Description
Use RStudio for all exercises. Efficiency is important. Use efficient programming techniques and modular
programming as discussed in class, and make use of functions we have already created when possible.
You should provide one script (a .R or .rmd file) that contains all the code and includes code comments
noting which code is for which exercises. You will also need to show and comment on the results, so place
the results in a Word (or Open Office or HTML or PDF) document and write sentences to answer the
questions, or use knitr to programmatically create your document. Script files must be the actual script
files, not unevaluated code pasted into some other document.
Include your name in the name for each file submitted (‘
‘JaneDoeHW4.R’). Any code based on code from elsewhere (e.g. code provided with the text) must
reference in code comments the source of the original code.
Some initial setup code is provided in HW4Setup.R in the Homework 4 directory in compass.
Exercises for All Students
1) Create a function computeMsgLLR2 which implements the following log of ratios of products of
probabilities formula for the log likelihood ratio statistic:
log�(∏ 𝑃𝑃(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝| 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)) 𝑖𝑖𝑖𝑖 𝑚𝑚𝑚𝑚𝑚𝑚 /(∏ 𝑃𝑃(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝| ℎ𝑎𝑎𝑎𝑎)) 𝑖𝑖𝑖𝑖 𝑚𝑚𝑚𝑚𝑚𝑚 � +
log((∏𝑛𝑛𝑛𝑛𝑛𝑛 𝑃𝑃(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎| 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) 𝑖𝑖𝑖𝑖 𝑚𝑚𝑚𝑚𝑚𝑚 )/(∏𝑛𝑛𝑛𝑛𝑛𝑛 𝑃𝑃(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎| ℎ𝑎𝑎𝑎𝑎) 𝑖𝑖𝑖𝑖 𝑚𝑚𝑚𝑚𝑚𝑚 ))
Compare the results from this definition with the results from the computeMsgLLR function used
in the text which used the sum of differences of log probabilities.
Specifically, compare accuracy for this formula compared to the one used in class (Hint: to estimate
relative accuracy you should look at (observed-expected)/expected, and treat the results from
computeMsgLLR2 as observed and the results from computeMsgLLR as expected) and note
any issues that arise with non-representable numbers (e.g. very large or very small intermediate
results that result in infinite, incorrect 0, or not a number results from your function).
2) Do exercise Q.13 from page 167 of Data Science in R: A Case Studies Approach to Computational
Reasoning and Problem Solving, by Deborah Nolan and Duncan Temple Lang. Within the exercise,
construct two functions: one that counts the number of yelling lines, and one that gives the
percentage.
3) Check that the hour feature in emailDF gives valid values for all of the email messages. Then
perform descriptive analysis to compare this feature for spam and ham, and comment on the
possibility of using this feature to classify email.
Additional Exercises for Graduate Students
4) Do exercise Q.14 from page 167 of Data Science in R: A Case Studies Approach to Computational
Reasoning and Problem Solving, by Deborah Nolan and Duncan Temple Lang.