pkc {boodd} | R Documentation |
Plot Kernel Density Estimates for Null and Alternative Distributions.
Description
Plots kernel density estimates for null and alternative distributions, showing the acceptance region for a hypothesis test and highlighting the type II error against an alternative hypothesis.
Usage
pkc(nul_dist, alt_dist, alpha)
Arguments
nul_dist |
Numeric vector representing the distribution under the null hypothesis. |
alt_dist |
Numeric vector representing the distribution under an alternative hypothesis. |
alpha |
Numeric value between |
Details
This function visualizes the kernel density estimates of two
distributions:
one under the null hypothesis and the other one under an alternative hypothesis.
It highlights the acceptance region of the test (using the significance
level alpha
)
and the region corresponding to type II error. This visual representation can
be useful for understanding the behaviour of bootstrapped test statistics and the
trade-off between type I and type II errors.
Value
Creates a plot showing the kernel density estimates of the null and alternative distributions with the relevant regions highlighted. The function does not return any values.
References
Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted..
Beran R. (1986). Simulated Power Functions. Ann. Statist., 14, 151 - 173.
See Also
Examples
# Example: Comparing null and alternative distributions
# Generate two normally distributed samples
set.seed(123)
null_dist <- rnorm(1000, mean = 0, sd = 1) # Null distribution
alt_dist <- rnorm(1000, mean = 0.5, sd = 1) # Alternative distribution
alpha <- 0.05 # Significance level
# Plot kernel density estimates
pkc(null_dist, alt_dist, alpha)