MBESS (version 4.1.0)

ss.aipe.cv: Sample size planning for the coefficient of variation given the goal of Accuracy in Parameter Estimation approach to sample size planning

Description

Determines the necessary sample size so that the expected confidence interval width for the coefficient of variation will be sufficiently narrow, optionally with a desired degree of certainty that the interval will not be wider than desired.

Usage

ss.aipe.cv(C.of.V = NULL, width = NULL, conf.level = 0.95, 
degree.of.certainty = NULL, assurance=NULL, certainty=NULL, 
mu = NULL, sigma = NULL, alpha.lower = NULL, alpha.upper = NULL, 
Suppress.Statement = TRUE, sup.int.warns = TRUE, ...)

Arguments

C.of.V

population coefficient of variation on which the sample size procedure is based

width

desired (full) width of the confidence interval

conf.level

confidence interval coverage; 1-Type I error rate

degree.of.certainty

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)

assurance

an alias for degree.of.certainty

certainty

an alias for degree.of.certainty

mu

population mean (specified with sigma when C.of.V is not specified)

sigma

population standard deviation (specified with mu when C.of.V) is not specified)

alpha.lower

Type I error for the lower confidence limit

alpha.upper

Type I error for the upper confidence limit

Suppress.Statement

Suppress a message restating the input specifications

sup.int.warns

suppress internal function warnings (e.g., warnings associated with qt)

...

for modifying parameters of functions this function calls

Value

See Also

ss.aipe.cv.sensitivity, cv

Examples

# Suppose one wishes to have a confidence interval with an expected width of .10 
# for a 99% confidence interval when the population coefficient of variation is .25.
ss.aipe.cv(C.of.V=.1, width=.1, conf.level=.99)

# Ensuring that the confidence interval will be sufficiently narrow with a 99% 
# certainty for the situation above.
ss.aipe.cv(C.of.V=.1, width=.1, conf.level=.99, degree.of.certainty=.99)