This does the modeling
##regis\practicum2\test>pandoc -f docx -t markdown "Chris Busch - practicum2 proposal.docx" -o foo.md
##pandoc --extract-media ./myMediaFolder input.docx -o output.md
rm(list = ls(all = TRUE)) #clear memory
library(stringr)
setwd("~/../practicum2")
source("common.R")
require(ggplot2)
## Loading required package: ggplot2
###############load all the data
bigdata=NULL
for(year in 2010:2015){
filenames=c(Sys.glob(paste0('data/county/',year,'*.csv')),
Sys.glob(paste0('data/irsclean/',year,'*.csv')),
Sys.glob(paste0('data/wonderclean/',year,'*.csv')))
yeardata=NULL
for(f in filenames){# f=filenames[2]
##gotta go by year
message(year,' ',f)
fn=str_match(f, '/(\\d+)')[,2]
d=read.csv(f,stringsAsFactors = T)
if(!is.null(yeardata)){
message('merging')
yeardata=(merge(yeardata,d,by=c('fips','Year')))
}else{
yeardata=d
}
}
message('storing')
if(is.null(bigdata)){
bigdata=yeardata
}else{
bigdata=dplyr::bind_rows(bigdata,yeardata)
}
}
## 2010 data/county/2010Ranked Measure Data.csv
## 2010 data/irsclean/2010-irs-soi.csv
## merging
## 2010 data/wonderclean/2010cdc.csv
## merging
## storing
## 2011 data/county/2011Additional Measure Data.csv
## 2011 data/county/2011Ranked Measure Data.csv
## merging
## 2011 data/irsclean/2011-irs-soi.csv
## merging
## 2011 data/wonderclean/2011cdc.csv
## merging
## storing
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## 2012 data/county/2012Additional Measure Data.csv
## 2012 data/county/2012Ranked Measure Data.csv
## merging
## 2012 data/irsclean/2012-irs-soi.csv
## merging
## 2012 data/wonderclean/2012cdc.csv
## merging
## storing
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## 2013 data/county/2013Additional Measure Data.csv
## 2013 data/county/2013Ranked Measure Data.csv
## merging
## 2013 data/irsclean/2013-irs-soi.csv
## merging
## 2013 data/wonderclean/2013cdc.csv
## merging
## storing
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## 2014 data/county/2014Additional Measure Data.csv
## 2014 data/county/2014Ranked Measure Data.csv
## merging
## 2014 data/irsclean/2014-irs-soi.csv
## merging
## 2014 data/wonderclean/2014cdc.csv
## merging
## storing
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## 2015 data/county/2015Additional Measure Data.csv
## 2015 data/county/2015Ranked Measure Data.csv
## merging
## 2015 data/irsclean/2015-irs-soi.csv
## merging
## 2015 data/wonderclean/2015cdc.csv
## merging
## storing
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
rm(yeardata)
rm(d)
###done loading
checkVar=function(var1){
message(var1,'not na=',length(which(!is.na(bigdata[[var1]]))))
print(table(!is.na(bigdata[[ var1 ]]),bigdata$Year,dnn=c(var1,'Year')))
summary(bigdata[[ var1 ]])
}
#this checks to see if a var was renamed
sapply(qw('diabetes.pct_diabetic pct_diabetic.diabetes'),checkVar)
## diabetes.pct_diabeticnot na=24566
## Year
## diabetes.pct_diabetic 2010 2011 2012 2013 2014 2015
## FALSE 6125 6120 0 0 0 0
## TRUE 0 0 6114 6120 6157 6175
## pct_diabetic.diabetesnot na=6120
## Year
## pct_diabetic.diabetes 2010 2011 2012 2013 2014 2015
## FALSE 6125 0 6114 6120 6157 6175
## TRUE 0 6120 0 0 0 0
## diabetes.pct_diabetic pct_diabetic.diabetes
## Min. 3.20000 3.000000
## 1st Qu. 9.00000 8.500000
## Median 10.50000 9.800000
## Mean 10.58208 9.922827
## 3rd Qu. 12.00000 11.300000
## Max. 21.60000 18.200000
## NA's 12245.00000 30691.000000
## #thisVar becomes thatVar
varsToCombine=
c(pct_diabetic.diabetes='diabetes.pct_diabetic',
some_college_post_secondary_education.psed='some_college_post_secondary_education.pct',
some_college_post_secondary_education.pct_psed='some_college_post_secondary_education.pct',
access_to_healthy_foods.pct.x='access_to_healthy_foods.pct_food',
access_to_recreational_facilities.rec_facility_rate='access_to_recreational_facilities.rec_fac_rate',
air_pollution_particulate_matter.average_daily_pm2_5='daily_fine_particulate_matter.average_pm25',
air_pollution_particulate_matter.average_daily_pm25='daily_fine_particulate_matter.average_pm25',
diabetic_monitoring.pct_receiving_hba1c='diabetic_screening.pct_hba1c'
)
for(n in names(varsToCombine)){
print(table(sign(bigdata[[ varsToCombine[n] ]]),bigdata$Year,dnn=c(varsToCombine[n],'Year (before)')))
bigdata[[ varsToCombine[n] ]] = ifelse(is.na(bigdata[[ varsToCombine[n] ]]),bigdata[[ n ]],bigdata[[ varsToCombine[n] ]])
bigdata[[ n ]]=NULL
print(table(sign(bigdata[[ varsToCombine[n] ]]),bigdata$Year,dnn=c(varsToCombine[n],'Year (after)')))
}
## Year (before)
## diabetes.pct_diabetic 2010 2011 2012 2013 2014 2015
## 1 0 0 6114 6120 6157 6175
## Year (after)
## diabetes.pct_diabetic 2010 2011 2012 2013 2014 2015
## 1 0 6120 6114 6120 6157 6175
## Year (before)
## some_college_post_secondary_education.pct 2010 2011 2012 2013 2014 2015
## 1 0 0 0 6120 6157 0
## Year (after)
## some_college_post_secondary_education.pct 2010 2011 2012 2013 2014 2015
## 1 0 6120 0 6120 6157 0
## Year (before)
## some_college_post_secondary_education.pct 2010 2011 2012 2013 2014 2015
## 1 0 6120 0 6120 6157 0
## Year (after)
## some_college_post_secondary_education.pct 2010 2011 2012 2013 2014 2015
## 1 0 6120 6114 6120 6157 0
## Year (before)
## access_to_healthy_foods.pct_food 2010 2011 2012 2013 2014 2015
## 0 68 33 0 0 0 0
## 1 6057 6078 0 0 0 0
## Year (after)
## access_to_healthy_foods.pct_food 2010 2011 2012 2013 2014 2015
## 0 68 33 35 0 0 0
## 1 6057 6078 6069 0 0 0
## Year (before)
## access_to_recreational_facilities.rec_fac_rate 2010 2011 2012 2013 2014
## 0 0 1180 1269 0 0
## 1 0 4940 4845 0 0
## Year (before)
## access_to_recreational_facilities.rec_fac_rate 2015
## 0 0
## 1 0
## Year (after)
## access_to_recreational_facilities.rec_fac_rate 2010 2011 2012 2013 2014
## 0 0 1180 1269 1320 0
## 1 0 4940 4845 4800 0
## Year (after)
## access_to_recreational_facilities.rec_fac_rate 2015
## 0 0
## 1 0
## Year (before)
## daily_fine_particulate_matter.average_pm25 2010 2011 2012 2013 2014 2015
## 1 0 0 0 6079 0 0
## Year (after)
## daily_fine_particulate_matter.average_pm25 2010 2011 2012 2013 2014 2015
## 1 0 0 0 6079 0 6134
## Year (before)
## daily_fine_particulate_matter.average_pm25 2010 2011 2012 2013 2014 2015
## 1 0 0 0 6079 0 6134
## Year (after)
## daily_fine_particulate_matter.average_pm25 2010 2011 2012 2013 2014 2015
## 1 0 0 0 6079 6119 6134
## Year (before)
## diabetic_screening.pct_hba1c 2010 2011 2012 2013 2014 2015
## 1 6084 6045 6107 6110 6154 0
## Year (after)
## diabetic_screening.pct_hba1c 2010 2011 2012 2013 2014 2015
## 1 6084 6045 6107 6110 6154 6163
################# define the predictors under consideration
yvar='Death.per.100k'
predictors=read.csv('data/predictors.csv',stringsAsFactors = F,na.strings = "")
predictors$column=coalesce(predictors$shorter,predictors$origcolumn)
##rename those pesky long column names
changelist=list()
for(i in 1:nrow(predictors)){
if(!is.na(predictors$shorter[i])){
changelist[[ predictors$origcolumn[i] ]]= predictors$shorter[i]
}
}
predictors[!is.na(predictors$shorter),c('shorter','origcolumn')]
## shorter
## 219 mentally_unhealthy_days
## 223 physically_unhealthy_days
## origcolumn
## 219 poor_mental_health_days.mentally_unhealthy
## 223 poor_physical_health_days.physically_unhealthy
bigdata=rename.columns(bigdata,changelist)
## poor_physical_health_days.physically_unhealthy renamed columns to physically_unhealthy_days
## poor_mental_health_days.mentally_unhealthy renamed columns to mentally_unhealthy_days
setdiff(names(bigdata),predictors$column) ##what is missing in the first compared to second
## [1] "Death.per.100k"
setdiff(predictors$column,names(bigdata)) ##what is missing in the first compared to second
## [1] "access_to_healthy_foods.pct.x"
## [2] "access_to_recreational_facilities.rec_facility_rate"
## [3] "air_pollution_particulate_matter.average_daily_pm2_5"
## [4] "air_pollution_particulate_matter.average_daily_pm25"
## [5] "diabetic_monitoring.pct_receiving_hba1c"
## [6] "pct_diabetic.diabetes"
## [7] "some_college_post_secondary_education.pct_psed"
## [8] "some_college_post_secondary_education.psed"
ignore=unique(predictors$column[predictors$predictor==0])
discardVars=c(ignore)
predictorVarsRaw=unique(predictors$column[predictors$predictor==1 & predictors$column!=yvar])
average.out=function(bigdata){
shush({
bigdata=bigdata[!is.na(bigdata$Death.per.100k),] ##only data with Death.per.100k
impute.df=as.data.frame(aggregate(bigdata,list(fips=bigdata$fips),FUN=function(x) mean(x,na.rm=T)))
})
impute.df
}
median.out=function(bigdata){
shush({
bigdata=bigdata[!is.na(bigdata$Death.per.100k),] ##only data with Death.per.100k
impute.df=as.data.frame(aggregate(bigdata,list(fips=bigdata$fips),FUN=function(x) median(x,na.rm=T)))
})
impute.df
}
impute.df=average.out(bigdata);
cor(bigdata[,qw('other_primary_care_providers.pcp_rate
previous_other_primary_care_providers_data.pcp_rate
previous_primary_care_physician_data_used_to_calculate_rankings.pcp_rate
primary_care_physicians.pcp_rate
')],use = "pairwise.complete.obs")
## other_primary_care_providers.pcp_rate
## other_primary_care_providers.pcp_rate 1.0000000
## previous_other_primary_care_providers_data.pcp_rate 0.9917949
## previous_primary_care_physician_data_used_to_calculate_rankings.pcp_rate NA
## primary_care_physicians.pcp_rate 0.5299042
## previous_other_primary_care_providers_data.pcp_rate
## other_primary_care_providers.pcp_rate 0.9917949
## previous_other_primary_care_providers_data.pcp_rate 1.0000000
## previous_primary_care_physician_data_used_to_calculate_rankings.pcp_rate NA
## primary_care_physicians.pcp_rate 0.5243828
## previous_primary_care_physician_data_used_to_calculate_rankings.pcp_rate
## other_primary_care_providers.pcp_rate NA
## previous_other_primary_care_providers_data.pcp_rate NA
## previous_primary_care_physician_data_used_to_calculate_rankings.pcp_rate 1.0000000
## primary_care_physicians.pcp_rate 0.9614487
## primary_care_physicians.pcp_rate
## other_primary_care_providers.pcp_rate 0.5299042
## previous_other_primary_care_providers_data.pcp_rate 0.5243828
## previous_primary_care_physician_data_used_to_calculate_rankings.pcp_rate 0.9614487
## primary_care_physicians.pcp_rate 1.0000000
fullness=function() {
years=c()
for(y in unique(bigdata$Year)){
years[[as.character(y)]]=0
for(n in names(bigdata)){
#catln(y,n,mean(!is.na(bigdata[[n]][bigdata$Year==y])))
years[[as.character(y)]]=years[[as.character(y)]]+mean(!is.na(bigdata[[n]][bigdata$Year==y]))
}
}
years
}
fullness()
## 2010 2011 2012 2013 2014 2015
## 68.32180 99.32582 115.05806 150.32745 126.24395 142.90623
bigdata hotdeck mean imputation to only impute those measures for the same fips county
for(n in names(bigdata)){
if(is.numeric(bigdata[[n]]) && any(is.na(bigdata[[n]]) )){
lookup=impute.df[[n]]
names(lookup)=as.character(impute.df$fips)
bigdata[[n]]=ifelse(is.na(bigdata[[n]]), lookup[as.character(bigdata$fips)] ,bigdata[[n]])
}
}
fullness()
## 2010 2011 2012 2013 2014 2015
## 294.1607 296.1165 296.0870 295.9634 295.9289 296.0363
require(usmap)
## Loading required package: usmap
## Warning: package 'usmap' was built under R version 3.4.2
for(age in unique(bigdata$Age.Grouping)){
# plot_counties(bigdata[bigdata$Age.Grouping==age,c('fips','Deaths')],
# yvar='Deaths',low='green',high='red',main=paste(age,"Deaths"))
plot_counties(winsor1Df(
median.out(bigdata[bigdata$Age.Grouping==age,c('fips','Death.per.100k')]),
ignore='fips',fraction=.05),
yvar='Death.per.100k',low='green',high='red',
main=paste(age,"Death Rates (Winsored)"),ylab='Deaths/\nPopulation\n*100k')
}
## void winsor(){ //generated by winsor1Df
## Death.per.100k =max( 3582.19444996898 ,min( 7819.82215733183 , Death.per.100k )); //limits are 518.116166354715 3582.19444996898 7819.82215733183 41463.4146341463
## }
## void winsor(){ //generated by winsor1Df
## Death.per.100k =max( 288.532980447823 ,min( 1355.51046017491 , Death.per.100k )); //limits are 137.962103908121 288.532980447823 1355.51046017491 3254.43786982249
## }
## void winsor(){ //generated by winsor1Df
## Death.per.100k =max( 67.1297848933185 ,min( 1515.55824885756 , Death.per.100k )); //limits are 31.5845054292382 67.1297848933185 1515.55824885756 3703.7037037037
## }
a100k=100000
n='ADULT' #hand executing this line allows one to step into the loop to bypass the for loop
for(n in unique(bigdata$Age.Grouping)){
d=bigdata[bigdata$Age.Grouping==n,]
d=d[!is.na(d$Deaths) & !is.na(d$Population),]
#plot(density(log(d$Population)),main=paste(n,'log(Age Group Populations) Density'))
m=sum(d$Deaths)/sum(d$Population)
priori=sum(d$Deaths,na.rm = T)/sum(d$Population,na.rm = T)
prioriweight=1/priori * 10
catln(n,priori,prioriweight)
priori=sum(d$Deaths[d$Population>prioriweight],na.rm = T)/sum(d$Population[d$Population>prioriweight],na.rm = T)
prioriweight=1/priori * 10
catln(n,priori,prioriweight)
##good
plot(d$Population, (d$Deaths)/(d$Population)*a100k,
col=rgb(1,0,0,0.2),log='x',main=paste('Deaths/Population for',n,'Group'),
xlab='Age Group Population in County',
ylab='Deaths/Population*100k in Age Group')
