Compute the Personalized Activity Index Based on a Negative Binomial Model

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Documentation for package ‘lmeNB’ version 1.3

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CP.ar1.se Compute a conditional probability of observing a set of counts as extreme as the new observations of a subjectvisit given the previous observations of the same subject based on the negative binomial mixed-effect AR(1) model.
CP.se Compute a conditional probability of observing a set of counts as extreme as the new observations of a subject given the previous observations from the same subject based on the negative binomial mixed effect independent model.
CP1.ar1 Compute a conditional probability of observing a set of counts as extreme as the new observations of a subjectvisit given the previous observations of the same subject based on the negative binomial mixed-effect AR(1) model.
dbb Calculate predicted values of E(Gi|Yi) given the estimates of parameters
densYijGivenYij_1AndGY Calculate predicted values of E(Gi|Yi) given the estimates of parameters
dens_Yi.gY Calculate predicted values of E(Gi|Yi) given the estimates of parameters
fitParaAR1 Performs the maximum likelihood estimation for the negative binomial mixed-effect AR(1) model
fitParaIND Performs the maximum likelihood estimation for the negative binomial mixed-effect independent model
fitSemiAR1 Fit the semi-parametric negative binomial mixed-effect AR(1) model.
fitSemiIND Fit the semi-parametric negative binomial mixed-effect independent model.
formulaToDat Performs the maximum likelihood estimation for the negative binomial mixed-effect independent model
index.batch The main function to compute the point estimates and 95% confidence intervals (for a parametric model) of the conditional probabilities Pr(q(Y[i,new])>=q(y[i,new])| Y[i,pre]=y[i,pre]) for multiple subjects.
int.denRE Calculate predicted values of E(Gi|Yi) given the estimates of parameters
int.numRE Calculate predicted values of E(Gi|Yi) given the estimates of parameters
jCP Compute a conditional probability of observing a set of counts as extreme as the new observations of a subject given the previous observations from the same subject based on the negative binomial mixed effect independent model.
jCP.ar1 Compute a conditional probability of observing a set of counts as extreme as the new observations of a subjectvisit given the previous observations of the same subject based on the negative binomial mixed-effect AR(1) model.
lmeNB Performs the maximum likelihood estimation for the negative binomial mixed-effect model. This function is a wrapper for 'fitParaIND', 'fitParaAR1', 'fitSemiIND' and 'fitSemiAR1'.
MCCP.ar1 Compute a conditional probability of observing a set of counts as extreme as the new observations of a subjectvisit given the previous observations of the same subject based on the negative binomial mixed-effect AR(1) model.
RElmeNB Calculate predicted values of E(Gi|Yi) given the estimates of parameters
rNBME.R Simulate a dataset from the negative binomial mixed-effect independent/AR(1) model