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

In statpsych: Statistical Methods for Psychologists

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

Description

Performs a computer simulation of confidence interval performance for two types of standardized mean differences in a paired-samples design. Sample data for each level of the within-subjects factor can be generated from five different population distributions. All distributions are scaled to have standard deviations of 1.0 at level 1.

Usage

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

Arguments

alpha	alpha level for 1-alpha confidence
n	sample size
sd.ratio	ratio of population standard deviations (sd2/sd1)
cor	correlation between paired measurements
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)
d	population standardized mean difference
rep	number of Monte Carlo samples

Value

Returns a 1-row matrix. The columns are:

• Coverage - Probability of confidence interval including population std mean difference

• Lower Error - Probability of lower limit greater than population std mean difference

• Upper Error - Probability of upper limit less than population std mean difference

• Ave CI Width - Average confidence interval width

Examples

sim.ci.stdmean.ps(.05, 20, 1.5, .8, 4, 4, .5, 2000)

# Should return (within sampling error):
#                         Coverage Lower Error Upper Error Ave CI Width   Ave Est
# Unweighted Standardizer   0.9095      0.0555       0.035    0.7354865 0.5186796
# Level 1 Standardizer      0.9525      0.0255       0.022    0.9330036 0.5058198

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