Membangun model lmer dengan efek acak

Dalam video, Anda mempelajari data tingkat kelahiran pada level county. County berada dalam negara bagian (state) dan mungkin negara bagian berkontribusi terhadap variabilitas. Pada latihan ini, Anda akan membangun serangkaian model mixed-effects menggunakan data tersebut.

Pada latihan ini, Anda akan membangun model hierarkis dengan intersep global (efek tetap/fixed-effect) dan efek acak untuk state. Anda kemudian akan melihat summary() dari model dan plot() dari residualnya. Seperti analisis regresi lainnya, memeriksa residual dapat membantu Anda melihat apakah ada masalah pada model.

Dengan lmer(), ada dua cara untuk melakukannya: y ~ 1 + (1 | random_effect) atau cara singkat, y ~ (1 | random_effect). Gunakan cara singkat pada latihan ini agar jawaban Anda lolos uji DataCamp.

Saat membangun model mixed-effect, memulai dari model sederhana seperti model intersep global dapat membantu memeriksa apakah ada masalah pada data atau kode. Global intercept mengasumsikan satu intersep dapat menggambarkan seluruh variabilitas dalam data. Salah satu cara memandang intersep global adalah bahwa Anda tidak dapat memodelkan data lebih baik daripada hanya memodelkan rataan tanpa menyertakan variabel prediktor lainnya.

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Build a lmer with State as a random effect
birth_rate_state_model <- 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: 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: Model efek campuran linear - Data angka kelahiran Exercise 2: Membangun model lmer dengan efek acak

Latihan Saat Ini

Exercise 3: Menyertakan efek tetap Exercise 4: Kemiringan efek-acak Exercise 5: Kemiringan efek-acak tak berkorelasi Exercise 6: Prediktor efek tetap dan acak Exercise 7: Memahami dan melaporkan keluaran dari lmer Exercise 8: Membandingkan keluaran print dan summary Exercise 9: Mengekstrak koefisien Exercise 10: Menampilkan hasil dari model lmer Exercise 11: Inferensi statistik dengan data kejahatan Maryland Exercise 12: Memvisualisasikan data kejahatan Maryland Exercise 13: Mengubah skala kemiringan (slope)Exercise 14: Pengujian hipotesis nol Exercise 15: Kontroversi seputar P-value Exercise 16: Perbandingan model dengan 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