Description Usage Arguments Value
Calculate the gradient of the log transformed ascertainment correction for sampling based on univariate Q_i
1 2 | logACi1q.score(subjectData, w.function, beta, sigma0, sigma1, rho, sigmae,
cutpoints, SampProb)
|
subjectData |
a list containing: yi, xi, zi |
w.function |
Sampling variable q_i function "mean", "intercept", "slope", "bivar". This only gets called if w.function in mean, slope, intercept |
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 2 to define low, medium and high values of $Q_i$). |
SampProb |
Sampling probabilities from within each region (vector of length 3 to define sampling probabilities within sampling regions |
gradient of the log transformed ascertainment correction under univariate $Q_i$
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.