R: Confidence Interval for a Regression Coefficient

ci.rc {MBESS}

R Documentation

Confidence Interval for a Regression Coefficient

Description

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.

Usage

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, ...)

Arguments

`b.k`	value of the regression coefficient for the kth predictor variable
`SE.b.k`	standard error for the kth predictor variable
`s.Y`	standard deviation of Y, the dependent variable
`s.X`	standard deviation of X, the predictor variable of interest
`N`	sample size
`K`	the number of predictors
`R2.Y_X`	the squared multiple correlation coefficient predicting Y from the k predictor variables
`R2.k_X.without.k`	the squared multiple correlation coefficient predicting the kth predictor variable (i.e., the predictor of interest) from the remaining K-1 predictor variables
`conf.level`	desired level of confidence for the computed interval (i.e., 1 - the Type I error rate)
`R2.Y_X.without.k`	the squared multiple correlation coefficient predicting Y from the K-1 predictor variable with the kth predictor of interest excluded
`t.value`	the t-value evaluating the null hypothesis that the population regression coefficient for the kth predictor equals zero
`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
`Noncentral`	`TRUE` or `FALSE` statement specifying whether or not the noncentral approach to confidence intervals should be used
`Suppress.Statement`	`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

Details

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.

Value

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.

Note

Not all of the values need to be specified, only those that contain all of the necessary information in order to compute the confidence interval (options are thus given for the values that need to be specified).

Author(s)

Ken Kelley (University of Notre Dame; KKelley@ND.Edu)

References

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.