# points(d$Population,
# (d$Deaths+10/2)/(d$Population+prioriweight/2), #*a100k,
# col='purple',pch='.')
abline(v=10*1/priori,col='gray');
grid()
abline(h=quantile((d$Deaths)/(d$Population)*a100k,0.95),col='blue') #winsor
text(10*1/priori,max((d$Deaths)/(d$Population)*a100k)*0.05,round(prioriweight))
##
# plot(d$Population,
# d$Deaths,pch=20,
# col=rgb(1,0,0,0.1),main=n,log='xy',xlab='Age Group Population',ylab='Deaths in Age Group');grid()
# points(sort(d$Population),sort(d$Population)*priori,type='l')
# abline(v=prioriweight,col='gray');grid()
# text(prioriweight,800,round(10*1/priori))
###
print(summary(d$Deaths))
catln(n,'priori',priori,
'prioriweight',prioriweight,
'need at least a pop of this size',min(d$Deaths)*1/priori,
" small counties ", sum(d$Population<round(10*1/priori)),
" big counties ",sum(d$Population>=round(10*1/priori) ))
}
## SENIOR 0.04299791 232.5694
## SENIOR 0.04298034 232.6645
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 101.0 210.0 624.5 502.0 45604.0
## SENIOR priori 0.04298034 prioriweight 232.6645 need at least a pop of this size 232.6645 small counties 461 big counties 17611
## ADULT 0.003633879 2751.88
## ADULT 0.003605117 2773.835
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 35.0 77.0 236.4 197.0 15835.0
## ADULT priori 0.003605117 prioriweight 2773.835 need at least a pop of this size 2773.835 small counties 2705 big counties 13128
## YOUTH 0.0008552847 11692.01
## YOUTH 0.000729517 13707.7
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 14.00 24.00 54.51 57.00 1182.00
## YOUTH priori 0.000729517 prioriweight 13707.7 need at least a pop of this size 13707.7 small counties 1605 big counties 1301
age='SENIOR' #hand executing this line allows one to step into the loop to bypass the for loop
age='ADULT'
age='YOUTH'
year=0
importance=data.frame()
trees=list()
perf.table=NULL
for(age in unique(bigdata$Age.Grouping)){
d=bigdata[bigdata$Age.Grouping==age & !is.na(bigdata$Death.per.100k),]
set.seed(7)
trainset=runif(nrow(d))<0.8
label=paste(age,ifelse(year==0,'',year))
mean(is.na(d$Death.per.100k))
d=winsor1Df(d,ignore = ignore,trace=F)
#trees handle missing data
#d=impute(d,ignore = ignore,missing.threshold = 0.25)
require(MASS)
predictorVars=intersect(names(d),predictorVarsRaw)
colinearvars=caret::findCorrelation(cor(d[,predictorVars],use="pairwise.complete.obs"),names = T)
colinearpos=caret::findCorrelation(cor(d[,predictorVars],use="pairwise.complete.obs"),names = F)
catln('co-linear variables to be ignored:',caret::findCorrelation(cor(d[,predictorVars],use="pairwise.complete.obs"),names = T))
if(! setequal(predictorVars[colinearpos],colinearvars)) stop('vars mismatch')
predictorVars=predictorVars[-colinearpos]
require(rpart)
require(rpart.plot)
library(partykit)
require(dplyr)
summary(d$Population)
mtree=rpart(ezformula(c(yvar,predictorVars)),d[trainset,],weights = d$Population[trainset],
control = rpart.control(cp = 0.005))
#printcp(mtree) # display the results
plotcp(mtree,main=label) # visualize cross-validation results
cp=mtree$cptable[which.min(mtree$cptable[,"xerror"]),"CP"] ##best CP
message(label,'cp=',cp)
mtree=prune(mtree,cp)
catln(label,'tree depth is',max(rpart:::tree.depth(as.numeric(rownames(mtree$frame)))))
print_rpart(mtree,digits=2,nlab = 'Counties:',ylab=paste0(yvar,':'))
agedata=bigdata[bigdata$Age.Grouping==age & !is.na(bigdata$Death.per.100k),]
if(nrow(agedata)!=nrow(d))stop('the winsored and not-winsored should be the same length')
catln(age,'all data')
cbind(agedata,node=round((predict(mtree,agedata,type='vector'))),
response=(predict(mtree,agedata,type='vector'))) %>%
dplyr::group_by(node) %>%
dplyr::summarise(counties=length(fips),
deaths.pred=round(sum(response/100000*Population)),
deaths.act=sum(Deaths),
age.pop=sum(Population),
#dr100k.mean=mean(Death.per.100k),
dr100k.fit=mean(response)) %>%
dplyr::mutate(dr100k.group=deaths.act*100000/age.pop) %>% as.data.frame %>% print
catln(age,'test data')
cbind(agedata[!trainset,],node=round((predict(mtree,agedata[!trainset,],type='vector'))),
response=(predict(mtree,agedata[!trainset,],type='vector'))) %>%
dplyr::group_by(node) %>%
dplyr::summarise(counties=length(fips),
deaths.pred=round(sum(response/100000*Population)),
deaths.act=sum(Deaths),
age.pop=sum(Population),
#dr100k.mean=mean(Death.per.100k),
dr100k.fit=mean(response)) %>%
dplyr::mutate(dr100k.group=deaths.act*100000/age.pop) %>% as.data.frame %>% print
importance=dplyr::bind_rows(importance,
cbind(data.frame(age=age,year=year),
as.data.frame(t(as.data.frame(mtree$variable.importance)))))
#savedPlots=list()
for(n in (used.rpart.vars(mtree))){
lm.m=lm(ezformula(c(yvar,n)),d[trainset,],weights = d$Population[trainset])
s.lm.m=summary(lm.m)
c.lm.m=coef(s.lm.m)
if(c.lm.m[2,"Pr(>|t|)"]<0.05){
catln(n,paste("slope=",signif(c.lm.m[2,"Estimate"],2),
"r^2=",signif(s.lm.m$adj.r.squared,2)))
plot(d[trainset,c(n,yvar)],ylab=yvar,
xlab=n,main=age,
sub=paste0(round(mtree$variable.importance[[n]]/sum(mtree$variable.importance)*100),'% importance'),
#col=rgb(0,0,0,0.1/2)
col=rgb(0,0,0,(log(d$Population[trainset])/log(max(d$Population[trainset])))/5)
);grid()
text(mean(d[[n]],na.rm=T),mean(d[[yvar]]),
paste("slope=",signif(c.lm.m[2,"Estimate"],2),
"\nr^2=",signif(s.lm.m$adj.r.squared,2)),
col=ifelse(c.lm.m[2,"Estimate"]<0,'darkgreen','red'),font=2,cex=1.5)
abline(lm.m,col='steelblue')
}
}
trees[[age]]=mtree;
#plot(d$Deaths,(predict(mtree)/100000)*d$Population,col=rgb(0,0,0,0.2),main=label);grid()
catln(label,'all',
'\nrmse deaths=',rmse(d$Deaths,(predict(mtree,d)/100000)*d$Population),
'\nrmse Deaths by priori=',rmse(d$Deaths,sum(d$Deaths)/sum(d$Population)*d$Population),
'\ntree Deaths rsq=',rsq(d$Deaths,(predict(mtree,d)/100000)*d$Population),
'\nprior Deaths rsq=',rsq(d$Deaths,sum(d$Deaths)/sum(d$Population)*d$Population),
'\nfitted Death.per.100k rmse=',rmse(d$Death.per.100k,predict(mtree,d)),
'\nweighted Death.per.100k rmse=',rmse(d$Death.per.100k,predict(mtree,d),weights = d$Population),
'\nfitted Death.per.100k rsq=',rsq(d$Death.per.100k,(predict(mtree,d))))
.=data.frame(#'Age Group'=label,
'Deaths RMSE'=rmse(d$Deaths[!trainset],((predict(mtree,d[!trainset,]))/100000)*d$Population[!trainset]),
'Deaths by Priori RMSE'=rmse(d$Deaths[!trainset],sum(d$Deaths[!trainset])/sum(d$Population[!trainset])*d$Population[!trainset]),
'Tree Deaths RSq'=rsq(d$Deaths[!trainset],(predict(mtree,d[!trainset,])/100000)*d$Population[!trainset]),
'Priori Deaths RSq'=rsq(d$Deaths[!trainset],sum(d$Deaths[!trainset])/sum(d$Population[!trainset])*d$Population[!trainset]),
'Fitted Death.per.100k RMSE'=rmse(d$Death.per.100k[!trainset],(predict(mtree,d[!trainset,]))),
'Weighted Death.per.100k RMSE'=rmse(d$Death.per.100k[!trainset],predict(mtree,d[!trainset,]),weights = d$Population[!trainset]),
'Fitted Death.per.100k RSq'=rsq(d$Death.per.100k[!trainset],(predict(mtree,d[!trainset,]))))
rownames(.)=age
if(is.null(perf.table))perf.table=.
else perf.table=rbind(perf.table,.)
print(summary(mtree))
}
## Loading required package: MASS
## co-linear variables to be ignored: pct_of_children_eligible_for_free_lunch teen_births.birth_rate homicides.homicide_rate sexually_transmitted_infections.rates_per_100000 excessive_drinking.pct high_housing_costs.pct violent_crime_rate hiv_prevalence_rate hiv_rate commuting_alone.pct_drive
## Loading required package: rpart
## Loading required package: rpart.plot
## Loading required package: grid
## Loading required package: dplyr
## Warning: package 'dplyr' was built under R version 3.4.2
##
## Attaching package: 'dplyr'
## The following object is masked _by_ '.GlobalEnv':
##
## coalesce
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## SENIOR cp=0.005
## SENIOR tree depth is 6
## n= 14460
##
## node), split, n, yval
## * denotes terminal node
##
## 1) root Counties:14460 Death.per.100k:4300
## 2) adult_obesity.pct_obese< 26 Counties:2191 Death.per.100k:3900
## 4) preventable_hospital_stays.hosp__rate< 57 Counties:1581 Death.per.100k:3800
## 8) other_primary_care_providers.pcp_rate< 68 Counties:1052 Death.per.100k:3700
## 16) unemployed.ratio< 0.07 Counties:508 Death.per.100k:3600
## 32) mammography_screening.pct>=74 Counties:54 Death.per.100k:3200 *
## 33) mammography_screening.pct< 74 Counties:454 Death.per.100k:3600
## 66) median_household_income>=8.1e+04 Counties:70 Death.per.100k:3300 *
## 67) median_household_income< 8.1e+04 Counties:384 Death.per.100k:3700 *
## 17) unemployed.ratio>=0.07 Counties:544 Death.per.100k:3900
## 34) social_associations.association_rate< 7.3 Counties:183 Death.per.100k:3800 *
## 35) social_associations.association_rate>=7.3 Counties:361 Death.per.100k:4100 *
## 9) other_primary_care_providers.pcp_rate>=68 Counties:529 Death.per.100k:4000
## 18) adult_obesity.pct_obese< 16 Counties:22 Death.per.100k:3400 *
## 19) adult_obesity.pct_obese>=16 Counties:507 Death.per.100k:4100 *
## 5) preventable_hospital_stays.hosp__rate>=57 Counties:610 Death.per.100k:4200
## 10) social_associations.association_rate< 6.1 Counties:77 Death.per.100k:3800 *
## 11) social_associations.association_rate>=6.1 Counties:533 Death.per.100k:4300
## 22) unemployed.ratio< 0.099 Counties:369 Death.per.100k:4200 *
## 23) unemployed.ratio>=0.099 Counties:164 Death.per.100k:4600 *
## 3) adult_obesity.pct_obese>=26 Counties:12269 Death.per.100k:4600
## 6) social_associations.association_rate< 8.8 Counties:2124 Death.per.100k:4200
## 12) adult_smoking.pct_smokers< 20 Counties:818 Death.per.100k:4000
## 24) primary_care_provider_rate.pcp< 42 Counties:166 Death.per.100k:3200
## 48) physical_inactivity.pct_physically_inactive< 24 Counties:34 Death.per.100k:2700 *
## 49) physical_inactivity.pct_physically_inactive>=24 Counties:132 Death.per.100k:4300 *
## 25) primary_care_provider_rate.pcp>=42 Counties:652 Death.per.100k:4100
## 50) injury_deaths.death_rate< 39 Counties:78 Death.per.100k:3700 *
## 51) injury_deaths.death_rate>=39 Counties:574 Death.per.100k:4100 *
## 13) adult_smoking.pct_smokers>=20 Counties:1306 Death.per.100k:4500
## 26) income_inequality.ratio< 4.4 Counties:476 Death.per.100k:4200 *
## 27) income_inequality.ratio>=4.4 Counties:830 Death.per.100k:4800 *
## 7) social_associations.association_rate>=8.8 Counties:10145 Death.per.100k:4700
## 14) physical_inactivity.pct_physically_inactive< 25 Counties:1586 Death.per.100k:4400 *
## 15) physical_inactivity.pct_physically_inactive>=25 Counties:8559 Death.per.100k:4800
## 30) access_to_parks.pct_park< 14 Counties:4422 Death.per.100k:4700
## 60) preventable_hospital_stays.hosp__rate< 64 Counties:1244 Death.per.100k:4400 *
## 61) preventable_hospital_stays.hosp__rate>=64 Counties:3178 Death.per.100k:4800 *
## 31) access_to_parks.pct_park>=14 Counties:4137 Death.per.100k:4900
## 62) median_household_income>=5.2e+04 Counties:480 Death.per.100k:4600 *
## 63) median_household_income< 5.2e+04 Counties:3657 Death.per.100k:5000 *
## SENIOR all data
## Warning: package 'bindrcpp' was built under R version 3.4.2
## node counties deaths.pred deaths.act age.pop dr100k.fit dr100k.group
## 1 2674 45 21588 21659 807286 2674.206 2682.940
## 2 3196 68 115344 115441 3609119 3195.912 3198.592
## 3 3263 91 161255 163354 4941412 3263.334 3305.816
## 4 3404 29 78373 78255 2302246 3404.182 3399.072
## 5 3660 95 107655 106612 2941527 3659.823 3624.376
## 6 3689 488 804212 806502 21800523 3688.956 3699.462
## 7 3757 223 672525 674100 17901841 3756.736 3765.535
## 8 3796 93 293166 291992 7722843 3796.091 3780.887
## 9 4083 437 341294 339971 8359615 4082.653 4066.826
## 10 4121 636 776557 778067 18843798 4121.023 4129.035
## 11 4128 713 1093669 1095347 26495287 4127.786 4134.120
## 12 4195 466 666555 669173 15889744 4194.878 4211.352
## 13 4233 589 332330 331236 7850776 4233.081 4219.150
## 14 4268 165 18587 18448 435522 4267.786 4235.837
## 15 4424 1566 456308 456279 10313613 4424.326 4424.046
## 16 4449 2038 1157043 1157498 26009674 4448.509 4450.260
## 17 4568 598 258440 258423 5657841 4567.829 4567.520
## 18 4586 197 325972 326373 7107596 4586.246 4591.890
## 19 4767 1030 449956 450335 9439133 4766.921 4770.936
## 20 4818 3984 959949 959437 19922227 4818.483 4815.912
## 21 4960 4521 2188147 2186906 44112484 4960.380 4957.567
## SENIOR test data
## node counties deaths.pred deaths.act age.pop dr100k.fit dr100k.group
## 1 2674 11 3378 3833 126323 2674.206 3034.285
## 2 3196 14 26165 26361 818703 3195.912 3219.849
## 3 3263 21 54365 56478 1665945 3263.334 3390.148
## 4 3404 7 11472 11354 336987 3404.182 3369.269
## 