Jags-Ymet-XmetMulti-Mrobust-Cameras-Run.R

graphics.off() # This closes all of R's graphics windows.
rm(list=ls())  # Careful! This clears all of R's memory!

library(ggplot2)
library(ggpubr)
library(ks)
library(rjags)
library(runjags)

# Set working directory if necessary
# setwd(" ")

#na.omit for cleanup, there were some empty cells
myData = na.omit(read.csv("camera_dataset.csv"))
colnames(myData) = c("Model", "Release.date",   "Max.resolution",   "Low.resolution",   "Effective.pixels",
                     "Zoom.wide",   "Zoom.tele", "Normal.focus.range",  "Macro.focus.range",
                     "Storage.included", "Weight.w.batteries", "Dimensions", "Price")

yName = "Price" ; xName = c("Release.date", "Max.resolution",   "Low.resolution",   "Effective.pixels",
                            "Zoom.wide",    "Zoom.tele", "Normal.focus.range",  "Macro.focus.range",
                            "Storage.included", "Weight.w.batteries", "Dimensions")
fileNameRoot = "Task9"

library(ggplot2)
head(myData)

summary(myData)
#graphics.off() # added to open plots in a new window. When plots are shown in the plots tab
windows( width=480 , height=480) #they have low quality
hist(myData$Price)
#graphics.off() # added to open plots in a new window. When plots are shown in the plots tab
windows( width=480 , height=480) #they have low quality
plot(kde(myData$Price))


# Scatter plots
p1 <- ggplot(myData, aes(x=Release.date, y=Price)) +
  geom_point()

p2 <- ggplot(myData, aes(x=Max.resolution, y=Price)) +
  geom_point()

p3 <- ggplot(myData, aes(x=Low.resolution, y=Price)) +
  geom_point()

p4 <- ggplot(myData, aes(x=Effective.pixels, y=Price)) +
  geom_point()

p5 <- ggplot(myData, aes(x=Zoom.wide, y=Price)) +
  geom_point()

p6 <- ggplot(myData, aes(x=Zoom.tele, y=Price)) +
  geom_point()

p7 <- ggplot(myData, aes(x=Normal.focus.range, y=Price)) +
  geom_point()

p8 <- ggplot(myData, aes(x=Macro.focus.range, y=Price)) +
  geom_point()

p9 <- ggplot(myData, aes(x=Storage.included, y=Price)) +
  geom_point()

p10 <- ggplot(myData, aes(x=Weight.w.batteries, y=Price)) +
  geom_point()

p11 <- ggplot(myData, aes(x=Dimensions, y=Price)) +
  geom_point()

figure <- ggarrange(p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, nrow = 4, ncol = 3)
#graphics.off() # added to open plots in a new window. When plots are shown in the plots tab
windows( width=480 , height=480) #they have low quality
figure


numSavedSteps = 3500
thinSteps = 10
nChains = 4

graphFileType = "eps"
#-------------------------------------------------------------------------------
# Load the relevant model into R's working memory:
source("Jags-Ymet-XmetMulti-Mrobust-Task9.R")
#-------------------------------------------------------------------------------
# Generate the MCMC chain:
startTime = proc.time()
xPred = c( 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 )

mcmcCoda = genMCMC( data=myData , xName=xName , yName=yName ,
                    numSavedSteps=numSavedSteps , thinSteps=thinSteps ,
                    saveName=fileNameRoot , nChains = nChains , xPred = xPred )
stopTime = proc.time()
duration = stopTime - startTime
show(duration)

# save.image(file='Task6Chains.RData')
# load('Task6Chains.RData')


#-------------------------------------------------------------------------------
# Display diagnostics of chain, for specified parameters:
parameterNames = varnames(mcmcCoda) # get all parameter names
for ( parName in parameterNames ) {
  diagMCMC( codaObject=mcmcCoda , parName=parName ,
            saveName=fileNameRoot , saveType=graphFileType )
}
#graphics.off()
#-------------------------------------------------------------------------------
# Get summary statistics of chain:

summaryInfo = smryMCMC( mcmcCoda ,
                        saveName=fileNameRoot  )
show(summaryInfo)
# Display posterior information:
plotMCMC( mcmcCoda , data=myData , xName=xName , yName=yName ,
          pairsPlot=TRUE , showCurve=FALSE ,
          saveName=fileNameRoot , saveType=graphFileType )
#-------------------------------------------------------------------------------