Menghitung odds-ratio

Pada latihan sebelumnya, kita melihat cara membandingkan efek rekomendasi teman terhadap penjualan. Namun, keluaran regresi bisa sulit dijelaskan dan terkadang odds-ratio lebih mudah digunakan. Menggunakan keluaran dari latihan sebelumnya, kita akan menghitung odds-ratio.

Penyegaran tentang odds-ratio:

Jika odds-ratio adalah 1,0, maka kedua peristiwa memiliki peluang yang sama untuk terjadi. Misalnya, jika odds-ratio untuk rekomendasi teman adalah 1,0, maka seorang teman tidak akan berpengaruh pada keputusan pembelian.
Jika odds-ratio kurang dari 1, maka rekomendasi teman akan menurunkan peluang terjadinya pembelian. Misalnya, odds-ratio sebesar 0,5 berarti rekomendasi teman memiliki peluang 1:2 atau 1 pembelian terjadi untuk setiap 2 kali tidak membeli.
Jika odds-ratio lebih dari 1, maka rekomendasi teman akan meningkatkan peluang terjadinya pembelian. Misalnya, odds-ratio sebesar 3,0 berarti rekomendasi teman memiliki peluang 3:1 atau 3 pembelian terjadi untuk setiap 1 kali tidak membeli.

Catatan tentang kode kursus: Sejak kursus ini diluncurkan, paket broom telah menghentikan dukungan untuk model lme4::lmer(). Jika Anda mencoba mengulanginya sendiri, Anda memerlukan paket broom.mixed, yang tersedia di cran.

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

Petunjuk latihan

Lihat summary() dari model_out.
Ekstrak koefisien dari model_out dengan fixef() lalu ubah menjadi odds-ratio dengan mengambil eksponensialnya. Ulangi dengan confint() untuk mendapatkan interval kepercayaan.
Hitung interval kepercayaan kemudian eksponensialkan efek friends pada pembelian menggunakan tidy(). Pastikan untuk mengatur parameter conf.int dan exponentiate.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Run the code to see how to calculate odds ratios
summary( ___) 
exp(___(model_out))
exp(___(model_out))

# Create the tidied output
tidy(model_out, conf.int = ___, exponentiate = ___)

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: 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: Kilasan cepat tentang GLM Exercise 2: Regresi logistik Exercise 3: Regresi Poisson Exercise 4: Memplot GLM Exercise 5: Data binomial Exercise 6: Data toksikologi Exercise 7: Contoh pemasaran Exercise 8: Menghitung odds-ratio

Latihan Saat Ini

Exercise 9: Data cacah Exercise 10: Klik-tayang internet Exercise 11: Klamidia menurut kelompok usia dan county Exercise 12: Menampilkan hasil klamidia

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