R: Sensitivity analysis for sample size planning from the...

ss.aipe.reg.coef.sensitivity {MBESS}

R Documentation

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; KKelley@ND.Edu)

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.