LogLikeC.score: Calculate the gradient of the conditional likelihood for the...

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

Description

Calculate the gradient of the conditional likelihood for the univariate and bivariate sampling cases across all subjects (CheeseCalc=FALSE) or the cheese part of the sandwich estimator if CheeseCalc=TRUE.

Usage

LogLikeC.score(y, x, z, w.function, id, beta, sigma0, sigma1, rho, sigmae,
  cutpoints, SampProb, SampProbi, CheeseCalc = FALSE)

Arguments

y	response vector
x	sum(n_i) by p design matrix for fixed effects
z	sum(n_i) by 2 design matric for random effects (intercept and slope)
w.function	options include "mean" "intercept" "slope" and "bivar"
id	sum(n_i) vector of subject ids
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 [bivariate Q_i: a vector of length 4 c(xlow, xhigh, ylow, yhigh); univariate Q_i: a vector of length K c(k1,k2, ... K) to define the cutpoints for Q_i based sampling regions]
SampProb	Sampling probabilities from within each region [bivariate Q_i: a vector of length 2 c(central region, outlying region); univariate Q_i: a vector of length K+1 with sampling probabilities for each region]
SampProbi	Subject specific sampling probabilities. A vector of length sum(n_i). Not used unless using weighted Likelihood
CheeseCalc	If FALSE, the function returns the gradient of the conditional log likelihood across all subjects. If TRUE, the cheese part of the sandwich esitmator is calculated.

Value

If CheeseCalc=FALSE, gradient of conditional log likelihood. If CheeseCalc=TRUE, the cheese part of the sandwich estimator is calculated.

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