index_batch_Bayes: The main function to compute the point estimates and 95%...

In lmeNBBayes: Compute the Personalized Activity Index Based on a Flexible Bayesian Negative Binomial Model

Description

Let m[i] be the number of pre-measurements and n[i] be the total number of repeated measures. Then the repeated measure of a subject can be divided into a pre-measurement set and a new measurement set as Y[i]=(Y[i,pre],Y[i,new]) , where Y[i,pre]=(y[i,1],\cdots,Y[i,m[i]]) and Y[i,new]=(Y[i,m[i]+1],...,Y[i,n[i]]) . Given an output of lmeNBBayes, this function computes the probability of observing the response counts as large as those new observations of subject i, y[i,new] conditional on the subject's previous observations y[i,pre] for subject i. That is, this function returns a point estimate and its asymptotic 95% confidence interval (for a parametric model) of the conditional probability for each subject:

Pr( Y[i,new+] ≥ y[i,new] | Y[i,pre]=y[i,pre]) , where Y[i,new+]=∑[j=m[i]+1]^{n[i]} Y[ij] .

Usage

index.batch.Bayes(data,labelnp,ID,olmeNBB,thin=NULL,printFreq=10^5,unExpIncrease=TRUE)

Arguments

data	See lmeNBBayes. This data does not have to be the same as the one used in the computations of negative binomial mixed effect regression (lmeNBBayes).
labelnp	See lmeNBBayes. nrow(data) == length(labelnp) must be satisfied.
ID	See the description in lmeNBBayes. nrow(data) == length(labelnp) must be satisfied.
olmeNBB	The output of the function lmeNBBayes.
thin	The frequency of thinning
printFreq	See the description in lmeNBBayes.
unExpIncrease	Internal use only. Should be always TRUE.

Value

condProb	(olmeNBB$para$B-olmeNBB$para$burnin)/thin by the number of patients, N (=length(unique(ID))), matrix, containing the MCMC samples of the conditional probability index for each patient at every selected iteration (after discarding burn-in and thinning). If some patients have 0 pre-scans or 0 new-scans, then NA is returned.
condProbSummary	4 by N matrix. The first column contains the posterior estimates of the conditional probability index. The second column contains the posterior SE. The third column contains the lower bound of the 95% credible interval. The fourth column contains the upper bound of the 95% credible interval.

Author(s)

Kondo, Y.

References

Kondo, Y., Zhao, Y. and Petkau, A.J., "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients".

See Also

lmeNBBayes getDIC dqmix

Examples

## See the examples of function lmeNBBayes

lmeNBBayes documentation built on May 1, 2019, 7:58 p.m.