5 3660 17 20872 19829 570292 3659.823 3476.991
## 6 3689 104 165680 168229 4491256 3688.956 3745.701
## 7 3757 40 121589 123175 3236555 3756.736 3805.744
## 8 3796 16 36541 35372 962590 3796.091 3674.669
## 9 4083 76 57433 56305 1406756 4082.653 4002.471
## 10 4121 129 178252 179772 4325441 4121.023 4156.154
## 11 4128 139 221235 222921 5359658 4127.786 4159.239
## 12 4195 97 198648 201252 4735485 4194.878 4249.871
## 13 4233 113 65760 64666 1553472 4233.081 4162.676
## 14 4268 33 4561 4457 106880 4267.786 4170.097
## 15 4424 321 90473 90426 2044898 4424.326 4422.030
## 16 4449 452 248280 248736 5581190 4448.509 4456.684
## 17 4568 119 47256 47230 1034531 4567.829 4565.354
## 18 4586 33 49314 49715 1075255 4586.246 4623.554
## 19 4767 200 90096 90479 1890033 4766.921 4787.165
## 20 4818 804 191822 191275 3980966 4818.483 4804.738
## 21 4960 866 430585 429279 8680491 4960.380 4945.331
## adult_obesity.pct_obese slope= 79 r^2= 0.3
## physical_inactivity.pct_physically_inactive slope= 73 r^2= 0.32
## social_associations.association_rate slope= 79 r^2= 0.25
## preventable_hospital_stays.hosp__rate slope= 16 r^2= 0.2
## injury_deaths.death_rate slope= 13 r^2= 0.14
## adult_smoking.pct_smokers slope= 68 r^2= 0.29
## unemployed.ratio slope= 3200 r^2= 0.032
## median_household_income slope= -0.02 r^2= 0.17
## other_primary_care_providers.pcp_rate slope= 3.4 r^2= 0.03
## mammography_screening.pct slope= -9.7 r^2= 0.0091
## primary_care_provider_rate.pcp slope= -0.71 r^2= 0.003
## income_inequality.ratio slope= 39 r^2= 0.0016
## access_to_parks.pct_park slope= -6 r^2= 0.047
## SENIOR all
## rmse deaths= 100.7995
## rmse Deaths by priori= 251.1529
## tree Deaths rsq= 0.996113
## prior Deaths rsq= 0.984624
## fitted Death.per.100k rmse= 1923.5
## weighted Death.per.100k rmse= 435.1074
## fitted Death.per.100k rsq= 0.04584812
## Call:
## rpart(formula = ezformula(c(yvar, predictorVars)), data = d[trainset,
## ], weights = d$Population[trainset], control = rpart.control(cp = 0.005))
## n= 14460
##
## CP nsplit rel error xerror xstd
## 1 0.258824362 0 1.0000000 1.0008793 0.0003132421
## 2 0.081461956 1 0.7411756 0.7599032 0.0002994681
## 3 0.028995912 2 0.6597137 0.6665662 0.0002921715
## 4 0.025553120 3 0.6307178 0.6401958 0.0002915996
## 5 0.023350731 4 0.6051646 0.6186134 0.0002892209
## 6 0.015967146 5 0.5818139 0.6046426 0.0002889076
## 7 0.014609014 6 0.5658468 0.5952839 0.0002888330
## 8 0.011201562 7 0.5512378 0.5791833 0.0002882086
## 9 0.010895643 8 0.5400362 0.5725608 0.0002882920
## 10 0.010182935 9 0.5291406 0.5705973 0.0002888298
## 11 0.009458984 10 0.5189576 0.5611237 0.0002886842
## 12 0.008731830 12 0.5000397 0.5529538 0.0002892816
## 13 0.007215268 13 0.4913078 0.5470928 0.0002886462
## 14 0.006865151 14 0.4840926 0.5381785 0.0002880015
## 15 0.006444455 15 0.4772274 0.5321912 0.0002878544
## 16 0.005735774 16 0.4707829 0.5207696 0.0002879072
## 17 0.005400564 17 0.4650472 0.5189931 0.0002872032
## 18 0.005345174 19 0.4542460 0.5163929 0.0002868944
## 19 0.005000000 20 0.4489009 0.5092958 0.0002868466
##
## Variable importance
## adult_obesity.pct_obese
## 17
## physical_inactivity.pct_physically_inactive
## 11
## college_degrees.pct
## 9
## households_with_high_housing_costs.pct
## 9
## diabetes.pct_diabetic
## 8
## severe_housing_problems.pct
## 7
## social_associations.association_rate
## 7
## preventable_hospital_stays.hosp__rate
## 3
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate
## 2
## long_commute_driving_alone.pct_drives
## 2
## demographics.pct_not_proficient_in_english
## 2
## injury_deaths.death_rate
## 2
## adult_smoking.pct_smokers
## 1
## unemployed.ratio
## 1
## pct_illiterate
## 1
## median_household_income
## 1
## air_pollution_ozone_days
## 1
## mammography_screening.rate
## 1
## other_primary_care_providers.pcp_rate
## 1
## mammography_screening.pct
## 1
## motor_vehicle_crash_deaths.mv_mortality_rate
## 1
## alcohol_impaired_driving_deaths.pct
## 1
## primary_care_provider_rate.pcp
## 1
## health_care_costs
## 1
## hiv_prevalence.rate
## 1
## income_inequality.ratio
## 1
## some_college.pct
## 1
## sahie.pct.uninsured
## 1
## access_to_parks.pct_park
## 1
## drug_poisoning_deaths.mortality_rate
## 1
##
## Node number 1: 14460 observations, complexity param=0.2588244
## mean=4300.236, MSE=418978.2
## left son=2 (2191 obs) right son=3 (12269 obs)
## Primary splits:
## adult_obesity.pct_obese < 26.35 to the left, improve=0.2588244, (0 missing)
## physical_inactivity.pct_physically_inactive < 24.48 to the left, improve=0.2575101, (2 missing)
## adult_smoking.pct_smokers < 18.33167 to the left, improve=0.2256138, (992 missing)
## social_associations.association_rate < 9.821322 to the left, improve=0.2122582, (97 missing)
## demographics.pct_not_proficient_in_english < 4.22358 to the right, improve=0.1695233, (2 missing)
## Surrogate splits:
## college_degrees.pct < 27.15661 to the right, agree=0.784, adj=0.473, (0 split)
## physical_inactivity.pct_physically_inactive < 22.85 to the left, agree=0.784, adj=0.473, (0 split)
## diabetes.pct_diabetic < 8.865152 to the left, agree=0.775, adj=0.451, (0 split)
## households_with_high_housing_costs.pct < 36.65 to the right, agree=0.767, adj=0.431, (0 split)
## severe_housing_problems.pct < 19.10782 to the right, agree=0.741, adj=0.369, (0 split)
##
## Node number 2: 2191 observations, complexity param=0.02899591
## mean=3904.995, MSE=222407.2
## left son=4 (1581 obs) right son=5 (610 obs)
## Primary splits:
## preventable_hospital_stays.hosp__rate < 57.235 to the left, improve=0.1327627, (49 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 65.25673 to the left, improve=0.1263855, (8 missing)
## social_associations.association_rate < 6.930659 to the left, improve=0.1197737, (15 missing)
## adult_smoking.pct_smokers < 16.865 to the left, improve=0.1150870, (65 missing)
## unemployed.ratio < 0.0837066 to the left, improve=0.1117071, (0 missing)
## Surrogate splits:
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 65.89869 to the left, agree=0.912, adj=0.686, (41 split)
## physical_inactivity.pct_physically_inactive < 21.70357 to the left, agree=0.792, adj=0.259, (8 split)
## married.pct < 0.2916978 to the right, agree=0.780, adj=0.215, (0 split)
## health_care_costs < 11703.18 to the left, agree=0.765, adj=0.163, (0 split)
## children_in_single_parent_households.pct < 37.56781 to the left, agree=0.763, adj=0.155, (0 split)
##
## Node number 3: 12269 observations, complexity param=0.08146196
## mean=4574.605, MSE=371714.2
## left son=6 (2124 obs) right son=7 (10145 obs)
## Primary splits:
## social_associations.association_rate < 8.840807 to the left, improve=0.15529100, (82 missing)
## physical_inactivity.pct_physically_inactive < 25.31 to the left, improve=0.14841010, (2 missing)
## adult_smoking.pct_smokers < 18.53 to the left, improve=0.12065990, (927 missing)
## median_household_income < 49635 to the right, improve=0.09786335, (2 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 71.33925 to the left, improve=0.08241451, (23 missing)
## Surrogate splits:
## demographics.pct_not_proficient_in_english < 5.883435 to the right, agree=0.781, adj=0.296, (80 split)
## long_commute_driving_alone.pct_drives < 38.65 to the right, agree=0.775, adj=0.279, (0 split)
## severe_housing_problems.pct < 20.28085 to the right, agree=0.769, adj=0.260, (0 split)
## households_with_high_housing_costs.pct < 39.55 to the right, agree=0.768, adj=0.255, (0 split)
## air_pollution_ozone_days < 23.5 to the right, agree=0.749, adj=0.194, (2 split)
##
## Node number 4: 1581 observations, complexity param=0.01460901
## mean=3797.528, MSE=193246.5
## left son=8 (1052 obs) right son=9 (529 obs)
## Primary splits:
## other_primary_care_providers.pcp_rate < 68.03784 to the left, improve=0.10737600, (9 missing)
## social_associations.association_rate < 10.46158 to the left, improve=0.09835927, (7 missing)
## long_commute_driving_alone.pct_drives < 33.15 to the right, improve=0.09229659, (7 missing)
## college_degrees.pct < 51.61495 to the right, improve=0.09088646, (21 missing)
## pct_illiterate < 10.75 to the right, improve=0.09075853, (17 missing)
## Surrogate splits:
## social_associations.association_rate < 10.96351 to the left, agree=0.795, adj=0.235, (2 split)
## primary_care_provider_rate.pcp < 204.0669 to the left, agree=0.791, adj=0.221, (3 split)
## high_school_graduation.pct_afgr < 69.2 to the right, agree=0.787, adj=0.206, (0 split)
## children_in_single_parent_households.pct < 37.81873 to the left, agree=0.787, adj=0.205, (4 split)
## chlamydia_rate.rates_per_100000 < 527.2 to the left, agree=0.786, adj=0.202, (0 split)
##
## Node number 5: 610 observations, complexity param=0.01596715
## mean=4180.887, MSE=191503.4
## left son=10 (77 obs) right son=11 (533 obs)
## Primary splits:
## social_associations.association_rate < 6.120965 to the left, improve=0.3043597, (8 missing)
## pct_illiterate < 19.85 to the right, improve=0.2753760, (12 missing)
## severe_housing_problems.pct < 24.45407 to the right, improve=0.2477433, (1 missing)
## married.pct < 0.2845878 to the left, improve=0.2471087, (0 missing)
## mammography_screening.rate < 52.862 to the left, improve=0.2443286, (55 missing)
## Surrogate splits:
## alcohol_impaired_driving_deaths.pct < 21.16731 to the left, agree=0.936, adj=0.774, (7 split)
## pct_illiterate < 20.55 to the right, agree=0.936, adj=0.773, (0 split)
## mammography_screening.rate < 57.47445 to the left, agree=0.928, adj=0.744, (0 split)
## hiv_prevalence.rate < 924.75 to the right, agree=0.925, adj=0.733, (0 split)
## households_with_high_housing_costs.pct < 49 to the right, agree=0.924, adj=0.731, (0 split)
##
## Node number 6: 2124 observations, complexity param=0.02335073
## mean=4217.313, MSE=285054.4
## left son=12 (818 obs) right son=13 (1306 obs)
## Primary splits:
## adult_smoking.pct_smokers < 20.255 to the left, improve=0.1855976, (148 missing)
## drug_poisoning_deaths.mortality_rate < 13.80764 to the left, improve=0.1685312, (428 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 73.69278 to the left, improve=0.1561667, (7 missing)
## physical_inactivity.pct_physically_inactive < 26.05 to the left, improve=0.1514961, (0 missing)
## uninsured_children.pct < 9.676387 to the right, improve=0.1483545, (2 missing)
## Surrogate splits:
## injury_deaths.death_rate < 62.25408 to the left, agree=0.811, adj=0.473, (134 split)
## diabetes.pct_diabetic < 10.22 to the left, agree=0.787, adj=0.407, (14 split)
## physical_inactivity.pct_physically_inactive < 25.72 to the left, agree=0.787, adj=0.407, (0 split)
## preventable_hospital_stays.hosp__rate < 66.635 to the left, agree=0.782, adj=0.394, (0 split)
## drug_poisoning_deaths.mortality_rate < 17.07774 to the left, agree=0.763, adj=0.340, (0 split)
##
## Node number 7: 10145 observations, complexity param=0.02555312
## mean=4736.444, MSE=326952
## left son=14 (1586 obs) right son=15 (8559 obs)
## Primary splits:
## physical_inactivity.pct_physically_inactive < 24.57333 to the left, improve=0.08058856, (2 missing)
## preventable_hospital_stays.hosp__rate < 59.15 to the left, improve=0.07578943, (197 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 65.798 to the left, improve=0.06706655, (16 missing)
## median_household_income < 51800 to the right, improve=0.06420144, (2 missing)
## income_inequality.ratio < 4.298682 to the left, improve=0.05959308, (63 missing)
## Surrogate splits:
## diabetes.pct_diabetic < 8.73 to the left, agree=0.814, adj=0.227, (0 split)
## college_degrees.pct < 28.24019 to the right, agree=0.795, adj=0.149, (2 split)
## some_college.pct < 71.51324 to the right, agree=0.793, adj=0.142, (0 split)
## health_care_costs < 7991.533 to the left, agree=0.788, adj=0.123, (0 split)
## median_household_income < 56084 to the right, agree=0.788, adj=0.123, (0 split)
##
## Node number 8: 1052 observations, complexity param=0.01089564
## mean=3710.324, MSE=164243.6
## left son=16 (508 obs) right son=17 (544 obs)
## Primary splits:
## unemployed.ratio < 0.06980548 to the left, improve=0.12878740, (0 missing)
## social_associations.association_rate < 10.54073 to the left, improve=0.10612200, (7 missing)
## college_degrees.pct < 50.56903 to the right, improve=0.08444942, (17 missing)
## drinking_water_violations.pct_pop_in_viol < 17.1353 to the left, improve=0.08253329, (32 missing)
## sahie.pct.uninsured < 20.43 to the right, improve=0.08057780, (44 missing)
## Surrogate