ss.aipe.sc.sensitivity: Sensitivity analysis for sample size planning for the...

In MBESS: The MBESS R Package

Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspective

Description

Performs a sensitivity analysis when planning sample size from the Accuracy in Parameter Estimation (AIPE) Perspective for the standardized ANOVA contrast.

Usage

ss.aipe.sc.sensitivity(true.psi = NULL, estimated.psi = NULL, c.weights, 
desired.width = NULL, selected.n = NULL, assurance = NULL, certainty=NULL, 
conf.level = 0.95, G = 10000, print.iter = TRUE, detail = TRUE, ...)

Arguments

true.psi	population standardized contrast
estimated.psi	estimated standardized contrast
c.weights	the contrast weights
desired.width	the desired full width of the obtained confidence interval
selected.n	selected sample size to use in order to determine distributional properties of at a given value of sample size
assurance	parameter to ensure that the obtained confidence interval width is narrower than the desired width with a specified degree of certainty (must be NULL or between zero and unity)
certainty	an alias for assurance
conf.level	the desired confidence interval coverage, (i.e., 1 - Type I error rate)
G	number of generations (i.e., replications) of the simulation
print.iter	to print the current value of the iterations
detail	whether the user needs a detailed (TRUE) or brief (FALSE) report of the simulation results; the detailed report includes all the raw data in the simulations
...	allows one to potentially include parameter values for inner functions

Value

psi.obs	observed standardized contrast in each iteration
Full.Width	vector of the full confidence interval width
Width.from.psi.obs.Lower	vector of the lower confidence interval width
Width.from.psi.obs.Upper	vector of the upper confidence interval width
Type.I.Error.Upper	iterations where a Type I error occurred on the upper end of the confidence interval
Type.I.Error.Lower	iterations where a Type I error occurred on the lower end of the confidence interval
Type.I.Error	iterations where a Type I error happens
Lower.Limit	the lower limit of the obtained confidence interval
Upper.Limit	the upper limit of the obtained confidence interval
replications	number of replications of the simulation
True.psi	population standardized contrast
Estimated.psi	estimated standardized contrast
Desired.Width	the desired full width of the obtained confidence interval
assurance	the value assigned to the argument assurance
Sample.Size.per.Group	sample size per group
Number.of.Groups	number of groups
mean.full.width	mean width of the obtained full conficence intervals
median.full.width	median width of the obtained full confidence intervals
sd.full.width	standard deviation of the widths of the obtained full confidence intervals
Pct.Width.obs.NARROWER.than.desired	percentage of the obtained full confidence interval widths that are narrower than the desired width
mean.Width.from.psi.obs.Lower	mean lower width of the obtained confidence intervals
mean.Width.from.psi.obs.Upper	mean upper width of the obtained confidence intervals
Type.I.Error.Upper	Type I error rate from the upper side
Type.I.Error.Lower	Type I error rate from the lower side

Author(s)

Ken Kelley (University of Notre Dame; [e-mail]); Keke Lai (University of California – Merced)

References

Cumming, G. & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions, Educational and Psychological Measurement, 61, 532–574.

Hedges, L. V. (1981). Distribution theory for Glass's Estimator of effect size and related estimators. Journal of Educational Statistics, 2, 107–128.

Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1–24.

Kelley, K., & Rausch, J. R. (2006). Sample size planning for the standardized mean difference: Accuracy in Parameter Estimation via narrow confidence intervals. Psychological Methods, 11 (4), 363–385.

Lai, K., & Kelley, K. (2007). Sample size planning for standardized ANCOVA and ANOVA contrasts: Obtaining narrow confidence intervals. Manuscript submitted for publication.

Steiger, J. H., & Fouladi, R. T. (1997). Noncentrality interval estimation and the evaluation of statistical methods. In L. L. Harlow, S. A. Mulaik, & J.H. Steiger (Eds.), What if there where no significance tests? (pp. 221–257). Mahwah, NJ: Lawrence Erlbaum.

See Also

ss.aipe.sc, ss.aipe.c, conf.limits.nct

MBESS documentation built on Oct. 26, 2023, 9:07 a.m.