在 geom_smooth 上绘制后验分布的 ggplot

Question

I'd like to recreate this plot from this blog post on Posterior predicted distribution for linear regression in JAGS ) using ggplot?

Knowing all the extras available for ggplot, what methods are there to go about this?

Answer 1

Here's a bare-bones example of what I've come up with using density to add a geom_path .

library(ggplot2)
#mydat <- read.csv("HtWt30.csv")

mydat <- structure(list(male = c(0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 
                                 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 
                                 0L, 0L, 1L, 0L), 
                        height = c(64, 62.3, 67.9, 64.2, 64.8, 57.5, 
                                   65.6, 70.2, 63.9, 71.1, 66.5, 68.1, 62.9, 75.1, 64.6, 69.2, 68.1, 
                                   72.6, 63.2, 64.1, 64.1, 71.5, 76, 69.7, 73.3, 61.7, 66.4, 65.7, 
                                   68.3, 66.9), 
                        weight = c(136.4, 215.1, 173.6, 117.3, 123.3, 96.5, 
                                   178.3, 191.1, 158, 193.9, 127.1, 147.9, 119, 204.4, 143.4, 124.4, 
                                   140.9, 164.7, 139.8, 110.2, 134.1, 193.6, 180, 155, 188.2, 187.4, 
                                   139.2, 147.9, 178.6, 111.1)), 
                   .Names = c("male", "height", "weight"), class = "data.frame", row.names = c(NA, -30L))


# smooth plot
g_smooth <- ggplot(mydat, aes(x = height, y = weight)) + geom_smooth()

# fake posterior at a height = 60
p60 <- data.frame(x = 60, y = rnorm(1000, mean = 145, sd = 10))
# density kernel 
d60 <- density(p60$y)
# calculate scaling factor so that density covers 1/20 of full x range
density_scaling <- ((max(mydat$height) - min(mydat$height)) / 20) / max(d60$y)
# convert to points
d60points <- data.frame(y = d60$x, x = 60 + d60$y * density_scaling)
# add path to plot
g_smooth <- g_smooth + geom_path(data = d60points, aes(x = x, y = y))

# fake posterior at a height = 70
p70 <-  data.frame(x = 60, y = rnorm(1000, mean = 165, sd = 10))
# density kernel 
d70 <- density(p70$y)
# calculate scaling factor so that density covers 1/20 of full x range
density_scaling <- ((max(mydat$height) - min(mydat$height)) / 20) / max(d70$y)
# convert to points
d70points <- data.frame(y = d70$x, x = 70 + d70$y * density_scaling)
# add path to plot
g_smooth <- g_smooth + geom_path(data = d70points, aes(x = x, y = y))

g_smooth