boot.math.a20.m<-predict(boot.math.giv.c.a.m,boot.c.a.m.imp)$pred
boot.c.a.m.imp$nhpovrt01<-a30
boot.read.a30.m<-predict(boot.read.giv.c.a.m,boot.c.a.m.imp)$pred
boot.math.a30.m<-predict(boot.math.giv.c.a.m,boot.c.a.m.imp)$pred
boot.read.uhat.a10.m<-ranger(
y=boot.read.a10.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.math.uhat.a10.m<-ranger(
y=boot.math.a10.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.read.uhat.a15.m<-ranger(
y=boot.read.a15.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.math.uhat.a15.m<-ranger(
y=boot.math.a15.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.read.uhat.a20.m<-ranger(
y=boot.read.a20.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.math.uhat.a20.m<-ranger(
y=boot.math.a20.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.read.uhat.a30.m<-ranger(
y=boot.read.a30.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.rd.m3$bestTune[,"min.node.size"],
mtry=rf.rd.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.math.uhat.a30.m<-ranger(
y=boot.math.a30.m,
x=boot.c.a.train,
num.trees=ntrees,
min.node.size=rf.mt.m3$bestTune[,"min.node.size"],
mtry=rf.mt.m3$bestTune[,"mtry"],
splitrule="variance",
respect.unordered.factors=TRUE,
case.weights=boot.wts.train,
seed=8675309)
boot.c.a.imp<-boot.eclsb[,c(vars.base,vars.covw1)]
boot.c.a.imp$nhpovrt01<-a5
boot.read.a10.mofa<-predict(boot.read.uhat.a10.m,boot.c.a.imp)$pred
boot.math.a10.mofa<-predict(boot.math.uhat.a10.m,boot.c.a.imp)$pred
boot.read.a15.mofa<-predict(boot.read.uhat.a15.m,boot.c.a.imp)$pred
boot.math.a15.mofa<-predict(boot.math.uhat.a15.m,boot.c.a.imp)$pred
boot.read.a20.mofa<-predict(boot.read.uhat.a20.m,boot.c.a.imp)$pred
boot.math.a20.mofa<-predict(boot.math.uhat.a20.m,boot.c.a.imp)$pred
boot.read.a30.mofa<-predict(boot.read.uhat.a30.m,boot.c.a.imp)$pred
boot.math.a30.mofa<-predict(boot.math.uhat.a30.m,boot.c.a.imp)$pred
boot.ate.rd.10v5<-weighted.mean(boot.read.a10,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nde.rd.10v5<-weighted.mean(boot.read.a10.mofa,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nie.rd.10v5<-weighted.mean(boot.read.a10,boot.wts.train)-weighted.mean(boot.read.a10.mofa,boot.wts.train)
boot.ate.mt.10v5<-weighted.mean(boot.math.a10,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nde.mt.10v5<-weighted.mean(boot.math.a10.mofa,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nie.mt.10v5<-weighted.mean(boot.math.a10,boot.wts.train)-weighted.mean(boot.math.a10.mofa,boot.wts.train)
boot.ate.rd.15v5<-weighted.mean(boot.read.a15,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nde.rd.15v5<-weighted.mean(boot.read.a15.mofa,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nie.rd.15v5<-weighted.mean(boot.read.a15,boot.wts.train)-weighted.mean(boot.read.a15.mofa,boot.wts.train)
boot.ate.mt.15v5<-weighted.mean(boot.math.a15,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nde.mt.15v5<-weighted.mean(boot.math.a15.mofa,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nie.mt.15v5<-weighted.mean(boot.math.a15,boot.wts.train)-weighted.mean(boot.math.a15.mofa,boot.wts.train)
