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Performs a computer simulation of confidence interval performance for two types of standardized mean differences in a 2-group design (see ci.stdmean2). Sample data for each group can be generated from five different population distributions. All distributions are scaled to have a standard deviation of 1.0 for group 1.

Usage

sim.ci.stdmean2(alpha, n1, n2, sd2, dist1, dist2, d, rep)

Arguments

alpha

alpha level for 1-alpha confidence

n1

sample size for group 1

n2

sample size for group 2

sd2

population standard deviation for group 2

dist1

type of distribution for group 1 (1, 2, 3, 4, or 5)

dist2

type of distribution for group 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 kurtosis = 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.stdmean2(.05, 20, 20, 1.5, 3, 4, .75, 5000)
#>                         Coverage Lower Error Upper Error Ave CI Width   Ave Est
#> Unweighted Standardizer   0.9182      0.0522      0.0296     1.341751 0.7837115
#> Group 1 Standardizer      0.9478      0.0282      0.0240     1.812745 0.7823249

# Should return (within sampling error):
#                         Coverage Lower Error Upper Error Ave CI Width   Ave Est
# Unweighted Standardizer   0.9058      0.0610      0.0332     1.342560 0.7838679
# Group 1 Standardizer      0.9450      0.0322      0.0228     1.827583 0.7862640