Time to Reperfusion 6

Data

setwd("c:/users/mark/documents/work/stemibayes")
library(rethinking)

## Loading required package: rstan

## Loading required package: ggplot2

## Loading required package: StanHeaders

## rstan (Version 2.12.1, packaged: 2016-09-11 13:07:50 UTC, GitRev: 85f7a56811da)

## For execution on a local, multicore CPU with excess RAM we recommend calling
## rstan_options(auto_write = TRUE)
## options(mc.cores = parallel::detectCores())

## Loading required package: parallel

## rethinking (Version 1.59)

par(mfrow=c(1,1))

dat<-read.csv("temp.csv",header = TRUE)
m<-data.frame(dat$Year,dat$DOW,dat$TOD,dat$DoorToLab)
colnames(m)<-c(
  "Year",
  "DOW",
  "TOD",
  "DoorToLab"
               )

Data Visualization

ggplot(m,aes(x=factor(Year),y=DoorToLab,fill=factor(Year)))+
  geom_boxplot()+xlab("Year")+ylab("Door To Lab (min)")+
  scale_fill_discrete(guide = guide_legend(title = "Year")) 

m$Year<-m$Year-2007

Model

tmp<-map2stan(
  alist(
    DoorToLab~dlnorm(mu,sigma),
    mu<-a+b0*Year+day[DOW]+b1*TOD+b2*TOD*TOD,
    a~dnorm(0,1),
    b0<-dnorm(0,1),
    day[DOW]~dnorm(0,1),
    b1~dnorm(0,1),
    b2~dnorm(0,1),
    sigma~dcauchy(0,1)
  ),
  data=m,
  iter=1e4
)

## 
## SAMPLING FOR MODEL 'DoorToLab ~ dlnorm(mu, sigma)' NOW (CHAIN 1).
## 
## Chain 1, Iteration:    1 / 10000 [  0%]  (Warmup)
## Chain 1, Iteration: 1000 / 10000 [ 10%]  (Warmup)
## Chain 1, Iteration: 2000 / 10000 [ 20%]  (Warmup)
## Chain 1, Iteration: 3000 / 10000 [ 30%]  (Warmup)
## Chain 1, Iteration: 4000 / 10000 [ 40%]  (Warmup)
## Chain 1, Iteration: 5000 / 10000 [ 50%]  (Warmup)
## Chain 1, Iteration: 5001 / 10000 [ 50%]  (Sampling)
## Chain 1, Iteration: 6000 / 10000 [ 60%]  (Sampling)
## Chain 1, Iteration: 7000 / 10000 [ 70%]  (Sampling)
## Chain 1, Iteration: 8000 / 10000 [ 80%]  (Sampling)
## Chain 1, Iteration: 9000 / 10000 [ 90%]  (Sampling)
## Chain 1, Iteration: 10000 / 10000 [100%]  (Sampling)
##  Elapsed Time: 8.27 seconds (Warm-up)
##                7.015 seconds (Sampling)
##                15.285 seconds (Total)
## 
## [1] "The following numerical problems occured the indicated number of times after warmup on chain 1"
##                                                                                         count
## Exception thrown at line 28: lognormal_log: Scale parameter is inf, but must be finite!    19
## [1] "When a numerical problem occurs, the Hamiltonian proposal gets rejected."
## [1] "See http://mc-stan.org/misc/warnings.html#exception-hamiltonian-proposal-rejected"
## [1] "If the number in the 'count' column is small,  do not ask about this message on stan-users."
## 
## SAMPLING FOR MODEL 'DoorToLab ~ dlnorm(mu, sigma)' NOW (CHAIN 1).
## WARNING: No variance estimation is
##          performed for num_warmup < 20
## 
## 
## Chain 1, Iteration: 1 / 1 [100%]  (Sampling)
##  Elapsed Time: 0 seconds (Warm-up)
##                0 seconds (Sampling)
##                0 seconds (Total)

## Computing WAIC

## Constructing posterior predictions

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Diagnostics

precis(tmp,depth=2)

##         Mean StdDev lower 0.89 upper 0.89 n_eff Rhat
## a       3.60   0.44       2.91       4.29  1361    1
## b0     -0.06   0.05      -0.14       0.02  3204    1
## day[1]  0.66   0.39       0.00       1.25  1362    1
## day[2]  0.08   0.40      -0.59       0.69  1401    1
## day[3]  0.42   0.39      -0.24       1.01  1383    1
## day[4]  0.46   0.39      -0.17       1.07  1338    1
## day[5]  0.55   0.39      -0.04       1.20  1368    1
## day[6]  0.86   0.39       0.24       1.48  1326    1
## day[7]  0.64   0.39       0.05       1.29  1391    1
## b1     -0.14   0.05      -0.21      -0.06  1607    1
## b2      0.01   0.00       0.00       0.01  1695    1
## sigma   1.04   0.05       0.95       1.12  2992    1

plot(tmp)

dashboard(tmp)

Prediction

yf<-function(y,t){
  nw<-data.frame(Year=y,DOW=1:7,TOD=t)
  pre<-as.vector(exp(link(tmp,data=nw,n=5000)))
  pf<-data.frame(rep(y,35000),rep(1:7,each=5000),pre)
  colnames(pf)<-c("Year","DOW","DoorToLab")
  return(pf)
}

lbl<-c("1"="Monday",
       "2"="Tuesday",
       "3"="Wednesday",
       "4"="Thursday",
       "5"="Friday",
       "6"="Saturday",
       "7"="Sunday"
       )
mgf<-function(t){
mg<-rbind(yf(1,t),yf(2,t),yf(3,t),yf(4,t),yf(5,t))
ggplot(data.frame(mg),aes(x=DoorToLab))+geom_histogram(aes(y=..density..),color="black",fill="red")+facet_grid(Year~DOW,labeller = labeller(DOW=lbl))+
  ggtitle(paste("Time: ",t,":00 hours"))}

mgt<-function(t){
mg<-rbind(yf(1,t),yf(2,t),yf(3,t),yf(4,t),yf(5,t))
ag<-aggregate(mg$DoorToLab,by=list(mg$DOW,mg$Year),FUN=median)
library(reshape2)
ggplot(ag, aes(Group.1,Group.2))+
    geom_tile(aes(fill = ag$x)) + 
    geom_text(aes(fill = ag$x, label = round(ag$x, 1)))+
    scale_fill_gradient(low = "white", high = "red",guide = guide_legend(title = "Door To Lab"))+
  xlab("Day of Week")+ylab("Year")+
  scale_x_discrete(
    limits=1:7,
    labels=c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"))+
  ggtitle(paste("Time:",t,":00 hours"))

}

for(i in c(2,8,12,16,20,22)){
  print(mgf(i))
  print(mgt(i))
  
}

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