Description Usage Arguments Value Author(s) References See Also Examples
Let m[i] be the number of premeasurements and n[i] be the total number of repeated measures.
Then the repeated measure of a subject can be divided into a premeasurement 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] .
1  index.batch.Bayes(data,labelnp,ID,olmeNBB,thin=NULL,printFreq=10^5,unExpIncrease=TRUE)

data 
See 
labelnp 
See 
ID 
See the description in 
olmeNBB 
The output of the function 
thin 
The frequency of thinning 
printFreq 
See the description in 
unExpIncrease 
Internal use only. Should be always TRUE. 
condProb 

condProbSummary 

Kondo, Y.
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".
1  ## See the examples of function lmeNBBayes

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