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).
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)
Value of the distribution with Lower.Limit noncentral value that has at its specified quantile F.value
Proportion of cases falling below Lower.Limit
Value of the distribution with Upper.Limit noncentral value that has at its specified quantile F.value
Proportion of cases falling above Upper.Limit
the observed F-value
the desired degree of confidence for the interval
the numerator degrees of freedom
the denominator degrees of freedom
Type I error for the lower confidence limit
Type I error for the upper confidence limit
tolerance for iterative convergence
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)
Ken Kelley (University of Notre Dame; KKelley@ND.Edu); Keke Lai (University of Califonia-Merced)
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).
ss.aipe.R2, ci.R2, conf.limits.nct
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)
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