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Copy pathDS2_CAB_v3.R
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DS2_CAB_v3.R
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# DS2_CAB_v3
# In cooperate changes in defining pre-op and post-op variables.
# Merge with dataset to get VUC.
############################## load data #####################
###check path
getwd()
setwd('C:/Users/heluy/OneDrive/Documents/Class/ISYE 6478 Practicum')
getwd()
###load as csv
mydata = read.csv("STS_Dataset_DEIDENTIFIED_v2.csv")
data.input2= read.csv("PPRRVU17_V1219.csv",skip=9)[,c(1,2,3,6)]
data.input2$CPT=paste(data.input2$HCPCS,data.input2$MOD,sep="")
dim(data.input2)
str(data.CPT)
str(test)
###### install all packages used
library(dplyr)
library(glmnet)
library(plotmo) # for plotres
# names(mydata)
# str(mydata)
#
# nrow(mydata)
# ncol(mydata)
# nrow(mydata)
#
# unique(mydata$Procedure.Type)
# summary(mydata$Procedure.Type)
# str(mydata$Procedure.Type)
#
#
# str(df,list.len=ncol(df))
# test=which(mydata$Procedure.Type==2)
# test=which(mydata[mydata$Procedure.Type==2,])
######select for CAB ######
#don't know why this is not working
#data=mydata[mydata$Procedure.Type==2,]
data=mydata[which(mydata$Procedure.Type==2),]
# unique(data$Intraop.Blood.Products)
# unique(data$Intraop.Blood.Products...Cryo.Units)
# unique(data$Intraop.Blood.Products...FFP.Units)
# unique(data$Intraop.Blood.Products...Platelet.Units)
# str(data$Intraop.Blood.Products...Platelet.Units)
# sum((data$Intraop.Blood.Products...Platelet.Units==2)==TRUE)
# data$Intraop.Blood.Products...Platelet.Units[7]==1
# unique(data$Intraop.Blood.Products...RBC.Units)
# unique(data$Intraop.Blood.Products.Refused)
# unique(data$Intraop.Blood.Products)
#data3=subset(mydata, Procedure.Type==2)
# unique(data$Procedure.Type)
# unique(data$op.Type)
#
# #some stats on null values
# a=apply(data, 2, function(x) 1-sum(is.na(x))/nrow(data))
# a
# hist(a)
# length(a)
#
#
# b=apply(data, 1, function(x) 1-sum(is.na(x))/ncol(data))
# b
# hist(b)
# length(b)
############ most conservative ##########
#### select only full
# ###revmove columns that are completely empty
#
df = data[,colSums(is.na(data))/nrow(data)!=1]
str(df,list.len=ncol(df))
# #df1 = data[,which(colSums(is.na(data))/nrow(data)!=1)]
# str(df)
#
# c=apply(df, 2, function(x) 1-sum(is.na(x))/nrow(df))
# c
# hist(c)
# length(c)
#
#
# d=apply(df, 1, function(x) 1-sum(is.na(x))/ncol(df))
# d
# hist(d)
# length(d)
# ###revmove columns that less than 75 complete
# df75 = data[,1-colSums(is.na(data))/nrow(data)>0.75]
# str(df75)
#
# c75=apply(df75, 2, function(x) 1-sum(is.na(x))/nrow(df75))
# c75
# hist(c75)
# length(c75)
#
# d75=apply(df75, 1, function(x) 1-sum(is.na(x))/ncol(df75))
# d
# hist(d75)
# length(d75)
#
# str(df75)
### data conversion
#some tests
# unique(df75$Hispanic.or.Latino.or.Spanish.Ethnicity)
# test=factor(df75$Hispanic.or.Latino.or.Spanish.Ethnicity)
# as.character(test)
# as.numeric(test)
# test2=as.numeric(as.character(df75$Hispanic.or.Latino.or.Spanish.Ethnicity))
#
# test[is.na(test)]=0
# df75.copy=df75
# df75.copy$Hispanic.or.Latino.or.Spanish.Ethnicity[is.na(df75.copy$Hispanic.or.Latino.or.Spanish.Ethnicity)]=0
#
# df75.copy
# unique(df75.copy$Hispanic.or.Latino.or.Spanish.Ethnicity)
# test3=factor(df75.copy$Hispanic.or.Latino.or.Spanish.Ethnicity)
# as.character(test3)
# as.numeric(test3)
# test4=as.numeric(as.character(df75.copy$Hispanic.or.Latino.or.Spanish.Ethnicity))
# sapply(df75, class)
# ### change type to factor
# #### don't use this, otherwise can't remove NA value in next section
# df75.copy=df75
# typeSet=c()
# for(i in names(df75)) {
# # i-th element of `u1` squared into `i`-th position of `usq`
# feature=df75[[i]]
# type=class(feature)
# typeSet=c(typeSet,type)
#
# print(i)
# print(type)
# print(length(unique(feature)))
# print('')
#
# if (type=='integer'&& length(unique(feature))>10 ){
# df75.copy[[i]]=as.numeric(feature)
# }else if (length(unique(feature))<=10 ){
# df75.copy[[i]]=as.factor(feature)
# }
# }
########### change data type to character and numeric
