plot

Ways of plotting map data in R (and python)

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Thanks to Scott Jackson, Daniel Ezra Johnson, David Morris, Michael Shvartzman, and Nathanial Smith for the recommendations and pointers to the packages mentioned below.

  • R:
    • The maps, mapsextra, and maptools packages provide data and tools to plot world, US, and a variety of regional maps (see also mapproj and mapdata). This, combined with ggplot2 is also what we used in Jaeger et al., (2011, 2012) to plot distributions over world maps. Here’s an example from ggplot2 with maps.
    Example of using ggplot2 combined with the maps package.
    Example use of ggplot2 combined with the maps package (similar to the graphs created for Jaeger et al., 2011, 2012).
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Plotting effects for glmer(, family=”binomial”) models

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UPDATE 12/15/10: Bug fix. Thanks to Christian Pietsch.

UPDATE 10/31/10: Some further updates and bug fixes. The code below is the updated one.

UPDATE 05/20/10: I’ve updated the code with a couple of extensions (both linear and binomial models should now work; the plot now uses ggplot2) and minor fixes (the code didn’t work if the model only had one fixed effect predictor).  I also wanted to be clear that the dashed lines in the plots aren’t confidence intervals. They are multiples of the standard error of the effect.

Here’s a new function for plotting the effect of predictors in multilevel logit models fitted in R using lmer() from the lme4 package. It’s based on code by Austin Frank and I also borrowed from Harald Baayen’s plotLMER.fnc() (package languageR). First a cool pic:

Predicted effect of speechrate on complementizer-mentioning
Predicted effect of speechrate on complementizer-mentioning

These plots contain the distribution of the predictor (x-axis) against the predicted values (based on the entire model, y-axis) using hexbinplot() from the package hexbin. On top of that, you see the model prediction fo the selected predictor along with confidence intervals. Note that the predictor is given in its original form (here speech rate) although it was entered into the model as the centered log-transformed speechrate. The plot consideres that. Of course, you can configure things.

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