I have a grouped dataframe which I would like to apply the bcp function to calculate for each point the posterior probability of there being a change at each point.
My data looks as follows:
# INSTALL PACMAN
if (!require("pacman", character.only = TRUE)) {
install.packages("pacman")
}
pacman::p_load(bcp,tidyverse)
df <- data.frame(
date = c(seq(Sys.Date(), by = -1, length.out = 1000), seq(Sys.Date(), by = -1, length.out = 1000)),
value = c(rnorm(200, mean = 20, sd = 1), rnorm(800, mean = 17, sd = 2), rnorm(400, mean = 200, sd = 3), rnorm(600, mean = 150, sd = 4)),
product = c(rep("A", 1000), rep("B", 1000))
)
By filtering my df to a single variable and assigning it to a new variable and applying bcp() I see it returns a list of 12
x <- df %>%
filter(product == "A")
y <- bcp(x$value)
I've tried using group_map which returns only two columns which is not ideal, I've no idea why only two columns are returned:
df %>%
group_by(product) %>%
group_map(~ bcp(.x$value))
I've also tried group_modify but I can't get the syntax correct to parse out the correct fields:
df %>%
group_by(product) %>%
group_modify(~ {
bcp::bcp(.x$value) %>%
tibble::enframe(name = "name", value = "value")
})
As well as:
df %>%
group_by(product) %>%
group_modify(~ bcp::bcp(.x$value) %>%
pluck("posterior.prob"))
Any guidance on how I can append the 'posterior.prob' from the bcp function to my original df on a per group basis would be greatly appreciated.
I'm not familiar with the bcp package but does this give you what you want?
posterior_prob <- map(df %>%
group_by(product) %>%
group_map(~ bcp(.x$value)), pluck("posterior.prob")) %>%
unlist()
df$posterior_prob_var <- posterior_prob
head(df)
# date value product posterior_prob_var
# 1 2023-05-10 21.90542 A 0.002
# 2 2023-05-09 19.61293 A 0.000
# 3 2023-05-08 20.46336 A 0.002
# 4 2023-05-07 21.22534 A 0.000
# 5 2023-05-06 19.37578 A 0.000
# 6 2023-05-05 18.94408 A 0.002