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

ss.aipe.rc.sensitivity {MBESS}

R Documentation

Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient

Description

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

Usage

ss.aipe.rc.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; default is `TRUE`
`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 (i.e., the probability that the observed interval will be no larger than desired).
`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 runs

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 when the simulation student within the function runs.

Value

`Results`	a matrix containing the empirical results from each of the `G` replication 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.

See ss.aipe.reg.coef.sensitivity in MBESS for more details.

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.