DMAR (version 1.0.0)

ss_aipe_R2: Sample Size Planning for Accuracy in Parameter Estimation for the Multiple Correlation Coefficient

Description

Determines necessary sample size for the multiple correlation coefficient so that the confidence interval for the population multiple correlation coefficient is sufficiently narrow. Optionally, there is a certainty parameter that allows one to be a specified percent certain that the observed interval will be no wider than desired.

Usage

ss_aipe_R2(
  population_R2 = NULL,
  conf_level = 0.95,
  width = NULL,
  random_predictors = TRUE,
  which_width = "Full",
  p = NULL,
  assurance = NULL,
  verify_ss = FALSE,
  tol = 1e-09,
  ...
)

Value

A 1-row data.frame with columns term and value. The term value is "necessary_N" and value is the necessary total sample size N given the input specifications.

Arguments

population_R2

Value of the population multiple correlation coefficient

conf_level

Confidence interval level (e.g., .95, .99, .90); 1-Type I error rate

width

Width of the confidence interval (see which_width)

random_predictors

Whether or not the predictor variables are random (set to TRUE) or are fixed (set to FALSE)

which_width

Defines the width that width refers to

p

The number of predictor variables

assurance

Value with which confidence can be placed that describes the likelihood of obtaining a confidence interval less than the value specified (e.g, .80, .90, .95)

verify_ss

Evaluates numerically via an internal Monte Carlo simulation the exact sample size given the specifications

tol

The tolerance of the iterative function ci_nc_t for convergence

...

For modifying the parameters of functions this function calls upon

Author

Ken Kelley [email protected]

Details

This function determines a necessary sample size so that the expected confidence interval width for the squared multiple correlation coefficient is sufficiently narrow (when assurance=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 (2008) 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. Two of its arguments pass through ...: g (default 500), the number of replications used for each candidate N in the coarse search that brackets the answer, and G (default 10000), the number used in the final pass that confirms the sample size near that bracket.

References

Algina, J. & Olejnik, S. (2000). Determining sample size for accurate estimation of the squared multiple correlation coefficient. Multivariate Behavioral Research, 35, 119--137. tools:::Rd_expr_doi("10.1207/s15327906mbr3501_5")

Kelley, K. (2007). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1--24. tools:::Rd_expr_doi("10.18637/jss.v020.i08")

Kelley, K. (2008). Sample size planning for the squared multiple correlation coefficient: Accuracy in parameter estimation via narrow confidence intervals. Multivariate Behavioral Research, 43, 524--555. tools:::Rd_expr_doi("10.1080/00273170802490632")

Kelley, K., & Maxwell, S. E. (2008). Sample size planning with applications to multiple regression: Power and accuracy for omnibus and targeted effects. In P. Alasuutari, L. Bickman, & J. Brannen (Eds.), The Sage handbook of social research methods (pp. 166--192). Sage.

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge. (See Chapter 3 on \(R^2\) as a model comparison effect size.)

Steiger, J. H., & Fouladi, R. T. (1992). R2: A computer program for interval estimation, power calculations, sample size estimation, and hypothesis testing in multiple regression. Behavior Research Methods, Instruments, & Computers, 24(4), 581--582. tools:::Rd_expr_doi("10.3758/BF03203611")

See Also

ci_R2, ci_nc_t, ss_aipe_R2_sensitivity

design_consequences for what a chosen design delivers: power, the Type S (sign) and Type M (exaggeration) errors of the significance filter, and the expected confidence interval width.

Examples

# 1. Closed form planner under random predictors (the typical case).
#    Sample size sufficient for the expected CI width on rho^2 to be .10.
ss_aipe_R2(population_R2 = .50, conf_level = .95, width = .10,
           which_width = "Full", p = 5, random_predictors = TRUE)

# 2. The same target under fixed predictors (planned dosing levels,
#    factorial covariates, and the like) needs a smaller N, since fixed
#    predictors contribute no sampling variability of their own.
ss_aipe_R2(population_R2 = .50, conf_level = .95, width = .10,
           which_width = "Full", p = 5, random_predictors = FALSE)

# 3. An assurance of .85, so that the realized width is no larger than the
#    target in 85 percent of replications rather than only on average,
#    needs a larger N than the expected width plan in (1).
ss_aipe_R2(population_R2 = .50, conf_level = .95, width = .10,
           which_width = "Full", p = 5, assurance = .85,
           random_predictors = TRUE)

# 4. verify_ss = TRUE follows the closed form approximation with an a
#    priori Monte Carlo simulation of the realized width, starting from
#    the closed form answer and returning the sample size the simulation
#    settles on, which is what a plan meant to be defended deserves. The
#    coarse search runs g replications per candidate N and the final pass
#    runs G; the small counts here keep the example quick, and a reported
#    plan deserves the defaults of g = 500 and G = 10000.
set.seed(113)
ss_aipe_R2(population_R2 = .50, conf_level = .95, width = .10,
           which_width = "Full", p = 5, random_predictors = TRUE,
           verify_ss = TRUE, g = 10, G = 30)