by David Lillis, Ph.D.

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. [click to continue…]

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R Is Not So Hard! A Tutorial, Part 16: Counting Values within Cases

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…

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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

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by David Lillis, Ph.D. 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 Send to KindleRelated PostsMixed Models for Logistic Regression in SPSS Chi-square test vs. Logistic Regression: Is a fancier test […]

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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.

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