Logistic regression
In toxicology studies, organisms are often dosed and binary outcomes often occur such as dead/alive or inhibited/mobile. This is called a dose-response study. For example, the response to different doses might be mortality (1) or survival (0) at the end of a study.
During this exercise, we will fit a logistic regression using all three methods described in the video. You have been given two datasets.
df_long, in a "long" format with each row corresponding to an observation (i.e., a 0 or 1).df_short, in an aggregated format with each row corresponding to a treatment (e.g., 6 successes, 4 failures, number of replicates = 10, proportion = 0.6).
When using the "wide" or "short" data frame, the "success, failure" methods for inputing logistic regression results require success and failure be a matrix. The easiest way to do this is with the cbind() function.
Tip: When working with data in the wild, always check to see what 0 and 1 correspond to. Different people use different notation and assumptions can cause problems for you if you assume wrong!
This exercise is part of the course
Hierarchical and Mixed Effects Models in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit a glm using data in a long format
fit_long <- glm(___ ~ ___, data = df_long,
family = "___")
summary(___)