bca_pvalue_for_lqm <-
function(estimate,
boot_vector,
model,
working_data)
{
boot.bca <- NA
# browser()
# boot.bca <- coxed::bca(na.omit(boot_vector))
# p_value <- boot.p_value::boot.p_value(na.omit(boot_vector), type = "bca", theta_null = 0)
standard_error <- stats::sd(boot_vector, na.rm = T)
mean_boot_vector <- mean(boot_vector, na.rm = T)
bias <- estimate - mean_boot_vector
bias_threshold <- 0.25 * standard_error
p_value <- 2 * stats::pt(-abs((estimate) / standard_error), (nrow(working_data) - model$edf))
ci_lower_limit <- estimate + stats::qt(0.05 / 2, (nrow(working_data) - model$edf)) * standard_error
ci_upper_limit <- estimate + stats::qt(1 - 0.05 / 2, (nrow(working_data) - model$edf)) * standard_error
#Bias-corrected p-value
if (abs(bias) > bias_threshold) {
p_value <- 2 * stats::pt(-abs((2 * estimate - mean_boot_vector) / standard_error), (nrow(working_data) - model$edf))
ci_lower_limit <- 2 * estimate - mean_boot_vector + stats::qt(0.05 / 2, (nrow(working_data) - model$edf)) * standard_error
ci_upper_limit <- 2 * estimate - mean_boot_vector + stats::qt(1 - 0.05 / 2, (nrow(working_data) - model$edf)) * standard_error
}
# return(c(ci_lower_limit,ci_upper_limit,p_value))
# DiCiccio, T. J. and B. Efron. (1996). Bootstrap Confidence Intervals. Statistical Science. 11(3):
# 189–212. https://doi.org/10.1214/ss/1032280214
# boot.bca <- coxed::bca(na.omit(boot_vector))
# if(boot.bca[1]<0 & boot.bca[2]>0)
# p_value <- 0.06
# else
# p_value <- 0.04
# return(c(boot.bca,p_value))
}
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