Kemiringan efek-acak

Pada latihan sebelumnya, Anda mengestimasi intersep efek-acak untuk setiap negara bagian. Ini memungkinkan Anda memperhitungkan bahwa masing-masing negara bagian memiliki intersepnya sendiri. Pada latihan ini, Anda akan mengestimasi kemiringan (slope) efek-acak untuk setiap negara bagian. Misalnya, mungkin log\(_{10}\)(total populasi tiap county), LogTotalPop, memengaruhi tingkat kelahiran suatu county DAN bervariasi menurut negara bagian.

Ingat dari video, sebuah slope efek-acak dapat diestimasi untuk setiap group menggunakan sintaks (slope | group) dengan lmer().

Pada latihan ini, sesuaikan model efek-campuran yang mengestimasi pengaruh usia rata-rata ibu dengan tetap memperhitungkan negara bagian dan total populasi sebagai efek-acak.

Bagaimana perbandingan keluaran dari model ini dengan model sebelumnya yang Anda bangun?

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Include the AverageAgeofMother as fixed-effect and State as a random-effect
model_a <- lmer(BirthRate ~ ___ + (___), county_births_data)
tidy(___)

# Include the AverageAgeofMother as fixed-effect and LogTotalPop and State as random-effects
model_b <- lmer(BirthRate ~ ___ + (___), county_births_data)
tidy(___)

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 Exercise 3: Menyertakan efek tetap Exercise 4: Kemiringan efek-acak

Latihan Saat Ini

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