BootstrapAPCEipwREparallel {aihuman} | R Documentation |
Bootstrap for estimating variance of APCE with random effects
Description
Estimate variance of APCE for frequentist analysis with random effects using bootstrap. See S7 for more details.
Usage
BootstrapAPCEipwREparallel(data, rep = 1000, formula, nAGQ = 1, size = 5)
Arguments
data |
A |
rep |
Size of bootstrap |
formula |
A formula of the model to fit. |
nAGQ |
Integer scalar - the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Defaults to 1, corresponding to the Laplace approximation. |
size |
The number of parallel computing. The default is |
Value
An object of class list
with the following elements:
P.D1.boot |
An array with dimension rep by (k+1) by (k+2) for quantity P(D(1)=d| R=r), dimension 1 is rep (size of bootstrap), dimension 2 is (k+1) values of D from 0 to k, dimension 3 is (k+2) values of R from 0 to k+1. |
P.D0.boot |
An array with dimension rep by (k+1) by (k+2) for quantity P(D(0)=d| R=r). |
APCE.boot |
An array with dimension rep by (k+1) by (k+2) for quantity P(D(1)=d| R=r)-P(D(0)=d| R=r). |
P.R.boot |
An array with dimension rep by (k+2) for quantity P(R=r) for r from 0 to (k+1). |
Examples
data(synth)
data(hearingdate_synth)
synth$CourtEvent_HearingDate = hearingdate_synth
set.seed(123)
boot_apce_re = BootstrapAPCEipwREparallel(synth, rep = 10,
formula = "Y ~ Sex + White + Age +
CurrentViolentOffense + PendingChargeAtTimeOfOffense +
PriorMisdemeanorConviction + PriorFelonyConviction +
PriorViolentConviction + (1|CourtEvent_HearingDate) +
D", size = 1) # adjust the size