REEMtree {REEMtree} | R Documentation |
Fit a RE-EM tree to data. This estimates a regression tree combined with a linear random effects model.
REEMtree(formula, data, random, subset = NULL, initialRandomEffects = rep(0, TotalObs), ErrorTolerance = 0.001, MaxIterations = 1000, verbose = FALSE, tree.control = rpart.control(), lme.control = lmeControl(returnObject = TRUE), method = "REML", correlation = NULL)
formula |
a formula, as in the lm or rpart function |
data |
a data frame in which to interpret the variables named in the formula (unlike in lm or rpart , this is not optional) |
random |
a description of the random effects, as a formula of the form ~1|g , where g is the grouping variable |
subset |
an optional logical vector indicating the subset of the rows of data that should be used in the fit. All observations are included by default. |
initialRandomEffects |
an optional vector giving initial values for the random effects to use in estimation |
ErrorTolerance |
when the difference in the likelihoods of the linear models of two consecutive iterations is less than this value, the RE-EM tree has converged |
MaxIterations |
maximum number of iterations allowed in estimation |
verbose |
if TRUE , the current estimate of the RE-EM tree will be printed after each iteration |
tree.control |
a list of control values for the estimation algorithm to replace the default values used to control the rpart algorithm. Defaults to an empty list. |
lme.control |
a list of control values for the estimation algorithm to replace the default values returned by the function lmeControl . Defaults to an empty list. |
method |
whether the linear model should be estimated with ML or REML |
correlation |
an optional corStruct object describing the within-group correlation structure; the available classes are given in corClasses |
an object of class REEMtree
Rebecca Sela rsela@stern.nyu.edu
Sela, Rebecca J., and Simonoff, Jeffrey S., “RE-EM Trees: A New Data Mining Approach for Longitudinal Data”.
rpart
, nlme
, REEMtree.object
, corClasses
data(simpleREEMdata) REEMresult<-REEMtree(Y~D+t+X, data=simpleREEMdata, random=~1|ID) # Estimation allowing for autocorrelation REEMresult<-REEMtree(Y~D+t+X, data=simpleREEMdata, random=~1|ID, correlation=corAR1()) # Random parameters model for the random effects REEMresult<-REEMtree(Y~D+t+X, data=simpleREEMdata, random=~1+X|ID) # Estimation with a subset sub <- rep(c(rep(TRUE, 10), rep(FALSE, 2)), 50) REEMresult<-REEMtree(Y~D+t+X, data=simpleREEMdata, random=~1|ID, subset=sub) # Dataset from the R library "AER" data("Grunfeld", package = "AER") REEMtree(invest ~ value + capital, data=Grunfeld, random=~1|firm) REEMtree(invest ~ value + capital, data=Grunfeld, random=~1|firm, correlation=corAR1()) REEMtree(invest ~ value + capital, data=Grunfeld, random=~1+year|firm) REEMtree(invest ~ value + capital, data=Grunfeld, random=~1+year|firm, correlation=corAR1()) REEMtree(invest ~ value + capital, data=Grunfeld, random=~1|firm/year)