ss.aipe.R2(Population.R2 = NULL, conf.level = 0.95, width = NULL,
Random.Predictors = TRUE, Random.Regressors, which.width = "Full", p = NULL,
K, degree.of.certainty = NULL, assurance=NULL, certainty=NULL,
verify.ss = FALSE, Tol = 1e-09, ...)which.width) TRUE) or are fixed (set to FALSE) Random.Predictors; Random.Regressors
overrides Random.Predictors width refers to p; K overrides p degree.of.certainty degree.of.certaintyconf.limits.nct for convergencedegree.of.certainty=NULL) so that the obtained confidence
interval is no larger than the value specified with some desired degree of
certainty (i.e., a probability that the obtained width is less than the specified
width). The method depends on whether or not the regressors are regarded as fixed
or random. This is the case because the distribution theory for the two cases is
different and thus the confidence interval procedure is conditional on the type of
regressors. The default methods are approximate but can be made exact with the
specification of verify.ss=TRUE, which performs an a priori Monte Carlo simulation study. Kelley (2007) and Kelley & Maxwell (2008) detail the methods used in the
function, with the former focusing on random regressors and the latter on fixed regressors.It is recommended that the option verify.ss should always be used! Doing so uses the method implied sample size as an estimate and then evaluates with an internal Monte Carlo simulation (i.e., via "brute-force" methods) the exact sample size given the goals specified. When verify.ss=TRUE, the default number of iterations is 10,000 but this can be changed by specifying G=5000 (or some other value; 10000 is the recommended) When verify.ss=TRUE is specified, an internal function verify.ss.aipe.R2 calls upon the ss.aipe.R2.sensitivity function for purposes of the
internal Monte Carlo simulation study. See the verify.ss.aipe.R2 function for arguments that can be passed from ss.aipe.R2 to verify.ss.aipe.R2.
Steiger, J. H. & Fouladi, R. T. (1992). R2: A computer program for interval estimation, power calculation, and hypothesis testing for the squared multiple correlation. Behavior research methods, instruments and computers, 4, 581--582.
Kelley, K. (2007). Sample size planning for the squared multiple correlation coefficient: Accuracy in parameter estimation via narrow confidence intervals, manuscripted submitted for publication.
Kelley, K. & Maxwell, S. E. (2008). Power and accuracy for omnibus and targeted effects: Issues of sample size planning with applications to multiple regression. In P. Alasuuta, J. Brannen, & L. Bickman (Eds.), Handbook of Social Research Methods (pp. 166--192). Newbury Park, CA: Sage.
ci.R2, conf.limits.nct, ss.aipe.R2.sensitivity ## Not run:
# # Returned sample size should be considered approximate; exact sample
# # size is obtained by specifying the argument 'verify.ss=TRUE' (see below).
# # ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# # p=5, Random.Predictors=TRUE)
# # Uncomment to run in order to get exact sample size.
# # ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# # p=5, Random.Predictors=TRUE, verify.ss=TRUE)
#
#
# # Same as above, except the predictor variables are considered fixed.
# # Returned sample size should be considered approximate; exact sample
# # size is obtained by specifying the argument 'verify.ss=TRUE'.
# # ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# # p=5, Random.Predictors=FALSE)
# # Uncomment to run in order to get exact sample size.
# #ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# #p=5, Random.Predictors=FALSE, verify.ss=TRUE)
#
#
# # Returned sample size should be considered approximate; exact sample
# # size is obtained by specifying the argument 'verify.ss=TRUE'.
# # ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# # p=5, degree.of.certainty=.85, Random.Predictors=TRUE)
# # Uncomment to run in order to get exact sample size.
# #ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# #p=5, degree.of.certainty=.85, Random.Predictors=TRUE, verify.ss=TRUE)
#
#
# # Same as above, except the predictor variables are considered fixed.
# # Returned sample size should be considered approximate; exact sample
# # size is obtained by specifying the argument 'verify.ss=TRUE'.
# # ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# # p=5, degree.of.certainty=.85, Random.Predictors=FALSE)
# # Uncomment to run in order to get exact sample size.
# #ss.aipe.R2(Population.R2=.50, conf.level=.95, width=.10, which.width="Full",
# #p=5, degree.of.certainty=.85, Random.Predictors=FALSE, verify.ss=TRUE)
# ## End(Not run)
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