boot.ate.rd.20v5<-weighted.mean(boot.read.a20,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nde.rd.20v5<-weighted.mean(boot.read.a20.mofa,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nie.rd.20v5<-weighted.mean(boot.read.a20,boot.wts.train)-weighted.mean(boot.read.a20.mofa,boot.wts.train)
boot.ate.mt.20v5<-weighted.mean(boot.math.a20,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nde.mt.20v5<-weighted.mean(boot.math.a20.mofa,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nie.mt.20v5<-weighted.mean(boot.math.a20,boot.wts.train)-weighted.mean(boot.math.a20.mofa,boot.wts.train)
boot.ate.rd.30v5<-weighted.mean(boot.read.a30,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nde.rd.30v5<-weighted.mean(boot.read.a30.mofa,boot.wts.train)-weighted.mean(boot.read.a5,boot.wts.train)
boot.nie.rd.30v5<-weighted.mean(boot.read.a30,boot.wts.train)-weighted.mean(boot.read.a30.mofa,boot.wts.train)
boot.ate.mt.30v5<-weighted.mean(boot.math.a30,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nde.mt.30v5<-weighted.mean(boot.math.a30.mofa,boot.wts.train)-weighted.mean(boot.math.a5,boot.wts.train)
boot.nie.mt.30v5<-weighted.mean(boot.math.a30,boot.wts.train)-weighted.mean(boot.math.a30.mofa,boot.wts.train)
return(
list(
boot.ate.rd.10v5,boot.nde.rd.10v5,boot.nie.rd.10v5,
boot.ate.mt.10v5,boot.nde.mt.10v5,boot.nie.mt.10v5,
boot.ate.rd.15v5,boot.nde.rd.15v5,boot.nie.rd.15v5,
boot.ate.mt.15v5,boot.nde.mt.15v5,boot.nie.mt.15v5,
boot.ate.rd.20v5,boot.nde.rd.20v5,boot.nie.rd.20v5,
boot.ate.mt.20v5,boot.nde.mt.20v5,boot.nie.mt.20v5,
boot.ate.rd.30v5,boot.nde.rd.30v5,boot.nie.rd.30v5,
boot.ate.mt.30v5,boot.nde.mt.30v5,boot.nie.mt.30v5))
}
boot.est.w1<-matrix(unlist(boot.est.w1),ncol=24,byrow=TRUE)
boot.ci.rd.10v5<-rbind(boot.ci.rd.10v5,boot.est.w1[,1:3])
boot.ci.mt.10v5<-rbind(boot.ci.mt.10v5,boot.est.w1[,4:6])
boot.ci.rd.15v5<-rbind(boot.ci.rd.15v5,boot.est.w1[,7:9])
boot.ci.mt.15v5<-rbind(boot.ci.mt.15v5,boot.est.w1[,10:12])
boot.ci.rd.20v5<-rbind(boot.ci.rd.20v5,boot.est.w1[,13:15])
boot.ci.mt.20v5<-rbind(boot.ci.mt.20v5,boot.est.w1[,16:18])
boot.ci.rd.30v5<-rbind(boot.ci.rd.30v5,boot.est.w1[,19:21])
boot.ci.mt.30v5<-rbind(boot.ci.mt.30v5,boot.est.w1[,22:24])
stopCluster(my.cluster)
rm(my.cluster)
}
### COMBINE MI ESTIMATES ###
est.rd.10v5<-est.mt.10v5<-matrix(data=NA,nrow=3,ncol=3)
est.rd.15v5<-est.mt.15v5<-matrix(data=NA,nrow=3,ncol=3)
est.rd.20v5<-est.mt.20v5<-matrix(data=NA,nrow=3,ncol=3)
est.rd.30v5<-est.mt.30v5<-matrix(data=NA,nrow=3,ncol=3)
for (i in 1:3) {
est.rd.10v5[i,1]<-round(mean(miest.rd.10v5[,i]),digits=3)
est.rd.10v5[i,2]<-round(quantile(boot.ci.rd.10v5[,i],prob=0.025),digits=3)
est.rd.10v5[i,3]<-round(quantile(boot.ci.rd.10v5[,i],prob=0.975),digits=3)
est.mt.10v5[i,1]<-round(mean(miest.mt.10v5[,i]),digits=3)
est.mt.10v5[i,2]<-round(quantile(boot.ci.mt.10v5[,i],prob=0.025),digits=3)
est.mt.10v5[i,3]<-round(quantile(boot.ci.mt.10v5[,i],prob=0.975),digits=3)
est.rd.15v5[i,1]<-round(mean(miest.rd.15v5[,i]),digits=3)
est.rd.15v5[i,2]<-round(quantile(boot.ci.rd.15v5[,i],prob=0.025),digits=3)
est.rd.15v5[i,3]<-round(quantile(boot.ci.rd.15v5[,i],prob=0.975),digits=3)
