mertree
(mixed-effects regression trees) is an alternative implementation of REEMtree
and REEMctree
that uses lme4
for efficient computation of mixed-effects models with large data sets.
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
:
# Assuming devtools is already installed devtools::install_github("bgreenwell/mertree")
Bug reports should be submitted to https://github.com/bgreenwell/mertree/issues.
# 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)
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.
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