Costruire il modello

Come parte del processo di costruzione del modello, inizia creando una semplice regressione di Poisson. Una glm() è più veloce di glmer() ed è più facile da fare il debug. Ad esempio, la regressione di Poisson può segnalare se hai dati non interi. Inoltre, la regressione di Poisson può darti intuizioni sul modello più complesso.

Se incontri problemi, segui le istruzioni per sistemare i dati.

Infine, costruisci e salva una regressione di Poisson per misure ripetute.

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Modelli gerarchici e a effetti misti in R

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# Model TotalIncidents predicted by Year and County
# Use a Poisson regression with the hate data.frame 
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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: Crash course on GLMs Exercise 2: Logistic regression Exercise 3: Poisson Regression Exercise 4: Plotting GLMs Exercise 5: Binomial data Exercise 6: Toxicology data Exercise 7: Marketing example Exercise 8: Calculating odds-ratios Exercise 9: Count data Exercise 10: Internet click-throughs Exercise 11: Chlamydia by age-group and county Exercise 12: Displaying chlamydia results

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: Introduzione alle misure ripetute Exercise 2: t-test appaiato Exercise 3: ANOVA per misure ripetute Exercise 4: Studio sul sonno Exercise 5: Esplorare i dati Exercise 6: Costruire i modelli Exercise 7: Confrontare regressioni e ANOVA Exercise 8: Rappresentare i risultati Exercise 9: Odio nello stato di NY?Exercise 10: Esplorare i dati sui crimini d’odio a NY Exercise 11: Costruire il modello

Esercizio in corso

Exercise 12: Interpretare i risultati del modello Exercise 13: Visualizzare i risultati Exercise 14: Ripasso dei modelli gerarchici in R Exercise 15: Conclusione