LogLikeC2: Calculate the conditional likelihood for the univariate and...

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

Description

Calculate the conditional likelihood for the univariate and bivariate sampling cases across all subjects (Keep.liC=FALSE) or the subject specific contributions to the conditional likelihood along with the log-transformed ascertainment correction for multiple imputation (Keep.liC=TRUE).

Usage

LogLikeC2(y, x, z, w.function, id, beta, sigma.vc, rho.vc, sigma.e,
  cutpoints, SampProb, Weights, Keep.liC = FALSE)

Arguments

y	response vector
x	sum(n_i) by p design matrix for fixed effects
z	sum(n_i) by q design matrix for random effects
w.function	sum(n_i) vector with possible values that include "mean" "intercept" "slope" and "bivar." There should be one unique value per subject
id	sum(n_i) vector of subject ids
beta	mean model parameter p-vector
sigma.vc	vector of variance components on standard deviation scale
rho.vc	vector of correlations among the random effects. The length should be q choose 2
sigma.e	std dev of the measurement error distribution
cutpoints	A matrix with the first dimension equal to sum(n_i). These cutpoints define the sampling regions [bivariate Q_i: each row is a vector of length 4 c(xlow, xhigh, ylow, yhigh); univariate Q_i: each row is a vector of length 2 c(k1,k2) to define the sampling regions, i.e., low, middle, high]. Each subject should have n_i rows of the same values.
SampProb	A matrix with the first dimension equal to sum(n_i). Sampling probabilities from within each region [bivariate Q_i: each row is a vector of length 2 c(central region, outlying region); univariate Q_i: each row is a vector of length 3 with sampling probabilities for each region]. Each subject should have n_i rows of the same values.
Weights	Subject specific sampling weights. A vector of length sum(n_i). Not used unless using weighted Likelihood
Keep.liC	If FALSE, the function returns the conditional log likelihood across all subjects. If TRUE, subject specific contributions and exponentiated subject specific ascertainment corrections are returned in a list.

Value

If Keep.liC=FALSE, conditional log likelihood. If Keep.liC=TRUE, a two-element list that contains subject specific likelihood contributions and exponentiated ascertainment corrections.

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