Class 14 (Module II) – Linear Mixed Models

9 Maio 2022, 09:30 • Elsa Maria Félix Gonçalves

Linear mixed model with two factors of random effects, balanced and diagonal matrices G e R (exercise 1, d). Linear mixed model with one factor of fixed effects and one factor of random effects, balanced and diagonal matrices G e R (exercise 3 a)).

Nota: Esta aula foi leccionada na quarta-feira, dia 18.5.22, das 15h às 17h30, na Sala S3, simultaneamente presencial e por zoom.

Class 13 (Module II) – Linear Mixed Models

4 Maio 2022, 15:00 • Elsa Maria Félix Gonçalves

The residual maximum likelihood method of estimation. Estimating fixed effects and predicting random effects: the mixed model equations. Tests of hypotheses for covariance parameters and fixed effects. Model selection (model comparison via likelihood ratio tests and via information criteria).
Case study (exercise 1, a) and b)): random model with one factor of random effects, balanced and diagonal matrices G e R.

Nota: Esta aula foi leccionada na segunda-feira, dia 16.5.22, das 15h às 17h30, na Sala 39, simultaneamente presencial e por zoom.

Class 12 (Module II) – Linear Mixed Models

2 Maio 2022, 09:30 • Elsa Maria Félix Gonçalves

Linear Mixed Models: some examples of application. General formulation of the model, properties and some particular cases.

Nota: Esta aula foi leccionada na sexta-feira, dia 13.5.22, das 10h30 às 13h, na Sala 47, simultaneamente presencial e por zoom.

Class 11 (Module II)

27 Abril 2022, 15:00 • Jorge Filipe Campinos Landerset Cadima

[GLM Slides 80-152 (except 126, 127)] [Video, Part 1] [ Video Part 2] Asymptotic inference theory for GLMs, based on the asymptotic properties of Maximum Likelihood estimators: confidence intervals and hypothesis tests for individual parameters or linear combinations of the parameters. Commands for inference in R and an example. GLMs with Poisson response variables and Log-linear models. An example with emergence data (GLM Exercise 5). Deviance in GLMs as a measure of goodness of fit: definition and interpretation. Deviance formulas for Poisson response models. Likelihood Ratio Tests (LRT) or Wilks' test to compare nested models, or as a goodness-of-fit test, for models that do not require the estimation of the dispersion parameter phi. Again the emergence data example (GLM Ex.5). ANCOVA-type GLMs: Exercises 1 and 10 (tobacco budworm data). Akaike Information Criteria in GLMs. The issue of an unknown dispersion parameter phi: the need for simplifying assumptions that make estimation possible. Deviance and Scaled Deviance. Residuals: Pearson Residuals and Deviance Residuals: definitions and interpretation for specific contexts. The Generalised Pearson Statistic and its use in estimating the dispersion parameter phi. AS brief note on the use of Log-linear models in the study of contingency tables.
Nota: Esta aula foi leccionada na segunda-feira, dia 9.5.22, das 9h45 às 12h25, na Sala S1, simultaneamente presencial e por zoom.

Extra Class

26 Abril 2022, 15:00 • Jorge Filipe Campinos Landerset Cadima