| ci.sc {MBESS} | R Documentation |
Function to obtain the confidence interval for a standardized contrast in a fixed effets analysis of variance context.
ci.sc(means = NULL, error.variance = NULL, c.weights = NULL, n = NULL, N = NULL, Psi = NULL, ncp = NULL, conf.level = 0.95, alpha.lower = NULL, alpha.upper = NULL, df.error = NULL, ...)
means |
a vector of the group means or the means of the particular level of the effect (for fixed effect designs) |
error.variance |
the common variance of the error (i.e., the mean square error) |
c.weights |
the contrast weights (the sum of the contrast weights should be zero) |
n |
sample sizes per group or level of the particular factor (if length 1 it is assumed that the per group/level sample sizes are equal) |
N |
total sample size |
Psi |
the (unstandardized) contrast effect, obtained by multiplying the jth mean by the jth contrast weight (this is the unstandardized effect) |
ncp |
the noncentrality paramter from the t-distribution |
conf.level |
desired level of confidence for the computed interval (i.e., 1 - the Type I error rate) |
alpha.lower |
the Type I error rate for the lower confidence interval limit |
alpha.upper |
the Type I error rate for the upper confidence interval limit |
df.error |
the degrees of freedom for the error. In one-way designs, this is simply N-length (means) and need not be specified; it must be specified if the design has multiple factors. |
... |
optional additional specifications for nested functions |
Lower.Conf.Limit.Standardized.Contrast |
the lower confidence limit for the standardized contrast |
Standardized.contrast |
standardized contrast |
Upper.Conf.Limit.Standardized.Contrast |
the upper confidence limit for the standardized contrast |
Be sure to use the error varaince and not its square root (i.e., the standard deviation of the errors).
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1-24.
Lai, K., \& Kelley, K. (2007). Sample size planning for standardized ANCOVA and ANOVA contrasts: Obtaining narrow confidence intervals. Manuscript submitted for publication.
Steiger, J. H. (2004). Beyond the F Test: Effect size confidence intervals and tests of close fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9, 164–182.
conf.limits.nct, ci.src, ci.smd, ci.smd.c, ci.sm, ci.c
ci.sc(means=c(2, 4, 9, 13), error.variance=1, c.weights=c(1, -1, -1, 1), n=c(3, 3, 3, 3), N=12, conf.level=.95) ci.sc(means=c(2, 4, 9, 13), error.variance=1, c.weights=c(1, -1, -1, 1), n=c(3, 3, 3, 3), N=12, conf.level=.95) ci.sc(means=c(1.6, 0), error.variance=1, c.weights=c(1, -1), n=c(10, 10), N=20, conf.level=.95)