Name: R package glmm: Generalised Linear Mixed Models
Start: 2018-05-23T18:00:00+01:00
End: 2018-05-23T19:00:00+01:00
Location: Room A1.01, Zeeman Building

Christina Knudson (St Thomas) has kindly offered to speak about her new R package 'glmm' for fitting generalized linear mixed models. A version of this talk was presented at the useR!2017 conference.

Christina is associate professor of statistics at the University of St Thomas, Minnesota, and co-organizer of the R Ladies Twin Cities user group.

Abstract:

Conducting frequentist likelihood-based inference for generalized linear mixed models is difficult because the likelihood is a high-dimensional integral. While some R packages perform inference on an alternative function (e.g. penalized quasi likelihood), the R package glmm approximates the entire likelihood function through Monte Carlo likelihood approximation. This package conducts maximum likelihood and enables all methods of likelihood-based inference. I will give a light introduction to Monte Carlo likelihood approximation, demonstrate the use of R package glmm, describe the package's capabilities, and compare results to other packages (e.g. lme4).

https://cran.r-project.org/web/packages/glmm/vignettes/intro.pdf

Lewis Rendell

David Selby

Warwick R User Group (R Programming Language)

R User Groups

R Consortium

Technology

Business Intelligence

Big Data

Open Source

Statistical Computing

User Group

Data Analytics

Data Visualization

Data Mining

R Project for Statistical Computing

Data Science

Programming in R

Applied Statistics

Computer Programming

R package glmm: Generalised Linear Mixed Models

Room A1.01, Zeeman Building

Share

Warwick R User Group (R Programming Language)

R package glmm: Generalised Linear Mixed Models

Warwick R User Group (R Programming Language)

Details

Sponsors

Members are also interested in