ci.reg.coef: Confidence interval for a regression coefficient

Description

A function to calculate a confidence interval around the population regression coefficient of interest using the standard approach and the noncentral approach when the regression coefficients are standardized.

Usage

ci.reg.coef(b.j, SE.b.j=NULL, s.Y=NULL, s.X=NULL, N, p, R2.Y_X=NULL,
R2.j_X.without.j=NULL, conf.level=0.95, R2.Y_X.without.j=NULL, 
t.value=NULL, alpha.lower=NULL, alpha.upper=NULL, Noncentral=FALSE, 
Suppress.Statement=FALSE, ...)

Arguments

b.j

value of the regression coefficient for the jth predictor variable

SE.b.j

standard error for the jth predictor variable

s.Y

standard deviation of Y, the dependent variable

s.X

standard deviation of $X_j$, the predictor variable of interest

sample size

the number of predictors

R2.Y_X

the squared multiple correlation coefficient predicting Y from the p predictor variables

R2.j_X.without.j

the squared multiple correlation coefficient predicting the jth predictor variable (i.e., the predictor of interest) from the remaining p-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.j

the squared multiple correlation coefficient predicting Y from the p-1 predictor variable with the jth predictor of interest excluded

t.value

the t-value evaluating the null hypothesis that the population regression coefficient for the jth 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, specifying whether or not the noncentral approach to confidence intervals should be used

Suppress.Statement

TRUE/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

Value

t-distribution.

Details

For standardized variables, do not specify the standard deviation of the variables and input the standardized regression coefficient for b.j.

References

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). Sample Size Planning with applications to multiple regression: Power and accuracy for omnibus and targeted effects. In P. Alasuuta, J. Brannen, & L. Bickman (Eds.), The Sage handbook of social research methods (pp. 166--192). Newbury Park, CA: Sage.