docs/stdmean.is.md

In cwendorf/dgb: DGB

Standardized Mean Difference from Two (Independent) Samples

ci.stdmean.is

Computes confidence interval for a population standardized mean difference in a 2-group design.

Arguments: - alpha: alpha level for 1-alpha confidence - mj: sample mean in group j - sdj: sample standard deviation in group j - nj: sample size in group j

Values: - estimate, SE, lower limit, and upper limit for equal variance and unequal variance methods plus single group standardizer

alpha = .05
m1 = 35.1
m2 = 26.7
sd1 = 7.32
sd2 = 6.98
n1 = 30
n2 = 30
ci.stdmean.is(alpha, m1, m2, sd1, sd2, n1, n2)

##                             Estimate        SE        LL       UL
## Equal Variances Not Assumed 1.174493 0.2844012 0.6170771 1.731909
## Equal Variances Assumed:    1.174493 0.2802826 0.6251494 1.723837
## Group 1 Standardizer:       1.147541 0.2975582 0.5643375 1.730744
## Group 2 Standardizer:       1.203438 0.3120525 0.5918268 1.815050

size.ci.stdmean.is

Computes sample size required to estimate a population standardized mean difference with desired precision in a 2-group design.

Arguments: - alpha: alpha level for 1-alpha confidence - d: planning value of standardized mean difference - w: desired confidence interval width - r: desired n2/n1 ratio

Values: - required sample size per group (or n1 if r not equal to 1)

alpha = .05
d = .75
w = .5
r = 1
size.ci.stdmean.is(alpha, d, w, r)

## [1] 132

cwendorf/dgb documentation built on May 3, 2022, 9:35 p.m.