Repo for Stat 461-ANOVA
I will use this repository to store and share data files and other resources for my sections of Stat 461-ANOVA at Penn State.
There are several key elements to this repo:
- Homework Template File
- activityHandouts
- dataFiles
- demoFiles
- rScripts
- exampleCode (archive)
- learnrTutorials (under development)
- writingReports (under development)
- Misc (-ellaneous)
I have included a template file you may use for assignments if you are using R Markdown. The template is based upon one developed by Alex Hayes.
This directory contains various RMD files for different activities used throughout the course.
This sub-directory contains various data files (mostly *.dat) that I have used in STAT 461 either as in-class examples, homework assignments, or projects.
This sub-directory will be the home of various R Markdown files and future files that help students learn R and develop fluency.
This sub-directory contains R files which define multiple helpful functions for Stat 461.
The checkSetup function will check your R setup for three things: 1) do you have a current version of R (i.e., 4.2.1+) do you have the necessary packages installed (if not, it will install them), 3) will ask you if you want to install TinyTeX (and install it if you do).
To use this function copy and paste the following code in your Console and hit Return:
source("https://raw.github.com/neilhatfield/STAT461/master/rScripts/checkSetup.R")
checkSetup()
The ANOVATools.R file contains a set of functions that I've written to help individuals perform several common tasks related to ANOVA. There are public and private functions in this file.
As of 3/20/2022, there are nine public functions: anova.PostHoc, anovaFixer, block.RelEff, hodgesLehmann, kw.PostHoc, probSup, pvalRound, sphericityPlot, and dscfTest.
The anova.PostHoc function takes three arguments: aov.obj-required, response-optional, and mainEffect-optional. The aov.obj should be the output of the function aov in R. The response and mainEffect arguments take a string value to identify the response of the model as well as the main effect of interest for post hoc analysis. These two optional arguments are useful for conducting Post Hoc analysis for Oneway + Blocking ANOVA as well as exploring Main Effects for a Factorial ANOVA.
The anova.PostHoc returns a data frame of effect sizes that can be formatted by another function (e.g., knitr::kable) consisting of the following columns: Pair , Cohen's d, Hedge's g, and the Probability of Superiority.
Future plans include adding on handling of interactions for Factorial ANOVAs.
The anovaFixer function will return a data frame for use in a kable call that adjusted denominators for mixed effect models. I'm currently pondering whether to retire this function.
The block.RelEff function takes three arguments, all required: aov.obj, blockName, trtName. The aov.obj should be the output of the function aov in R using a formula that contains a block term. The blockName should be a string identifying the column name of the block, and trtName should be a string identifying the column name for the treatment.
The block.RelEff will return a string giving the relative efficiency of the randomized complete block design used when compared to a complete randomized design. A relative efficiency of x means that we would need x times as many measurement units per treatment level as what we used in the RCB design to produce the same level of information.
The hodgesLehmann function takes two vectors of values, x and y, and calculates the value of Hodges-Lehman .
The kw.PostHoc function takes two arguments, both required: x, and g. The x argument should be the response vector and g a vector of group membership (i.e., the factor).
The kw.PostHoc function returns a data frame of effect sizes that can be formatted by another function (e.g., knitr::kable) consisting of the following columns: Pair , Hodges-Lehmann , and the Probability of Superiority.
The probSup function takes a Cohen's d value as the input and returns the Probability of Superiority, via
The pvalRound function looks at a value (particularly p-values) and if the value is less than a given values (0.0001 be default) will return the character string "< 0.0001", otherwise will return the value rounded to four decimal places. This is to help limit instances were rounding make p-values look like zero. The argument digits, which defaults to 4, controls the comparison value, i.e., 1*10^(-1*digits) and the rounnding.
The sphericityPlot provides a scatter plot of the pairwise differences of the treatments for each subject in a Within Subjects Repeated Measures design.
The dscfTest is a function of two vectors, both required: response containing the response values and factor containing the levels of the factor. The two vectors should be in the same order. This function is a wrapper for NSM3::pSDCFlig function for doing the Steel-Dwass-Critchlow-Fligner post hoc test in a One-way nonparametric setting. This will return a data frame ready for kable consisting of the comparisons and their (adjusted) p-values.
The anovaScreens function takes a data frame (via dataFrame) and two strings (via response and factor) to apply the screens idea and returns a data frame with the new screen columns. Error checking is done on the two string arguments to ensure that they 1) are just a single character string, and 2) are found in the names of the supplied data frame. Currently, anovaScreens only works for Oneway ANOVA models.
As of 3/30/2021, there is one private function which acts as a helper function: .strsplitN.
The .strsplitN function splits strings generated by dunn.test::dunn.test on the "-" and returns Nth word.
The shadowgram function is under development. The goal is the bring the shadowgram from JMP to R via ggplot2. While the current code functions, there is still more work to be done to fine tune the function.
You can access this function through the following means
source("https://raw.github.com/neilhatfield/STAT461/master/rScripts/shadowgram.R")
# Example usage
shadowgram(
dataVec = oreoData$Filling.Mass,
label = "Filling mass (g)",
layers = 60,
aStep = 4,
color = "blue"
)
This sub-directory is acting as an archive of individual R files that served as example code for Spring 2020.
This sub-directory is where I'm working on creating some learnr based tutorials for STAT 461. They are in rather incomplete states.
This is a new (as of Spring 2022) directory where I'm placing materials related to helping students write reports of their results in the ANOVA context.