Given the sample data sampleDT below, I would appreciate any help to iteratively fit the brms model below n times, and each time extract the means and sigma from the brmsfit object brm.fit.n and add them to the data frame sampleDT.
If n=10, then there should be 10 columns of means and 10 columns of sigma added to the data frame.
My attempt below does not work as intended. It allows me to run the brms model n times and generate the means and sigma n times, but does not add them to the data frame - one column for each means and one column for each sigma from each run - as intended.
#sample data
sampleDT<-structure(list(id = 1:10, N = c(10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L), A = c(62L, 96L, 17L, 41L, 212L, 143L, 143L,
143L, 73L, 73L), B = c(3L, 1L, 0L, 2L, 170L, 21L, 0L, 33L, 62L,
17L), C = c(0.05, 0.01, 0, 0.05, 0.8, 0.15, 0, 0.23, 0.85, 0.23
), employer = c(1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L), F = c(0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L), G = c(1.94, 1.19, 1.16,
1.16, 1.13, 1.13, 1.13, 1.13, 1.12, 1.12), H = c(0.14, 0.24,
0.28, 0.28, 0.21, 0.12, 0.17, 0.07, 0.14, 0.12), dollar.wage_1 = c(1.94,
1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_2 = c(1.93,
1.18, 3.15, 3.15, 1.12, 1.12, 2.12, 1.12, 1.11, 1.11), dollar.wage_3 = c(1.95,
1.19, 3.16, 3.16, 1.14, 1.13, 2.13, 1.13, 1.13, 1.13), dollar.wage_4 = c(1.94,
1.18, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_5 = c(1.94,
1.19, 3.16, 3.16, 1.14, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_6 = c(1.94,
1.18, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_7 = c(1.94,
1.19, 3.16, 3.16, 1.14, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_8 = c(1.94,
1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_9 = c(1.94,
1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_10 = c(1.94,
1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12)), row.names = c(NA,
-10L), class = "data.frame")
#my attempt
map_dfc(1:10, function(i) {
brm.fit.n <-brm(dollar.wage_1 ~ A + B + C + employer + F + G + H,
data=sampleDT, iter = 200, family = gaussian())
sampleDT$mean.n<-fitted(brm.fit.n)[, 1]
sampleDT$sd.n<-summary(brm.fit.n)$spec_pars[1]
return(sampleDT)
})
This question has also been posted here. Thanks in advance for any help.
The two things you need to do to adapt your existing code into a function are:
n timesThere are lots of ways to do both parts, one option is purrr::map_dfr which can do both, applying the fit multiple times and creating a dataframe.
Instead of a brm model, which takes some time to fit, I've fit a simple linear model to random data instead, you would just have to replace those parts with your fitting code and save the mean and sd instead of the intercept and slope:
library(purrr)
# 1:10 - will repeat 10 times
map_dfr(1:10, function(i) {
random_data = data.frame(
x = rnorm(30),
y = rnorm(30)
)
fit = lm(y ~ x, data = random_data)
intercept = coef(fit)[1]
slope = coef(fit)[2]
return(data.frame(intercept, slope))
}, .id = "sim_num")
Which gives a dataframe as output:
sim_num intercept slope
1 1 0.274903632 -0.03529736
2 2 -0.005134599 -0.22063748
3 3 -0.134999713 0.12090366
4 4 -0.216886033 0.21265679
5 5 0.261365432 0.02434036
6 6 0.067069791 0.23180334
7 7 -0.235138217 0.28360061
8 8 -0.117489553 0.10781101
9 9 -0.150288480 0.03086797
10 10 -0.031814194 -0.04075479