df.copy=df
typeSet=c()
for(i in names(df)) {
# i-th element of `u1` squared into `i`-th position of `usq`
feature=df[[i]]
type=class(feature)
typeSet=c(typeSet,type)
print(i)
print(type)
print(length(unique(feature)))
print('----------------------------------')
if (type=='integer'&& length(unique(feature))>10 ){
df.copy[[i]]=as.numeric(feature)
}else if (length(unique(feature))<=10 ){
df.copy[[i]]=as.character(feature)
}
}
#####check the original feature data type
str(df,list.len=ncol(df))
#####check the new feature data type
str(df.copy,list.len=ncol(df.copy))
# length(unique(df75$ Other.Replacement.Approaches))
# length(unique(df75$ Other.Card.Other.Describe))
#
# unique(df75.copy$Other.Card.Other.Describe)
# unique(df75.copy$X..Hospital.Deaths)
# unique(df75.copy$Hispanic.or.Latino.or.Spanish.Ethnicity)
# unique(df75.copy$Deidentified.Record.ID)
# unique(df75.copy$Surgeon)
# unique(df75.copy$ RF.Hemoglobin)
# unique(df75.copy$ CPT.1.Code...2)
##################### manually change some features
###############change some features to character
unique(df.copy$CPT.1.Code...1)
unique(df.copy$CPT.1.Code...2)
unique(df.copy$CPT.1.Code...3)
df.copy$CPT.1.Code...1=as.character(df$CPT.1.Code...1)
df.copy$CPT.1.Code...2=as.character(df$CPT.1.Code...2)
df.copy$CPT.1.Code...3=as.character(df$CPT.1.Code...3)
df.copy$CPT.1.Code...4=as.character(df$CPT.1.Code...4)
df.copy$CPT.1.Code...5=as.character(df$CPT.1.Code...5)
df.copy$CPT.1.Code...6=as.character(df$CPT.1.Code...6)
df.copy$Surgeon=as.character(df$Surgeon)
unique(df.clean$CPT.1.Code...1)
###############change some features to numeric
df.copy$Deidentified.Patient.Age=as.numeric(df$Deidentified.Patient.Age)
df.copy$RF.High.Sensitivity.CRP.or.Ultra.sensitive.CRP=as.numeric(df$RF.High.Sensitivity.CRP.or.Ultra.sensitive.CRP)
# str(df.copy$Deidentified.Patient.Age)
# str(df.copy$RF.High.Sensitivity.CRP.or.Ultra.sensitive.CRP)
############## merge two data frame
data.CPT=df.copy[c('CPT.1.Code...1','CPT.1.Code...2','CPT.1.Code...3','CPT.1.Code...4','CPT.1.Code...5','CPT.1.Code...6')]
data.input2$CPT=as.character(data.input2$CPT)
dim(data.input2)
str(data.CPT)
dim(data.CPT)
dim(df.copy)
dim(df.copy)
str(data.CPT)
str(data.RVU)
dim(data.CPT)
dim(data.input2)
data.join=left_join(data.CPT, data.input2[c('CPT','RVU','DESCRIPTION')], by = c("CPT.1.Code...1"="CPT"))
dim(data.join)
data.join=left_join(data.join, data.input2[c('CPT','RVU','DESCRIPTION')], by = c("CPT.1.Code...2"="CPT"))
dim(data.join)
data.join=left_join(data.join, data.input2[c('CPT','RVU','DESCRIPTION')], by = c("CPT.1.Code...3"="CPT"))
dim(data.join)
data.join=left_join(data.join, data.input2[c('CPT','RVU','DESCRIPTION')], by = c("CPT.1.Code...4"="CPT"))
dim(data.join)
data.join=left_join(data.join, data.input2[c('CPT','RVU','DESCRIPTION')], by = c("CPT.1.Code...5"="CPT"))
dim(data.join)
data.join=left_join(data.join, data.input2[c('CPT','RVU','DESCRIPTION')], by = c("CPT.1.Code...6"="CPT"))
dim(data.join)
variables.RVU=c(grep("RVU", names(data.join), value=TRUE))
variables.RVU
# data.join2=merge(data.join, data.input2, all.x=TRUE,by.x = "CPT.1.Code...6", by.y = "HCPCS",no.dups = TRUE)
# dim(data.join2)
#
# data.join2[which(data.join2$CPT.1.Code...6=="93631"),]
#
#
# unique(data.CPT$CPT.1.Code...5)
#
# dim(data.RVU)
data.RVU=data.join[variables.RVU]
data.RVU[is.na(data.RVU)] = 0
sumRVU=rowSums (data.RVU, na.rm = FALSE, dims = 1)
