MEsvm: Mixed Effect support vector machine

Description Usage Arguments Value Author(s) References

View source: R/MEsvm.R View source: R/MEsvm.R

Description

Train a Mixed Effect support vector machine for binary outcome.

Usage

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MEsvm(form, dat, groups = NULL, 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

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

likelihoodCheck

(logical) to use log likelihood of glmer to check for convergence?

...

Further arguments passed to or from other methods.

lme.family

glmer control

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

svmfit

fitted svm model

glmer.fit

fitted mixed effect logistic regression model

logLik

log likelihood of mixed effect logistic regression

random.effects

random effect parameter estimates

svm.form

svm 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

fitted.decision

fitted decision values for final model

train.perf

various performance measures for final model on training set

threshold

classification cut-off

Author(s)

Che Ngufor <[email protected]>

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 Jan. 15, 2020, 3:23 a.m.