RElmeNB: Calculate predicted values of E(Gi|Yi) given the estimates of...

In lmeNB: Compute the Personalized Activity Index Based on a Negative Binomial Model

Description

Compute predicted values of random effects for each patient

Usage

RElmeNB(theta, alpha, betas, delta, formula, ID, Vcode = NULL,
        data, AR, RE, rel.tol = .Machine$double.eps^0.8, expG = FALSE)

Arguments

theta	A scalar containing the estimated variance of the random effect distribution, theta.
alpha	A scalar containing the estimated dispersion parameter, alpha.

betas

A vector containing the estimated regression coefficients, beta.

delta	AR(1) parameter, delta
ID	See lmeNB.
Vcode	Necessary only if the AR(1) model is used. See lmeNB.
RE	The distribution of random effects G[i]. If RE="G" then the random effects are assumed to be from the gamma distribution. If RE="N" then they are assumed to be form the log-normal distribution. The current version of RElmeNB only accept parametric model.
AR	See lmeNB.
formula	See lmeNB.
data	See lmeNB.
rel.tol	relative tolerance for the integration of the random effect. passed to integrate function.
expG	Internal use only

Value

return the predicted RE of each patient.

Author(s)

Zhao, Y. and Kondo, Y.

References

Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients. (2013) Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., Journal of the American Statistical Association.

See Also

The main function to fit the Negative Binomial mixed-effect model: lmeNB,

The subroutines of this function is: fitParaIND, fitParaAR1, fitSemiIND, fitSemiAR1,

The subroutines of index.batch to compute the conditional probability index: jCP.ar1, CP1.ar1, MCCP.ar1, CP.ar1.se, CP.se, jCP,

The functions to generate simulated datasets: rNBME.R.

Examples

## See the examples in help files of rNBME.R.

lmeNB documentation built on May 2, 2019, 3:34 p.m.