docs/lc.mean.bs.md

In cwendorf/dgb: DGB

Linear Contrast of Means (Between Subjects)

ci.lc.mean.bs

Computes confidence interval for a linear contrast of population means in a between-subjects design.

Arguments: - alpha: alpha level for 1-alpha confidence - m: vector of sample means - sd: vector of sample standard deviations - n: vector of sample sizes - c: vector of contrast coefficients

Values: - estimate, SE, df, lower limit, upper limit for both equal variance and unequal variance methods

alpha = .05
m = c(33.5, 37.9, 38.0, 44.1)
sd = c(3.84, 3.84, 3.65, 4.98)
n = c(10,10,10,10)
c = c(.5, .5, -.5, -.5)
ci.lc.mean.bs(alpha, m, sd, n, c)

##                              Estimate       SE       df        LL        UL
## Equal Variances Assumed:        -5.35 1.300136 36.00000 -7.986797 -2.713203
## Equal Variances Not Assumed:    -5.35 1.300136 33.52169 -7.993583 -2.706417

size.ci.lc.mean.bs

Computes sample size required to estimate a linear contrast of population mean with desired precision in a between-subjects design.

Arguments: - alpha: alpha level for 1-alpha confidence - var: planning value of average within-group DV variance - w: desired confidence interval width - c: vector of contrast coefficients

Values: - required sample size per group

alpha = .05
var = 5.62
w = 2
c = c(.5, .5, -1)
size.ci.lc.mean.bs(alpha, var, w, c)

##      [,1]
## [1,]   34

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