MErf: Mixed Effect random forest

In nguforche/MEml: Mixed Effect Machine Learning

Description

Trains a Mixed Effect random forest for longitudinal continuous and binary data. A rule based version or these methods using the inTree package is also implemented(see [1])

Usage

MErf(form, dat, groups = NULL, family, rand.vars = "1", para = NULL,
  tol = 1e-05, max.iter = 100, include.RE = FALSE, verbose = FALSE,
  maxdepth = 5, glmer.Control = glmerControl(optimizer = "bobyqa"),
  nAGQ = 0, likelihoodCheck = TRUE, K = 3, decay = 0.05, ...)

Arguments

form	formula
dat	data.frame with predictors
groups	character name of the column containing the group identifier
family	a GLM family: continuous data set family = "gaussian", binary data set family = "binomial" see glm and family
rand.vars	random effect variables
para	named list of gbm training parameters
tol	convergence tolerance
max.iter	maximum number of iterations
include.RE	(logical) to include random effect Zb as predictor in gbm?
verbose	verbose for lme4
glmer.Control	glmer or lmer control
likelihoodCheck	(logical) to use log likelihood of glmer to check for convergence?
...	Further arguments passed to or from other methods.
type	of predictions of gbm to pass to lme4 as population estimates (these will be used as offset)

Value

An object of class MEgbm; a list with items

rf.fit	fitted random forest model
glmer.fit	fitted mixed effect logistic regression model
logLik	log likelihood of mixed effect logistic regression
random.effects	random effect parameter estimates
glmer.form	lmer4 formula
glmer.CI	estimates of mixed effect logistic regression with approximate confidence intervals on the logit scale. More accurate values can be obtained by bootstrap
threshold	classification cut-off
predRules	fitted rules
Y.star	fitted "transform" outcome. This is the same as the predicted outcome for binary data

Author(s)

Che Ngufor <[e-mail]>

References

Che Ngufor, Holly Van Houten, Brian S. Caffo , Nilay D. Shah, Rozalina G. McCoy Mixed Effect Machine Learning: a framework for predicting longitudinal change in hemoglobin A1c, in Journal of Biomedical Informatics, 2018

nguforche/MEml documentation built on April 20, 2020, 7:26 a.m.