#add to data set
df.copy$sumRVU=sumRVU
# variables.description=c(grep("DESCRIPTION", names(data.join), value=TRUE))
# variables.description
#
# data.description=data.join[variables.description]
# colnames(data.description) = c("CPT.DESCRIPTION.1.", "CPT.DESCRIPTION.2.","CPT.DESCRIPTION.3.",
# "CPT.DESCRIPTION.4.","CPT.DESCRIPTION.5.","CPT.DESCRIPTION.6.")
#
# ###
# df.copy$CPT.DESCRIPTION.1.=as.factor(data.description$CPT.DESCRIPTION.1.)
###############remove some features
df.copy$DRG..=NULL
df.copy$Op.Category=NULL
df.copy$Other.Card.Other.Describe=NULL
df.copy$CPT.1.Code...1=NULL
df.copy$CPT.1.Code...2=NULL
df.copy$CPT.1.Code...3=NULL
df.copy$CPT.1.Code...4=NULL
df.copy$CPT.1.Code...5=NULL
df.copy$CPT.1.Code...6=NULL
######################################## remove NA values
df.clean=df.copy
for(i in names(df.copy)) {
feature=df.copy[[i]]
type=class(feature)
feature=df.clean[[i]]
print(type)
#print(unique(feature))
if (type=='numeric'){
df.clean[[i]][is.na(df.copy[[i]])]=mean(df.copy[[i]][!is.na(df.copy[[i]])])
}else {
df.clean[[i]][is.na(df.copy[[i]])]='NA'
df.clean[[i]]=as.factor(df.clean[[i]])
}
}
###########check again the feature tyes
#orignal
str(df.copy,list.len=ncol(df.copy))
#cleaned set
str(df.clean,list.len=ncol(df.clean))
#make sure all NA are removed
sum(is.na(df.clean))
###################check and remove factors that only has 1 level
l=sapply(df.clean, function(x) is.factor(x))
one=sapply(df.clean[l], function(x) length(levels(x)))
sum(one==1)
one
names(which(one==1))
length(unique(df$Procedure.Type))
length(unique(df$Other.Replacement.Approaches))
length(unique(df$Valve))
for(i in names(which(one==1))) {
feature=df.clean[[i]]
df.clean[[i]]=NULL
}
sum(names(df.clean) %in% names(which(one==1)))
str(df.clean,list.len=ncol(df.clean))
#set up print
getOption("max.print")
options(max.print=1000000)
####### predicted variables
#contains key word "predicted"
variables.predicted=grep("Predicted", names(df.clean), value=TRUE)
variables.predicted
###### blood products
# in-op blood products contain "intraop"
# post-op blood products contain "blood prod"
variables.bloodProd=grep("Blood.Prod", names(df.clean), value=TRUE)
variables.bloodProd
###### ventilation
#cotains key word "Ventilation"
# post-op
variables.ventilation=grep("Ventilation", names(df.clean), value=TRUE)
variables.ventilation
##### length of stay
# contain key word "LOS"
# post-op
variables.LOS=grep("LOS", names(df.clean), value=TRUE)
variables.LOS
##### cost variables
# contain key word "cost"
variables.cost=grep("Cost", names(df.clean), value=TRUE)
variables.cost
#### mortality variables
variables.mortality=grep("Mort", names(df.clean), value=TRUE)
variables.mortality=variables.mortality[variables.mortality!='Avg.APR.Risk.of.Mortality']
variables.mortality
unique(df.clean$Mort.Location)
unique(df.clean$Mort.Prim.Cause)
unique(df.clean$Mort.30d.Status)
unique(df.clean$Mort.DC.Status)
unique(df.clean$Mort.Op.Death)
#### readmission variables
variables.readmission=grep("Readm", names(df.clean), value=TRUE)
variables.readmission
variables.readmission[1:2]
unique(df.clean$Readmit...30.Days.from.DOP)
unique(df.clean$X..30.day.Readmissions)
#### post-op variables
variables.postOp=grep("Post", names(df.clean), value=TRUE)
variables.postOp
variables.postOp
unique(df.clean$In.Hospital.Post.Op.Events)
####### action taken on Aortic
#### contains key word "Aortic
#INTRA-OP
