Description Usage Arguments Value Author(s) References Examples
Trains a Mixed Effect gradient boosted machine
for longitudinal continuous, binary and count data. A rule based version or these methods
using the inTree
package is also implemented(see [1])
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | MEgbm(X, Y, groups = NULL, rand.vars = "1", para = NULL,
lme.family = binomial, tol = 0.00001, max.iter = 100,
include.RE = TRUE, verbose = FALSE, likelihoodCheck = TRUE, ...)
MEgbm(X, Y, groups = NULL, rand.vars = "1", para = NULL,
lme.family = binomial, tol = 0.00001, max.iter = 100,
include.RE = TRUE, verbose = FALSE, likelihoodCheck = TRUE, ...)
MEgbmRules(form, dat, groups = NULL, rand.vars = "1", para = NULL,
tol = 0.00001, max.iter = 100, include.RE = FALSE,
verbose = FALSE, maxdepth = 5,
glmer.Control = glmerControl(optimizer = "bobyqa", check.nobs.vs.nRE =
"ignore", check.nobs.vs.nlev = "ignore"), nAGQ = 0,
likelihoodCheck = TRUE, K = 3, decay = 0.05, ...)
MEgbmRules(form, dat, groups = NULL, rand.vars = "1", para = NULL,
tol = 0.00001, max.iter = 100, include.RE = FALSE,
verbose = FALSE, maxdepth = 5,
glmer.Control = glmerControl(optimizer = "bobyqa", check.nobs.vs.nRE =
"ignore", check.nobs.vs.nlev = "ignore"), nAGQ = 0,
likelihoodCheck = TRUE, K = 3, decay = 0.05, ...)
|
X |
data.frame with predictors |
Y |
binary response vector |
groups |
character name of the column containing the group identifier |
rand.vars |
random effect variables |
para |
named list of gbm training parameters |
lme.family |
glmer control |
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 |
likelihoodCheck |
(logical) to use log likelihood of glmer to check for convergence? |
... |
Further arguments passed to or from other methods. |
gbm.dist |
gbm loss function |
type |
of predictions of gbm to pass to lme4 as population estimates (these will be used as offset) |
An object of class MEgbm; a list with items
gbmfit |
fitted gbm model |
glmer.fit |
fitted mixed effect logistic regression model |
logLik |
log likelihood of mixed effect logistic regression |
random.effects |
random effect parameter estimates |
boost.form |
gbm formula for fitted model |
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 |
fitted.probs |
fitted probabilites for final model |
fitted.class |
fitted class labels for final model |
train.perf |
various performance measures for final model on training set |
threshold |
classification cut-off |
Che Ngufor <Ngufor.Che@mayo.edu>
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
1 2 3 4 5 6 |
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