Memvisualisasikan data kejahatan Maryland

Sebelum menyesuaikan model, membuat plot data dapat membantu untuk melihat apakah ada tren atau titik data yang menonjol, keberadaan pencilan, atau atribut lain dari data yang perlu dipertimbangkan ke depan. Dengan ggplot2, Anda dapat memplot garis per county dan menelaah bagaimana kejahatan berubah seiring waktu. Untuk latihan ini, telaah data kejahatan Maryland (md_crime). Data ini mencakup Year, jumlah Crime kekerasan di county, serta nama County.

Untuk mengeksplorasi data ini, terlebih dahulu plot titik data untuk setiap county sepanjang waktu. Ini memungkinkan Anda melihat bagaimana masing-masing county berubah dari waktu ke waktu. Alih-alih menggunakan estetika seperti color, di sini digunakan group karena terlalu banyak county untuk dibedakan warnanya dengan mudah. Setelah memplot data mentah, tambahkan garis tren untuk setiap county.

Baik titik yang dihubungkan (geom_line) maupun garis tren (geom_smooth) memberikan wawasan tentang apakah diperlukan random effect dan jenisnya. Jika semua titik tampak memiliki rentang dan rataan yang serupa, intersep random effect mungkin tidak penting. Demikian pula, jika tren tampak konsisten di seluruh county (mis., garis tren terlihat mirip atau paralel antarkelompok), kemiringan random effect mungkin tidak diperlukan.

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot the change in crime through time by County
plot1 <- 
	ggplot(data = md_crime, 
           aes(x = ___, y = ___, group = ___)) +
    geom_line() + 
    theme_minimal() +
    ylab("Major crimes reported per county")
print(plot1)

# Add the trend line for each county
plot1 + ___

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 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

Latihan Saat Ini

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