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

Description Usage Arguments Value

View source: R/Functions2.R

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

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

SampProbi

Subject specific sampling probabilities. 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.