ci.omega2 {MBESS} | R Documentation |
Confidence Interval for omega-squared (
) for between-subject fixed-effects ANOVA and ANCOVA designs (and partial omega-squared
for between-subject multifactor ANOVA and ANCOVA designs)
Description
Function to obtain the exact confidence interval using the non-central -distribution for omega-squared or partial omega-squared in between-subject fixed-effects ANOVA and ANCOVA designs.
Usage
ci.omega2(F.value = NULL, df.1 = NULL, df.2 = NULL, N = NULL, conf.level = 0.95,
alpha.lower = NULL, alpha.upper = NULL, ...)
Arguments
F.value |
The value of the |
df.1 |
numerator degrees of freedom |
df.2 |
denominator degrees of freedom |
N |
total sample size (i.e., the number of individual entities in the data) |
conf.level |
confidence interval coverage (i.e., 1-Type I error rate), default is .95 |
alpha.lower |
Type I error for the lower confidence limit |
alpha.upper |
Type I error for the upper confidence limit |
... |
allows one to potentially include parameter values for inner functions |
Details
The confidence level must be specified in one of following two ways: using
confidence interval coverage (conf.level
), or lower and upper confidence
limits (alpha.lower
and alpha.upper
). The value returned is the confidence
interval limits for the population (or partial
).
This function uses the confidence interval transformation principle (Steiger, 2004) to transform the confidence limits for the noncentality parameter to the confidence limits for the population's (partial) omega-squared (). The confidence interval for the noncentral
-parameter can be obtained
from the
conf.limits.ncf
function in MBESS, which is used internally within this function.
Value
Returns the confidence limits for (partial) omega-sqaured.
lower_Limit_omega2 |
lower limit for omega-squared |
lower_Limit_omega2 |
upper limit for omega-squared |
Author(s)
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
References
Fleishman, A. I. (1980). Confidence intervals for correlation ratios. Educational and Psychological Measurement, 40, 659–670.
Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1–24.
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.
See Also
ci.srsnr
, ci.snr
, conf.limits.ncf
Examples
## To illustrate the calculation of the confidence interval for noncentral
## F parameter,Bargman (1970) gave an example in which a 5-group ANOVA with
## 11 subjects in each group is conducted and the observed F value is 11.2213.
## This exmaple continued to be used in Venables (1975), Fleishman (1980),
## and Steiger (2004). If one wants to calculate the exact confidence interval
## for omega-squared of that example, this function can be used.
ci.omega2(F.value=11.221, df.1=4, df.2=50, N=55)
ci.omega2(F.value=11.221, df.1=4, df.2=50, N=55, conf.level=.90)