output_code/runOnData.R
In jackiemauro/ShiftEst:
#################################################################################
# this file includes all code run on the data #
# without access to the dataset it will unfortunately not be reproducible #
# we provide it for clarity #
#################################################################################
library(ggplot2)
source('R/doubleShiftRangeWrapper.R')
# read in the data
dat <- read.csv('~jacquelinemauro/MergedData.csv')[,-1]
# generate covariates and outcomes
dat$no.visits.last.loc <- (1 - dat$visitslastlocyn1)
dat$no.childvisits <- (1 - dat$childyn)
dat$maxtime <- apply(dat[,2:26],1,max)
dat <- dat[-which(dat$NCRecid.Event == 'reincarceration'),] #drop if they go back for parole violation
attach(dat)
startdummy = which(names(dat)=='alb'); enddummy = which(names(dat)=='pit')
options(na.action='na.pass')
county.f = factor(dat$CountyClass); county.dummies = model.matrix(~county.f)[,-c(1,2)]
county.dummies2 = model.matrix(~county.f)[,-c(1)]
minName.f = factor(dat$minTimeName); minName.dummies = model.matrix(~minName.f)[,-c(1,2)]
# use prison closest to home as a dummy to indicate location
covs = cbind(white,loslastloc,minName.dummies,ageyrs,urban,priorarrests,married,violent,lsirscore,
highschoolgrad,custody_level,numofpriorinc,mh,dat[,c(startdummy:(enddummy-1))],numoftotalmisconducts)
detach(dat)
old.names <- names(covs)
names(covs) <- sapply(c(1:dim(covs)[2]), function(k) paste('x',k,sep = ""))
#### summary statistics ####
library(plyr)
library(xtable)
sumstats = ddply(dat, .(visitslastlocyn1), summarize,
recidivate = mean(NCRecid3,na.rm = T),
#minTime = mean(minTime,na.rm = T),
currentTime = mean(total_time,na.rm = T),
county = mean(CountyClass, na.rm = T),
lengthofstay = mean(loslastloc,na.rm = T),
white = mean(white,na.rm = T),
age = mean(ageyrs,na.rm = T),
urban = mean(urban,na.rm = T),
married = mean(married,na.rm = T),
mentalhealth = mean(mh,na.rm = T),
highschool = mean(highschoolgrad,na.rm = T),
violent = mean(violent,na.rm = T),
score = mean(lsirscore,na.rm = T),
numberofvisits = mean(visitslastloc1,na.rm = T),
custodylevel =mean(custody_level,na.rm = T),
priorarrests = mean(priorarrests,na.rm = T),
priorincarcerations = mean(numofpriorinc,na.rm = T),
misconducts = mean(numofmisconductslastloc,na.rm = T)
)
sumstats_county = ddply(cbind(dat,county.dummies2), .(visitslastlocyn1), summarize,
recidivate = mean(NCRecid3,na.rm = T),
#minTime = mean(minTime,na.rm = T),
currentTime = mean(total_time,na.rm = T),
county2 = mean(county.f2, na.rm = T),
county3 = mean(county.f3, na.rm = T),
county4 = mean(county.f4, na.rm = T),
county5 = mean(county.f5, na.rm = T),
county6 = mean(county.f6, na.rm = T),
county7 = mean(county.f7, na.rm = T),
county8 = mean(county.f8, na.rm = T),
lengthofstay = mean(loslastloc,na.rm = T),
white = mean(white,na.rm = T),
age = mean(ageyrs,na.rm = T),
urban = mean(urban,na.rm = T),
married = mean(married,na.rm = T),
mentalhealth = mean(mh,na.rm = T),
highschool = mean(highschoolgrad,na.rm = T),
violent = mean(violent,na.rm = T),
score = mean(lsirscore,na.rm = T),
numberofvisits = mean(visitslastloc1,na.rm = T),
custodylevel =mean(custody_level,na.rm = T),
priorarrests = mean(priorarrests,na.rm = T),
priorincarcerations = mean(numofpriorinc,na.rm = T),
misconducts = mean(numofmisconductslastloc,na.rm = T)
)
sds = ddply(dat, .(visitslastlocyn1), summarize,
recidivate = sd(NCRecid3,na.rm = T),
#minTime = sd(minTime,na.rm = T),
currentTime = sd(total_time,na.rm = T),
lengthofstay = sd(loslastloc,na.rm = T),
white = sd(white,na.rm = T),
age = sd(ageyrs,na.rm = T),
urban = sd(urban,na.rm = T),
married = sd(married,na.rm = T),
mentalhealth = sd(mh,na.rm = T),
highschool = sd(highschoolgrad,na.rm = T),
