visualization

When fitting a linear model we make two assumptions about the distribution of residuals:

A Simple Slopes Analysis is a follow-up procedure to regression modeling that helps us investigate and interpret “significant” interactions. The analysis is often employed for interactions between two numeric predictors, but it can be applied to other types of interactions as well. To motivate why we might be interested in this type of analysis, consider the following research question:

Does the length of time in a managerial position (X) and a manager’s ability (Z) help explain or predict a manager’s self-assurance (Y)?

In this article, we demonstrate how to include mathematical symbols and formulas in plots created with R. This can mean adding a formula in the title of the plot, adding symbols to axis labels, annotating a plot with some math, and so on.

This article assumes basic familiarity with the use and interpretation of logistic regression, odds and probabilities, and true/false positives/negatives. The examples are coded in R. ROC curves and AUC have important limitations, and I encourage reading through the section at the end of the article to get a sense of when and why the tools can be of limited use.

Note: This version of the article contains static images of maps generated with Leaflet. You can view a version with interactive maps here. 

What is Shiny?

Shiny is an R package that facilitates the creation of interactive web apps using R code, which can be hosted locally, on the shinyapps server, or on your own server. Shiny apps can range from extremely simple to incredibly sophisticated. They can be written purely with R code or supplemented with HTML, CSS, or JavaScript. Visit R studio’s shiny gallery to view some examples.

I've been struggling off and on for literally months trying to create and export a print layout using Python for QGIS 3. Or PyQGIS 3 for short. I have finally figured out may of the ins and outs of the process and hopefully this will serve as a guide to save someone else a lot of effort and time.

I was recently working on a project in QGIS 3 with a member of UVA Health's Oncology department. This person wanted to take a set of patient data (after identifying info had been removed) and after doing some other stuff, apply a graduated color scheme to the results, shading them from light to dark based on intensity.

You can find a sample dataset for this project here:

https://github.com/epurpur/PyQGIS-Scripts/blob/master/TestZipCodes.zip

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.

I'm still looking at the rhetoric from the presidential debates, this time focusing on the first general election debate between Hillary Clinton and Donald Trump.

Data sets provided by the US Census Bureau, such as the Decennial Census and American Community Survey (ACS), are widely used by researchers, among others. You can certainly find and download census data from the Census Bureau website, from the licensed data source Social Explorer, or from other free sources such as IPUMS-USA and then load the data into a statistical package or other software to analyze or present the data.

Plotting with color in R is kind of like painting a room in your house: You have to pick some colors. R has some default colors ready to go, but it's only natural to want to play around and try some different combinations. In this article, we'll look at some ways you can define new color palettes for plotting in R.

To begin, let's use the palette() function to see what colors are currently available:

Hurdle Models are a class of models for count data that help handle excess zeros and overdispersion. To motivate their use, let's look at some data in R. The following data come with the AER package. It is a sample of 4,406 individuals, aged 66 and over, who were covered by Medicare in 1988. One of the variables the data provide is number of physician office visits.

When it comes to modeling counts (i.e., whole numbers greater than or equal to 0), we often start with Poisson regression. This is a generalized linear model where a response is assumed to have a Poisson distribution conditional on a weighted sum of predictors. For example, we might model the number of documented concussions to NFL quarterbacks as a function of snaps played and the total years experience of his offensive line. However, one potential drawback of Poisson regression is that it may not accurately describe the variability of the counts.

Logistic regression is a popular and effective way of modeling a binary response. For example, we might wonder what influences a person to volunteer, or not volunteer, for psychological research. Some do, some don’t. Are there independent variables that would help explain or distinguish between those who volunteer and those who don’t? Logistic regression gives us a mathematical model that we can we use to estimate the probability of someone volunteering given certain independent variables.

When doing linear modeling or ANOVA it’s useful to examine whether or not the effect of one variable depends on the level of one or more variables. If it does then we have what is called an “interaction”. This means variables combine or interact to affect the response. The simplest type of interaction is the interaction between two two-level categorical variables. Let’s say we have gender (male and female), treatment (yes or no), and a continuous response measure. If the response to treatment depends on gender, then we have an interaction.

You ran a linear regression analysis and the stats software spit out a bunch of numbers. The results were significant (or not). You might think that you’re done with analysis. No, not yet. After running a regression analysis, you should check if the model works well for the data.

The QQ plot, or quantile-quantile plot, is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a normal or exponential. For example, if we run a statistical analysis that assumes our residuals are normally distributed, we can use a normal QQ plot to check that assumption. It's just a visual check, not an air-tight proof, so it is somewhat subjective.

An important component of data analysis is graphing. Stata provides excellent graphics facility for quickly exploring and visualizing your data. For example, let's load the auto data set that comes with Stata (1978 Automobile Data) and make two scatterplots and then two boxplots: