De resultaten van een lmer begrijpen en rapporteren

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Deze oefening maakt deel uit van de cursus

Hiërarchische en Mixed-Effects-modellen in R

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4.7+

Begin de cursus gratis

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: Lineair mixed-effectsmodel - Geboortecijfers Exercise 2: Een lmer-model bouwen met random effects Exercise 3: Een fixed effect opnemen Exercise 4: Random-effects hellingen Exercise 5: Niet-gecorreleerde random-effect helling Exercise 6: Vaste en willekeurige voorspeller Exercise 7: De resultaten van een lmer begrijpen en rapporteren

Huidige oefening

Exercise 8: print- en summary-uitvoer vergelijken Exercise 9: Coëfficiënten extraheren Exercise 10: De resultaten van een lmer-model weergeven Exercise 11: Statistische inferentie met criminaliteitsgegevens uit Maryland Exercise 12: Maryland-misdaaddata visualiseren Exercise 13: Herschalen van hellingen Exercise 14: Toetsen van nulhypothesen Exercise 15: Discussies rond p-waarden Exercise 16: Modellen vergelijken met 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: 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