Kemiringan efek-acak

Pada latihan sebelumnya, Anda melihat cara membuat kode intersep efek-acak. Sekarang Anda akan melihat cara membuat kode kemiringan (slope) efek-acak. Dengan sintaks lme4, lmer() menggunakan (countinuous_predictor | random_effect_group) untuk kemiringan efek-acak. Ketika lme4 memperkirakan kemiringan efek-acak, ia juga memperkirakan intersep efek-acak. scale() mengubah skala variabel prediktor mathkind untuk membuat model lebih stabil secara numerik. Tanpa perubahan ini, lmer() tidak dapat menyesuaikan model.

Pada latihan sebelumnya, Anda memperkirakan intersep efek-acak untuk setiap kelas dan satu slope untuk seluruh data. Di sini, Anda akan memperkirakan intersep efek-acak untuk setiap kelas dan kemiringan efek-acak untuk setiap kelas. Seperti halnya intersep efek-acak, kemiringan efek-acak berasal dari sebaran bersama dari semua kemiringan efek-acak.

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Rescale mathkind to make the model more stable
student_data <-
	student_data %>%
    mutate(mathkind_scaled = scale(mathkind))

# Build lmer models
lmer_intercept <- lmer(___ ~ ___ + (1 | ___),
                       data = ___)
lmer_slope     <- lmer(___ ~ (___ | ___),
                       data = ___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus 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: Apa itu model hierarkis?Exercise 2: Contoh himpunan data hierarkis Exercise 3: Data siswa multi-level Exercise 4: Menjelajahi multi-level: Kelas dan sekolah Exercise 5: Bagian-bagian dari sebuah regresi Exercise 6: Intersep Exercise 7: Kemiringan dan regresi berganda Exercise 8: Efek-acak dalam regresi dengan data sekolah Exercise 9: Intersep efek-acak Exercise 10: Kemiringan efek-acak

Latihan Saat Ini

Exercise 11: Membangun model sekolah Exercise 12: Menafsirkan model sekolah

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: 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