The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
An incredibly useful tool in evaluating and comparing predictive models is the ROC curve.
Its name is indeed strange. ROC stands for Receiver Operating Characteristic. Its origin is from sonar back in the 1940s. ROCs were used to measure how well a sonar signal (e.g., from an enemy submarine) could be detected from noise (a school of fish).
ROC curves are a nice way to see how any predictive model can distinguish between the true positives and negatives. [Read more…] about What Is an ROC Curve?
Relative Risk and Odds Ratios are often confused despite being unique concepts. Why?
Well, both measure association between a binary outcome variable and a continuous or binary predictor variable. [Read more…] about The Difference Between Relative Risk and Odds Ratios
Effect size statistics are expected by many journal editors these days.
If you’re running an ANOVA, t-test, or linear regression model, it’s pretty straightforward which ones to report.
Things get trickier, though, once you venture into other types of models. [Read more...] about Effect Size Statistics in Logistic Regression
by David Lillis, Ph.D.
In our last article, we learned about model fit in Generalized Linear Models on binary data using the glm() command. We continue with the same glm on the mtcars data set (regressing the vs variable on the weight and engine displacement).
Now we want to plot our model, along with the observed data.
Although we ran a model with multiple predictors, it can help interpretation to plot the predicted probability that vs=1 against each predictor separately. So first we fit a glm for only [Read more…] about Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities
by David Lillis, Ph.D.
Ordinary Least Squares regression provides linear models of continuous variables. However, much data of interest to statisticians and researchers are not continuous and so other methods must be used to create useful predictive models.
The glm() command is designed to perform generalized linear models (regressions) on binary outcome data, count data, probability data, proportion data and many other data types.
In this blog post, we explore the use of R’s glm() command on one such data type. Let’s take a look at a simple example where we model binary data.
One great thing about logistic regression, at least for those of us who are trying to learn how to use it, is that the predictor variables work exactly the same way as they do in linear regression.
Dummy coding, interactions, quadratic terms–they all work the same way.
In pretty much every regression procedure in every stat software, the default way to code categorical variables is with dummy coding.
All dummy coding means is recoding the original categorical variable into a set of binary variables that have values of one and zero. You may find it helpful to [Read more…] about How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels