A function used to plan sample size from the accuracy in parameter estimation approach for a standardized regression coefficient of interest given the input specification.
1 2 3 4 5 6  ss.aipe.src(Rho2.Y_X = NULL, Rho2.k_X.without.k = NULL, K = NULL,
beta.k = NULL, width, which.width = "Full", sigma.Y = 1, sigma.X.k = 1,
RHO.XX = NULL, Rho.YX = NULL, which.predictor = NULL,
alpha.lower = NULL, alpha.upper = NULL, conf.level = .95,
degree.of.certainty = NULL, assurance=NULL, certainty=NULL,
Suppress.Statement = FALSE)

Rho2.Y_X 
Population value of the squared multiple correlation coefficient 
Rho2.k_X.without.k 
Population value of the squared multiple correlation coefficient predicting the kth predictor variable from the remaining p1 predictor variables 
K 
the number of predictor variables 
beta.k 
the regression coefficient for the kth predictor variable (i.e., the predictor of interest) 
width 
the desired width of the confidence interval 
which.width 
which width ( 
sigma.Y 
the population standard deviation of Y (i.e., the dependent variables) 
sigma.X.k 
the population standard deviation of the kth X variable (i.e., the predictor variable of interest) 
RHO.XX 
Population correlation matrix for the p predictor variables 
Rho.YX 
Population p length vector of correlation between the dependent variable (Y) and the p independent variables 
which.predictor 
identifies which of the p predictors is of interest 
alpha.lower 
Type I error rate for the lower confidence interval limit 
alpha.upper 
Type I error rate for the upper confidence interval limit 
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, which
yields an approximate sample size to be verified with function 
assurance 
an alias for 
certainty 
an alias for 
Suppress.Statement 

Not all of the arguments need to be specified, only those that provide all of the necessary information so that the sample size can be determined for the conditions specified.
Returns the necessary sample size in order for the goals of accuracy in parameter estimation to be satisfied for the confidence interval for a particular regression coefficient given the input specifications.
As discussed in Kelley and Maxwell (2008), the sample size planning approach from the AIPE perspective used in this function is only an approximation.
This function calls upon ss.aipe.reg.coef
in MBESS but has a different naming
scheme. See ss.aipe.reg.coef
for more details.
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
Kelley, K. & Maxwell, S. E. (2003). Sample size for Multiple Regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8, 305–321.
Kelley, K. & Maxwell, S. E. (2008). Sample Size Planning with applications to multiple regression: Power and accuracy for omnibus and targeted effects. In P. Alasuuta, J. Brannen, & L. Bickman (Eds.), The Sage handbook of social research methods (pp. 166–192). Newbury Park, CA: Sage.
ss.aipe.reg.coef.sensitivity
, conf.limits.nct
,
ss.aipe.reg.coef
, ss.aipe.rc
1 2 3 4 5 6 7 8 9 10  # Exchangable correlation structure
# Rho.YX < c(.3, .3, .3, .3, .3)
# RHO.XX < rbind(c(1, .5, .5, .5, .5), c(.5, 1, .5, .5, .5), c(.5, .5, 1, .5, .5),
# c(.5, .5, .5, 1, .5), c(.5, .5, .5, .5, 1))
# ss.aipe.src(width=.1, which.width="Full", sigma.Y=1, sigma.X=1, RHO.XX=RHO.XX,
# Rho.YX=Rho.YX, which.predictor=1, conf.level=1.05)
# ss.aipe.src(width=.1, which.width="Full", sigma.Y=1, sigma.X=1, RHO.XX=RHO.XX,
# Rho.YX=Rho.YX, which.predictor=1, conf.level=1.05, degree.of.certainty=.85)

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