est.mt.15v5[i,1]<-round(mean(miest.mt.15v5[,i]),digits=3)
est.mt.15v5[i,2]<-round(quantile(boot.ci.mt.15v5[,i],prob=0.025),digits=3)
est.mt.15v5[i,3]<-round(quantile(boot.ci.mt.15v5[,i],prob=0.975),digits=3)
est.rd.20v5[i,1]<-round(mean(miest.rd.20v5[,i]),digits=3)
est.rd.20v5[i,2]<-round(quantile(boot.ci.rd.20v5[,i],prob=0.025),digits=3)
est.rd.20v5[i,3]<-round(quantile(boot.ci.rd.20v5[,i],prob=0.975),digits=3)
est.mt.20v5[i,1]<-round(mean(miest.mt.20v5[,i]),digits=3)
est.mt.20v5[i,2]<-round(quantile(boot.ci.mt.20v5[,i],prob=0.025),digits=3)
est.mt.20v5[i,3]<-round(quantile(boot.ci.mt.20v5[,i],prob=0.975),digits=3)
est.rd.30v5[i,1]<-round(mean(miest.rd.30v5[,i]),digits=3)
est.rd.30v5[i,2]<-round(quantile(boot.ci.rd.30v5[,i],prob=0.025),digits=3)
est.rd.30v5[i,3]<-round(quantile(boot.ci.rd.30v5[,i],prob=0.975),digits=3)
est.mt.30v5[i,1]<-round(mean(miest.mt.30v5[,i]),digits=3)
est.mt.30v5[i,2]<-round(quantile(boot.ci.mt.30v5[,i],prob=0.025),digits=3)
est.mt.30v5[i,3]<-round(quantile(boot.ci.mt.30v5[,i],prob=0.975),digits=3)
}
rlabel<-c('ATE','NDE','NIE')
output.rd.10v5<-data.frame(est.rd.10v5,row.names=rlabel)
output.mt.10v5<-data.frame(est.mt.10v5,row.names=rlabel)
output.rd.15v5<-data.frame(est.rd.15v5,row.names=rlabel)
output.mt.15v5<-data.frame(est.mt.15v5,row.names=rlabel)
output.rd.20v5<-data.frame(est.rd.20v5,row.names=rlabel)
output.mt.20v5<-data.frame(est.mt.20v5,row.names=rlabel)
output.rd.30v5<-data.frame(est.rd.30v5,row.names=rlabel)
output.mt.30v5<-data.frame(est.mt.30v5,row.names=rlabel)
colnames(output.rd.10v5)<-colnames(output.mt.10v5)<-c('estimate','ll.pct.95ci','ul.pct.95ci')
colnames(output.rd.15v5)<-colnames(output.mt.15v5)<-c('estimate','ll.pct.95ci','ul.pct.95ci')
colnames(output.rd.20v5)<-colnames(output.mt.20v5)<-c('estimate','ll.pct.95ci','ul.pct.95ci')
colnames(output.rd.30v5)<-colnames(output.mt.30v5)<-c('estimate','ll.pct.95ci','ul.pct.95ci')
### PRINT RESULTS ###
sink("C:\\Users\\wodtke\\Desktop\\projects\\nhood_mediation_toxins\\programs\\_LOGS\\24_create_table_S4_log.txt")
cat("===========================================\n")
cat("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
cat("Reading Test Scores\n")
cat("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
cat("*** 10% vs. 5% Poverty ***\n")
print(output.rd.10v5)
cat(" \n")
cat("*** 15% vs. 5% Poverty ***\n")
print(output.rd.15v5)
cat(" \n")
cat("*** 20% vs. 5% Poverty ***\n")
print(output.rd.20v5)
cat(" \n")
cat("*** 30% vs. 5% Poverty ***\n")
print(output.rd.30v5)
cat("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
cat("Math Test Scores\n")
cat("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
cat("*** 10% vs. 5% Poverty ***\n")
print(output.mt.10v5)
cat(" \n")
cat("*** 15% vs. 5% Poverty ***\n")
print(output.mt.15v5)
cat(" \n")
cat("*** 20% vs. 5% Poverty ***\n")
print(output.mt.20v5)
cat(" \n")
cat("*** 30% vs. 5% Poverty ***\n")
print(output.mt.30v5)
cat("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
cat("===========================================\n")
print(startTime)
print(Sys.time())
sink()