问题
I am still getting used to converting formulas to code in R. I am trying to work through the empirical Bayes chapter (chapter 6) in Computer Age Statistical Inference. I would like to produce plot 6.4, but in order to do so I need to find the marginal distribution of a function of the data.
To obtain the the data and plot it I did the following.
nodes <- read.table("https://web.stanford.edu/~hastie/CASI_files/DATA/nodes.txt",
header = T)
nodes %>%
ggplot(aes(x=x/n))+
geom_histogram(bins = 30)+
theme_bw()+
labs(x = "nodes",
n = "p=x/n")
He then tries the following model
With the following calculations
To get the plot I am trying to reproduce.
I am used to using standard R functions for inference such as lm()
and
glm()
, but now that I need to maximize a mle to find a marginal distribution I am lost.
来源:https://stackoverflow.com/questions/45290265/reproduce-a-prior-density-plot-in-r