# c(grep("Aort", names(df.clean), value=TRUE))
# "Assessment.of.Aorta.Plaque" is not an action
# "Aortic.Occlusion" is an action
# variables.aortic=c(grep("Aortic.Procedure", names(df.clean), value=TRUE),'Aorta.Procedure.Performed','Aortic.Cross.Clamp...Partial','Aortic.Occlusion')
# variables.aortic
variables.aortic=c(grep("Aortic", names(df.clean), value=TRUE))
variables.aortic
variables.VD=c(grep("VD", names(df.clean), value=TRUE))
variables.VD
variables.RF=c(grep("RF", names(df.clean), value=TRUE))
variables.RF
variables.procedure=c(grep("Procedure", names(df.clean), value=TRUE))
variables.procedure
variables.approach=c(grep("Approach", names(df.clean), value=TRUE))
variables.approach
variables.operative=c(grep("Operative", names(df.clean), value=TRUE))
variables.operative
####### action taken on Aortic
#### contains key word "Cardioplegia"
#INTRA-op
variables.cardioplegia=c(grep("Cardioplegia", names(df.clean), value=TRUE))
variables.cardioplegia
####INTRA-op
variables.cerebral=c(grep("Cerebral", names(df.clean), value=TRUE))
variables.cerebral
#### INTRA-op
variables.circulatory=c(grep("Circulatory", names(df.clean), value=TRUE))
variables.circulatory
### INTRA-op
variables.cannulation=grep("Cannulation", names(df.clean), value=TRUE)
variables.cannulation
### medication
variables.meds=grep("Meds", names(df.clean), value=TRUE)
variables.meds
### CPB, action taken by surgeon
variables.CPB=grep("CPB", names(df.clean), value=TRUE)
variables.CPB
### ction taken by surgeon
variables.clamp=grep("Clamp", names(df.clean), value=TRUE)
variables.clamp
variables.dist=grep("Dist", names(df.clean), value=TRUE)
variables.dist
variables.IABP=grep("IABP", names(df.clean), value=TRUE)
variables.IABP
variables.chest=grep("Chest", names(df.clean), value=TRUE)
variables.chest
variables.discharge=grep("Discharge", names(df.clean), value=TRUE)
variables.discharge
variables.cardiopulmonary=grep("Cardiopulmonary", names(df.clean), value=TRUE)
variables.cardiopulmonary
variables.death=grep("Death", names(df.clean), value=TRUE)
variables.death
variables.otherCard=grep("Other.Card", names(df.clean), value=TRUE)
variables.otherCard=variables.otherCard[variables.otherCard != "Prev.Other.Card.PPM"]
variables.otherCard
variables.combined=grep("Combined", names(df.clean), value=TRUE)
variables.combined
variables.intra=grep("Intra", names(df.clean), value=TRUE)
variables.intra
variables.prev=grep("Prev", names(df.clean), value=TRUE)
variables.prev
variables.pre=grep("Pre", names(df.clean), value=TRUE)
variables.pre
grep("Death", names(df.clean), value=TRUE)
grep("Comps", names(df.clean), value=TRUE)
grep("Other.Card", names(df.clean), value=TRUE)
grep("Non.Card", names(df.clean), value=TRUE)
grep("Death", names(df.clean), value=TRUE)
grep("Comps", names(df.clean), value=TRUE)
str(df.clean,list.len=ncol(df.clean))
#response variables to regress on or irelevant
#post-op: 'Total.Hrs.ICU'
#irelavent:
variables.other1=c("STS.Data.Version",'Case.Mix.Index','Deidentified.Record.ID','Surgeon','Total.Hrs.ICU',
'Comps.Pulm.ARDS','Total.OR.Time','Cardiac.Symptoms...At.Time.Of.Surgery','Hospital.Name')
variables.other1
variables.other2=c("STS.Data.Version",'Case.Mix.Index','Deidentified.Record.ID')
variables.other2
variables.otherAction=c('CAB','Robot.Used',"VAD.Device","Other.Non.Card","IMA.Artery.Used")
