rejection {distfreereg} | R Documentation |
Compute Rejection Rates of a Distribution-Free Test
Description
Compute the rejection rates of the tests simulated in a compare
object. Specifically, this function estimates the rejection rates of the tests conducted with specified statistics of the hypothesis that the mean function is test_mean
when the true mean function is true_mean
.
Usage
rejection(object, alpha = 0.05, stat = names(object[["observed_stats"]]), ...)
Arguments
object |
Object of class |
alpha |
Numeric vector; specifies the |
stat |
Character vector; specifies the names of the statistics to use. The default value computes the rejection rate associated with every statistic in |
... |
Additional arguments to pass to |
Value
Data frame containing estimated rejection rates and associated Monte Carlo standard errors, with one row for each combination of stat
and alpha
elements.
Warning
The reported Monte Carlo standard error does not account for the uncertainty of the estimation of the 1-\alpha
quantiles of the distribution of simulated statistics. The number of Monte Carlo simulations should be large enough to make this estimate sufficiently accurate that it can be considered known for practical purposes. The standard errors of estimated quantiles can be calculated using the mcmcse package.
Author(s)
Jesse Miller
See Also
Examples
# In practice, set "reps" larger than 200.
set.seed(20240201)
n <- 100
func <- function(X, theta) theta[1] + theta[2]*X
Sig <- rWishart(1, df = n, Sigma = diag(n))[,,1]
theta <- c(2,5)
X <- matrix(rexp(n, rate = 1))
cdfr <- compare(true_mean = func, true_X = X, true_covariance = list(Sigma = Sig),
test_mean = func, X = X, covariance = list(Sigma = Sig), reps = 200,
prog = Inf, theta = theta, theta_init = rep(1, length(theta)))
rejection(cdfr)
rejection(cdfr, stat = "CvM")
rejection(cdfr, alpha = c(0.1, 0.2))