Hello,
Your code is correctly extracting the bootstrapped r-squared confidence
intervals calculated using the adjusted bootstrap percentile (BCa) method.
If you want the p-values, do
summary(fit)$coefficients[, 4]
in the function.
Also, function boot calls a function statistic that expects data and
indices as 1st and 2nd arguments. Argument formula should go after these
ones. In your case that doesn't make any difference because you are
passing *named* arguments but it's better to keep to the rules, you'll
be avoiding potential/future problems. Rewrite the function as
rsq <- function(data, indices, formula) {
d <- data[indices, ] # allows boot to select sample
fit <- lm(formula, data = d)
summary(fit)$r.square
}
Hope this helps,
Rui Barradas
Às 18:18 de 02/01/20, Saaim Khan escreveu:
I've been trying to get the pvalue of my samples from a bootstrap rsquared test
in R. I'm not very good with statistics so could someone please take a look at
my below code and point me in the right direction with regards to how I can
extract the p-values per sample (basically input a Descriptor_Score value and
output a p-value based on the bootstrap model)? If my understanding is
incorrect, please let me know. This code is mostly from API.
# Bootstrap 95% CI for R-Squared
library(boot)
# function to obtain R-Squared from the data
rsq <- function(formula, data, indices) {
d <- data[indices,] # allows boot to select sample
fit <- lm(formula, data=d)
return(summary(fit)$r.square)
}
###vdw beta bootstrap
`results[b]_vdw` = boot(data=rescored_beta, statistic=rsq,
R=10000, formula=Descriptor_Score~vdw)
# get 95% confidence interval
confb_vdw=boot.ci(`results[b]_vdw`)
cib_vdw = confb_vdw$bca[ , c(4, 5)]
Thanks!
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