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…)
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.