conf.limits.ncf: Confidence limits for noncentral F parameters

In MBESS: The MBESS R Package

Confidence limits for noncentral F parameters

Description

Function to determine the noncentral parameter that leads to the observed F-value, so that a confidence interval around the population F-value can be conducted. Used for forming confidence intervals around noncentral parameters (given the monotonic relationship between the F-value and the noncentral value).

Usage

conf.limits.ncf(F.value = NULL, conf.level = .95, df.1 = NULL, 
df.2 = NULL, alpha.lower = NULL, alpha.upper = NULL, tol = 1e-09,
Jumping.Prop = 0.1)

Arguments

F.value	the observed F-value
conf.level	the desired degree of confidence for the interval
df.1	the numerator degrees of freedom
df.2	the denominator degrees of freedom
alpha.lower	Type I error for the lower confidence limit
alpha.upper	Type I error for the upper confidence limit
tol	tolerance for iterative convergence
Jumping.Prop	Value used in the iterative scheme to determine the noncentral parameters necessary for confidence interval construction using noncentral F-distributions (0 < Jumping.Prop < 1) (users should not need to change this value)

Details

This function is the relied upon by the ci.R2 and ss.aipe.R2. If the function fails (or if a function relying upon this function fails), adjust the Jumping.Prop (to a smaller value).

Value

Lower.Limit	Value of the distribution with Lower.Limit noncentral value that has at its specified quantile F.value
Prob.Less.Lower	Proportion of cases falling below Lower.Limit
Upper.Limit	Value of the distribution with Upper.Limit noncentral value that has at its specified quantile F.value
Prob.Greater.Upper	Proportion of cases falling above Upper.Limit

Author(s)

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

See Also

ss.aipe.R2, ci.R2, conf.limits.nct

Examples

conf.limits.ncf(F.value = 5, conf.level = .95, df.1 = 5, 
df.2 = 100)

# A one sided confidence interval.
conf.limits.ncf(F.value = 5, conf.level = NULL, df.1 = 5, 
df.2 = 100, alpha.lower = .05, alpha.upper = 0, tol = 1e-09,
Jumping.Prop = 0.1)

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