Logistic regression on flight delay data using R

(!******************************************************
   Mosel R Example Problems
   ========================

   file flightdelay.mos
   ````````````````````
   Perform logistic regression on flight delay data
   -- Graphical output via R --
   
  (c) 2015 Fair Isaac Corporation
      author: L.Varghese, Jul. 2015
*******************************************************!)
model flightdelay
  uses "mmsystem"
  uses "r"
  
  parameters
    GRDEVICE = "png" ! Set to 'png', 'pdf' or '' to change the type of document
  end-parameters
  
  ! Input data
  declarations
    Flights: range                        ! Flight index
    Carrier: array(Flights) of string     ! Carrier Code: AA,B6,DL,US,UA,VX
    Delay: array(Flights) of integer      ! Whether flight was delayed or not: 1,0
    Weekday: array(Flights) of integer    ! Weekday: 1=Monday,7=Sunday
    Departure: array(Flights) of integer  ! Departure Hour: 0-23
    Arrival: array(Flights) of integer    ! Arrival Hour: 0-23
    ArrvRegion: array(Flights) of string  ! Arrival Region: NE,SE,SW,W,M,ISL

    ! Misc
    t: text
  end-declarations
  
  ! Read data from csv file
  initializations from "mmsheet.csv:flightdelay.csv"
    [Carrier,Delay,Weekday,Departure,Arrival,ArrvRegion] as "[A2:G7902](#1,#2,#3,#4,#5,#6,#7)"
  end-initializations
  
  ! Print out R errorstream
  setparam("Rverbose", true)
  
  ! Set up R dataframe
  Rset("carrier",Carrier)
  Reval('MyData <- data.frame(carrier=carrier)')
  Rset('MyData$delay',Delay)
  Rset('MyData$weekday',Weekday)
  Rset('MyData$departure',Departure)
  Rset('MyData$arrival',Arrival)
  Rset('MyData$arrvregion',ArrvRegion)
  
  ! Print 5 rows of the dataframe
  writeln("Printing 5 rows of input data:")
  Rprint('head(MyData,n=5)')
  writeln
  
  ! Print summary stats of each column
  writeln("Summary statistics of input data")
  Rprint('summary(MyData)')
  writeln
  
  ! Set weekday, departure and arrival to factor variables 
  Reval('MyData$weekday <- factor(MyData$weekday);MyData$departure <- factor(MyData$departure);MyData$arrival <- factor(MyData$arrival)')
  
  ! Run a logistic regression on delay
  Reval('mylogit <- glm(delay ~ carrier + weekday + departure + arrvregion, data = MyData, family = "binomial")')  
  ! Print summary statistics of the model
  writeln("Summary Statistics for the Logistic Regression")
  Rprint('summary(mylogit)')
  
  !**** Plot Sensitivity and Specificity for different  
  !**** cutoff values of probability  
  Reval('library(graphics)')             ! Load R graphics library
  Reval('library(grDevices)')            ! Load graphical device library
  
  Reval('s = seq(.01,.99,length=1000)')  ! Create % y axis
  Reval('stats = matrix(0,1000,2)')      ! Create a 1000x2 matrix
  
  ! Calculate specificity and sensitivity for each cutoff point
  Reval('for(i in 1:1000){
   predDelay = (mylogit$fit > s[i])
   w = which(MyData$delay == 1)
   sensitivity = mean(predDelay[w] == 1)
   specificity = mean(predDelay[-w] == 0)
   temp = t(as.matrix(c(sensitivity, specificity)))
   colnames(temp) = c("sensitivity", "specificity")
   stats[i,] = temp
  }')
  
  ! Plot the graph
  if (GRDEVICE.size>0) then
    Reval(GRDEVICE+'("flightdelay.'+GRDEVICE+'")') ! Open graphical file
  end-if
  Reval('plot(s,stats[,1],xlab="% Cutoff",ylab="Specificity/Sensitivity",type="l",lwd=2,col=2)')
  Reval('lines(s,stats[,2],col=3,lwd=2);box()')
  Reval('legend(0.7,0.8,col=c(2,3),lwd=c(2,2),c("Sensitivity","Specificity"))')
  if (GRDEVICE.size=0) then              ! Wait for user entry
    Reval('dev.flush()')                 ! Print plots
    writeln("Press enter to continue...")
    dummy := readtextline(t)
  end-if
  Reval('dev.off()')                     ! Close graphics device
  
end-model