After R is done running a long process, you may need to notify the operator to check the R console and provide the next commands. Without installing any more software or creating any batch files or VBS scripts, here is a simple way to create the popup notice in Windows Continue reading →
When applying the stats::lag() function to a data frame, you probably expect it will pad the missing time periods with NA, but lag() doesn’t. For example: Nothing happened. Here is an alternative lag function made for this situation. It pads … Continue reading →
nnet2sas() version 1 introduced a way to export a nnet() model trained in R to Base SAS through metaprogramming, and now nnet2sas() version 2 introduces support for variable centering and scaling as implemented in caret::train(). See the link for version … Continue reading →
In R we’ll generate similar continuous distributions for two groups and give a brief overview of statistical tests and visualizations to compare the groups. Though the fake data are normally distributed, we use methods for various kinds of continuous distributions. … Continue reading →
This R code draws individual growth plots as shown in “Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence” by Judith D. Singer and John B. Willett, an excellent book on multilevel modeling and survival analysis. This code recreates figure … Continue reading →
Some R code designed for ggplot2 0.8.9 is not compatible with ggplot2 0.9.0, and today the ggplot2 web site has outdated documentation which gives this broken example: Dennis Murphy points to the ggplot2 0.9.0 transition guide from where I derived … Continue reading →
If you do parallel processing in R on Windows, then you probably have heard of the doSMP package. However, it was recently removed from the CRAN repository with the terse message: Package ‘doSMP’ was removed from the CRAN repository. Revolution … Continue reading →
Artificial neural networks are commonly thought to be used just for classification because of the relationship to logistic regression: neural networks typically use a logistic activation function and output values from 0 to 1 like logistic regression. However, the worth … Continue reading →
This R code fits an artificial neural network in R and generates Base SAS code, so new records can be scored entirely in Base SAS. This is intended to be a simple, elegant, fast solution. You don’t need SAS Enterprise … Continue reading →
This code shows how to easily plot a beautiful confidence interval diagram in R. First, let’s input the raw data. We’ll be making two confidence intervals for two samples of 10. In case you curious, the data represents samples from … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
