\[%% % Add your macros here; they'll be included in pdf and html output. %% \newcommand{\R}{\mathbb{R}} % reals \newcommand{\E}{\mathbb{E}} % expectation \renewcommand{\P}{\mathbb{P}} % probability \DeclareMathOperator{\logit}{logit} \DeclareMathOperator{\logistic}{logistic} \DeclareMathOperator{\sd}{sd} \DeclareMathOperator{\var}{var} \DeclareMathOperator{\cov}{cov} \DeclareMathOperator{\cor}{cor} \DeclareMathOperator{\Normal}{Normal} \DeclareMathOperator{\LogNormal}{logNormal} \DeclareMathOperator{\Poisson}{Poisson} \DeclareMathOperator{\Beta}{Beta} \DeclareMathOperator{\Binom}{Binomial} \DeclareMathOperator{\Gam}{Gamma} \DeclareMathOperator{\Exp}{Exponential} \DeclareMathOperator{\Cauchy}{Cauchy} \DeclareMathOperator{\Unif}{Unif} \DeclareMathOperator{\Dirichlet}{Dirichlet} \DeclareMathOperator{\Wishart}{Wishart} \DeclareMathOperator{\StudentsT}{StudentsT} \DeclareMathOperator{\Weibull}{Weibull} \newcommand{\given}{\;\vert\;} \]
Assignment: Your task is to use Rmarkdown to write a short report, readable by a technically literate person. The code you used should not be visible in the final report (unless you have a good reason to show it).
Due: Submit your work via Canvas by the end of the day (midnight) on Tuesday, February 22nd. Please submit both the Rmd file and the resulting html or pdf file. You can work with other members of class, but I expect each of you to construct and run all of the scripts yourself.
In class (Week 16 slides) we have analyzed the “hurricane lizards” dataset, available in the ../Datasets/Hurricane_lizards directory. Please write up this analysis into a report. You may use the code that we wrote in class, although you should not use it uncritically (e.g., make sure that graph axes are properly labeled). As one additional analysis, please fit the model without the quadratic term and compare the results. (A formal model comparison is not necessary, only a comparison of the relevant results.)
In particular, you may restrict your report to mean finger area and femur length (as analyzed in class, but you can include more variables if you want). You may omit the initial exploratory set of \(t\)-tests, as well as the posterior predictive checks done at the end. But, do be sure to include plots of the raw data, and to report on convergence of the MCMC (this can be brief, and needn’t include plots).