Regression Trees with Random Effects for Longitudinal (Panel) Data

Description

This package estimates regression trees with random effects as a way to use data mining techniques to describe longitudinal or panel data.

Details

Author(s)

Rebecca Sela rsela@stern.nyu.edu

References

Sela, Rebecca J., and Simonoff, Jeffrey S., “RE-EM Trees: A Data Mining Approach for Longitudinal and Clustered Data”, Machine Learning (2011).

Examples

data(simpleREEMdata)
REEMresult<-REEMtree(Y~D+t+X, data=simpleREEMdata, random=~1|ID)
print(REEMresult)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(REEMtree)
Loading required package: nlme
Loading required package: rpart
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/REEMtree/REEMtree-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: REEMtree-package
> ### Title: Regression Trees with Random Effects for Longitudinal (Panel)
> ###   Data
> ### Aliases: REEMtree-package
> ### Keywords: tree
> 
> ### ** Examples
> 
> data(simpleREEMdata)
> REEMresult<-REEMtree(Y~D+t+X, data=simpleREEMdata, random=~1|ID)
> print(REEMresult)
[1] "*** RE-EM Tree ***"
n= 600 

node), split, n, deviance, yval
      * denotes terminal node

1) root 600 1092.91700  1.3639420  
  2) D< 0.5 300  339.28840  0.5006719  
    4) X>=0.2503819 89   62.24319 -0.3697058 *
    5) X< 0.2503819 211  181.18360  0.8677981 *
  3) D>=0.5 300  306.48750  2.2272130  
    6) t< 5.5 125  111.18930  1.6862250 *
    7) t>=5.5 175  132.58370  2.6136330 *
[1] "Estimated covariance matrix of random effects:"
            (Intercept)
(Intercept)    3.660086
[1] "Estimated variance of errors: 0.887508271560221"
[1] "Log likelihood:  -914.122394149391"
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>