ci.R(R = NULL, df.1 = NULL, df.2 = NULL, conf.level = 0.95,
Random.Predictors = TRUE, Random.Regressors, F.value = NULL,
N = NULL, K=NULL, alpha.lower = NULL, alpha.upper = NULL, ...)Random.Predictors; Random.Regressors overrides
Random.Predictors ci.R2 in MBESS package.This function can be used with random predictor variables (Random.Predictors=TRUE) or
when predictor variables are fixed (Random.Predictors=FALSE). In many applications in the behavioral,
educational, and social sciences, predictor variables are random, which is the default for this
function.
For random predictors, the function implements the procedure of Lee (1971), which was implemented
by Algina and Olejnik (2000; specifically in their ci.smcc.bisec.sas SAS script). When
Random.Predictors=TRUE, the function implements code that is in part based on the Alginia and
Olejnik (2000) SAS script.
When Random.Predictors=FALSE, and thus the predictors are planned and thus fixed in hypothetical
replications of the study, the confidence limits are based on a noncentral F-distribution (see
conf.limits.ncf).
Lee, Y. S. (1971). Some results on the sampling distribution of the multiple correlation coefficient. Journal of the Royal Statistical Society, B, 33, 117--130.
Smithson, M. (2003). Confidence intervals. New York, NY: Sage Publications.
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.
Steiger, J. H. & Fouladi, R. T. (1992). R2: A computer program for interval estimation, power calculation, and hypothesis testing for the squared multiple correlation. Behavior research methods, instruments and computers, 4, 581--582.
ci.R2, ss.aipe.R2, conf.limits.nct