As you may have read in one of our recent newsletters, this month The Analysis Factor hit two milestones:
We’re quite happy about both, and seriously grateful to all members of our community.
So to celebrate and to say thanks, we decided to do a giveaway to 6 randomly-chosen newsletter subscribers.
I just sent emails to the 6 winners this morning.
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 (more…)
In the last article, we saw how to create a simple Generalized Linear Model on binary data using the glm() command. We continue with the same glm on the mtcars data set (more…)
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.
In Part 14, let’s see how to create pie charts in R. Let’s create a simple pie chart using the pie() command. As always, we set up a vector of numbers and then we plot them.
B <- c(2, 4, 5, 7, 12, 14, 16) (more…)
One of our instructors–David Lillis–recently gave a talk in front of the Wellington R Users Group highlighting 15 Tips for using the R statistical programming language aimed at the beginner.
Below is a video recording of his presentation…