violent = sd(violent,na.rm = T),
score = sd(lsirscore,na.rm = T),
numberofvisits = sd(visitslastloc1,na.rm = T),
custodylevel =sd(custody_level,na.rm = T),
priorarrests = sd(priorarrests,na.rm = T),
priorincarcerations = sd(numofpriorinc,na.rm = T),
misconducts = sd(numofmisconductslastloc,na.rm = T)
)
out.tab = rbind(sumstats[1,],sds[1,],sumstats[2,],sds[2,])
library(xtable)
x = xtable(t(sumstats))
print(x, type = 'latex','summaryStatsNoparole.tex')
out.tab = rbind(sumstats[1,],sds[1,],sumstats[2,],sds[2,])
library(xtable)
x = xtable(t(sumstats_county))
print(x, type = 'latex','summaryStatsNoparoleCounty.tex')
x = xtable(t(out.tab))
print(x, type = 'latex','summaryStatsNoparoleSds.tex')
########## run double shift estimators ########
# main results
delta1 <- c(20,40,60,90,120)
delta2 <- -delta1
output1 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5, pos.cutoff = 100)
p <- plot.cace.double(output1)
write.csv(matrix(unlist(output1[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftEven_5foldRR.csv')
ggsave(plot = p, filename = 'DubShiftEven_5foldRR.png',height = 4, width = 7)
# child visitation
delta1 <- c(20,40,60,90,120)
delta2 <- -delta1
output1c <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.childvisits,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5, pos.cutoff = 100)
p <- plot.cace.double(output1c)
write.csv(matrix(unlist(output1c[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftEvenChild_5foldRR.csv')
ggsave(plot = p, filename = 'DubShiftEvenChild_5foldRR.png',height = 4, width = 7)
# using less flexible conditional density estimator
delta1 <- c(20,40,60,90,120)
delta2 <- -delta1
output1 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'glm',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5, pos.cutoff = 100)
p <- plot.cace.double(output1)
write.csv(matrix(unlist(output1[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftEven_glm_5foldRR.csv')
ggsave(plot = p, filename = 'DubShiftEven_glm_5foldRR.png',height = 4, width = 7)
################# other intervention options ####################
# instead of double shift, create moving windows for different client populations(not done)
# 20 minute window
delta1 <- c(-100,-40,0,20,60,120)
delta2 <- c(-120,-60,-20,0,40,60)
output2 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 2,
pos.cutoff = 50)
p2 <- plot.cace.double(output2)
write.csv(matrix(unlist(output2[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftWindow.csv')
ggsave(plot = p2, filename = 'DubShiftWindow.png',height = 4, width = 7)
# 30 minute window
delta1 <- c(-90,-60,-30,0,30,60,90,120)
delta2 <- c(-120,-90,-60,-30,0,30,60,90)
output3 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 2,
pos.cutoff = 50)
p3 <- plot.cace.double(output3)
write.csv(matrix(unlist(output3[-5]),ncol = length(delta1),byrow = T),file = 'dubshift30Window.csv')
ggsave(plot = p3, filename = 'DubShift30Window.png',height = 4, width = 7)
# 60 minute window
delta1 <- c(-120,-60,0,60,120,180)
delta2 <- c(-180,-120,-60,0,60,120)
output4 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5,
pos.cutoff = 50)
p4 <- plot.cace.double(output4)
write.csv(matrix(unlist(output4[-5]),ncol = length(delta1),byrow = T),file = 'dubshift60Window.csv')
ggsave(plot = p4, filename = 'DubShift60Window.png',height = 4, width = 7)
############ run tsls #################
library(AER)
library(xtable)
df <- data.frame(subset(dat, select = c('NCRecid3', 'no.visits.last.loc', 'total_time')), covs)
df <- df[complete.cases(df),]
names(df) <- c('Recid', 'no.visits.last.loc', 'total_time', old.names)
# linear specification
tsls.form <- as.formula(paste("Recid~no.visits.last.loc+",paste(names(covs),collapse = "+"),"| total_time +",paste(names(covs),collapse = "+")))