#
# variables.combined
#
# variables.aortic
# variables.exclude1=union(variables.predicted, variables.bloodProd, variables.ventilation, variables.LOS,
# variables.cost, variables.mortality, variables.readmission, variables.postOp,
# variables.aortic, variables.cardioplegia, variables.cerebral, variables.circulatory,
# variables.cannulation, variables.CPB, variables.clamp, variables.dist,
# variables.IABP, variables.chest, variables.discharge, variables.cardiopulmonary,
# variables.combined,variables.death,variables.otherCard,variables.intra,
# variables.otherAction, variables.other1)
#
# variables.exclude1
variables.exclude1=unique(c(variables.predicted, variables.bloodProd, variables.ventilation, variables.LOS,
variables.cost, variables.mortality, variables.readmission, variables.postOp,
variables.aortic, variables.cardioplegia, variables.cerebral, variables.circulatory,
variables.cannulation, variables.CPB, variables.clamp, variables.dist,
variables.IABP, variables.chest, variables.discharge, variables.cardiopulmonary,
variables.combined,variables.death,variables.otherCard,variables.intra,
variables.procedure,variables.approach,
variables.otherAction, variables.other1))
variables.include1=unique(c(variables.VD, variables.RF, variables.prev))
variables.include1
variables.exclude.preOp=setdiff(variables.exclude1,variables.include1)
variables.exclude.preOp
X.preOp=df.clean[!(names(df.clean) %in% variables.exclude1)]
names(X.preOp)
variables.discharge
str(X.preOp,list.len=ncol(X.preOp))
#
# variables.intraOpBlood=grep("Intraop.Blood", names(df.clean), value=TRUE)
# variables.intraOpBlood
#
# variables.CPT=grep("CPT", names(df.clean), value=TRUE)
# variables.CPT
test=unique()
variables.ventilation
variables.postOp
variables.death
variables.discharge
variables.exclude2=unique(c(variables.predicted, variables.LOS,
variables.cost, variables.mortality, variables.readmission,
variables.death,variables.other2))
X.postOp=df.clean[!(names(df.clean) %in% variables.exclude2)]
names(X.postOp)
str(X.postOp,list.len=ncol(X.postOp))
####
# discharge
# Cardiogenic.Shock2
# MI.When2
# Prior.MI2
# Resuscitation2
# Meds.Aspirin.Within.Five.Days2
# Meds.Beta.Blocker.Therapy.For.More.Than.2.Weeks.Prior.To.Surgery2
# Meds.Beta.Blockers.Within.24.Hours2
# Meds
# Dominance
# Num.Dis.Vessels
# Hemo.Data.EF
# Num.Dis.Vessels2
# VD
# Appropriate.
# CPB.Utilization2
# Cross.Clamp.Time..min.
# Incidence
# Lowest.Hematocrit .
# Lowest.Intra.op.Hemoglobin
# Op.Category
unique(data$Op.Category)
# Operative.Approach
# Other.Card
unique(data$Other.Card)
# Other.Non.Card
unique(data$Other.Non.Card)
# Status
# Urgent.Or.Emergent.Reason
unique(data$Urgent.Or.Emergent.Reason)
# Dist.Anast...Vein
# Dist.Anast
# IMA.Dist.Anast
# IABP
# IABP.When.Inserted
# VAD.Device
# Other.Card.
# Chest.Tube.Loss
# Comps.Pulm.ARDS
# Total.OR.Time
# Discharge
################ variable selection #######################
######################################### Pre-Op Direct Variable Cost ########################################
y.DVC=df.clean$Average.Variable.Direct.Cost
length(y.DVC)
y.DVC=y.DVC[df.clean$Mort.Mortality!=1]
length(y.DVC)
dim(X.preOp)
X.DVC.preOp=X.preOp[df.clean$Mort.Mortality!=1,]
dim(X.DVC.preOp)
#only keep live person
str(df.clean$Mort.Mortality)
sum(df.clean$Mort.Mortality==1)
y2.DVC=log(y.DVC)
length(y2.DVC)
mm.X.DVC.preOp = model.matrix(~ ., data=X.DVC.preOp)
########## histogram distribution
library(scales)
df.summary.DVC=data.frame(as.integer(y.DVC),y2.DVC)
df.summary.DVC
names(df.summary.DVC)=c('DVC','logDVC')
# Draw with black outline, white fill
ggplot(df.summary.DVC, aes(x=DVC)) +
geom_histogram(colour="black", fill="white")+
