BootstrapAPCEipwRE {aihuman}

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

BootstrapAPCEipwRE(data, rep = 1000, formula, CourtEvent_HearingDate, nAGQ = 1)

Arguments

data

A data.frame or matrix of which columns consists of pre-treatment covariates, a binary treatment (Z), an ordinal decision (D), and an outcome variable (Y). The column names of the latter three should be specified as "Z", "D", and "Y" respectively.

rep

Size of bootstrap

formula

A formula of the model to fit.

CourtEvent_HearingDate

The court event hearing date.

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.

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 = BootstrapAPCEipwRE(synth, rep = 10, formula = "Y ~ Sex + White + Age + 
                                  CurrentViolentOffense + PendingChargeAtTimeOfOffense + 
                                  PriorMisdemeanorConviction + PriorFelonyConviction + 
                                  PriorViolentConviction + (1|CourtEvent_HearingDate) + D", 
                                  CourtEvent_HearingDate = hearingdate_synth)