tsls<- ivreg(tsls.form, data = df)
print(xtable(summary(tsls)$coefficients),type = "latex",file = "TSLS_output_RR.tex")
# more flexible specification -- just putting default splines on all non-binary variables
library(splines)
tsls.spline <- ivreg(Recid ~ no.visits.last.loc + white + bs(loslastloc) + minName.fCHS +
minName.fCOA + minName.fCRE + minName.fDAL + minName.fFRA +
minName.fFRS + minName.fFYT + minName.fGRA + minName.fGRE +
minName.fGRN + minName.fHOU + minName.fHUN + minName.fLAU +
minName.fMER + minName.fPIT + minName.fPNG + minName.fQUE +
minName.fRET + minName.fROC + minName.fSMR + minName.fWAM +
bs(ageyrs) + urban + bs(priorarrests) + married + violent + bs(lsirscore) +
highschoolgrad + custody_level + bs(numofpriorinc) + mh + alb +
cam + chs + coa + cre + dal + fra + frs + fyt + gra + gre +
grn + hou + hun + lau + mah + mer + png + que + ret + roc +
smi + smr + wam + bs(numoftotalmisconducts) | bs(total_time) + white +
bs(loslastloc) + minName.fCHS +
minName.fCOA + minName.fCRE + minName.fDAL + minName.fFRA +
minName.fFRS + minName.fFYT + minName.fGRA + minName.fGRE +
minName.fGRN + minName.fHOU + minName.fHUN + minName.fLAU +
minName.fMER + minName.fPIT + minName.fPNG + minName.fQUE +
minName.fRET + minName.fROC + minName.fSMR + minName.fWAM +
bs(ageyrs) + urban + bs(priorarrests) + married + violent + bs(lsirscore) +
highschoolgrad + custody_level + bs(numofpriorinc) + mh + alb +
cam + chs + coa + cre + dal + fra + frs + fyt + gra + gre +
grn + hou + hun + lau + mah + mer + png + que + ret + roc +
smi + smr + wam + bs(numoftotalmisconducts), data = df)
summary(tsls.spline)
print(xtable(summary(tsls.spline)$coefficients),type = "latex",file = "TSLS_splines_output_RR.tex")
jackiemauro/ShiftEst documentation built on Feb. 14, 2020, 2:46 a.m.
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#################################################################################
# this file includes all code run on the data #
# without access to the dataset it will unfortunately not be reproducible #
# we provide it for clarity #
#################################################################################
library(ggplot2)
source('R/doubleShiftRangeWrapper.R')
# read in the data
dat <- read.csv('~jacquelinemauro/MergedData.csv')[,-1]
# generate covariates and outcomes
dat$no.visits.last.loc <- (1 - dat$visitslastlocyn1)
dat$no.childvisits <- (1 - dat$childyn)
dat$maxtime <- apply(dat[,2:26],1,max)
dat <- dat[-which(dat$NCRecid.Event == 'reincarceration'),] #drop if they go back for parole violation
attach(dat)
startdummy = which(names(dat)=='alb'); enddummy = which(names(dat)=='pit')
options(na.action='na.pass')
county.f = factor(dat$CountyClass); county.dummies = model.matrix(~county.f)[,-c(1,2)]
county.dummies2 = model.matrix(~county.f)[,-c(1)]
minName.f = factor(dat$minTimeName); minName.dummies = model.matrix(~minName.f)[,-c(1,2)]
# use prison closest to home as a dummy to indicate location
covs = cbind(white,loslastloc,minName.dummies,ageyrs,urban,priorarrests,married,violent,lsirscore,
highschoolgrad,custody_level,numofpriorinc,mh,dat[,c(startdummy:(enddummy-1))],numoftotalmisconducts)
detach(dat)
old.names <- names(covs)
names(covs) <- sapply(c(1:dim(covs)[2]), function(k) paste('x',k,sep = ""))
#### summary statistics ####
library(plyr)
library(xtable)
sumstats = ddply(dat, .(visitslastlocyn1), summarize,
recidivate = mean(NCRecid3,na.rm = T),
#minTime = mean(minTime,na.rm = T),
currentTime = mean(total_time,na.rm = T),
county = mean(CountyClass, na.rm = T),
lengthofstay = mean(loslastloc,na.rm = T),
white = mean(white,na.rm = T),
age = mean(ageyrs,na.rm = T),
urban = mean(urban,na.rm = T),
married = mean(married,na.rm = T),
mentalhealth = mean(mh,na.rm = T),
