exceedProb: Confidence intervals for the exceedance probability

In exceedProb: Confidence Intervals for Exceedance Probability

Description

This function obtains confidence intervals for exceedance probability

Usage

exceedProb(cutoff, theta_hat, sd_hat, alpha, d, n, m, interval = c(-100,
  100), lower_tail = FALSE)

Arguments

cutoff	Cutoff values (scalar or vector)
theta_hat	Point estimate for the parameter of interest
sd_hat	Estimated standard deviation for the parameter of interest (Note: not the standard error)
alpha	Significance level
d	Number of parameters in the general linear model
n	Number of observations in the initial study
m	Number of observations in the replication study
interval	Interval within which to search for roots
lower_tail	If TRUE, reports lower tail probabilities

Value

ep Exceedance probability with confidence intervals

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)

exceedProb documentation built on Aug. 27, 2019, 9:02 a.m.