ci.cv {MBESS} | R Documentation |
Function to calculate the confidence interval for the population coefficient of variation using the noncentral t
-distribution.
ci.cv(cv=NULL, mean = NULL, sd = NULL, n = NULL, data = NULL, conf.level = 0.95, alpha.lower = NULL, alpha.upper = NULL, ...)
cv |
coefficient of variation |
mean |
sample mean |
sd |
sample standard deviation (square root of the unbiased estimate of the variance) |
n |
sample size |
data |
vector of data for which the confidence interval for the coefficient of variation is to be calculated |
conf.level |
desired confidence level (1-Type I error rate) |
alpha.lower |
the proportion of values beyond the lower limit of the confidence interval (cannot be used with conf.level ). |
alpha.upper |
the proportion of values beyond the upper limit of the confidence interval (cannot be used with conf.level ). |
... |
allows one to potentially include parameter values for inner functions |
Uses the noncentral t
-distribution to calculate the confidence interval for the population coefficient of variation.
Lower.Limit.CofV |
Lower confidence interval limit |
Prob.Less.Lower |
Proportion of the distribution beyond Lower.Limit.CofV |
Upper.Limit.CofV |
Upper confidence interval limit |
Prob.Greater.Upper |
Proportion of the distribution beyond Upper.Limit.CofV |
C.of.V |
Observed coefficient of variation |
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
Johnson, B. L., & Welch, B. L. (1940). Applications of the non-central t-distribution. Biometrika, 31, 362–389.
Kelley, K. (2007). Sample size planning for the coefcient of variation from the accuracy in parameter estimation approach. Behavior Research Methods, 39 (4), 755-766.
Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1-24.
McKay, A. T. (1932). Distribution of the coefficient of variation and the extended t distribution, Journal of the Royal Statistical Society, 95, 695–698.
set.seed(113) N <- 15 X <- rnorm(N, 5, 1) mean.X <- mean(X) sd.X <- var(X)^.5 ci.cv(mean=mean.X, sd=sd.X, n=N, alpha.lower=.025, alpha.upper=.025, conf.level=NULL) ci.cv(data=X, conf.level=.95) ci.cv(cv=sd.X/mean.X, n=N, conf.level=.95)