highschool = mean(highschoolgrad,na.rm = T),
violent = mean(violent,na.rm = T),
score = mean(lsirscore,na.rm = T),
numberofvisits = mean(visitslastloc1,na.rm = T),
custodylevel =mean(custody_level,na.rm = T),
priorarrests = mean(priorarrests,na.rm = T),
priorincarcerations = mean(numofpriorinc,na.rm = T),
misconducts = mean(numofmisconductslastloc,na.rm = T)
)
sumstats_county = ddply(cbind(dat,county.dummies2), .(visitslastlocyn1), summarize,
recidivate = mean(NCRecid3,na.rm = T),
#minTime = mean(minTime,na.rm = T),
currentTime = mean(total_time,na.rm = T),
county2 = mean(county.f2, na.rm = T),
county3 = mean(county.f3, na.rm = T),
county4 = mean(county.f4, na.rm = T),
county5 = mean(county.f5, na.rm = T),
county6 = mean(county.f6, na.rm = T),
county7 = mean(county.f7, na.rm = T),
county8 = mean(county.f8, na.rm = T),
lengthofstay = mean(loslastloc,na.rm = T),
white = mean(white,na.rm = T),
age = mean(ageyrs,na.rm = T),
urban = mean(urban,na.rm = T),
married = mean(married,na.rm = T),
mentalhealth = mean(mh,na.rm = T),
highschool = mean(highschoolgrad,na.rm = T),
violent = mean(violent,na.rm = T),
score = mean(lsirscore,na.rm = T),
numberofvisits = mean(visitslastloc1,na.rm = T),
custodylevel =mean(custody_level,na.rm = T),
priorarrests = mean(priorarrests,na.rm = T),
priorincarcerations = mean(numofpriorinc,na.rm = T),
misconducts = mean(numofmisconductslastloc,na.rm = T)
)
sds = ddply(dat, .(visitslastlocyn1), summarize,
recidivate = sd(NCRecid3,na.rm = T),
#minTime = sd(minTime,na.rm = T),
currentTime = sd(total_time,na.rm = T),
lengthofstay = sd(loslastloc,na.rm = T),
white = sd(white,na.rm = T),
age = sd(ageyrs,na.rm = T),
urban = sd(urban,na.rm = T),
married = sd(married,na.rm = T),
mentalhealth = sd(mh,na.rm = T),
highschool = sd(highschoolgrad,na.rm = T),
violent = sd(violent,na.rm = T),
score = sd(lsirscore,na.rm = T),
numberofvisits = sd(visitslastloc1,na.rm = T),
custodylevel =sd(custody_level,na.rm = T),
priorarrests = sd(priorarrests,na.rm = T),
priorincarcerations = sd(numofpriorinc,na.rm = T),
misconducts = sd(numofmisconductslastloc,na.rm = T)
)
out.tab = rbind(sumstats[1,],sds[1,],sumstats[2,],sds[2,])
library(xtable)
x = xtable(t(sumstats))
print(x, type = 'latex','summaryStatsNoparole.tex')
out.tab = rbind(sumstats[1,],sds[1,],sumstats[2,],sds[2,])
library(xtable)
x = xtable(t(sumstats_county))
print(x, type = 'latex','summaryStatsNoparoleCounty.tex')
x = xtable(t(out.tab))
print(x, type = 'latex','summaryStatsNoparoleSds.tex')
########## run double shift estimators ########
# main results
delta1 <- c(20,40,60,90,120)
delta2 <- -delta1
output1 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5, pos.cutoff = 100)
p <- plot.cace.double(output1)
write.csv(matrix(unlist(output1[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftEven_5foldRR.csv')
ggsave(plot = p, filename = 'DubShiftEven_5foldRR.png',height = 4, width = 7)
# child visitation
delta1 <- c(20,40,60,90,120)
delta2 <- -delta1
output1c <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.childvisits,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5, pos.cutoff = 100)
p <- plot.cace.double(output1c)
write.csv(matrix(unlist(output1c[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftEvenChild_5foldRR.csv')
ggsave(plot = p, filename = 'DubShiftEvenChild_5foldRR.png',height = 4, width = 7)
# using less flexible conditional density estimator
delta1 <- c(20,40,60,90,120)
delta2 <- -delta1
output1 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'glm',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5, pos.cutoff = 100)
p <- plot.cace.double(output1)
write.csv(matrix(unlist(output1[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftEven_glm_5foldRR.csv')