splits:
## demographics.pct_not_proficient_in_english < 15.46075 to the left, agree=0.688, adj=0.350, (0 split)
## diabetic_screening.pct_hba1c < 79.23486 to the right, agree=0.686, adj=0.346, (0 split)
## single_parent_households.pct < 11.37577 to the right, agree=0.644, adj=0.258, (0 split)
## liquor_store_density.rate < 1.547533 to the right, agree=0.643, adj=0.256, (0 split)
## low_birthweight.pct_lbw < 6.502618 to the right, agree=0.639, adj=0.249, (0 split)
##
## Node number 9: 529 observations, complexity param=0.01018293
## mean=4035.557, MSE=194997.1
## left son=18 (22 obs) right son=19 (507 obs)
## Primary splits:
## adult_obesity.pct_obese < 16.41775 to the left, improve=0.2767254, (0 missing)
## college_degrees.pct < 47.85632 to the right, improve=0.2677454, (4 missing)
## pct_illiterate < 16.75 to the right, improve=0.2669924, (7 missing)
## severe_housing_problems.pct < 24.94777 to the right, improve=0.2583796, (0 missing)
## some_college.pct < 80.3951 to the right, improve=0.2563665, (0 missing)
## Surrogate splits:
## college_degrees.pct < 51.87942 to the right, agree=0.966, adj=0.718, (0 split)
## long_commute_driving_alone.pct_drives < 44.7 to the right, agree=0.964, adj=0.697, (0 split)
## motor_vehicle_crash_deaths.mv_mortality_rate < 4.888512 to the left, agree=0.964, adj=0.695, (0 split)
## injury_deaths.death_rate < 33.46756 to the left, agree=0.964, adj=0.694, (0 split)
## limited_access_to_healthy_foods.pct < 0.005 to the left, agree=0.963, adj=0.686, (0 split)
##
## Node number 10: 77 observations
## mean=3796.091, MSE=65375.53
##
## Node number 11: 533 observations, complexity param=0.006865151
## mean=4332.245, MSE=159964.1
## left son=22 (369 obs) right son=23 (164 obs)
## Primary splits:
## unemployed.ratio < 0.09917085 to the left, improve=0.2181193, (0 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 78.05615 to the left, improve=0.1457259, (2 missing)
## preventable_hospital_stays.hosp__rate < 70.95 to the left, improve=0.1359340, (16 missing)
## long_commute_driving_alone.pct_drives < 36.65 to the right, improve=0.1353127, (2 missing)
## adult_smoking.pct_smokers < 18.32 to the left, improve=0.1302174, (36 missing)
## Surrogate splits:
## single_parent_households.pct < 10.69067 to the right, agree=0.770, adj=0.344, (0 split)
## liquor_store_density.rate < 2.676875 to the right, agree=0.766, adj=0.334, (0 split)
## diabetic_screening.pct_hba1c < 80.48071 to the right, agree=0.743, adj=0.267, (0 split)
## taxcredits.ratio < 0.3321441 to the left, agree=0.736, adj=0.249, (0 split)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 80.5505 to the left, agree=0.720, adj=0.201, (0 split)
##
## Node number 12: 818 observations, complexity param=0.00873183
## mean=4044.027, MSE=205480.2
## left son=24 (166 obs) right son=25 (652 obs)
## Primary splits:
## primary_care_provider_rate.pcp < 42.13645 to the left, improve=0.1551356, (19 missing)
## wages.avg < 22.34552 to the left, improve=0.1514776, (0 missing)
## income_inequality.ratio < 3.763576 to the left, improve=0.1505764, (12 missing)
## mammography_screening.pct < 77.99824 to the right, improve=0.1496536, (4 missing)
## dentists.dentist_rate < 27.35067 to the left, improve=0.1463942, (0 missing)
## Surrogate splits:
## primary_care_physicians.pcp_rate < 23.01542 to the left, agree=0.990, adj=0.747, (17 split)
## wages.avg < 22.63266 to the left, agree=0.971, adj=0.286, (2 split)
## taxcredits.ratio < 0.2407121 to the left, agree=0.970, adj=0.278, (0 split)
## mammography_screening.pct < 79.65746 to the right, agree=0.970, adj=0.274, (0 split)
## some_college.pct < 39.54731 to the left, agree=0.970, adj=0.264, (0 split)
##
## Node number 13: 1306 observations, complexity param=0.01120156
## mean=4524.133, MSE=278642.7
## left son=26 (476 obs) right son=27 (830 obs)
## Primary splits:
## income_inequality.ratio < 4.406645 to the left, improve=0.2540602, (7 missing)
## uninsured_children.pct < 7.900187 to the right, improve=0.2341967, (1 missing)
## limited_access_to_healthy_foods.pct < 4.329132 to the right, improve=0.2113412, (1 missing)
## low_birthweight.pct_lbw < 9.35 to the left, improve=0.2035723, (0 missing)
## income_inequality.gini < 44.55 to the left, improve=0.1957590, (2 missing)
## Surrogate splits:
## income_inequality.gini < 43.95 to the left, agree=0.820, adj=0.604, (7 split)
## homicide_rate < 5.6933 to the left, agree=0.742, adj=0.432, (0 split)
## low_birthweight.pct_lbw < 8.515 to the left, agree=0.739, adj=0.426, (0 split)
## children_eligible_for_free_lunch.pct < 50.02899 to the left, agree=0.729, adj=0.405, (0 split)
## uninsured_children.pct < 7.769359 to the right, agree=0.715, adj=0.373, (0 split)
##
## Node number 14: 1586 observations
## mean=4448.509, MSE=213831.7
##
## Node number 15: 8559 observations, complexity param=0.009458984
## mean=4827.972, MSE=328178.9
## left son=30 (4422 obs) right son=31 (4137 obs)
## Primary splits:
## access_to_parks.pct_park < 13.5 to the left, improve=0.03883445, (1011 missing)
## income_inequality.ratio < 4.298921 to the left, improve=0.03862518, (58 missing)
## preventable_hospital_stays.hosp__rate < 61.535 to the left, improve=0.03624648, (168 missing)
## diabetic_screening.pct_hba1c < 82.83689 to the right, improve=0.03508955, (3 missing)
## median_household_income < 49684 to the right, improve=0.03481088, (2 missing)
## Surrogate splits:
## motor_vehicle_crash_deaths.mv_mortality_rate < 17.56553 to the right, agree=0.753, adj=0.299, (831 split)
## sahie.pct.uninsured < 15.18583 to the right, agree=0.750, adj=0.291, (60 split)
## access_to_exercise_opportunities.pct_with < 58.20196 to the left, agree=0.747, adj=0.282, (109 split)
## dentists.dentist_rate < 30.88099 to the left, agree=0.742, adj=0.268, (8 split)
## some_college.pct < 51.85744 to the left, agree=0.735, adj=0.247, (0 split)
##
## Node number 16: 508 observations, complexity param=0.005400564
## mean=3570.458, MSE=159965.1
## left son=32 (54 obs) right son=33 (454 obs)
## Primary splits:
## mammography_screening.pct < 73.84467 to the right, improve=0.1188342, (2 missing)
## sahie.pct.uninsured < 20.1875 to the right, improve=0.1161690, (29 missing)
## some_college.pct < 52.90441 to the left, improve=0.1079978, (4 missing)
## access_to_healthy_foods.pct_food < 61.5828 to the right, improve=0.1025952, (4 missing)
## median_household_income < 85933 to the right, improve=0.1001463, (0 missing)
## Surrogate splits:
## mammography_screening.rate < 72.4327 to the right, agree=0.947, adj=0.558, (0 split)
## diabetes.pct_diabetic < 9.85 to the right, agree=0.932, adj=0.431, (2 split)
## fast_food_restaurants.pct_foods < 37.77769 to the left, agree=0.920, adj=0.329, (0 split)
## some_college.pct < 52.90441 to the left, agree=0.916, adj=0.296, (0 split)
## sahie.pct.uninsured < 20.1875 to the right, agree=0.913, adj=0.270, (0 split)
##
## Node number 17: 544 observations, complexity param=0.005735774
## mean=3861.558, MSE=124845.6
## left son=34 (183 obs) right son=35 (361 obs)
## Primary splits:
## social_associations.association_rate < 7.337901 to the left, improve=0.1858073, (3 missing)
## severe_housing_problems.pct < 18.44201 to the right, improve=0.1576965, (0 missing)
## could_not_see_doctor_due_to_cost.pct_couldnt_access < 9.15 to the right, improve=0.1288276, (16 missing)
## violent_crime.rate < 204.1354 to the right, improve=0.1274380, (8 missing)
## uninsured_children.pct < 4.356934 to the right, improve=0.1272138, (0 missing)
## Surrogate splits:
## access_to_recreational_facilities.rec_fac_rate < 12.51896 to the left, agree=0.866, adj=0.582, (3 split)
## teen_birth_rate < 24.45 to the right, agree=0.864, adj=0.579, (0 split)
## children_eligible_for_free_lunch.pct < 26.18888 to the right, agree=0.863, adj=0.574, (0 split)
## pct_illiterate < 10.55 to the right, agree=0.862, adj=0.572, (0 split)
## sexually_transmitted_infections.chlamydia_rate < 259.9 to the right, agree=0.855, adj=0.548, (0 split)
##
## Node number 18: 22 observations
## mean=3404.182, MSE=92072.08
##
## Node number 19: 507 observations
## mean=4121.023, MSE=147664.4
##
## Node number 22: 369 observations
## mean=4194.878, MSE=134245
##
## Node number 23: 164 observations
## mean=4586.246, MSE=108113
##
## Node number 24: 166 observations, complexity param=0.006444455
## mean=3192.941, MSE=1044677
## left son=48 (34 obs) right son=49 (132 obs)
## Primary splits:
## physical_inactivity.pct_physically_inactive < 23.95 to the left, improve=0.5337147, (0 missing)
## mammography_screening.rate < 66.92715 to the right, improve=0.5124461, (27 missing)
## could_not_see_doctor_due_to_cost.pct_couldnt_access < 12.95 to the left, improve=0.4393661, (45 missing)
## income_inequality.ratio < 3.949739 to the left, improve=0.4078268, (8 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 68.46535 to the left, improve=0.4046166, (1 missing)
## Surrogate splits:
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 68.46535 to the left, agree=0.951, adj=0.849, (0 split)
## sahie.pct.uninsured < 20.575 to the left, agree=0.925, adj=0.770, (0 split)
## mammography_screening.pct < 61.58763 to the right, agree=0.906, adj=0.710, (0 split)
## social_associations.association_rate < 5.848229 to the left, agree=0.899, adj=0.690, (0 split)
## college_degrees.pct < 12.44625 to the right, agree=0.891, adj=0.666, (0 split)
##
## Node number 25: 652 observations, complexity param=0.005345174
## mean=4080.581, MSE=136990.8
## left son=50 (78 obs) right son=51 (574 obs)
## Primary splits:
## injury_deaths.death_rate < 38.98509 to the left, improve=0.1449049, (8 missing)
## adult_smoking.pct_smokers < 14.795 to the left, improve=0.1222545, (29 missing)
## mentally_unhealthy_days < 3.315 to the left, improve=0.1105845, (24 missing)
## long_commute_driving_alone.pct_drives < 44.3 to the right, improve=0.1104462, (4 missing)
## dentists.dentist_rate < 62.13478 to the left, improve=0.1034940, (0 missing)
## Surrogate splits:
## health_care_costs < 12092.46 to the right, agree=0.923, adj=0.238, (8 split)
## children_in_poverty.pct < 44.55 to the right, agree=0.916, adj=0.171, (0 split)
## sahie.pct.uninsured < 30.11667 to the right, agree=0.915, adj=0.160, (0 split)
## drug_poisoning_deaths.mortality_rate < 4.981702 to the left, agree=0.914, adj=0.144, (0 split)
## income_inequality.gini < 48.75 to the right, agree=0.912, adj=0.127, (0 split)
##
## Node number 26: 476 observations
## mean=4233.081, MSE=186597.7
##
## Node number 27: 830 observations
## mean=4766.921, MSE=225814.2
##
## Node number 30: 4422 observations, complexity param=0.009458984
## mean=4683.863, MSE=409354.8
## left son=60 (1244 obs) right son=61 (3178 obs)
## Primary splits:
## preventable_hospital_stays.hosp__rate < 63.9 to the left, improve=0.08601551, (82 missing)
## income_inequality.ratio < 4.500345 to the left, improve=0.08190500, (32 missing)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 91.45407 to the left, improve=0.08153864, (10 missing)
## physical_inactivity.pct_physically_inactive < 29.23 to the left, improve=0.07034809, (1 missing)
## median_household_income < 45907.5 to the right, improve=0.06694624, (1 missing)
## Surrogate splits:
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 70.2685 to the left, agree=0.859, adj=0.587, (73 split)
