logACi2q.score: Gradient of the log of the ascertainment correction piece for...

In schildjs/ods4lda: Outcome Dependent Sampling (ODS) Designs for Linear Mixed Models Data Analysis

Description

Calculate the gradient of the log transformed ascertainment correction under designs that sample based on a bivariate Q_i (numerically)

Usage

logACi2q.score(subjectData, w.function, beta, sigma0, sigma1, rho, sigmae,
  cutpoints, SampProb)

Arguments

subjectData	a list containing: yi, xi, zi
w.function	Sampling variable q_i function "mean", "intercept", "slope", "bivar". This only gets called if w.function="bivar"
beta	mean model parameter p-vector
sigma0	std dev of the random intercept distribution
sigma1	std dev of the random slope distribution
rho	correlation between the random intercept and slope
sigmae	std dev of the measurement error distribution
cutpoints	cutpoints defining the sampling regions. (a vector of length 4 c(xlow, xhigh, ylow, yhigh))
SampProb	Sampling probabilities from within each region (vector of length 2 c(central region, outlying region)).

Value

gradient of the log transformed ascertainment correction under the bivariate sampling design

schildjs/ods4lda documentation built on March 16, 2020, 8:16 a.m.