# Comparing continuous distributions with R

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. I put this together while working with data from an odd distribution involving money where…

# Plotting individual growth charts

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 2.5 on page 32 with the caption, “OLS summaries of how individuals change over time.…

# Scales and transformations in ggplot2 0.9.0

Some R code designed for ggplot2 0.8.9 is not compatible with ggplot2 0.9.0, and today the ggplot2 web site has outdated documentation which gives this broken example: Dennis Murphy points to the ggplot2 0.9.0 transition guide from where I derived a solution: The transition guide has more details about transformations such as log, log10, sqrt,…

# doSMP removed from CRAN

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 Analytics develops the doSMP package and promptly sent me this explanation: The doSMP package relies…

# Using neural network for regression

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 of neural networks to model complex, non-linear hypothesis is desirable for many real world problems—including…

# Train neural network in R, predict in SAS

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 Miner, IML, or any other special licenses, and R is free. You don’t need PMML.…

# Confidence interval diagram in R

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 a survey of how many minutes it takes to drive from home to school at…