# Pymer4¶

Love multi-level-modeling using lme4 in R, but prefer to work in the scientific Python ecosystem? This package has got you covered! It’s a small convenience package wrapping the basic functionality of lme4 for compatibility with python.

This package’s main purpose is to provide a clean interface that hides the back-and-forth code required when moving between R and Python. In other words a user can work completely in Python, never having to deal with R, but get (most) of lme4’s goodness. Behind the scenes this package simply uses rpy2 to pass objects between languages, compute what’s needed, parse everything, and convert to Python types (e.g. numpy arrays, pandas dataframes, etc).

This package can also fit standard regression models with a few extra bells and whistles compared to R’s lm() (Currently this only includes linear models)

TL;DR This package is your new simple Pythonic drop-in replacement for lm() or glmer() in R.

Github Repo Source

If you use this software please cite as: Jolly, (2018). Pymer4: Connecting R and Python for Linear Mixed Modeling. Journal of Open Source Software, 3(31), 862, https://doi.org/10.21105/joss.00862

## Features¶

This package has some extra goodies to make life a bit easier, namely:

• For multi-level models (i.e. glmer()):

• Automatic inclusion of p-values in model output using lmerTest
• Automatic inclusion of confidence intervals in model output
• Automatic conversion and calculation of odds-ratios and probabilities for logit models
• Easy access to model fits, residuals, and random effects as pandas dataframes
• Random effects plotting using seaborn
• Easy post-hoc tests with multiple-comparisons correction via lsmeans
• Easy model predictions on new data
• Easy generation of new data from a fitted model
• Optional p-value computation via within cluster permutation testing (experimental)
• For standard linear models (i.e. lm())

• Automatic inclusion of confidence intervals in model output
• Easy computation of empirically bootstrapped 95% confidence intervals
• Easy computation of cluster-robust, heteroscedasticity-robust or auto-correlation-robust, ‘sandwich estimators’ for standard errors (note: these are not the same as auto-regressive models)
• Permutation tests on model parameters
• Data simulation

• Highly customizable functions for generating data useful for standard regression models and multi-level models
• Data visualization

• Convenience methods for plotting model estimates, including random-effects terms

## Installation¶

pymer4 since version 0.6.0 is only compatible with Python 3. Versions 0.5.0 and lower will work with Python 2, but will not contain any new features. pymer4 also requires a working R installation with specific packages installed and it will not install R or these packages for you. However, you can follow either option below to easily handle these dependencies.

### Option 1 (simpler but slower model fitting)¶

If you don’t have R installed and you use the Anaconda Python distribution simply run the following commands to have Anaconda install R and the required packages for you. This is fairly painless installation, but model fitting will be slower than if you install R and pymer4 separately and configure them (option 2).

1. conda install -c conda-forge r r-base r-lmertest r-lsmeans rpy2
2. pip install pymer4
3. Test the installation to see if it’s working by running python -c "from pymer4.test_install import test_install; test_install()"
4. If there are errors follow the guide below

### Option 2 (potentially trickier, but faster model fitting)¶

This method assumes you already have R installed. If not install first install it from the R Project website. Then complete the following steps:

1. Install the required R packages by running the following command from within R: install.packages(c('lme4','lmerTest','lsmeans'))
2. Install pymer4 by running the following command in a terminal: pip install pymer4
3. Test the installation to see if it’s working by running the following command in a terminal: python -c "from pymer4.test_install import test_install; test_install()"
4. If there are errors follow the guide below

### Install issues¶

Some users have issues installing pymer4 on recent versions of macOS. This is due to compiler issues that give rpy2 (a package dependency of pymer4) some issues during install. Here’s a fix that should work for that:

1. Install homebrew if you don’t have it already by running the command at the link (it’s a great pacakage manager for macOS). To check if you already have it, do which brew in your Terminal. If nothing pops up you don’t have it.

2. Fix brew permissions: sudo chown -R $(whoami)$(brew --prefix)/* (this is necessary on macOS Sierra or higher (>= macOS 10.12))

3. Update homebrew brew update

4. Install the xz uitility brew install xz

5. At this point you can try to re-install pymer4 and re-test the installation. If it still doesn’t work follow the next few steps below

6. Install an updated compiler: brew install gcc, or if you have homebrew already, brew upgrade gcc

7. Enable the new compiler for use:

export CC="$(find brew info gcc | grep usr | sed 's/(.*//' | awk '{printf$1"/bin"}' -name 'x86*gcc-?')"
export CFLAGS="-W"

8. If the above results in any error output (it should return nothing) you might need to manually find out where the new compiler is installed. To do so use brew info gcc and cd into the directory that begins with /usr in the output of that command. From there cd into bin and look for a file that begins with x86 and ends with gcc-7. It’s possible that the directory ends with gcc-8 or a higher number based on how recently you installed from homebrew. In that case, just use the latest version. Copy the full path to that file and run the following:

export CC= pathYouCopiedInQuotes
export CFLAGS="-W"

9. Finally install rpy2 using the new compiler you just installed: conda install -c conda-forge rpy2 if you followed Option 1 above or pip install rpy2 if you followed Option 2

10. Now you should be able to pip install pymer4 :)