## health_care_costs < 9180.875 to the left, agree=0.752, adj=0.275, (9 split)
## college_degrees.pct < 19.7649 to the right, agree=0.733, adj=0.217, (0 split)
## physical_inactivity.pct_physically_inactive < 27.2619 to the left, agree=0.721, adj=0.183, (0 split)
## mammography_screening.pct < 69.19766 to the right, agree=0.712, adj=0.156, (0 split)
##
## Node number 31: 4137 observations, complexity param=0.007215268
## mean=4915.069, MSE=258980.4
## left son=62 (480 obs) right son=63 (3657 obs)
## Primary splits:
## median_household_income < 51848 to the right, improve=0.06074987, (1 missing)
## physical_inactivity.pct_physically_inactive < 28.9 to the left, improve=0.05055453, (1 missing)
## wages.avg < 34.18568 to the right, improve=0.05041817, (0 missing)
## social_associations.association_rate < 21.35227 to the left, improve=0.04270936, (26 missing)
## injury_deaths.death_rate < 65.45651 to the left, improve=0.04001469, (85 missing)
## Surrogate splits:
## children_in_poverty.pct < 14.05 to the left, agree=0.914, adj=0.253, (1 split)
## wages.avg < 47.68349 to the right, agree=0.906, adj=0.182, (0 split)
## pct_illiterate < 6.55 to the left, agree=0.903, adj=0.164, (0 split)
## adjusted.gross.income.avg < 68.57197 to the right, agree=0.898, adj=0.114, (0 split)
## some_college.pct < 76.01044 to the right, agree=0.895, adj=0.088, (0 split)
##
## Node number 32: 54 observations
## mean=3195.912, MSE=191489.3
##
## Node number 33: 454 observations, complexity param=0.005400564
## mean=3621.231, MSE=134097.2
## left son=66 (70 obs) right son=67 (384 obs)
## Primary splits:
## median_household_income < 80853.5 to the right, improve=0.1807546, (0 missing)
## college_degrees.pct < 50.56903 to the right, improve=0.1759987, (13 missing)
## long_commute_driving_alone.pct_drives < 49.2 to the right, improve=0.1625147, (4 missing)
## limited_access_to_healthy_foods.pct < 2.176528 to the left, improve=0.1452561, (0 missing)
## injury_deaths.death_rate < 42.57526 to the left, improve=0.1432071, (10 missing)
## Surrogate splits:
## college_degrees.pct < 46.55999 to the right, agree=0.951, adj=0.691, (0 split)
## food_environment_index < 9.085718 to the right, agree=0.924, adj=0.519, (0 split)
## motor_vehicle_crash_deaths.mv_mortality_rate < 5.846693 to the left, agree=0.923, adj=0.514, (0 split)
## injury_deaths.death_rate < 34.98867 to the left, agree=0.921, adj=0.504, (0 split)
## wages.avg < 69.66999 to the right, agree=0.920, adj=0.498, (0 split)
##
## Node number 34: 183 observations
## mean=3756.736, MSE=72419.48
##
## Node number 35: 361 observations
## mean=4082.653, MSE=163366.6
##
## Node number 48: 34 observations
## mean=2674.206, MSE=191757.7
##
## Node number 49: 132 observations
## mean=4267.786, MSE=1099119
##
## Node number 50: 78 observations
## mean=3659.823, MSE=112477.7
##
## Node number 51: 574 observations
## mean=4127.786, MSE=117650.5
##
## Node number 60: 1244 observations
## mean=4424.326, MSE=391618.7
##
## Node number 61: 3178 observations
## mean=4818.483, MSE=365492.9
##
## Node number 62: 480 observations
## mean=4567.829, MSE=221241.2
##
## Node number 63: 3657 observations
## mean=4960.38, MSE=246117.8
##
## Node number 66: 70 observations
## mean=3263.334, MSE=113612.6
##
## Node number 67: 384 observations
## mean=3688.956, MSE=109148.2
##
## n= 14460
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 14460 87348740000000 4300.236
## 2) adult_obesity.pct_obese< 26.35 2191 18998860000000 3904.995
## 4) preventable_hospital_stays.hosp__rate< 57.235 1581 11880200000000 3797.528
## 8) other_primary_care_providers.pcp_rate< 68.03784 1052 7389859000000 3710.324
## 16) unemployed.ratio< 0.06980548 508 3739209000000 3570.458
## 32) mammography_screening.pct>=73.84467 54 534334800000 3195.912 *
## 33) mammography_screening.pct< 73.84467 454 2760356000000 3621.231
## 66) median_household_income>=80853.5 70 372134300000 3263.334 *
## 67) median_household_income< 80853.5 384 1889275000000 3688.956 *
## 17) unemployed.ratio>=0.06980548 544 2698930000000 3861.558
## 34) social_associations.association_rate< 7.337901 183 1062052000000 3756.736 *
## 35) social_associations.association_rate>=7.337901 361 1135865000000 4082.653 *
## 9) other_primary_care_providers.pcp_rate>=68.03784 529 3214257000000 4035.557
## 18) adult_obesity.pct_obese< 16.41775 22 180945500000 3404.182 *
## 19) adult_obesity.pct_obese>=16.41775 507 2143845000000 4121.023 *
## 5) preventable_hospital_stays.hosp__rate>=57.235 610 4585905000000 4180.887
## 10) social_associations.association_rate< 6.120965 77 441955100000 3796.091 *
## 11) social_associations.association_rate>=6.120965 533 2749240000000 4332.245
## 22) unemployed.ratio< 0.09917085 369 1497403000000 4194.878 *
## 23) unemployed.ratio>=0.09917085 164 652174200000 4586.246 *
## 3) adult_obesity.pct_obese>=26.35 12269 45741900000000 4574.605
## 6) social_associations.association_rate< 8.840807 2124 10935510000000 4217.313
## 12) adult_smoking.pct_smokers< 20.255 818 5037650000000 4044.027
## 24) primary_care_provider_rate.pcp< 42.13645 166 1054712000000 3192.941
## 48) physical_inactivity.pct_physically_inactive< 23.95 34 130579900000 2674.206 *
## 49) physical_inactivity.pct_physically_inactive>=23.95 132 361216700000 4267.786 *
## 25) primary_care_provider_rate.pcp>=42.13645 652 3220224000000 4080.581
## 50) injury_deaths.death_rate< 38.98509 78 266711000000 3659.823 *
## 51) injury_deaths.death_rate>=38.98509 574 2486618000000 4127.786 *
## 13) adult_smoking.pct_smokers>=20.255 1306 3858199000000 4524.133
## 26) income_inequality.ratio< 4.406645 476 1175063000000 4233.081 *
## 27) income_inequality.ratio>=4.406645 830 1704694000000 4766.921 *
## 7) social_associations.association_rate>=8.840807 10145 27690800000000 4736.444
## 14) physical_inactivity.pct_physically_inactive< 24.57333 1586 4368257000000 4448.509 *
## 15) physical_inactivity.pct_physically_inactive>=24.57333 8559 21090510000000 4827.972
## 30) access_to_parks.pct_park< 13.5 4422 9910143000000 4683.863
## 60) preventable_hospital_stays.hosp__rate< 63.9 1244 3238057000000 4424.326 *
## 61) preventable_hospital_stays.hosp__rate>=63.9 3178 5826245000000 4818.483 *
## 31) access_to_parks.pct_park>=13.5 4137 10373750000000 4915.069
## 62) median_household_income>=51848 480 1022939000000 4567.829 *
## 63) median_household_income< 51848 3657 8720562000000 4960.380 *
## co-linear variables to be ignored: pct_of_children_eligible_for_free_lunch teen_births.birth_rate homicides.homicide_rate adjusted.gross.income.avg sexually_transmitted_infections.rates_per_100000 excessive_drinking.pct high_housing_costs.pct violent_crime_rate hiv_prevalence_rate hiv_rate commuting_alone.pct_drive
## ADULT cp=0.005
## ADULT tree depth is 5
## n= 12675
##
## node), split, n, yval
## * denotes terminal node
##
## 1) root Counties:12675 Death.per.100k:360
## 2) motor_vehicle_crash_deaths.mv_mortality_rate< 17 Counties:5416 Death.per.100k:330
## 4) injury_deaths.death_rate< 54 Counties:1843 Death.per.100k:270
## 8) adult_obesity.pct_obese< 25 Counties:512 Death.per.100k:240 *
## 9) adult_obesity.pct_obese>=25 Counties:1331 Death.per.100k:310
## 18) injury_deaths.death_rate< 39 Counties:155 Death.per.100k:240 *
## 19) injury_deaths.death_rate>=39 Counties:1176 Death.per.100k:320
## 38) physical_inactivity.pct_physically_inactive< 25 Counties:670 Death.per.100k:310 *
## 39) physical_inactivity.pct_physically_inactive>=25 Counties:506 Death.per.100k:370 *
## 5) injury_deaths.death_rate>=54 Counties:3573 Death.per.100k:390
## 10) diabetes.pct_diabetic< 10 Counties:1907 Death.per.100k:350
## 20) wages.avg>=36 Counties:896 Death.per.100k:330
## 40) physical_inactivity.pct_physically_inactive< 20 Counties:248 Death.per.100k:300 *
## 41) physical_inactivity.pct_physically_inactive>=20 Counties:648 Death.per.100k:350 *
## 21) wages.avg< 36 Counties:1011 Death.per.100k:410
## 42) chlamydia_rate.rates_per_100000>=2.5e+02 Counties:355 Death.per.100k:380 *
## 43) chlamydia_rate.rates_per_100000< 2.5e+02 Counties:656 Death.per.100k:490 *
## 11) diabetes.pct_diabetic>=10 Counties:1666 Death.per.100k:460
## 22) median_household_income>=4.1e+04 Counties:999 Death.per.100k:440 *
## 23) median_household_income< 4.1e+04 Counties:667 Death.per.100k:540 *
## 3) motor_vehicle_crash_deaths.mv_mortality_rate>=17 Counties:7259 Death.per.100k:610
## 6) median_household_income>=3.9e+04 Counties:3495 Death.per.100k:530
## 12) mental_health_providers.mph_rate>=0.6 Counties:2320 Death.per.100k:510
## 24) access_to_recreational_facilities.rec_fac_rate>=4.5 Counties:1804 Death.per.100k:500 *
## 25) access_to_recreational_facilities.rec_fac_rate< 4.5 Counties:516 Death.per.100k:650 *
## 13) mental_health_providers.mph_rate< 0.6 Counties:1175 Death.per.100k:710 *
## 7) median_household_income< 3.9e+04 Counties:3764 Death.per.100k:740
## 14) access_to_recreational_facilities.rec_fac_rate>=0.33 Counties:2407 Death.per.100k:700
## 28) motor_vehicle_crash_deaths.mv_mortality_rate< 21 Counties:677 Death.per.100k:620 *
## 29) motor_vehicle_crash_deaths.mv_mortality_rate>=21 Counties:1730 Death.per.100k:770 *
## 15) access_to_recreational_facilities.rec_fac_rate< 0.33 Counties:1357 Death.per.100k:970 *
## ADULT all data
## node counties deaths.pred deaths.act age.pop dr100k.fit dr100k.group
## 1 236 191 71400 71231 30253811 236.0030 235.4447
## 2 241 628 628286 622234 260634916 241.0600 238.7378
## 3 300 319 222774 223156 74311496 299.7840 300.2981
## 4 309 870 466598 464074 151008722 308.9876 307.3160
## 5 352 800 463522 464005 131596720 352.2293 352.5962
## 6 374 653 162150 162096 43327334 374.2434 374.1195
## 7 383 438 145181 145163 37915070 382.9116 382.8636
## 8 436 1258 492113 491294 112942122 435.7212 434.9963
## 9 491 799 69216 69439 14109818 490.5498 492.1325
## 10 499 2277 355343 356459 71172707 499.2683 500.8366
## 11 539 840 203304 202061 37697675 539.3013 536.0039
## 12 621 848 114734 114954 18471374 621.1452 622.3359
## 13 650 632 42934 42128 6605965 649.9298 637.7267
## 14 714 1460 59280 59331 8303426 713.9179 714.5364
## 15 767 2141 192141 190801 25052181 766.9651 761.6143
## 16 975 1679 64271 64459 6593752 974.7333 977.5770
## ADULT test data
## node counties deaths.pred deaths.act age.pop dr100k.fit dr100k.group
## 1 236 36 9671 9547 4097974 236.0030 232.9688
## 2 241 116 126929 124353 52654624 241.0600 236.1673
## 3 300 71 58548 58943 19530162 299.7840 301.8050
## 4 309 201 100354 97849 32478198 308.9876 301.2760
## 5 352 152 85822 86305 24365446 352.2293 354.2106
## 6 374 147 35972 35918 9611845 374.2434 373.6848
## 7 383 83 24101 24083 6294194 382.9116 382.6225
## 8 436 259 96444 95625 22134275 435.7212 432.0223
## 9 491 142 10734 10949 2188245 490.5498 500.3553
## 10 499 473 70386 71502 14097781 499.2683 507.1862
## 11 539 173 54478 53215 10101556 539.3013 526.8000
## 12 621 171 22225 22443 3578062 621.1452 627.2390
## 13 650 116 8625 7815 1327054 649.9298 588.8984
## 14 714 285 11498 11541 1610491 713.9179 716.6138
## 15 767 411 40411 39057 5268944 766.9651 741.2681
## 16 975 322 12365 12455 1268526 974.7333 981.8482
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## motor_vehicle_crash_deaths.mv_mortality_rate slope= 20 r^2= 0.56
## diabetes.pct_diabetic slope= 58 r^2= 0.47
## physical_inactivity.pct_physically_inactive slope= 21 r^2= 0.44
## injury_deaths.death_rate slope= 6.4 r^2= 0.54
## median_household_income slope= -0.0068 r^2= 0.35
## mental_health_providers.mph_rate slope= -1.6 r^2= 0.15
## adult_obesity.pct_obese slope= 21 r^2= 0.37
## wages.avg slope= -7.7 r^2= 0.34
## access_to_recreational_facilities.rec_fac_rate slope= -11 r^2= 0.097
## chlamydia_rate.rates_per_100000 slope= 0.049 r^2= 0.0049
## ADULT all
## rmse deaths= 70.36103
## rmse Deaths by priori= 233.1597
## tree Deaths rsq= 0.9872463
## prior Deaths rsq= 0.9451815
## fitted Death.per.100k rmse= 261.9283
## weighted Death.per.100k rmse= 79.12766
## fitted Death.per.100k rsq= 0.5002931
## Call:
## rpart(formula = ezformula(c(yvar, predictorVars)), data = d[trainset,
## ], weights = d$Population[trainset], control = rpart.control(cp = 0.005))
## n= 12675
##
## CP nsplit rel error xerror xstd
## 1 0.387573355 0 1.0000000 1.0008092 0.00011610187
## 2 0.126231042 1 0.6124266 0.6132801 0.00007317471
## 3 0.057157247 2 0.4861956 0.4890044 0.00006973413
## 4 0.046117609 3 0.4290384 0.4341584 0.00006219423
## 5 0.024064699 4 0.3829207 0.3883235 0.00006026314
## 6 0.017480628 5 0.3588560 0.3665534 0.00005974281
## 7 0.012759840 6 0.3413754 0.3493192 0.00005554479
## 8 0.011760296 7 0.3286156 0.3337649 0.00005244683
## 9 0.010781808 8 0.3168553 0.3279402 0.00005172415
## 10 0.009357202 9 0.3060735 0.3185148 0.00005053259
## 11 0.008837985 10 0.2967163 0.3148207 0.00005004606
## 12 0.005789113 11 0.2878783 0.3025517 0.00004914691
## 13 0.005680555 12 0.2820892 0.2914575 0.00004784434
## 14 0.005194775 13 0.2764086 0.2847439 0.00004720771
## 15 0.005165097 14 0.2712138 0.2801932 0.00004677474
## 16 0.005000000 15 0.2660487 0.2783297 0.00004671009
##
## Variable importance
## motor_vehicle_crash_deaths.mv_mortality_rate
## 25
## diabetes.pct_diabetic
## 8
## physical_inactivity.pct_physically_inactive
## 8
## injury_deaths.death_rate
## 8
## median_household_income
## 7
## access_to_exercise_opportunities.pct_with
## 6
## college_degrees.pct
## 6
## mental_health_providers.mph_rate
## 6
## drug_poisoning_deaths.mortality_rate
## 4
## adult_smoking.pct_smokers
## 4
## long_commute_driving_alone.pct_drives
## 3
## adult_obesity.pct_obese
## 2
## wages.avg
## 2
## children_in_poverty.pct
## 2
## access_to_recreational_facilities.rec_fac_rate
