https://github.com/bgreenwell/mertree
Regression trees for longitudinal and clustered data
https://github.com/bgreenwell/mertree
Last synced: about 1 month ago
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Regression trees for longitudinal and clustered data
- Host: GitHub
- URL: https://github.com/bgreenwell/mertree
- Owner: bgreenwell
- Created: 2015-12-24T01:25:43.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-09-07T13:42:11.000Z (over 8 years ago)
- Last Synced: 2025-02-12T21:18:37.746Z (3 months ago)
- Language: R
- Homepage:
- Size: 254 KB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.Rmd
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README
# mertree
[](https://travis-ci.org/bgreenwell/mertree)
[](https://codecov.io/github/bgreenwell/mertree?branch=master)
`mertree` (mixed-effects regression trees) is an alternative implementation of [`REEMtree`](http://pages.stern.nyu.edu/~jsimonof/REEMtree/) and [`REEMctree`](http://people.stern.nyu.edu/jsimonof/unbiasedREEM/) that uses [`lme4`](https://cran.r-project.org/web/packages/lme4/index.html) for efficient computation of mixed-effects models with large data sets.
## Installation
Package `mertree` is not currently available from CRAN, but the development version is hosted on GitHub at https://github.com/bgreenwell/mertree and can be downloaded using [`devtools`](https://github.com/hadley/devtools):
```r
# Assuming devtools is already installed
devtools::install_github("bgreenwell/mertree")
```
Bug reports should be submitted to https://github.com/bgreenwell/mertree/issues.
## Basic usage
```{r, warning=FALSE}
# Load required packages
library(mertree)
library(pdp)
# Fit a mixed-effects regression tree
fm <- mertree(y ~ time + x1 + x2 + x3 + x4 + x5 + x6 + (1 | subject),
data = simd, unbiased = TRUE, do.trace = TRUE)
# Partial dependence of response on time
partial(fm, pred.var = "time", plot = TRUE, train = simd)
# Partial dependence of response on covariates (notice x3 and x6 are flat!)
par(mfrow = c(3, 2))
for (i in 1:6) {
partial(fm, pred.var = paste0("x", i), plot = TRUE, train = simd)
}
# Is there an interaction between x1 and x4?
partial(fm, pred.var = c("x1", "x4"), plot = TRUE, train= simd)
```
## References
Rebecca J. Sela and Jeffrey S. Simonoff (2012). "RE-EM Trees: A Data Mining Approach for Longitudinal and Clustered Data". _Machine Learning_, 86(2), 169-207.
Wei Fu and Jeffrey S. Simonoff (2015), "Unbiased Regression Trees for Longitudinal and Clustered Data". _Computational Statistics and Data Analysis_, 88, 53-74.
Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). "Fitting Linear Mixed-Effects Models Using lme4". _Journal of Statistical Software_, 67(1), 53-74. doi:10.18637/jss.v067.i01.