linear model

The Difference Between Link Functions and Data Transformations

Generalized linear models—and generalized linear mixed models—are called generalized linear because they connect a model’s outcome to its predictors in a linear way. The function used to make this connection is called a link function. Link functions sounds like an exotic term, but they’re actually much simpler than they sound.

For example, Poisson regression (commonly used for outcomes that are counts) makes use of a natural log link function as follows:

Member Training: Generalized Linear Models

by guest Leave a Comment

In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).

For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.

[Read more…]

Using Pairwise Comparisons to Help you Interpret Interactions in Linear Regression

by Jeff Meyer 6 Comments

In a previous post we discussed using marginal means to explain an interaction to a non-statistical audience. The output from a linear regression model can be a bit confusing. This is the model that was shown.

In this model, BMI is the outcome variable and there are three predictors:

What Are Nested Models?

by Karen Grace-Martin Leave a Comment

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
Linear Mixed Models
Generalized Linear Mixed Models
Parametric Survival Analysis models, like Weibull models
Structural Equation Models

That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require [Read more…] about What Are Nested Models?

Incorporating Graphs in Regression Diagnostics with Stata

by Jeff Meyer Leave a Comment

by Jeff Meyer

You put a lot of work into preparing and cleaning your data. Running the model is the moment of excitement.

You look at your tables and interpret the results. But first you remember that one or more variables had a few outliers. Did these outliers impact your results? [Read more…] about Incorporating Graphs in Regression Diagnostics with Stata

R Is Not So Hard! A Tutorial, Part 5: Fitting an Exponential Model

by guest 10 Comments

by David Lillis, Ph.D.

In Part 3 and Part 4 we used the lm() command to perform least squares regressions. We saw how to check for non-linearity in our data by fitting polynomial models and checking whether they fit the data better than a linear model. Now let’s see how to fit an exponential model in R.

As before, we will use a data set of counts (atomic disintegration events that take place within a radiation source), taken with a Geiger counter at a nuclear plant.

The counts were registered over a 30 second period for a short-lived, man-made radioactive compound. We read in the data and subtract the background count of 623.4 counts per second in order to obtain [Read more...] about R Is Not So Hard! A Tutorial, Part 5: Fitting an Exponential Model