lmeNB (version 1.2)

index.batch: Compute the point estimate and its 95of the conditional probability Pr(q(Y_i,new)>=q(y_i,new)| Y_i,pre=y_i,pre)

Description

Given an output of mle.fun, mle.ar1.fun, mle.a3.fun, mle.ar1.non3 or lmeNB. This function computes the probability of observing the response counts as large as those new observations of subject i y_i,new=(y_i,m_i+1,...,y_i,ni) conditional on the subject's previous observations y_i,pre(y_i,1,...,y_i,m_i).That is, this function returns a point estimate and its asymptotic 95 Pr(q(Y_i,new)>=q(y_i,new)| Y_i,pre=y_i,pre).

The standard error is not produced when the semi-parametric approach is employed.A scalar statistic to summarize the new response counts can be either the total count, q(Y_i,new)=sum_{j=m_i+1}^ni Y_ij, or the maximum, q(Y_i,new)=max { Y_ij;j=m_i+1,...,n_i }.See Zhao et al., for more details.

Usage

index.batch(data,labelnp, ID, Vcode,olmeNB,subset=NULL, 
            qfun = "sum", IPRT = TRUE,  i.se = TRUE, iMC = FALSE)

Arguments

data

A data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. The each row must contains the data corresponding to the repeated measure j of subjects and the rows (i,j)s must b

labelnp

A vector of length the total number of repeated measures, indicating new measures by TRUE. For examples, if patient i has a n_i repeated measures and the last n_i-m_i+1 measures are new, then then lab

ID

A vector of length sum_i^N n_i, containing the subject IDs. i.e., c(rep(1,n_1),rep(2,n_2),...,rep(N,n_N))

Vcode

A vector of length the total number of repeated measures, containing the indices of time point. For example, there are three subjectss and two subjects do not have missing visits and completed five visits while the other subjects missed a visit at

olmeNB

Output of mle.fun,mle.ar1.fun,mle.a3.fun,mle.ar1.non3<

subset

An optional expression indicating the subset of the subjects of that the index should be computed.

qfun

If qfun="sum", a scalar statistic to summarize the new response counts is the total count. If qfun="max", a scalar statistic to summarize the new response counts is the maximum.

IPRT

print control.

i.se

If i.se=TRUE then the standard errors of the estimator of the conditional probability are returned for the output of mle.fun or mle.ar1.fun

iMC

Necessary for the AR(1) outputs,mle.ar1.fun and mle.ar1.non3. If iMC=TRUE then the function MCCP.ar1 is called and the

Value

• The N by 4 (3, if hide the SE) numeric matrix, containing the point estimate of the conditional probability, and the lower and the upper bounds of the 95

Details

The standard error of the point estimate on the logit scale is constructed using the delta method for the parametric model, where distributional assumption was made for random effects.

References

Zhao, Y., Li, D.K.B., Petkau, J.A., Riddehough, A. & Traboulsee, A. Detection of unusual increases in MRI lesion counts in multiple sclerosis patients.

See Also

A wrapper function to fit negative binomial mixed effect model: lmeNB The functions to fit the relevant models: mle.fun, mle.ar1.fun, mle.a3.fun, mle.ar1.non3,

The subroutines of index.batch: 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 linked R-functions.