De data verkennen

Een van de eerste dingen die je met nieuwe gegevenssets doet, is ze plotten vóór de analyse. In deze oefening ga je de data plotten met ggplot2 en een figuur van publicatiekwaliteit maken. Zo kun je de data echt zien.

Deze data onderzoekt of twee verschillende medicijnen de hoeveelheid slaap van mensen veranderen. De responsvariabele is de hoeveelheid extra slaap die een patiënt krijgt. De voorspellende variabele is de medicijn-group. De ID van elke patiënt maakt een repeated-measures-analyse mogelijk. Dit is een random-intercept en komt overeen met het basiseffect van het geven van een slaapmiddel aan iemand. We zijn hier niet geïnteresseerd in hoeveel iemand in totaal slaapt, maar alleen in de verandering in slaap tussen de groepen.

De data.frame sleep bevat deze variabelen.

Deze oefening maakt deel uit van de cursus

Hiërarchische en Mixed-Effects-modellen in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Plot the raw data
ggplot(data = ___, aes(x = ___, y = ___)) +
	geom_point()

Code bewerken en uitvoeren

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: 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: Een introductie tot herhaalde metingen Exercise 2: Gepaard t‑toets Exercise 3: Repeated measures ANOVA Exercise 4: Slaapstudie Exercise 5: De data verkennen

Huidige oefening

Exercise 6: Modellen bouwen Exercise 7: Regressies en ANOVA's vergelijken Exercise 8: Resultaten plotten Exercise 9: Haat in de staat NY?Exercise 10: NY-hategegevens verkennen Exercise 11: Het model bouwen Exercise 12: Modelresultaten interpreteren Exercise 13: De resultaten weergeven Exercise 14: Herhaling: hiërarchische modellen in R Exercise 15: Conclusie