Pengantar

vignette menjelaskan cara menggunakan paket tidybayes untuk mengekstraksi tidy data frame dari model brms, dan penarikan dari distribusi posterior variabel model, kecocokan, dan prediksi dari brms :: brm. Untuk pengenalan yang lebih umum tentang tidybayes, lihat vignette(“tidybayes”).

Untuk menjalankan vignette membutuhkan package sebagai berikut :

library(magrittr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(purrr)
## 
## Attaching package: 'purrr'
## The following object is masked from 'package:magrittr':
## 
##     set_names
library(forcats)
library(tidyr)
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:magrittr':
## 
##     extract
library(modelr)
library(ggdist)
library(tidybayes)
library(ggplot2)
library(cowplot)
library(rstan)
## Loading required package: StanHeaders
## rstan (Version 2.21.2, GitRev: 2e1f913d3ca3)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
## 
## Attaching package: 'rstan'
## The following object is masked from 'package:tidyr':
## 
##     extract
## The following object is masked from 'package:magrittr':
## 
##     extract
library(brms)
## Loading required package: Rcpp
## Loading 'brms' package (version 2.14.4). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
## 
## Attaching package: 'brms'
## The following object is masked from 'package:rstan':
## 
##     loo
## The following objects are masked from 'package:tidybayes':
## 
##     dstudent_t, pstudent_t, qstudent_t, rstudent_t
## The following objects are masked from 'package:ggdist':
## 
##     dstudent_t, pstudent_t, qstudent_t, rstudent_t
## The following object is masked from 'package:stats':
## 
##     ar
library(ggrepel)
library(RColorBrewer)
library(gganimate)
## No renderer backend detected. gganimate will default to writing frames to separate files
## Consider installing:
## - the `gifski` package for gif output
## - the `av` package for video output
## and restarting the R session
theme_set(theme_tidybayes() + panel_border())

Opsi ini membantu Stan bekerja lebih cepat:

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

Kita mengambil dataset :

set.seed(5)
n = 10
n_condition = 5
ABC =
  tibble(
    condition = rep(c("A","B","C","D","E"), n),
    response = rnorm(n * 5, c(0,1,2,1,-1), 0.5)
  )
head(ABC, 10)
## # A tibble: 10 x 2
##    condition response
##    <chr>        <dbl>
##  1 A           -0.420
##  2 B            1.69 
##  3 C            1.37 
##  4 D            1.04 
##  5 E           -0.144
##  6 A           -0.301
##  7 B            0.764
##  8 C            1.68 
##  9 D            0.857
## 10 E           -0.931
ABC %>%
  ggplot(aes(y = condition, x = response)) +
  geom_point()

m = brm(
  response ~ (1|condition), 
  data = ABC, 
  prior = c(
    prior(normal(0, 1), class = Intercept),
    prior(student_t(3, 0, 1), class = sd),
    prior(student_t(3, 0, 1), class = sigma)
  ),
  control = list(adapt_delta = .99)
)
## Compiling Stan program...
## Trying to compile a simple C file
## Running /usr/lib/R/bin/R CMD SHLIB foo.c
## gcc -std=gnu99 -I"/usr/share/R/include" -DNDEBUG   -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/Rcpp/include/"  -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/"  -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/unsupported"  -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/BH/include" -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/StanHeaders/include/src/"  -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/StanHeaders/include/"  -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppParallel/include/"  -I"/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1     -fpic  -g -O2 -fdebug-prefix-map=/build/r-base-V28x5H/r-base-3.6.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -g  -c foo.c -o foo.o
## In file included from /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/Eigen/Core:88:0,
##                  from /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/Eigen/Dense:1,
##                  from /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
##                  from <command-line>:0:
## /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name ‘namespace’
##  namespace Eigen {
##  ^~~~~~~~~
## /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:17: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute__’ before ‘{’ token
##  namespace Eigen {
##                  ^
## In file included from /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/Eigen/Dense:1:0,
##                  from /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
##                  from <command-line>:0:
## /home/suhartono/R/x86_64-pc-linux-gnu-library/3.6/RcppEigen/include/Eigen/Core:96:10: fatal error: complex: No such file or directory
##  #include <complex>
##           ^~~~~~~~~
## compilation terminated.
## /usr/lib/R/etc/Makeconf:168: recipe for target 'foo.o' failed
## make: *** [foo.o] Error 1
## Start sampling

Membandingkan tingkat suatu faktor

Jika kita ingin membandingkan rata-rata dari setiap kondisi, bandingkan_levels () memfasilitasi perbandingan nilai beberapa variabel di seluruh tingkat faktor. Secara default, ini menghitung semua perbedaan berpasangan.

Mari kita peragakan bandingkan_levels () dengan ggdist :: stat_halfeye ():

#N.B. the syntax for compare_levels is experimental and may change
m %>%
  spread_draws(r_condition[condition,]) %>%
  compare_levels(r_condition, by = condition) %>%
  ggplot(aes(y = condition, x = r_condition)) +
  stat_halfeye()

Jika Anda lebih suka plot “caterpillar”, yang diurutkan berdasarkan nilai rata-rata perbedaannya, Anda dapat menyusun ulang faktor tersebut sebelum merencanakan:

m %>%
  spread_draws(r_condition[condition,]) %>%
  compare_levels(r_condition, by = condition) %>%
  ungroup() %>%
  mutate(condition = reorder(condition, r_condition)) %>%
  ggplot(aes(y = condition, x = r_condition)) +
  stat_halfeye() +
  geom_vline(xintercept = 0, linetype = "dashed")

Daftar pustaka

  1. http://mjskay.github.io/tidybayes/articles/tidy-brms.html