LogLikeCAndScore2: Calculate the ascertainment corrected log likelihood and...

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

Usage

LogLikeCAndScore2(params, y, x, z, id, w.function, cutpoints, SampProb,
  Weights, ProfileCol = NA, Keep.liC = FALSE)

Arguments

params	parameter vector c(beta, log(sigma0), log(sigma1), rho, sigmae)
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)
id	sum(n_i) vector of subject ids
w.function	sum(n_i) vector with possible values that include "mean" (mean of response series), "intercept" (intercept of the regression of Yi ~ zi where zi is the design matrix for the random effects (solve(t.zi \item cutpointsA 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. \item SampProbA 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. \item WeightsSubject specific sampling weights. A vector of length sum(n_i). Not used unless using weighted Likelihood \item ProfileColthe column number(s) for which we want fixed at the value of param. Maimizing the log likelihood for all other parameters while fixing these columns at the values of params[ProfileCol] \item Keep.liCIf TRUE outputs subject specific conditional log lileihoods to be used for the imputation procedure described in the AOAS paper keep z sum(n_i) by 2 design matric for random effects (intercept and slope)

The conditional log likelihood with a "gradient" attribute (if Keep.liC=FALSE) and subject specific contributions to the conditional likelihood if Keep.liC=TRUE). Calculate the ascertainment corrected log likelihood and score

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