geom_vline(aes(xintercept=mean(DVC)),
color="blue", linetype="dashed", size=1)+
scale_x_continuous(labels = comma)+
geom_text(data = df.summary.DVC, aes(label = round(mean(DVC)), y=1000, x=mean(DVC)),hjust=-0.5, color="blue", size=3.5)+
ggtitle("Histogram of DVC of CABG")+
labs(x="DVC in $",y="Counts")
ggplot(df.summary.DVC, aes(x=logDVC)) +
geom_histogram(colour="black", fill="white")+
geom_vline(aes(xintercept=mean(logDVC)),
color="blue", linetype="dashed", size=1)+
scale_x_continuous(labels = comma)+
geom_text(data = df.summary.DVC, aes(label = round(mean(logDVC),2), y=400, x=mean(logDVC)),hjust=-0.5, color="blue", size=3.5)+
ggtitle("Histogram of Log-transformed DVC of CABG")+
labs(x="Log-transformed DVC",y="Counts")
##lm: no y transform
lm.VDC = lm(y.DVC ~.,data=X.DVC.preOp)
summary(lm.VDC)
layout(matrix(c(1,2,3,4),2,2))
plot(lm.VDC)
##lm: y transform
lm2.VDC = lm(y2.DVC ~.,data=X.DVC.preOp)
summary(lm2.VDC)
layout(matrix(c(1,2,3,4),2,2))
plot(lm2.VDC)
###### log transform
elastNet2.DVC.cv=cv.glmnet(mm.X.DVC.preOp,y2.DVC, alpha=0.5,nfolds=10)
elastNet2.DVC = glmnet(mm.X.DVC.preOp, y2.DVC, alpha = 0.5, nlambda = 100)
coef.elastNet2.DVC=coef(elastNet2.DVC,s=elastNet2.DVC.cv$lambda.min)
plot(elastNet2.DVC,xvar="lambda")
abline(v=log(elastNet2.DVC.cv$lambda.min),col='black',lty = 2)
sig.elastNet2.DVC=coef.elastNet2.DVC[which(coef.elastNet2.DVC[,1]!=0),1]
round(sig.elastNet2.DVC,2)
r2.elastNet2.DVC.cv <- elastNet2.DVC.cv$glmnet.fit$dev.ratio[which(elastNet2.DVC.cv$glmnet.fit$lambda == elastNet2.DVC.cv$lambda.min)]
r2.elastNet2.DVC.cv
pos.elastNet2.DVC=exp(coef.elastNet2.DVC[which(coef.elastNet2.DVC[,1]>0),])
pos.elastNet2.DVC=round(pos.elastNet2.DVC[order(-pos.elastNet2.DVC)],2)
pos.elastNet2.DVC
names(pos.elastNet2.DVC)
values(pos.elastNet2.DVC)
typeof(names(pos.elastNet2.DVC))
typeof((pos.elastNet2.DVC))
neg.elastNet2.DVC=exp(coef.elastNet2.DVC[which(coef.elastNet2.DVC[,1]<0),])
neg.elastNet2.DVC=round(neg.elastNet2.DVC[order(neg.elastNet2.DVC)],2)
neg.elastNet2.DVC
# newdata.coef.elastNet2.DVC=data.frame(names(pos.elastNet2.DVC)[2:21],pos.elastNet2.DVC[2:21],
# names(neg.elastNet2.DVC)[1:20],neg.elastNet2.DVC[1:20])
# names(newdata.coef.elastNet2.DVC)=c('Positive','Value1','Negative','Value2')
# str(X.postOp)
#
library(ggplot2)
# typeof(pos.elastNet2.DVC)
# qplot(1,pos.elastNet2.DVC, geom="boxplot")
# g <- ggplot(newdata.coef.elastNet2.DVC,x='Positive',y='Value1')
# # Number of cars in each class:
# g + geom_bar()
#
# p<-ggplot(data=newdata.coef.elastNet2.DVC, aes(x= reorder(Positive, Value1), y=Value1)) +
# geom_bar(stat="identity", fill="steelblue")+
# coord_flip()+geom_text(aes(label=Value1), hjust=0.3,vjust=0, color="blue", size=3.5)
# p
unique(surgeon)
unique(as.character(surgeon))
unique(as.factor(surgeon))
unique(as.numeric(surgeon))
fitted.elastNet2.DVC=predict(elastNet2.DVC, newx = mm.X.DVC.preOp, s = elastNet2.DVC.cv$lambda.min)
residual.elastNet2.DVC=y2.DVC-fitted.elastNet2.DVC
surgeon=as.character(as.numeric(df.clean$Surgeon))[df.clean$Mort.Mortality!=1]
hospital=as.factor(df.clean$Hospital.Name)[df.clean$Mort.Mortality!=1]
robot=as.factor(df.clean$Robot.Used)[df.clean$Mort.Mortality!=1]
newdata.elastNet2.DVC=data.frame(fitted.elastNet2.DVC,residual.elastNet2.DVC,surgeon,hospital,robot)
#residual plot
p=ggplot(data=newdata.elastNet2.DVC, aes(x=fitted.elastNet2.DVC, y=residual.elastNet2.DVC)) + geom_point()
p +labs(title="Residual plot of log transformed Pre-Op DVC model for CABG", x ="Fitted log(DVC)", y = "Residual")
