Multivariate Multiple Regression is the method of modeling multiple responses, or dependent variables, with a single set of predictor variables. For example, we might want to model both math and reading SAT scores as a function of gender, race, parent income, and so forth. This allows us to evaluate the relationship of, say, gender with […]

# StatLab Articles

## Visualizing the Effects of Proportional-Odds Logistic Regression

Proportional-odds logistic regression is often used to model an ordered categorical response. By “ordered”, we mean categories that have a natural ordering, such as “Disagree”, “Neutral”, “Agree”, or “Everyday”, “Some days”, “Rarely”, “Never”. For a primer on proportional-odds logistic regression, see our post, Fitting and Interpreting a Proportional Odds Model. In this post we demonstrate […]

## Getting started with the purrr package in R

If you’re wondering what exactly the purrr package does, then this blog post is for you. Before we get started, we should mention the Iteration chapter in R for Data Science by Garrett Grolemund and Hadley Wickham. We think this is the most thorough and extensive introduction to the purrr package currently available (at least […]

## Working with dates and time in R using the lubridate package

Sometimes we have data with dates and/or times that we want to manipulate or summarize. A common example in the health sciences is time-in-study. A subject may enter a study on Feb 12, 2008 and exit on November 4, 2009. How many days was the person in the study? (Donâ€™t forget 2008 was a leap […]

## The Wilcoxon Rank Sum Test

The Wilcoxon Rank Sum Test is often described as the non-parametric version of the two-sample t-test. You sometimes see it in analysis flowcharts after a question such as “is your data normal?” A “no” branch off this question will recommend a Wilcoxon test if you’re comparing two groups of continuous measures. So what is this […]

## Pairwise comparisons of proportions

Pairwise comparison means comparing all pairs of something. If I have three items A, B and C, that means comparing A to B, A to C, and B to C. Given n items, I can determine the number of possible pairs using the binomial coefficient: $$ \frac{n!}{2!(n – 2)!} = \binom {n}{2}$$ Using the R […]

## Stata Basics: foreach and forvalues

There are times we need to do some repetitive tasks in the process of data preparation, analysis or presentation, for instance, computing a set of variables in a same manner, rename or create a series of variables, or repetitively recode values of a number of variables. In this post, I show a few of simple […]

## Stata Basics: Reshape Data

In this post, I use a few examples to illustrate the two common data forms: wide form and long form, and how to convert datasets between the two forms – here we call it “reshape” data. Reshaping often needed when you work with datasets that contain variables with some kinds of sequences, say, time-series data. […]

## Stata Basics: Combine Data (Append and Merge)

When I first started working with data, which was in a statistics class, we mostly used clean and completed dataset as examples. Later on, I realize it’s not always the case when doing research or data analysis for other purposes; in reality, we often need to put two or more dataset together to be able […]

## Stata Basics: Subset Data

Sometimes only parts of a dataset mean something to you. In this post, we show you how to subset a dataset in Stata, by variables or by observations. We use the census.dta dataset installed with Stata as the sample data. Subset by variables * Load the data > sysuse census.dta (1980 Census data by state) […]