• Medientyp: E-Book; Video
  • Titel: Estimating fixed-effect coefficients in count models - GLMM vs marginal models
  • Beteiligte: Lancelot, Renaud [Verfasser:in]; Hengl, Tomislav [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [Erscheinungsort nicht ermittelbar]: OpenGeoHub Foundation, 2021
  • Erschienen in: MOOD Science Webinars ; (Jan. 2021)
  • Umfang: 1 Online-Ressource (1208 MB, 00:39:32:15)
  • Sprache: Englisch
  • DOI: 10.5446/52154
  • Identifikator:
  • Schlagwörter: rstats ; Covid-19 ; MOOD
  • Entstehung:
  • Anmerkungen: Audiovisuelles Material
  • Beschreibung: Renaud Lancelot is a veterinary epidemiologist with 20-year experience in field research, mostly in continental Africa and Madagascar. His research focuses on livestock infectious diseases of tropical origin and their vectors. Renaud explains the differences and appropriate applications of Generalized linear mixed models (GLMMs) - extensions to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects - and marginal models which are used when estimating fixed effects. He also discusses different model types as they relate to a case study on COVID-19 mortality rates and lockdown measures
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung - Nicht kommerziell (CC BY-NC)