## 1
## preventable_hospital_stays.hosp__rate
## 1
## children_in_single_parent_households.pct
## 1
## driving_alone_to_work.pct_drive
## 1
## limited_access_to_healthy_foods.pct
## 1
##
## Node number 1: 12675 observations, complexity param=0.3875734
## mean=364.2569, MSE=23564.04
## left son=2 (5416 obs) right son=3 (7259 obs)
## Primary splits:
## motor_vehicle_crash_deaths.mv_mortality_rate < 16.9804 to the left, improve=0.3839362, (335 missing)
## injury_deaths.death_rate < 64.29935 to the left, improve=0.3678195, (12 missing)
## diabetes.pct_diabetic < 10.32364 to the left, improve=0.3598620, (1 missing)
## college_degrees.pct < 24.84014 to the right, improve=0.3378618, (11 missing)
## physical_inactivity.pct_physically_inactive < 28.27444 to the left, improve=0.3260522, (1 missing)
## Surrogate splits:
## access_to_exercise_opportunities.pct_with < 58.61636 to the right, agree=0.902, adj=0.262, (328 split)
## physical_inactivity.pct_physically_inactive < 30.30556 to the left, agree=0.900, adj=0.249, (6 split)
## college_degrees.pct < 14.84097 to the right, agree=0.898, adj=0.235, (1 split)
## mental_health_providers.mph_rate < 5.45 to the right, agree=0.898, adj=0.230, (0 split)
## diabetes.pct_diabetic < 12.30385 to the left, agree=0.892, adj=0.190, (0 split)
##
## Node number 2: 5416 observations, complexity param=0.126231
## mean=326.8133, MSE=10282.81
## left son=4 (1843 obs) right son=5 (3573 obs)
## Primary splits:
## injury_deaths.death_rate < 54.15686 to the left, improve=0.3335287, (6 missing)
## adult_smoking.pct_smokers < 18.39 to the left, improve=0.3231496, (104 missing)
## diabetes.pct_diabetic < 9.930714 to the left, improve=0.3215485, (1 missing)
## median_household_income < 50667 to the right, improve=0.3023767, (1 missing)
## college_degrees.pct < 26.92086 to the right, improve=0.2949028, (5 missing)
## Surrogate splits:
## drug_poisoning_deaths.mortality_rate < 11.73105 to the left, agree=0.824, adj=0.614, (0 split)
## long_commute_driving_alone.pct_drives < 37.25 to the right, agree=0.730, adj=0.407, (0 split)
## adult_smoking.pct_smokers < 18.4 to the left, agree=0.727, adj=0.401, (3 split)
## motor_vehicle_crash_deaths.mv_mortality_rate < 10.50784 to the left, agree=0.722, adj=0.390, (0 split)
## median_household_income < 51456.5 to the right, agree=0.714, adj=0.374, (2 split)
##
## Node number 3: 7259 observations, complexity param=0.05715725
## mean=608.1646, MSE=41454.1
## left son=6 (3495 obs) right son=7 (3764 obs)
## Primary splits:
## median_household_income < 39392 to the right, improve=0.2441317, (0 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 22.7746 to the left, improve=0.2214078, (198 missing)
## mental_health_providers.mph_rate < 0.6 to the right, improve=0.2134086, (8 missing)
## college_degrees.pct < 13.16847 to the right, improve=0.2039336, (6 missing)
## access_to_recreational_facilities.rec_fac_rate < 0.6687665 to the right, improve=0.1869118, (1 missing)
## Surrogate splits:
## children_in_poverty.pct < 28.75 to the left, agree=0.812, adj=0.489, (0 split)
## wages.avg < 31.3082 to the right, agree=0.779, adj=0.399, (0 split)
## diabetes.pct_diabetic < 12.60417 to the left, agree=0.749, adj=0.316, (0 split)
## physical_inactivity.pct_physically_inactive < 31.15778 to the left, agree=0.748, adj=0.313, (0 split)
## college_degrees.pct < 12.29639 to the right, agree=0.736, adj=0.280, (0 split)
##
## Node number 4: 1843 observations, complexity param=0.0240647
## mean=273.1774, MSE=4046.38
## left son=8 (512 obs) right son=9 (1331 obs)
## Primary splits:
## adult_obesity.pct_obese < 24.95 to the left, improve=0.2971914, (0 missing)
## injury_deaths.death_rate < 38.7985 to the left, improve=0.2667106, (2 missing)
## adult_smoking.pct_smokers < 15.59 to the left, improve=0.2504855, (22 missing)
## diabetes.pct_diabetic < 8.304167 to the left, improve=0.2469641, (1 missing)
## college_degrees.pct < 27.24505 to the right, improve=0.2379269, (5 missing)
## Surrogate splits:
## limited_access_to_healthy_foods.pct < 4.517023 to the left, agree=0.763, adj=0.487, (0 split)
## adult_smoking.pct_smokers < 15.59 to the left, agree=0.763, adj=0.487, (0 split)
## diabetes.pct_diabetic < 8.12 to the left, agree=0.747, adj=0.452, (0 split)
## driving_alone_to_work.pct_drive < 78.35703 to the left, agree=0.747, adj=0.452, (0 split)
## access_to_exercise_opportunities.pct_with < 93.52691 to the right, agree=0.746, adj=0.450, (0 split)
##
## Node number 5: 3573 observations, complexity param=0.04611761
## mean=390.7844, MSE=10197.51
## left son=10 (1907 obs) right son=11 (1666 obs)
## Primary splits:
## diabetes.pct_diabetic < 10.07 to the left, improve=0.2695403, (0 missing)
## median_household_income < 44720.6 to the right, improve=0.2522379, (0 missing)
## physical_inactivity.pct_physically_inactive < 26.69286 to the left, improve=0.2509207, (0 missing)
## adult_smoking.pct_smokers < 19.885 to the left, improve=0.2129811, (82 missing)
## wages.avg < 36.07804 to the right, improve=0.2122985, (0 missing)
## Surrogate splits:
## physical_inactivity.pct_physically_inactive < 25.72 to the left, agree=0.794, adj=0.435, (0 split)
## adult_obesity.pct_obese < 29.65 to the left, agree=0.749, adj=0.314, (0 split)
## preventable_hospital_stays.hosp__rate < 65.91 to the left, agree=0.745, adj=0.302, (0 split)
## children_in_single_parent_households.pct < 38.78801 to the left, agree=0.742, adj=0.295, (0 split)
## median_household_income < 44223.8 to the right, agree=0.729, adj=0.258, (0 split)
##
## Node number 6: 3495 observations, complexity param=0.01275984
## mean=531.5937, MSE=22682.4
## left son=12 (2320 obs) right son=13 (1175 obs)
## Primary splits:
## mental_health_providers.mph_rate < 0.6 to the right, improve=0.1573519, (3 missing)
## access_to_recreational_facilities.rec_fac_rate < 4.476977 to the right, improve=0.1297916, (0 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 22.77923 to the left, improve=0.1287799, (88 missing)
## college_degrees.pct < 15.97005 to the right, improve=0.1105543, (0 missing)
## preventable_hospital_stays.hosp__rate < 83.89 to the left, improve=0.1057088, (15 missing)
## Surrogate splits:
## primary_care_provider_rate.pcp < 18.14577 to the right, agree=0.909, adj=0.064, (3 split)
## physical_inactivity.pct_physically_inactive < 35.35 to the left, agree=0.906, adj=0.029, (0 split)
## fast_food_restaurants.pct_foods < 71.11143 to the left, agree=0.906, adj=0.027, (0 split)
## some_college.pct < 36.31884 to the right, agree=0.905, adj=0.022, (0 split)
## married.pct < 0.5272176 to the left, agree=0.904, adj=0.015, (0 split)
##
## Node number 7: 3764 observations, complexity param=0.01748063
## mean=740.3331, MSE=46267.02
## left son=14 (2407 obs) right son=15 (1357 obs)
## Primary splits:
## access_to_recreational_facilities.rec_fac_rate < 0.3333333 to the right, improve=0.1823829, (1 missing)
## mental_health_providers.mph_rate < 0.6 to the right, improve=0.1766176, (5 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 22.06412 to the left, improve=0.1510220, (110 missing)
## median_household_income < 34046.2 to the right, improve=0.1444368, (0 missing)
## mammography_screening.pct < 57.55155 to the right, improve=0.1230853, (1 missing)
## Surrogate splits:
## college_degrees.pct < 6.874589 to the right, agree=0.875, adj=0.064, (1 split)
## primary_care_provider_rate.pcp < 11.9189 to the right, agree=0.875, adj=0.058, (0 split)
## primary_care_physicians.pcp_rate < 14.55286 to the right, agree=0.874, adj=0.052, (0 split)
## fast_food_restaurants.pct_foods < 72.65396 to the left, agree=0.873, adj=0.044, (0 split)
## access_to_exercise_opportunities.pct_with < 14.72906 to the right, agree=0.873, adj=0.044, (0 split)
##
## Node number 8: 512 observations
## mean=241.06, MSE=1199.397
##
## Node number 9: 1331 observations, complexity param=0.008837985
## mean=310.6197, MSE=4760.897
## left son=18 (155 obs) right son=19 (1176 obs)
## Primary splits:
## injury_deaths.death_rate < 38.7985 to the left, improve=0.2008820, (1 missing)
## physical_inactivity.pct_physically_inactive < 24.91 to the left, improve=0.1987558, (1 missing)
## college_degrees.pct < 25.0477 to the right, improve=0.1502515, (0 missing)
## diabetes.pct_diabetic < 8.67 to the left, improve=0.1355016, (1 missing)
## demographics.pct_not_proficient_in_english < 1.621169 to the right, improve=0.1332136, (1 missing)
## Surrogate splits:
## health_care_costs < 12072.11 to the right, agree=0.885, adj=0.215, (0 split)
## adult_smoking.pct_smokers < 13.355 to the left, agree=0.885, adj=0.215, (0 split)
## wages.avg < 59.61102 to the right, agree=0.872, adj=0.130, (1 split)
## drug_poisoning_deaths.mortality_rate < 4.566742 to the left, agree=0.872, adj=0.125, (0 split)
## children_in_poverty.pct < 44.05 to the right, agree=0.871, adj=0.122, (0 split)
##
## Node number 10: 1907 observations, complexity param=0.01078181
## mean=350.994, MSE=5827.4
## left son=20 (896 obs) right son=21 (1011 obs)
## Primary splits:
## wages.avg < 36.07804 to the right, improve=0.1737923, (0 missing)
## social_associations.association_rate < 11.32763 to the left, improve=0.1654727, (2 missing)
## median_household_income < 51408 to the right, improve=0.1521359, (0 missing)
## college_degrees.pct < 26.99055 to the right, improve=0.1425542, (0 missing)
## adult_smoking.pct_smokers < 19.985 to the left, improve=0.1383303, (19 missing)
## Surrogate splits:
## median_household_income < 44759.7 to the right, agree=0.878, adj=0.424, (0 split)
## college_degrees.pct < 19.6033 to the right, agree=0.830, adj=0.198, (0 split)
## contributions.ratio < 0.7906229 to the right, agree=0.829, adj=0.191, (0 split)
## physically_unhealthy_days < 3.97 to the left, agree=0.819, adj=0.147, (0 split)
## access_to_exercise_opportunities.pct_with < 71.1032 to the right, agree=0.818, adj=0.142, (0 split)
##
## Node number 11: 1666 observations, complexity param=0.0117603
## mean=459.8623, MSE=10263.82
## left son=22 (999 obs) right son=23 (667 obs)
## Primary splits:
## median_household_income < 41145.5 to the right, improve=0.1868458, (0 missing)
## college_degrees.pct < 16.23905 to the right, improve=0.1828298, (0 missing)
## wages.avg < 33.80492 to the right, improve=0.1601795, (0 missing)
## some_college.pct < 59.81483 to the right, improve=0.1462554, (6 missing)
## taxcredits.ratio < 0.2816365 to the right, improve=0.1383583, (0 missing)
## Surrogate splits:
## income_inequality.ratio < 5.705983 to the left, agree=0.869, adj=0.438, (0 split)
## inadequate_social_support.pct_no_emotional < 24.038 to the left, agree=0.868, adj=0.436, (0 split)
## food_insecurity.pct_insecure < 21.625 to the left, agree=0.862, adj=0.407, (0 split)
## children_in_poverty.pct < 33.25 to the left, agree=0.859, adj=0.395, (0 split)
## severe_housing_problems.pct < 22.69124 to the left, agree=0.854, adj=0.374, (0 split)
##
## Node number 12: 2320 observations, complexity param=0.005680555
## mean=512.0234, MSE=16291.14
## left son=24 (1804 obs) right son=25 (516 obs)
## Primary splits:
## access_to_recreational_facilities.rec_fac_rate < 4.476977 to the right, improve=0.10797330, (0 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 20.8635 to the left, improve=0.09896063, (39 missing)
## college_degrees.pct < 11.69338 to the right, improve=0.08851621, (0 missing)
## access_to_exercise_opportunities.pct_with < 54.38523 to the right, improve=0.08368683, (0 missing)
## preventable_hospital_stays.hosp__rate < 83.92 to the left, improve=0.08260993, (4 missing)
## Surrogate splits:
## primary_care_physicians.pcp_rate < 11.62487 to the right, agree=0.920, adj=0.052, (0 split)
## college_degrees.pct < 9.237706 to the right, agree=0.920, adj=0.051, (0 split)
## primary_care_provider_rate.pcp < 26.03554 to the right, agree=0.919, adj=0.043, (0 split)
## dentists.dentist_rate < 11.39345 to the right, agree=0.919, adj=0.038, (0 split)
## preventable_hospital_stays.hosp__rate < 106.38 to the left, agree=0.918, adj=0.037, (0 split)
##
## Node number 13: 1175 observations
## mean=713.9179, MSE=45415.44
##
## Node number 14: 2407 observations, complexity param=0.009357202
## mean=704.3366, MSE=33528.82
## left son=28 (677 obs) right son=29 (1730 obs)
## Primary splits:
## motor_vehicle_crash_deaths.mv_mortality_rate < 21.37521 to the left, improve=0.1562181, (25 missing)
## mental_health_providers.mph_rate < 0.6 to the right, improve=0.1365845, (1 missing)
## median_household_income < 34660 to the right, improve=0.1175709, (0 missing)
## mammography_screening.pct < 57.55155 to the right, improve=0.1071864, (1 missing)
## college_degrees.pct < 10.16521 to the right, improve=0.1054515, (1 missing)
## Surrogate splits:
## injury_deaths.death_rate < 84.91093 to the left, agree=0.741, adj=0.397, (25 split)
## college_degrees.pct < 14.45925 to the right, agree=0.682, adj=0.258, (0 split)
## some_college.pct < 47.63203 to the right, agree=0.677, adj=0.246, (0 split)
## access_to_exercise_opportunities.pct_with < 66.25781 to the right, agree=0.669, adj=0.229, (0 split)
## preventable_hospital_stays.hosp__rate < 71.07 to the left, agree=0.669, adj=0.229, (0 split)
##
## Node number 15: 1357 observations
## mean=974.7333, MSE=65834.01
##
## Node number 18: 155 observations
## mean=236.003, MSE=2116.901
##
## Node number 19: 1176 observations, complexity param=0.005789113
## mean=323.4387, MSE=4094.288
## left son=38 (670 obs) right son=39 (506 obs)
## Primary splits:
## physical_inactivity.pct_physically_inactive < 24.91 to the left, improve=0.1793633, (1 missing)
## demographics.pct_not_proficient_in_english < 1.621402 to the right, improve=0.1496125, (1 missing)
## access_to_exercise_opportunities.pct_with < 66.72005 to the right, improve=0.1463830, (1 missing)
## wages.avg < 35.24007 to the right, improve=0.1456332, (0 missing)
## college_degrees.pct < 25.0477 to the right, improve=0.1428851, (0 missing)
## Surrogate splits:
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 83.64189 to the left, agree=0.841, adj=0.280, (1 split)
