Sometimes you need to know if your data set contains elements that meet some criterion or a particular set of criteria.
For example, a common data cleaning task is to check if you have missing data (NAs) lurking somewhere in a large data set.
Or you may need to check if you have zeroes or negative numbers, or numbers outside a given range.
In such cases, the any() and all() commands are very helpful. You can use them to interrogate R about the values in your data. (more…)
SPSS has the Count Values within Cases option, but R does not have an equivalent function. Here are two functions that you might find helpful, each of which counts values within cases inside a rectangular array. (more…)
Combining the length() and which() commands gives a handy method of counting elements that meet particular criteria.
b <- c(7, 2, 4, 3, -1, -2, 3, 3, 6, 8, 12, 7, 3)
b
Let’s count the 3s in the vector b. (more…)
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…)