DescTools (version 0.99.19)

Gmean: Geometric Mean and Standard Deviation

Description

Calculates the geometric mean, its confidence interval and the geometric standard deviation of a vector x.

Usage

Gmean(x, method = c("classic", "boot"), conf.level = NA, sides = c("two.sided","left","right"), na.rm = FALSE, ...)
Gsd(x, na.rm = FALSE)

Arguments

x

a positive numeric vector. An object which is not a vector is coerced (if possible) by as.vector.

method

a vector of character strings representing the type of intervals required. The value should be any subset of the values "classic", "boot". See boot.ci.

conf.level

confidence level of the interval. Default is NA.

sides

a character string specifying the side of the confidence interval, must be one of "two.sided" (default), "left" or "right". You can specify just the initial letter. "left" would be analogue to a hypothesis of "greater" in a t.test.

na.rm

logical, indicating whether NA values should be stripped before the computation proceeds. Defaults to FALSE.

...

further arguments are passed to the boot function. Supported arguments are type ("norm", "basic", "stud", "perc", "bca"), parallel and the number of bootstrap replicates R. If not defined those will be set to their defaults, being "basic" for type, option "boot.parallel" (and if that is not set, "no") for parallel and 999 for R.

Value

Details

To compute the geometric mean, log(x) is first calculated, before the arithmetic mean and its confidence interval are computed by MeanCI.

So the geometric mean and geometric sd are restricted to positive inputs (because otherwise the answer can have an imaginary component). Hence if any argument is negative, the result will be NA. If any argument is zero, then the geometric mean is zero.

The geometric mean is defined as $$\sqrt[n]{x_{1}\cdot x_{2}\cdot x_{3} \ldots \cdot x_{n}}$$

and its confidence interval is given as exp(MeanCI(log(x))). Use sapply to calculate the measures from data frame, resp. from a matrix.

References

Snedecor, G. W., Cochran, W. G. Cochran (1989) Statistical Methods, 8th ed. Ames, IA: Iowa State University Press

See Also

mean, Hmean

Examples

x <- runif(5)
Gmean(x)

m <- matrix(runif(50), nrow = 10)
apply(m, 2, Gmean)

sapply(as.data.frame(m), Gmean)