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Simulates confidence interval coverage probability for a 2-group mean difference

Source: R/statpsych1.R
sim.ci.mean2.Rd

Performs a computer simulation of separate variance and pooled variance confidence interval performance for a population mean difference in a 2-group design. Sample data within 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.mean2(alpha, n1, n2, sd2, dist1, dist2, rep)

Arguments

alpha

alpha level for 1-alpha confidence

n1

sample size in group 1

n2

sample size in 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)

rep

number of Monte Carlo samples

Value

Returns a 1-row matrix. The columns are:

  • Coverage - probability of confidence interval including population mean difference

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

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

  • Ave CI Width - average confidence interval width

Examples

sim.ci.mean2(.05, 30, 25, 1.5, 1, 1, 1000)
#>                              Coverage Lower Error Upper Error Ave CI Width
#> Equal Variances Assumed:        0.952       0.021       0.027     1.350563
#> Equal Variances Not Assumed:    0.959       0.017       0.024     1.408989

# Should return (within sampling error):
#                              Coverage Lower Error Upper Error Ave CI Width
# Equal Variances Assumed:      0.93988      0.0322     0.02792     1.354437
# Equal Variances Not Assumed:  0.94904      0.0262     0.02476     1.411305

sim.ci.mean2(.05, 30, 25, 1.5, 4, 5, 1000)
#>                              Coverage Lower Error Upper Error Ave CI Width
#> Equal Variances Assumed:        0.937       0.049       0.014     1.350916
#> Equal Variances Not Assumed:    0.942       0.048       0.010     1.408283

# Should return (within sampling error):
#                              Coverage Lower Error Upper Error Ave CI Width
# Equal Variances Assumed:      0.93986     0.04022     0.01992     1.344437
# Equal Variances Not Assumed:  0.94762     0.03862     0.01376     1.401305