ggsave(plot = p, filename = 'DubShiftEven_glm_5foldRR.png',height = 4, width = 7)
################# other intervention options ####################
# instead of double shift, create moving windows for different client populations(not done)
# 20 minute window
delta1 <- c(-100,-40,0,20,60,120)
delta2 <- c(-120,-60,-20,0,40,60)
output2 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 2,
pos.cutoff = 50)
p2 <- plot.cace.double(output2)
write.csv(matrix(unlist(output2[-5]),ncol = length(delta1),byrow = T),file = 'dubshiftWindow.csv')
ggsave(plot = p2, filename = 'DubShiftWindow.png',height = 4, width = 7)
# 30 minute window
delta1 <- c(-90,-60,-30,0,30,60,90,120)
delta2 <- c(-120,-90,-60,-30,0,30,60,90)
output3 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 2,
pos.cutoff = 50)
p3 <- plot.cace.double(output3)
write.csv(matrix(unlist(output3[-5]),ncol = length(delta1),byrow = T),file = 'dubshift30Window.csv')
ggsave(plot = p3, filename = 'DubShift30Window.png',height = 4, width = 7)
# 60 minute window
delta1 <- c(-120,-60,0,60,120,180)
delta2 <- c(-180,-120,-60,0,60,120)
output4 <- double.shift.range(y = dat$NCRecid3,z = dat$total_time,x = covs,
a = dat$no.visits.last.loc,delta1 = delta1,delta2=delta2,
Y.est = 'superlearner', A.est = 'superlearner',Z.est = 'ranger',
zmin = min(dat$minTime),zmax = max(dat$maxtime), nfolds = 5,
pos.cutoff = 50)
p4 <- plot.cace.double(output4)
write.csv(matrix(unlist(output4[-5]),ncol = length(delta1),byrow = T),file = 'dubshift60Window.csv')
ggsave(plot = p4, filename = 'DubShift60Window.png',height = 4, width = 7)
############ run tsls #################
library(AER)
library(xtable)
df <- data.frame(subset(dat, select = c('NCRecid3', 'no.visits.last.loc', 'total_time')), covs)
df <- df[complete.cases(df),]
names(df) <- c('Recid', 'no.visits.last.loc', 'total_time', old.names)
# linear specification
tsls.form <- as.formula(paste("Recid~no.visits.last.loc+",paste(names(covs),collapse = "+"),"| total_time +",paste(names(covs),collapse = "+")))
tsls<- ivreg(tsls.form, data = df)
print(xtable(summary(tsls)$coefficients),type = "latex",file = "TSLS_output_RR.tex")
# more flexible specification -- just putting default splines on all non-binary variables
library(splines)
tsls.spline <- ivreg(Recid ~ no.visits.last.loc + white + bs(loslastloc) + minName.fCHS +
minName.fCOA + minName.fCRE + minName.fDAL + minName.fFRA +
minName.fFRS + minName.fFYT + minName.fGRA + minName.fGRE +
minName.fGRN + minName.fHOU + minName.fHUN + minName.fLAU +
minName.fMER + minName.fPIT + minName.fPNG + minName.fQUE +
minName.fRET + minName.fROC + minName.fSMR + minName.fWAM +
bs(ageyrs) + urban + bs(priorarrests) + married + violent + bs(lsirscore) +
highschoolgrad + custody_level + bs(numofpriorinc) + mh + alb +
cam + chs + coa + cre + dal + fra + frs + fyt + gra + gre +
grn + hou + hun + lau + mah + mer + png + que + ret + roc +
smi + smr + wam + bs(numoftotalmisconducts) | bs(total_time) + white +
bs(loslastloc) + minName.fCHS +
minName.fCOA + minName.fCRE + minName.fDAL + minName.fFRA +
minName.fFRS + minName.fFYT + minName.fGRA + minName.fGRE +
minName.fGRN + minName.fHOU + minName.fHUN + minName.fLAU +
minName.fMER + minName.fPIT + minName.fPNG + minName.fQUE +
minName.fRET + minName.fROC + minName.fSMR + minName.fWAM +
bs(ageyrs) + urban + bs(priorarrests) + married + violent + bs(lsirscore) +
highschoolgrad + custody_level + bs(numofpriorinc) + mh + alb +
cam + chs + coa + cre + dal + fra + frs + fyt + gra + gre +
grn + hou + hun + lau + mah + mer + png + que + ret + roc +
smi + smr + wam + bs(numoftotalmisconducts), data = df)
summary(tsls.spline)
print(xtable(summary(tsls.spline)$coefficients),type = "latex",file = "TSLS_splines_output_RR.tex")
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