A function to calculate a confidence interval for the population regression coefficient of interest using the standard approach and the noncentral approach when the regression coefficients are standardized.
ci.rc(b.k, SE.b.k = NULL, s.Y = NULL, s.X = NULL, N, K, R2.Y_X = NULL,
R2.k_X.without.k = NULL, conf.level = 0.95, R2.Y_X.without.k = NULL,
t.value = NULL, alpha.lower = NULL, alpha.upper = NULL,
Noncentral = FALSE, Suppress.Statement = FALSE, ...)Returns the confidence limits for the standardized regression coefficients of interest from the standard approach to confidence interval formation or from the noncentral approach to confidence interval formation using the noncentral t-distribution.
value of the regression coefficient for the kth predictor variable
standard error for the kth predictor variable
standard deviation of Y, the dependent variable
standard deviation of X, the predictor variable of interest
sample size
the number of predictors
the squared multiple correlation coefficient predicting Y from the k predictor variables
the squared multiple correlation coefficient predicting the kth predictor variable (i.e., the predictor of interest) from the remaining K-1 predictor variables
desired level of confidence for the computed interval (i.e., 1 - the Type I error rate)
the squared multiple correlation coefficient predicting Y from the K-1 predictor variable with the kth predictor of interest excluded
the t-value evaluating the null hypothesis that the population regression coefficient for the kth predictor equals zero
the Type I error rate for the lower confidence interval limit
the Type I error rate for the upper confidence interval limit
TRUE or FALSE statement specifying whether or not the noncentral approach to
confidence intervals should be used
TRUE or FALSE statement specifying whether or not a statement should be printed
that identifies the type of confidence interval formed
optional additional specifications for nested functions
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
This function calls upon ci.reg.coef in MBESS, but has a different naming system. See ci.reg.coef for more details.
For standardized variables, do not specify the standard deviation of the variables and input the
standardized regression coefficient for b.k.
Kelley, K. (2007). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1--24.
Kelley, K. & Maxwell, S. E. (2003). Sample size for Multiple Regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8, 305--321.
Kelley, K. & Maxwell, S. E. (2008). Power and accuracy for omnibus and targeted effects: Issues of sample size planning with applications to Multiple Regression. Handbook of Social Research Methods, J. Brannon, P. Alasuutari, and L. Bickman (Eds.). New York, NY: Sage Publications.
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.
ss.aipe.reg.coef, conf.limits.nct, ci.reg.coef, ci.src