Description
Confidence Intervals for Exceedance Probability.
Description
Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) <arXiv:1803.03356> for more information.
README.md
exceedProb
This R package computes exceedance probabilities and associated confidence intervals. It currently only supports general linear models.
Installation
install.packages("exceedProb")
Examples
library(exceedProb)
# Sample mean -----------------------------------------------------------------
n <- 100
x <- rnorm(n = n)
theta_hat <- mean(x)
sd_hat <- sd(x)
cutoff <- seq(from = theta_hat - 0.5, to = theta_hat + 0.5, by = 0.1)
exceedProb(cutoff = cutoff,
theta_hat = theta_hat,
sd_hat = sd_hat,
alpha = 0.05,
d = 1,
n = n,
m = n)
# Linear regression -----------------------------------------------------------
n <- 100
beta <- c(1, 2)
x <-runif(n = n, min = 0, max = 10)
y <- rnorm(n = n, mean = cbind(1, x) %*% beta, sd = 1)
j <- 2
fit <- lm(y ~ x)
theta_hat <- coef(fit)[j]
sd_hat <- sqrt(n * vcov(fit)[j, j])
cutoff <- seq(from = theta_hat - 0.5, to = theta_hat + 0.5, by = 0.1)
exceedProb(cutoff = cutoff,
theta_hat = theta_hat,
sd_hat = sd_hat,
alpha = 0.05,
d = length(beta),
n = n,
m = n)
References
Segal, B. D. (submitted). Towards replicability with confidence intervals for the exceedance probability.