使用 bayesplot 绘制多个模型的后验参数估计值

Question

我正在使用出色的绘图库 bayesplot 来可视化我使用 rstanarm 估计的模型的后验概率区间。我想通过将系数的后验区间放到同一个图上来以图形方式比较不同模型的绘图。

例如，想象一下，对于两个不同的模型，我从三个参数 beta1, beta2, beta3 的后部抽取了 1000 次：

# load the plotting library
library(bayesplot)
#> This is bayesplot version 1.6.0
#> - Online documentation and vignettes at mc-stan.org/bayesplot
#> - bayesplot theme set to bayesplot::theme_default()
#>    * Does _not_ affect other ggplot2 plots
#>    * See ?bayesplot_theme_set for details on theme setting
library(ggplot2)

# generate fake posterior draws from model1
fdata <- matrix(rnorm(1000 * 3), ncol = 3)
colnames(fdata) <- c('beta1', 'beta2', 'beta3')

# fake posterior draws from model 2
fdata2 <- matrix(rnorm(1000 * 3, 1, 2), ncol = 3)
colnames(fdata2) <- c('beta1', 'beta2', 'beta3')

Bayesplot 为单个模型绘图制作了出色的可视化效果，它是 ggplot2 'under the hood' 所以我可以根据需要进行自定义：

# a nice plot of 1
color_scheme_set("orange")
mcmc_intervals(fdata) + theme_minimal() + ggtitle("Model 1")

# a nice plot of 2
color_scheme_set("blue")
mcmc_intervals(fdata2) + ggtitle("Model 2")

但我想要实现的是将这两个模型一起绘制在同一个图上，这样对于每个系数我都有两个区间并且可以通过将颜色映射到模型来区分哪个区间是哪个区间。但是我不知道该怎么做。有些东西不起作用：

# doesnt work
mcmc_intervals(fdata) + mcmc_intervals(fdata2)
#> Error: Don't know how to add mcmc_intervals(fdata2) to a plot

# appears to pool
mcmc_intervals(list(fdata, fdata2))

关于如何做到这一点有什么想法吗？或者如何根据后验绘制矩阵手动完成？

^{由 reprex package (v0.2.1)}

创建于 2018-10-18

Answer 1

我在 GitHub 的 bayesplot 页面上问了这个问题，得到了回复 (Issue #232)。

Answer 2

为了让答案也贴在这里，我扩展了来自@Manny T (https://github.com/stan-dev/bayesplot/issues/232)

link 处的代码

# simulate having posteriors for two different models each with parameters beta[1],..., beta[4]
posterior_1 <- matrix(rnorm(4000), 1000, 4)
posterior_2 <- matrix(rnorm(4000), 1000, 4)
colnames(posterior_1) <- colnames(posterior_2) <- paste0("beta[", 1:4, "]")

# use bayesplot::mcmc_intervals_data() function to get intervals data in format easy to pass to ggplot
library(bayesplot)
combined <- rbind(mcmc_intervals_data(posterior_1), mcmc_intervals_data(posterior_2))
combined$model <- rep(c("Model 1", "Model 2"), each = ncol(posterior_1))

# make the plot using ggplot 
library(ggplot2)
theme_set(bayesplot::theme_default())
pos <- position_nudge(y = ifelse(combined$model == "Model 2", 0, 0.1))
ggplot(combined, aes(x = m, y = parameter, color = model)) + 
  geom_linerange(aes(xmin = l, xmax = h), position = pos, size=2)+
  geom_linerange(aes(xmin = ll, xmax = hh), position = pos)+
  geom_point(position = pos, color="black")

如果你像我一样，你会想要 80% 和 90% 的可信区间（而不是 50% 的内部区间）并且可能想要翻转坐标，让我们在 0 处添加一条虚线（模型估计没有改变）。你可以这样做：

# use bayesplot::mcmc_intervals_data() function to get intervals data in format easy to pass to ggplot
library(bayesplot)
combined <- rbind(mcmc_intervals_data(posterior_1,prob=0.8,prob_outer = 0.9), mcmc_intervals_data(posterior_2,prob=0.8,prob_outer = 0.9))
combined$model <- rep(c("Model 1", "Model 2"), each = ncol(posterior_1))

# make the plot using ggplot 
library(ggplot2)
theme_set(bayesplot::theme_default())
pos <- position_nudge(y = ifelse(combined$model == "Model 2", 0, 0.1))
ggplot(combined, aes(x = m, y = parameter, color = model)) + 
  geom_linerange(aes(xmin = l, xmax = h), position = pos, size=2)+
  geom_linerange(aes(xmin = ll, xmax = hh), position = pos)+
  geom_point(position = pos, color="black")+
  coord_flip()+
  geom_vline(xintercept=0,linetype="dashed")

关于最后一个，有几点需要注意。我添加了 prob_outer = 0.9，尽管这是默认设置，只是为了展示您可以如何更改外部可信区间。虚线是用 geom_vline 和 xintercept = 创建的，而不是 geom_hline 和 yintercept = ，因为 coord_flip（一切都颠倒了）。所以如果你不翻转轴，你将需要做相反的事情。

Answer 3

我浪费了比我愿意承认的更多的时间来写这篇文章，所以不妨 post 在这里。这是一个结合了上述建议的函数（目前）适用于 rstanarm 和 brms 模型对象。

compare_posteriors <- function(..., dodge_width = 0.5) {
  dots <- rlang::dots_list(..., .named = TRUE)
  draws <- lapply(dots, function(x) {
    if (class(x)[1] == "stanreg") {
        posterior::subset_draws(posterior::as_draws(x$stanfit),
            variable = names(fixef(x))
        )
    } else if (class(x)[1] == "brmsfit") {
        brm_draws <- posterior::subset_draws(posterior::as_draws(x$fit),
            variable = paste0("b_", rownames(fixef(x)))
        )
        posterior::variables(brm_draws) <- stringr::str_split(posterior::variables(brm_draws), "_", simplify = T)[, 2]
        posterior::rename_variables(brm_draws, `(Intercept)` = Intercept)
    } else {
        stop(paste0(class(x)[1], " objects not supported."))
    }
  })
  intervals <- lapply(draws, bayesplot::mcmc_intervals_data)
  combined <- dplyr::bind_rows(intervals, .id = "model")
  ggplot(combined, aes(x = m, y = parameter, color = model, group = model)) +
    geom_linerange(aes(xmin = l, xmax = h), size = 2, position = position_dodge(dodge_width)) +
    geom_linerange(aes(xmin = ll, xmax = hh), position = position_dodge(dodge_width)) +
    geom_point(color = "black", position = position_dodge(dodge_width)) +
    geom_vline(xintercept = 0, linetype = "dashed")
}

用法：

compare_posteriors(mod1, mod2, mod3)

使用 bayesplot 绘制多个模型的后验参数估计值

Plotting posterior parameter estimates from multiple models with bayesplot

r

ggplot2

rstan

rstanarm