sim.ci.median.ps: Simulates confidence interval coverage probability for a...

In statpsych: Statistical Methods for Psychologists

Simulates confidence interval coverage probability for a median difference in a paired-samples design

Description

Performs a computer simulation of confidence interval performance for a median difference in a paired-samples design. Sample data within each level of the within-subjects factor can be generated from bivariate population distributions with five different marginal distributions. All distributions are scaled to have standard deviations of 1.0 at level 1. Bivariate random data with specified marginal skewness and kurtosis are generated using the unonr function in the mnonr package.

Usage

sim.ci.median.ps(alpha, n, sd.ratio, cor, dist1, dist2, rep)

Arguments

alpha	alpha level for 1-alpha confidence
n	sample size
sd.ratio	ratio of population standard deviations
cor	population correlation of paired observations
dist1	type of distribution at level 1 (1, 2, 3, 4, or 5)
dist2	type of distribution at level 2 (1, 2, 3, 4, or 5) 1 = Gaussian (skewness = 0 and excess kurtosis = 0) 2 = platykurtic (skewness = 0 and excess kurtosis = -1.2) 3 = leptokurtic (skewness = 0 and excess kurtsois = 6) 4 = moderate skew (skewness = 1 and excess kurtosis = 1.5) 5 = large skew (skewness = 2 and excess kurtosis = 6)
rep	number of Monte Carlo samples

Value

Returns a 1-row matrix. The columns are:

• Coverage - probability of confidence interval including population median difference

• Lower Error - probability of lower limit greater than population median difference

• Upper Error - probability of upper limit less than population median difference

• Ave CI Width - average confidence interval width

Examples

# Only 1000 reps because CRAN runs all examples and requires short execution time.
sim.ci.median.ps(.05, 30, 1.5, .7, 4, 3, 1000)

# Should return (within sampling error):
#      Coverage Lower Error Upper Error Ave CI Width
# [1,]    0.961       0.026       0.013    0.9435462

statpsych documentation built on July 9, 2023, 6:50 p.m.