ss.aipe.reg.coef.sensitivity: Sensitivity analysis for sample size planning from the...

In MBESS: The MBESS R Package

Sensitivity analysis for sample size planning from the Accuracy in Parameter Estimation Perspective for the (standardized and unstandardized) regression coefficient

Description

Performs a sensitivity analysis when planning sample size from the Accuracy in Parameter Estimation Perspective for the standardized or unstandardized regression coefficient.

Usage

ss.aipe.reg.coef.sensitivity(True.Var.Y = NULL, True.Cov.YX = NULL,
True.Cov.XX = NULL, Estimated.Var.Y = NULL, Estimated.Cov.YX = NULL,
Estimated.Cov.XX = NULL, Specified.N = NULL, which.predictor = 1, 
w = NULL, Noncentral = FALSE, Standardize = FALSE, conf.level = 0.95, 
degree.of.certainty = NULL, assurance=NULL, certainty=NULL,
 G = 1000, print.iter = TRUE)

Arguments

True.Var.Y	Population variance of the dependent variable (Y)
True.Cov.YX	Population covariances vector between the p predictor variables and the dependent variable (Y)
True.Cov.XX	Population covariance matrix of the p predictor variables
Estimated.Var.Y	Estimated variance of the dependent variable (Y)
Estimated.Cov.YX	Estimated covariances vector between the p predictor variables and the dependent variable (Y)
Estimated.Cov.XX	Estimated Population covariance matrix of the p predictor variables
Specified.N	Directly specified sample size (instead of using Estimated.Rho.YX and Estimated.RHO.XX)
which.predictor	identifies which of the p predictors is of interest
w	desired confidence interval width for the regression coefficient of interest
Noncentral	specify with a TRUE or FALSE statement whether or not the noncentral approach to sample size planning should be used
Standardize	specify with a TRUE or FALSE statement whether or not the regression coefficient will be standardized
conf.level	desired level of confidence for the computed interval (i.e., 1 - the Type I error rate)
degree.of.certainty	degree of certainty that the obtained confidence interval will be sufficiently narrow
assurance	an alias for degree.of.certainty
certainty	an alias for degree.of.certainty
G	the number of generations/replication of the simulation student within the function
print.iter	specify with a TRUE/FALSE statement if the iteration number should be printed as the simulation within the function runts

Details

Direct specification of True.Rho.YX and True.RHO.XX is necessary, even if one is interested in a single regression coefficient, so that the covariance/correlation structure can be specified when the simulation student within the function runs.

Value

Results	a matrix containing the empirical results from each of the G replications of the simulation
Specifications	a list of the input specifications and the required sample size
Summary.of.Results	summary values for the results of the sensitivity analysis (simulation study) given the input specification

Note

Note that when True.Rho.YX=Estimated.Rho.YX and True.RHO.XX=Estimated.RHO.XX, the results are not literally from a sensitivity analysis, rather the function performs a standard simulation study. A simulation study can be helpful in order to determine if the sample size procedure under or overestimates necessary sample size.

Author(s)

Ken Kelley (University of Notre Dame; [e-mail])

References

Kelley, K. & Maxwell, S. E. (2003). Sample size for Multiple Regression: Obtaining regression coefficients that are accuracy, not simply significant. Psychological Methods, 8, 305–321.

See Also

ss.aipe.reg.coef, ci.reg.coef

MBESS documentation built on Aug. 8, 2025, 7:24 p.m.