English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Maximum likelihood estimation of generalized linear mixed models(GLMMs) isdifficult due to marginalization of the random effects. Computing derivativesof a fitted GLMM's likelihood (with respect to model parameters) is alsodifficult, especially because the derivatives are not by-products of popularestimation algorithms. In this paper, we describe GLMM derivatives along with aquadrature method to efficiently compute them, focusing on lme4 models with asingle clustering variable. We describe how psychometric results related to IRTare helpful for obtaining these derivatives, as well as for verifying thederivatives' accuracies. After describing the derivative computation methods,we illustrate the many possible uses of these derivatives, including robuststandard errors, score tests of fixed effect parameters, and likelihood ratiotests of non-nested models. The derivative computation methods and applicationsdescribed in the paper are all available in easily-obtained R packages.

Computation and application of generalized linear mixed model derivatives using lme4