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1. In this problem we are are going to use Bayesian Aldrich-McKelvey Scaling to analyze the 1980 Liberal-Conservative Seven Point Scale. In order to run the program you will need to install JAGS on your computer. For WINDOWS you can get it here:
   
   WINDOWS Version of JAGS
   
   For the MAC Go to Marty Plummer's Blog and he has the latest *.dmg files for MAC OS X:
   
   JAGS News -- You can download JAGS-Mavericks-3.4.0.dmg from this page.
   
   It is necessary to install JAGS to run rjags on the MAC or WINDOWS.
   
   Once these are installed download the R program:
   BAM_ANES1980_issuescale.r
   
   and the BUGS code that it utilizes:
   
   BAM_JAGScode.bug
   
   
   1. Run BAM_ANES1980_issuescale.r and show the plot that appears. (Note that this is the command in the code plot(zhat[,1:3]).)
      
      
   2. Issue the command summary(zhat) and report the results NEATLY FORMATTED.
      
      
   3. Issue the command summary(ML.result) and report the results NEATLY FORMATTED (inspect the code, this is form the aldmck() function.
      
      
   4. Issue the command plot(ML.result) and report the resulting plot.
      
      
   5. Plot the point estimates from aldmck() -- ML.result$stimuli against the means from BAM contained in summary(zhat).
   
   
2. In this problem we are are going to use Bayesian Aldrich-McKelvey Scaling to analyze the 2012 Liberal-Conservative Seven Point Scale. For Windows download the R program:
   BAM_L-R_2012_ANES2.r
   
   and the BUGS code that it utilizes:
   
   BAM_JAGScode.bug
   
   and the 2012 ANES data:
   
   ANES2012.RDA
   
   
   and put them in the same directory.
   
   For MACs download the R program:
   
   BAM_L-R_2012_ANES.r.
   
   This MAC version uses ftp to download the BUGs code and the data file. However, if you know how to use the directory structure in the MAC you can simply modify the R program to read the Bugs code and data file locally.
   
   Run BAM_L-R_2012_ANES.r/BAM_L-R_2012_ANES2.r (THIS WILL TAKE A LONG TIME!! BE PATIENT.)
   
   
   1. Issue the command summary(MLE.result) and report the results NEATLY FORMATTED (inspect the code, this is form the aldmck() function.
      
      
   2. Issue the command plot(MLE.result) and report the resulting plot.
      
      
   3. Issue the command print(round(bayes.out, 3)) and report the result.
      
      
   4. Plot the point estimates from aldmck() -- MLE_result$stimuli -- against the means from BAM contained in print(round(bayes.out, 3)).

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