View source: R/geometric_mean_sd.R
gmean | R Documentation |
Return the geometric mean, variance, and standard deviation,
gmean(x, na_rm = FALSE)
gvar(x, na_rm = FALSE)
gsd(x, na_rm = FALSE)
x |
a numeric vector |
na_rm |
a logical value indicating whether |
a numeric value
gmean_sd
for easy formatting of the geometric mean and
standard deviation. vignette("summary-statistics", package =
"qwraps2")
.
gmean(mtcars$mpg)
identical(gmean(mtcars$mpg), exp(mean(log(mtcars$mpg))))
gvar(mtcars$mpg)
identical(gvar(mtcars$mpg),
exp(var(log(mtcars$mpg)) * (nrow(mtcars) - 1) / nrow(mtcars)))
gsd(mtcars$mpg)
identical(gsd(mtcars$mpg),
exp(sqrt( var(log(mtcars$mpg)) * (nrow(mtcars) - 1) / nrow(mtcars))))
#############################################################################
set.seed(42)
x <- runif(14, min = 4, max = 70)
# geometric mean - four equivalent ways to get the same result
prod(x) ^ (1 / length(x))
exp(mean(log(x)))
1.2 ^ mean(log(x, base = 1.2))
gmean(x)
# geometric variance
gvar(x)
# geometric sd
exp(sd(log(x))) ## This is wrong (incorrect sample size)
exp(sqrt((length(x) - 1) / length(x)) * sd(log(x))) ## Correct calculation
gsd(x)
# Missing data will result in and NA being returned
x[c(2, 4, 7)] <- NA
gmean(x)
gmean(x, na_rm = TRUE)
gvar(x, na_rm = TRUE)
gsd(x, na_rm = TRUE)
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