## preventable_hospital_stays.hosp__rate < 65.4 to the left, agree=0.836, adj=0.258, (0 split)
## wages.avg < 35.24007 to the right, agree=0.833, adj=0.246, (0 split)
## contributions.ratio < 0.7447357 to the right, agree=0.829, adj=0.230, (0 split)
## binge_drinking.pct < 13.495 to the right, agree=0.827, adj=0.218, (0 split)
##
## Node number 20: 896 observations, complexity param=0.005165097
## mean=334.496, MSE=3039.772
## left son=40 (248 obs) right son=41 (648 obs)
## Primary splits:
## physical_inactivity.pct_physically_inactive < 19.65 to the left, improve=0.2025019, (0 missing)
## some_college.pct < 78.52289 to the right, improve=0.1899559, (0 missing)
## adult_smoking.pct_smokers < 18.55 to the left, improve=0.1774663, (2 missing)
## diabetes.pct_diabetic < 7.49 to the left, improve=0.1744028, (0 missing)
## preventable_hospital_stays.hosp__rate < 47.15 to the left, improve=0.1627104, (0 missing)
## Surrogate splits:
## preventable_hospital_stays.hosp__rate < 45.23 to the left, agree=0.913, adj=0.743, (0 split)
## preventable_hospital_stays_ambulatory_care_sensitive_conditions.acsc_rate < 51.31477 to the left, agree=0.888, adj=0.669, (0 split)
## diabetes.pct_diabetic < 8.22 to the left, agree=0.846, adj=0.543, (0 split)
## adult_obesity.pct_obese < 23.95 to the left, agree=0.823, adj=0.476, (0 split)
## driving_alone_to_work.pct_drive < 78.40481 to the left, agree=0.811, adj=0.441, (0 split)
##
## Node number 21: 1011 observations, complexity param=0.005194775
## mean=412.3807, MSE=11418.69
## left son=42 (355 obs) right son=43 (656 obs)
## Primary splits:
## chlamydia_rate.rates_per_100000 < 248.95 to the right, improve=0.2017369, (0 missing)
## households_with_high_housing_costs.pct < 30.45 to the right, improve=0.1838919, (0 missing)
## social_associations.association_rate < 11.163 to the left, improve=0.1781584, (2 missing)
## sexually_transmitted_infections.chlamydia_rate < 362.4812 to the right, improve=0.1581570, (0 missing)
## fast_food_restaurants.pct_foods < 48.75019 to the right, improve=0.1478975, (0 missing)
## Surrogate splits:
## sexually_transmitted_infections.chlamydia_rate < 285.8643 to the right, agree=0.923, adj=0.721, (0 split)
## food_environment_index < 7.597611 to the left, agree=0.849, adj=0.449, (0 split)
## violent_crime.rate < 219.6522 to the right, agree=0.837, adj=0.403, (0 split)
## food_insecurity.pct_insecure < 14.15 to the right, agree=0.814, adj=0.322, (0 split)
## fast_food_restaurants.pct_foods < 41.82962 to the right, agree=0.800, adj=0.269, (0 split)
##
## Node number 22: 999 observations
## mean=435.7212, MSE=5989.755
##
## Node number 23: 667 observations
## mean=539.3013, MSE=16099.74
##
## Node number 24: 1804 observations
## mean=499.2683, MSE=12620.57
##
## Node number 25: 516 observations
## mean=649.9298, MSE=35199.76
##
## Node number 28: 677 observations
## mean=621.1452, MSE=19919.39
##
## Node number 29: 1730 observations
## mean=766.9651, MSE=34641.85
##
## Node number 38: 670 observations
## mean=308.9876, MSE=2130.497
##
## Node number 39: 506 observations
## mean=374.2434, MSE=7682.959
##
## Node number 40: 248 observations
## mean=299.784, MSE=2332.652
##
## Node number 41: 648 observations
## mean=352.2293, MSE=2470.988
##
## Node number 42: 355 observations
## mean=382.9116, MSE=6529.838
##
## Node number 43: 656 observations
## mean=490.5498, MSE=15972.78
##
## n= 12675
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 12675 19308130000000 364.2569
## 2) motor_vehicle_crash_deaths.mv_mortality_rate< 16.9804 5416 7304307000000 326.8133
## 4) injury_deaths.death_rate< 54.15686 1843 1563452000000 273.1774
## 8) adult_obesity.pct_obese< 24.95 512 249451000000 241.0600 *
## 9) adult_obesity.pct_obese>=24.95 1331 849356500000 310.6197
## 18) injury_deaths.death_rate< 38.7985 155 55369320000 236.0030 *
## 19) injury_deaths.death_rate>=38.7985 1176 623342200000 323.4387
## 38) physical_inactivity.pct_physically_inactive< 24.91 670 252530500000 308.9876 *
## 39) physical_inactivity.pct_physically_inactive>=24.91 506 259034700000 374.2434 *
## 5) injury_deaths.death_rate>=54.15686 3573 3303569000000 390.7844
## 10) diabetes.pct_diabetic< 10.07 1907 1197847000000 350.9940
## 20) wages.avg>=36.07804 896 492481300000 334.4960
## 40) physical_inactivity.pct_physically_inactive< 19.65 248 127785800000 299.7840 *
## 41) physical_inactivity.pct_physically_inactive>=19.65 648 264967200000 352.2293 *
## 21) wages.avg< 36.07804 1011 497189200000 412.3807
## 42) chlamydia_rate.rates_per_100000>=248.95 355 206479200000 382.9116 *
## 43) chlamydia_rate.rates_per_100000< 248.95 656 190408600000 490.5498 *
## 11) diabetes.pct_diabetic>=10.07 1666 1215277000000 459.8623
## 22) median_household_income>=41145.5 999 543916800000 435.7212 *
## 23) median_household_income< 41145.5 667 444290500000 539.3013 *
## 3) motor_vehicle_crash_deaths.mv_mortality_rate>=16.9804 7259 4520509000000 608.1646
## 6) median_household_income>=39392 3495 1566147000000 531.5937
## 12) mental_health_providers.mph_rate>=0.6 2320 1015815000000 512.0234
## 24) access_to_recreational_facilities.rec_fac_rate>=4.476977 1804 720317900000 499.2683 *
## 25) access_to_recreational_facilities.rec_fac_rate< 4.476977 516 185816400000 649.9298 *
## 13) mental_health_providers.mph_rate< 0.6 1175 303962600000 713.9179 *
## 7) median_household_income< 39392 3764 1850763000000 740.3331
## 14) access_to_recreational_facilities.rec_fac_rate>=0.3333333 2407 1162664000000 704.3366
## 28) motor_vehicle_crash_deaths.mv_mortality_rate< 21.37521 677 296665600000 621.1452 *
## 29) motor_vehicle_crash_deaths.mv_mortality_rate>=21.37521 1730 685327900000 766.9651 *
## 15) access_to_recreational_facilities.rec_fac_rate< 0.3333333 1357 350581000000 974.7333 *
## co-linear variables to be ignored: teen_births.birth_rate children_eligible_for_free_lunch.pct food_insecurity.pct_insecure wages.avg homicides.homicide_rate sexually_transmitted_infections.chlamydia_rate sexually_transmitted_infections.rates_per_100000 violent_crime.rate hiv_prevalence.rate hiv_prevalence_rate driving_alone_to_work.pct_drive households_with_high_housing_costs.pct sahie.pct.uninsured binge_drinking.pct
## YOUTH cp=0.0173159759897632
## YOUTH tree depth is 3
## n= 2313
##
## node), split, n, yval
## * denotes terminal node
##
## 1) root Counties:2313 Death.per.100k: 86
## 2) demographics.pct_not_proficient_in_english>=3.9 Counties:908 Death.per.100k: 69
## 4) social_associations.association_rate< 7.3 Counties:370 Death.per.100k: 57 *
## 5) social_associations.association_rate>=7.3 Counties:538 Death.per.100k:100
## 10) access_to_exercise_opportunities.pct_with>=83 Counties:360 Death.per.100k: 93 *
## 11) access_to_exercise_opportunities.pct_with< 83 Counties:178 Death.per.100k:240 *
## 3) demographics.pct_not_proficient_in_english< 3.9 Counties:1405 Death.per.100k:170
## 6) access_to_exercise_opportunities.pct_with>=71 Counties:1002 Death.per.100k:150
## 12) access_to_exercise_opportunities.pct_with>=86 Counties:408 Death.per.100k:120 *
## 13) access_to_exercise_opportunities.pct_with< 86 Counties:594 Death.per.100k:220 *
## 7) access_to_exercise_opportunities.pct_with< 71 Counties:403 Death.per.100k:460 *
## YOUTH all data
## node counties deaths.pred deaths.act age.pop dr100k.fit dr100k.group
## 1 57 467 64214 63811 112330034 57.16540 56.80671
## 2 93 434 33492 32970 36033472 92.94713 91.49826
## 3 122 512 29466 28751 24187179 121.82282 118.86876
## 4 224 747 18960 18743 8446910 224.46242 221.89179
## 5 237 228 5606 5519 2365179 237.00361 233.34386
## 6 459 518 8473 8612 1845729 459.06937 466.59071
## YOUTH test data
## node counties deaths.pred deaths.act age.pop dr100k.fit dr100k.group
## 1 57 97 12046 12001 21072618 57.16540 56.95068
## 2 93 74 5928 5417 6377396 92.94713 84.94062
## 3 122 104 7079 6358 5810586 121.82282 109.42098
## 4 224 153 4440 4223 1978192 224.46242 213.47776
## 5 237 50 1240 1153 523013 237.00361 220.45341
## 6 459 115 1790 1910 389905 459.06937 489.86292
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## demographics.pct_not_proficient_in_english slope= -4.4 r^2= 0.095
## access_to_exercise_opportunities.pct_with slope= -3.3 r^2= 0.096
## social_associations.association_rate slope= 15 r^2= 0.15
## YOUTH all
## rmse deaths= 39.99027
## rmse Deaths by priori= 78.00859
## tree Deaths rsq= 0.9072014
## prior Deaths rsq= 0.9117817
## fitted Death.per.100k rmse= 446.975
## weighted Death.per.100k rmse= 85.34004
## fitted Death.per.100k rsq= 0.3502687
## Call:
## rpart(formula = ezformula(c(yvar, predictorVars)), data = d[trainset,
## ], weights = d$Population[trainset], control = rpart.control(cp = 0.005))
## n= 2313
##
## CP nsplit rel error xerror xstd
## 1 0.13456289 0 1.0000000 1.0016483 0.0006912473
## 2 0.08714994 1 0.8654371 0.8712310 0.0006112062
## 3 0.03312596 2 0.7782872 0.8153507 0.0005250473
## 4 0.03007403 3 0.7451612 0.7765289 0.0004993224
## 5 0.02365492 4 0.7150872 0.7456848 0.0004905201
## 6 0.01731598 5 0.6914323 0.7356099 0.0004763674
##
## Variable importance
## demographics.pct_not_proficient_in_english
## 24
## access_to_exercise_opportunities.pct_with
## 23
## social_associations.association_rate
## 10
## severe_housing_problems.pct
## 5
## commuting_alone.pct_drive
## 5
## motor_vehicle_crash_deaths.mv_mortality_rate
## 4
## high_housing_costs.pct
## 4
## diabetes.pct_diabetic
## 3
## access_to_parks.pct_park
## 3
## college_degrees.pct
## 3
## dentists.dentist_rate
## 2
## access_to_recreational_facilities.rec_fac_rate
## 2
## pct_illiterate
## 2
## other_primary_care_providers.pcp_rate
## 2
## primary_care_provider_rate.pcp
## 2
## limited_access_to_healthy_foods.pct
## 2
## some_college.pct
## 1
## excessive_drinking.pct
## 1
## physically_unhealthy_days
## 1
## teen_birth_rate
## 1
## adult_smoking.pct_smokers
## 1
##
## Node number 1: 2313 observations, complexity param=0.1345629
## mean=85.66406, MSE=10208.18
## left son=2 (908 obs) right son=3 (1405 obs)
## Primary splits:
## demographics.pct_not_proficient_in_english < 3.873321 to the right, improve=0.13456290, (0 missing)
## social_associations.association_rate < 7.547315 to the left, improve=0.11589750, (1 missing)
## high_housing_costs.pct < 30.29605 to the right, improve=0.10937370, (0 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 14.68285 to the left, improve=0.09595310, (1 missing)
## long_commute_driving_alone.pct_drives < 30.65 to the right, improve=0.09399445, (1 missing)
## Surrogate splits:
## social_associations.association_rate < 9.795606 to the left, agree=0.867, adj=0.247, (0 split)
## severe_housing_problems.pct < 16.35279 to the right, agree=0.865, adj=0.234, (0 split)
## commuting_alone.pct_drive < 82.35599 to the left, agree=0.864, adj=0.232, (0 split)
## high_housing_costs.pct < 31.38978 to the right, agree=0.854, adj=0.172, (0 split)
## diabetes.pct_diabetic < 10.50417 to the left, agree=0.849, adj=0.147, (0 split)
##
## Node number 2: 908 observations, complexity param=0.03007403
## mean=68.50856, MSE=3199.906
## left son=4 (370 obs) right son=5 (538 obs)
## Primary splits:
## social_associations.association_rate < 7.301414 to the left, improve=0.11649660, (0 missing)
## long_commute_driving_alone.pct_drives < 30.7 to the right, improve=0.10707980, (0 missing)
## high_housing_costs.pct < 46.73384 to the right, improve=0.07899071, (0 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 15.63478 to the left, improve=0.07721895, (0 missing)
## pct_illiterate < 17.15 to the right, improve=0.07669416, (1 missing)
## Surrogate splits:
## access_to_recreational_facilities.rec_fac_rate < 11.7317 to the left, agree=0.855, adj=0.437, (0 split)
## pct_illiterate < 12.45 to the right, agree=0.851, adj=0.420, (0 split)
## other_primary_care_providers.pcp_rate < 67.70461 to the left, agree=0.849, adj=0.410, (0 split)
## demographics.pct_not_proficient_in_english < 5.968491 to the right, agree=0.840, adj=0.377, (0 split)
## primary_care_provider_rate.pcp < 163.076 to the left, agree=0.835, adj=0.357, (0 split)
##
## Node number 3: 1405 observations, complexity param=0.08714994
## mean=165.7342, MSE=35133.15
## left son=6 (1002 obs) right son=7 (403 obs)
## Primary splits:
## access_to_exercise_opportunities.pct_with < 70.575 to the right, improve=0.14317820, (1 missing)
## access_to_parks.pct_park < 22.5 to the right, improve=0.11819240, (8 missing)
## college_degrees.pct < 19.36358 to the right, improve=0.10176070, (0 missing)
## high_housing_costs.pct < 30.49355 to the right, improve=0.08775901, (0 missing)
## dentists.dentist_rate < 52.69253 to the right, improve=0.08135788, (0 missing)
## Surrogate splits:
## college_degrees.pct < 18.17608 to the right, agree=0.949, adj=0.083, (1 split)
## physically_unhealthy_days < 4.71 to the left, agree=0.948, adj=0.058, (0 split)
## teen_birth_rate < 71.82796 to the left, agree=0.948, adj=0.058, (0 split)
## motor_vehicle_crash_deaths.mv_mortality_rate < 21.7016 to the left, agree=0.947, adj=0.049, (0 split)
## adult_smoking.pct_smokers < 28.275 to the left, agree=0.947, adj=0.046, (0 split)
##
## Node number 4: 370 observations
## mean=57.1654, MSE=1230.264
##
## Node number 5: 538 observations, complexity param=0.02365492
## mean=101.3722, MSE=7453.597
## left son=10 (360 obs) right son=11 (178 obs)
## Primary splits:
## access_to_exercise_opportunities.pct_with < 82.59758 to the right, improve=0.15330970, (0 missing)
## college_degrees.pct < 17.23121 to the right, improve=0.12165630, (0 missing)
## motor_vehicle_crash_deaths.mv_mortality_rate < 14.37506 to the left, improve=0.10994210, (0 missing)
## high_housing_costs.pct < 30.90253 to the right, improve=0.08266816, (0 missing)
## dentists.dentist_rate < 45.15872 to the right, improve=0.08082371, (0 missing)
## Surrogate splits:
## motor_vehicle_crash_deaths.mv_mortality_rate < 14.97141 to the left, agree=0.970, adj=0.484, (0 split)
## some_college.pct < 53.57157 to the right, agree=0.962, adj=0.358, (0 split)
## college_degrees.pct < 17.83316 to the right, agree=0.962, adj=0.357, (0 split)
## dentists.dentist_rate < 44.50392 to the right, agree=0.961, adj=0.339, (0 split)
## access_to_parks.pct_park < 13.5 to the right, agree=0.960, adj=0.315, (0 split)
##
## Node number 6: 1002 observations, complexity param=0.03312596
## mean=148.546, MSE=22421.61
## left son=12 (408 obs) right son=13 (594 obs)
## Primary splits:
## access_to_exercise_opportunities.pct_with < 85.6941 to the right, improve=0.09048102, (0 missing)
## access_to_parks.pct_park < 22.5 to the right, improve=0.06748198, (1 missing)
## high_housing_costs.pct < 30.4761 to the right, improve=0.05777937, (0 missing)
## dentists.dentist_rate < 53.61436 to the right, improve=0.05159972, (0 missing)
## long_commute_driving_alone.pct_drives < 16.75 to the right, improve=0.04868044, (0 missing)
## Surrogate splits:
## access_to_parks.pct_park < 34.5 to the right, agree=0.834, adj=0.363, (0 split)
## limited_access_to_healthy_foods.pct < 9.110028 to the left, agree=0.818, adj=0.300, (0 split)
## motor_vehicle_crash_deaths.mv_mortality_rate < 14.93367 to the left, agree=0.805, adj=0.252, (0 split)
## excessive_drinking.pct < 12.95 to the right, agree=0.791, adj=0.198, (0 split)
## dentists.dentist_rate < 50.45416 to the right, agree=0.788, adj=0.188, (0 split)
##
## Node number 7: 403 observations
## mean=459.0694, MSE=160982.7
##
## Node number 10: 360 observations
## mean=92.94713, MSE=3269.302
##
## Node number 11: 178 observations
## mean=237.0036, MSE=55275.81
##
## Node number 12: 408 observations
## mean=121.8228, MSE=9427.879
##
## Node number 13: 594 observations
## mean=224.4624, MSE=51542.7
##
## n= 2313
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 2313 1521599000000 85.66406
## 2) demographics.pct_not_proficient_in_english>=3.873321 908 392806500000 68.50856
## 4) social_associations.association_rate< 7.301414 370 112270700000 57.16540 *
## 5) social_associations.association_rate>=7.301414 538 234775200000 101.37220
## 10) access_to_exercise_opportunities.pct_with>=82.59758 360 96954670000 92.94713 *
## 11) access_to_exercise_opportunities.pct_with< 82.59758 178 101827200000 237.00360 *
## 3) demographics.pct_not_proficient_in_english< 3.873321 1405 924041700000 165.73420
## 6) access_to_exercise_opportunities.pct_with>=70.575 1002 557071900000 148.54600
## 12) access_to_exercise_opportunities.pct_with>=85.6941 408 173252300000 121.82280 *
## 13) access_to_exercise_opportunities.pct_with< 85.6941 594 333415200000 224.46240 *
## 7) access_to_exercise_opportunities.pct_with< 70.575 403 234362500000 459.06940 *
require(gridExtra)
## Loading required package: gridExtra
## Warning: package 'gridExtra' was built under R version 3.4.1
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
g <- tableGrob(signif(t(perf.table),2))
grid.newpage()
grid.draw(g)
for(age in unique(bigdata$Age.Grouping)){
mtree=trees[[age]]
data=mtree$variable.importance
names(data)=abbreviate(names.arg = names(data),
minlength = floor(mean(nchar(names(data)))+sd(nchar(names(data)))))
print(lattice::barchart( rev(data/sum(data)*100),main=age,
xlab='Variable Importance'))
catln(age,'very important vars:',names(mtree$variable.importance)[(mtree$variable.importance/max(mtree$variable.importance)*100)>50])
}
## SENIOR very important vars: adult_obesity.pct_obese physical_inactivity.pct_physically_inactive college_degrees.pct households_with_high_housing_costs.pct
## ADULT very important vars: motor_vehicle_crash_deaths.mv_mortality_rate
## YOUTH very important vars: demographics.pct_not_proficient_in_english access_to_exercise_opportunities.pct_with
for(age in unique(bigdata$Age.Grouping)){
mtree=trees[[age]]
prp(mtree,varlen=ceiling(max(nchar(names(mtree$variable.importance)))),cex=0.8,nn=F,main=age,box.palette="GnRd",fallen.leaves = F)
}
for(age in unique(bigdata$Age.Grouping)){
mtree=trees[[age]]
print(
plot_counties(data.frame(Death.per.100k=(round(predict(mtree,impute.df,type='vector'))),
fips=impute.df$fips),'Death.per.100k',low='green',high='red',
main=paste(age,'Fitted'),print = F)
)
}
#importance
#View(importance)
###################END OF TREE
#https://stackoverflow.com/questions/23714052/ggplot-mapping-us-counties-problems-with-visualization-shapes-in-r
#####################
Lantz (2015) suggests the elbow method for cluster size determination.
Compute and plot wss for k = 1 to k = 17:
require(factoextra)
## Loading required package: factoextra
## Warning: package 'factoextra' was built under R version 3.4.2
## Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ
require(cluster)
## Loading required package: cluster
## Warning: package 'cluster' was built under R version 3.4.2
age='SENIOR'
age='YOUTH'
allclusters=list()
allcentermeans=list()
for(age in unique(bigdata$Age.Grouping)){
shush({
mtree=trees[[age]]
vip=names(mtree$variable.importance)[(mtree$variable.importance/max(mtree$variable.importance))>0.3]
data=as.data.frame(aggregate(bigdata[,vip],
list(fips=bigdata$fips),FUN=function(x) mean(x,na.rm=T)))
fips=data$fips
data$fips=NULL
data=winsor1Df(data,trace=F)
odata=data
data=impute(data,missing.threshold = 0.1,trace=F)
data=as.data.frame(scale(keepNumeric( data)))
})
#'VIF Double Check
#'
#' The HH library allows for the calculation Variance Inflation Factor for checking for collinearity
#' without requiring a response variable.
catln('removing multi-collinear vars via vif:',names(data)[HH::vif(data)>=10])
data=data[,HH::vif(data)<10]
# myvif=car::vif(lm(ezformula(c(yvar,predictorVars)), impute(d[,c(yvar,predictorVars)],trace=F)))
# names(myvif)[myvif > 10] # problem?
#
set.seed(7)
k.max <- 8 # Maximal number of clusters
wss <- sapply(1:k.max, function(k){set.seed(17); kmeans(data, k, nstart=5 )$tot.withinss})
plot(1:k.max, wss, type="b", pch = 19, frame = FALSE, xlab="Number of clusters K",
ylab="Total within-clusters sum of squares",main=paste(age,'elbow method'));grid()
bestK=4
set.seed(7)
cl=kmeans(data, bestK, nstart=5,iter.max = 30 )
print(fviz_cluster(cl, data=data,stand = FALSE, geom = "point",
pointsize = 1,main=paste(age,'Cluster Plot')))
cl$clustering=cl$cluster
allclusters[[age]]=cl
data$fips=fips
plot_counties(df = data.frame(fips=data$fips,cluster=as.factor(cl$clustering)),
main=age,yvar = 'cluster')
center.means=NULL
for(i in sort(unique(cl$clustering))){
w=impute.df$fips %in% data$fips[cl$clustering==i]
catln(age,'cluster=',i,'deathRate per 100k:',sum(1.0*impute.df$Deaths[w])/
sum(1.0*impute.df$Population[w])*100000,
'counties:',length(unique(impute.df$fips[w])))
.=as.data.frame(colMeans(odata[cl$clustering==i,],na.rm = T))
names(.)=paste0('Cluster',i)
if(is.null(center.means)) center.means=.
else center.means=cbind(center.means,.)
}
allcentermeans[[age]]=center.means
######################
}
## removing multi-collinear vars via vif:
## SENIOR cluster= 1 deathRate per 100k: 2393.272 counties: 716
## SENIOR cluster= 2 deathRate per 100k: 1673.197 counties: 991
## SENIOR cluster= 3 deathRate per 100k: 1274.992 counties: 942
## SENIOR cluster= 4 deathRate per 100k: 809.0095 counties: 419
## removing multi-collinear vars via vif:
## ADULT cluster= 1 deathRate per 100k: 1294.359 counties: 1304
## ADULT cluster= 2 deathRate per 100k: 847.3716 counties: 701
## ADULT cluster= 3 deathRate per 100k: 1875.764 counties: 750
## ADULT cluster= 4 deathRate per 100k: 1857.314 counties: 313
## removing multi-collinear vars via vif:
## YOUTH cluster= 1 deathRate per 100k: 1884.152 counties: 1022
## YOUTH cluster= 2 deathRate per 100k: 2344.622 counties: 472
## YOUTH cluster= 3 deathRate per 100k: 1162.552 counties: 1368
## YOUTH cluster= 4 deathRate per 100k: 717.2535 counties: 206
draw.table=function(data,main){
library(grid)
library(gridExtra)
library(gtable)
t1 <- tableGrob(data)
title <- textGrob(main) #,gp=gpar(fontsize=50)
padding <- unit(5,"mm")
table <- gtable_add_rows(
t1,
heights = grobHeight(title) + padding,
pos = 0)
table <- gtable_add_grob(
table,
title,
1, 1, 1, ncol(table))
grid.newpage()
grid.draw(table)
}
for(age in unique(bigdata$Age.Grouping)){
data=allcentermeans[[age]]
names(data)=abbreviate(names.arg = names(data),
minlength = floor(mean(nchar(names(data)))))
draw.table(signif(data,2),main=age)
gplots::textplot(signif(data,2),valign='top');title(age)
}
for(age in unique(bigdata$Age.Grouping)){
cl=allclusters[[age]]
data=as.data.frame(cl$centers)
names(data)=abbreviate(names.arg = names(data),
minlength = floor(mean(nchar(names(data)))+sd(nchar(names(data)))))
ezplot2(data,xlab='cluster',col=darken(rainbow(1+ncol(data))),
title = paste(age,'Cluster Centers'),type='bar')
}
## Loading required package: reshape2
## Warning: package 'reshape2' was built under R version 3.4.1
## Loading required package: ggthemes
#
# #+ fig.width=7, fig.height=5
# for(age in unique(bigdata$Age.Grouping)){
# cl=allclusters[[age]]
# mtree=trees[[age]]
# vip=names(mtree$variable.importance)[(
# mtree$variable.importance/max(mtree$variable.importance))>0.3]
# vip=base::intersect(colnames(cl$centers),vip)
# catln(age,vip)
# data=as.data.frame(cl$centers[,vip,drop=F])
#
# names(data)=abbreviate(names.arg = names(data),
# minlength = floor(mean(nchar(names(data)))+sd(nchar(names(data)))))
#
# ezplot2(data,xlab='cluster',col=darken(rainbow(1+ncol(data))),
# title = paste(age,'Very Important Cluster Centers'),type='bar')
# }
#### end
#### end