Dati di tossicologia

Una tossicologa si chiede se l’esposizione a una sostanza chimica aumenti la mortalità al crescere della dose. Ha esposto organismi da test a diverse dosi di una sostanza e ha registrato il numero di morti in ciascuna vasca di prova. Ogni dose è stata ripetuta in tre giorni diversi.

Per prima cosa, costruisci un GLM. Partire da un modello semplice rende il debugging più facile. Inoltre, in seguito confronterai i risultati con GLMER. Questo modello include dose e replicate come effetti fissi.

Poi, costruisci un GLMER. Questa volta, dose è un effetto fisso e replicate è un intercetta a effetti casuali.

Infine, esamina le stime dei coefficienti con coef() per ciascun modello. Nota come le stime dell’intercetta vengono visualizzate in modo diverso nei due modelli.

Questo esercizio fa parte del corso

Modelli gerarchici e a effetti misti in R

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Fit glm_out and look at its coefficient estimates
glm_out <- glm(___ ~ ___ + ___,
               family = "___", data = df)
coef(___)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Modelli gerarchici e a effetti misti in R

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The first chapter provides an example of when to use a mixed-effect and also describes the parts of a regression. The chapter also examines a student test-score dataset with a nested structure to demonstrate mixed-effects.

Exercise 1: What is a hierarchical model?Exercise 2: Examples of hierarchical datasets Exercise 3: Multi-level student data Exercise 4: Exploring multiple-levels: Classrooms and schools Exercise 5: Parts of a regression Exercise 6: Intercepts Exercise 7: Slopes and multiple regression Exercise 8: Random-effects in regressions with school data Exercise 9: Random-effect intercepts Exercise 10: Random-effect slopes Exercise 11: Building the school model Exercise 12: Interpreting the school model

This chapter providers an introduction to linear mixed-effects models. It covers different types of random-effects, describes how to understand the results for linear mixed-effects models, and goes over different methods for statistical inference with mixed-effects models using crime data from Maryland.

Exercise 1: Linear mixed effect model- Birth rates data Exercise 2: Building a lmer model with random effects Exercise 3: Including a fixed effect Exercise 4: Random-effect slopes Exercise 5: Uncorrelated random-effect slope Exercise 6: Fixed- and random-effect predictor Exercise 7: Understanding and reporting the outputs of a lmer Exercise 8: Comparing print and summary output Exercise 9: Extracting coefficients Exercise 10: Displaying the results from a lmer model Exercise 11: Statistical inference with Maryland crime data Exercise 12: Visualizing Maryland crime data Exercise 13: Rescaling slopes Exercise 14: Null hypothesis testing Exercise 15: Controversies around P-values Exercise 16: Model comparison with ANOVA

This chapter extends linear mixed-effects models to include non-normal error terms using generalized linear mixed-effects models. By altering the model to include a non-normal error term, you are able to model more kinds of data with non-linear responses. After reviewing generalized linear models, the chapter examines binomial data and count data in the context of mixed-effects models.

Exercise 1: Corso intensivo sui GLM Exercise 2: Regressione logistica Exercise 3: Regressione di Poisson Exercise 4: Rappresentare graficamente i GLM Exercise 5: Dati binomiali Exercise 6: Dati di tossicologia

Esercizio in corso

Exercise 7: Esempio di marketing Exercise 8: Calcolare gli odds-ratio Exercise 9: Dati di conteggio Exercise 10: Click-through su Internet Exercise 11: Clamidia per fascia d’età e contea Exercise 12: Visualizzare i risultati sulla clamidia

This chapter shows how repeated-measures analysis is a special case of mixed-effect modeling. The chapter begins by reviewing paired t-tests and repeated measures ANOVA. Next, the chapter uses a linear mixed-effect model to examine sleep study data. Lastly, the chapter uses a generalized linear mixed-effect model to examine hate crime data from New York state through time.

Exercise 1: An introduction to repeated measures Exercise 2: Paired t-test Exercise 3: Repeated measures ANOVA Exercise 4: Sleep study Exercise 5: Exploring the data Exercise 6: Building models Exercise 7: Comparing regressions and ANOVAs Exercise 8: Plotting results Exercise 9: Hate in NY state?Exercise 10: Exploring NY hate data Exercise 11: Building the model Exercise 12: Interpreting model results Exercise 13: Displaying the results Exercise 14: Hierarchical models in R review Exercise 15: Conclusion