ss.aipe.reg.coef.sensitivity {MBESS} | R Documentation |
Performs a sensitivity analysis when planning sample size from the Accuracy in Parameter Estimation Perspective for the standardized or unstandardized regression coefficient.
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)
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 /FALSE statement whether or not the noncentral approach to sample size planning should be used |
Standardize |
specify with a TRUE /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 |
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.
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 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.
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
Kelley, K. & Maxwell, S. E. (2003). Sample size for Multiple Regression: {O}btaining regression coefficients that are accuracy, not simply significant. Psychological Methods, 8, 305–321.
ss.aipe.reg.coef
, ci.reg.coef