acml.lmem: Fitting function: ACML for a linear mixed effects model...

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

Description

Fitting function: ACML or WL for a linear mixed effects model (random intercept and slope)

Usage

acml.lmem(formula.fixed, formula.random, data, id, w.function = "mean",
  InitVals, cutpoints = c(0, 5), SampProb = NA, SampProbiWL = NA,
  ProfileCol = NA)

Arguments

formula.fixed	formula for the fixed effects (of the form y~x)
formula.random	formula for the random effects (of the form ~z)
data	data frame (should contain everything in formula.fixed, formula.random, id, and SampProbiWL)
id	sum(n_i) vector of subject ids
w.function	options include "mean" "intercept" "slope" and "bivar"
InitVals	starting values for c(beta, log(sigma0), log(sigma1), rho, log(sigmae))
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]
SampProbiWL	Subject specific sampling probabilities. A vector of length sum(n_i). Not used unless using weighted Likelihood
ProfileCol	the 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]

Value

Ascertainment corrected Maximum likelihood: Ests, covar, LogL, code, robcov

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