#ANOVA
anova.elastNet2.DVC=aov(residual.elastNet2.DVC~surgeon)
summary(anova.elastNet2.DVC)
library(gplots)
par(mfrow=c(1,1))
par(mar=c(9, 3.5, 3, 1), mgp=c(2.4, 0.8, 0), las=3)
plotmeans(exp(residual.elastNet2.DVC)~surgeon,xlab='',mean.labels = TRUE,barwidth=1,barcol='blue',
ylab="Risk Adjusted Average Value in $", main="Risk Adjusted Average Variable Direct Cost"
,connect=FALSE)
#Visualization
#violin plot of the Direct Variable Cost Index by Surgeons
p = ggplot(newdata.elastNet2.DVC, aes(x=surgeon, y=exp(residual.elastNet2.DVC),color=robot)) +
geom_violin(trim=TRUE) + coord_flip()+
labs(title="Ratio of Actual DVC / Expected DVC by Surgeons",y="Ratio", x = "Surgeons")+
geom_hline(yintercept = 1, color = "red", size=1.5)+
ylim(0.5, 3)
p + geom_jitter(position=position_jitter(0.3))+ geom_boxplot(width=0.3,color='black')
#violin plot of the absolute Direct Variable Cost by Surgeons
p = ggplot(newdata.elastNet2.DVC, aes(x=surgeon, y=exp(y2.DVC)-exp(fitted.elastNet2.DVC),color=robot)) +
geom_violin(trim=TRUE) + coord_flip()+
labs(title="Cost Difference from Expected DVC by Surgeons",y="Cost Difference from Expected DVC in $", x = "Surgeons")+
geom_hline(yintercept = 0, color = "red", size=1.5)+
ylim(-25000, 60000)
p + geom_jitter(position=position_jitter(0.3))+ geom_boxplot(width=0.3,color='black')
#violin plot of the Direct Variable Cost Index by Surgeons and hospital
p = ggplot(newdata.elastNet2.DVC, aes(x=hospital, y=exp(residual.elastNet2.DVC),color=hospital)) +
geom_violin(trim=TRUE) + coord_flip()+
labs(title="Direct Variable Cost Index by Surgeons in Various hospitals",x="Direct Variable Cost Index", y = "Surgeons")
p + geom_boxplot(width=0.3,color='black')
# library(ggplot2)
#
# newdata=data.frame(riskAd.elastNet.DVC,surgeon)
#
#
# ggplot(data=newdata, aes(x=surgeon, y=riskAd.elastNet.DVC, fill=surgeon)) +
# geom_bar(stat="identity")
#violin plot
# p <- ggplot(newdata, aes(x=surgeon, y=log(riskAd.elastNet.DVC),color=surgeon)) +
# geom_violin(trim=TRUE) + coord_flip()
#
# p + stat_summary(fun.y=median, geom="point", size=2, color="red")
#
# p + geom_boxplot(width=0.1)
#
# p + stat_summary(fun.data=mean_sdl, mult=1,
# geom="pointrange", color="red")
#
# p + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.01,bins=1)
#
# p + geom_jitter(position=position_jitter(0.3))+ geom_boxplot(width=0.3,color='black')+ theme(legend.position="none")
######################################### Post-Op Direct Variable Cost ########################################
y.DVC=df.clean$Average.Variable.Direct.Cost
y.DVC=y.DVC[df.clean$Mort.Mortality!=1]
length(y.DVC)
y2.DVC=log(y.DVC)
length(y2.DVC)
X.DVC.postOp=X.postOp[df.clean$Mort.Mortality!=1,]
dim(X.DVC.postOp)
mm.X.DVC.postOp = model.matrix(~ ., data=X.DVC.postOp)
dim(mm.X.DVC.postOp)
str(X.DVC.postOp,list.len=ncol(df.clean))
###################check and remove factors that only has 1 level
one.DVC.postOp=sapply(X.DVC.postOp, function(x) length(unique(x)))
sum(one.DVC.postOp==1)
one.DVC.postOp
names(which(one.DVC.postOp==1))
length(unique(df$Procedure.Type))
length(unique(df$Other.Replacement.Approaches))
length(unique(df$Valve))
for(i in names(which(one.DVC.postOp==1))) {
feature=X.DVC.postOp[[i]]
X.DVC.postOp[[i]]=NULL
}
sum(names(X.DVC.postOp) %in% names(which(one.DVC.postOp==1)))
str(df.clean,list.len=ncol(df.clean))
##lm model prior to transformation
lm.VDC.postOp = lm(y.DVC ~.,data=X.DVC.postOp)
