Blog

Mighty Metrika focuses on statistical methods and mathematics for the analysis of small sample size data. As such, the project runs the risk of people with small sample sizes using tools and methods from mightymetrika.com and becoming over confident in their results because they used "small sample size methods." The long term rigorous goal to combat this disservice is to host citizen science projects, include simulation function in R packages, and share simulation results from the literature and from mightymetrika.com tools through blogs. A short and quick way to combat misuse is through the Who Said It Best series. The series will share some of the best warnings from the small sample size statistical literature. In the Conclusion section of Daniel McNeish's paper Challenging Conventional Wisdom for Multivariate Statistical Models With Small Samples he shares a clear and wonderfully worded warning:

This is a quick blog post to list some of the essential resources that I needed to get a citizen science app up and running. The app uses: R Shiny PostgreSQL Pool AWS EC2 The post is basically a way for me to bookmark resources that I found useful and also as a way to say thank you to the folks that put these resources up online.

In 'mmibain' v0.2.0, the unit tests are passing at the moment, but on r-devel-linux-x86_64-debian-clang it really seems to be hit or miss. I believe that when the test fails it is do to the new BFfe function which is a case-by-case type implementation of ' bain ' for linear models; however, I used a unit test which relies on a synthetic data set where I generated random numbers and then just used the rep() function to group observations by participants. As such, the data generating process does fit the statistical model and sometimes the random data set that is generated does not make it through bain::bain() without error. I have already changed the unit test and corresponding Roxygen2 documentation example on the Mighty Metrika GitHub and this blog post will walk through the new data and model. But just for further context, here is the original code that sometimes runs through and sometimes throws and error.

mmibain 0.2.0 is now available on CRAN: https://CRAN.R-project.org/package=mmibain . The updated package has a new function and a corresponding app. 

Building More Raw Data Plots with scdtb

Building Raw Data Plots with scdtb

Single Case Design Toolbox

Mighty Metrika Interface to Cluster Adjusted t Statistics

In a recent blog post we discussed the process for reading in variables correctly. The gist was this: If you want your variable treated as a factor (i.e., a categorical variable) then ensure that the values have letters. This is still good advice. But an ongoing (note: CRAN is closed for the holidays so the updates are taking a while) update to Mighty Metrika tools will have another way to make sure your variables are being handled correctly. This blog will give a basic overview on using this new method. Other blogs posts which will drop within the next few weeks will also feature this new method. First, let's use mmirestriktor to read in the data_f_grpnum.csv file which gave us issues in the Handling Factors in Formulas .