summary(lm.VDC.postOp)
layout(matrix(c(1,2,3,4),2,2))
plot(lm.VDC.postOp)
##lm model after transformation
lm2.VDC.postOp = lm(y2.DVC ~.,data=X.DVC.postOp)
summary(lm2.VDC.postOp)
layout(matrix(c(1,2,3,4),2,2))
plot(lm2.VDC.postOp)
### elasticNet
#### post-transformation
elastNet2.DVC.postOp.cv=cv.glmnet(mm.X.DVC.postOp,y2.DVC, alpha=0.5,nfolds=10)
elastNet2.DVC.postOp = glmnet(mm.X.DVC.postOp, y2.DVC, alpha = 0.5, nlambda = 100)
coef.elastNet2.DVC.postOp=coef(elastNet2.DVC.postOp,s=elastNet2.DVC.postOp.cv$lambda.min)
plot(elastNet2.DVC.postOp,xvar="lambda")
abline(v=log(elastNet2.DVC.postOp.cv$lambda.min),col='black',lty = 2)
#R square
r2.elastNet2.DVC.postOp.cv <- elastNet2.DVC.postOp.cv$glmnet.fit$dev.ratio[which(elastNet2.DVC.postOp.cv$glmnet.fit$lambda == elastNet2.DVC.postOp.cv$lambda.min)]
r2.elastNet2.DVC.postOp.cv
#significant variables
sig.elastNet2.DVC.postOp=coef.elastNet2.DVC.postOp[which(coef.elastNet2.DVC.postOp[,1]!=0),1]
round(sig.elastNet2.DVC.postOp,2)
pos.elastNet2.DVC.postOp=exp(coef.elastNet2.DVC.postOp[which(coef.elastNet2.DVC.postOp[,1]>0),])
pos.elastNet2.DVC.postOp=round(pos.elastNet2.DVC.postOp[order(-pos.elastNet2.DVC.postOp)],2)
pos.elastNet2.DVC.postOp
neg.elastNet2.DVC.postOp=exp(coef.elastNet2.DVC.postOp[which(coef.elastNet2.DVC.postOp[,1]<0),])
neg.elastNet2.DVC.postOp=round(neg.elastNet2.DVC.postOp[order(neg.elastNet2.DVC.postOp)],2)
neg.elastNet2.DVC.postOp
# newdata.coefCompare.elastNet2.DVC=data.frame(names(pos.elastNet2.DVC)[2:13],pos.elastNet2.DVC[2:13],
# names(pos.elastNet2.DVC.postOp)[2:13],pos.elastNet2.DVC.postOp[2:13])
# type=c(rep("Pre-Op", 12), rep("Post-Op", 12))
# name=c(names(pos.elastNet2.DVC)[2:13],names(pos.elastNet2.DVC.postOp)[2:13])
# value=c(pos.elastNet2.DVC[2:13],pos.elastNet2.DVC.postOp[2:13])
# df.pos.coef.compare=data.frame(name,value,type)
#
# p <-ggplot(df.pos.coef.compare, aes(x= reorder(name, value), y=value))
# p +geom_bar(stat = "identity", aes(fill = type), position = "dodge")+coord_flip()+
# geom_text(aes(label=value), hjust=1.5,vjust=0, color="black", size=3.5)+
# labs(title="Compounding Rate of Positive Factors on DVC Index",x="Factors", y = "Compounding Rate")
type
neg.elastNet2.DVC
#top of all pre-op factors
type=c(rep("Positive", 6), rep("Negative", 6))
name=c(names(pos.elastNet2.DVC)[2:7],names(neg.elastNet2.DVC)[1:6])
value=c(pos.elastNet2.DVC[2:7],neg.elastNet2.DVC[1:6])
df.coef.compare1=data.frame(name,value,type)
df.coef.compare1=df.coef.compare1[order(-value),]
p <-ggplot(df.coef.compare1, aes(x= reorder(name, value), y=value))
p +geom_bar(stat = "identity", aes(fill = type), position = "dodge")+coord_flip()+
scale_fill_brewer(palette="Blues")+
geom_text(aes(label=value), hjust=1.5,vjust=0, color="black", size=3.5)+
labs(title="Compounding Rate of Top Positive and Negative Pre-Op Factors on DVC Index",x="Factors", y = "Compounding Rate")
#top of all post-op factors
type=c(rep("Positive", 6), rep("Negative", 6))
name=c(names(pos.elastNet2.DVC.postOp)[2:7],names(neg.elastNet2.DVC.postOp)[1:6])
value=c(pos.elastNet2.DVC.postOp[2:7],neg.elastNet2.DVC.postOp[1:6])
df.coef.compare2=data.frame(name,value,type)
df.coef.compare2=df.coef.compare2[order(-value),]
df.coef.compare2
p <-ggplot(df.coef.compare2, aes(x= reorder(name, value), y=value))
p +geom_bar(stat = "identity", aes(fill = type), position = "dodge")+coord_flip()+
scale_fill_brewer(palette="Reds")+
geom_text(aes(label=value), hjust=1.5,vjust=0, color="black", size=3.5)+