moments: Moments

In metinbulus/cosa: Bound Constrained Optimal Sample Size Allocation

Description

If data (vector) is provided use emp.moment() function, otherwise for truncated normal distribution use tnorm.moment(), and for uniform distribution use unif.moment().

Usage

  tnorm.moment(mu = 0, sigma = 1, k1 = -Inf, k2 = Inf, order = 1, central = FALSE)
  unif.moment(k1 = 0, k2 = 1, order = 1, central = FALSE)
  emp.moment(x, order = 1, central = FALSE, absolute = FALSE, na.rm = FALSE)

Arguments

mu	mean of truncated normal - applies to tnorm.moment().
sigma	standard deviation of truncated normal - applies to tnorm.moment().
k1	left truncation point for truncated normal distribution or lower bound for uniform distribution.
k2	right truncation point for truncated normal distribution or upper bound for uniform distribution.
order	+ int; order of moment
x	a vector of values - applies to emp.moment().
central	logical; if TRUE produces central moments.
absolute	logical; if TRUE produces absolute moments - applies to emp.moment().
na.rm	logical; if TRUE removes missing values - applies to emp.moment().

Examples

tnorm.moment(k1 = -20, k2 = 20, order = 4, central = FALSE)
emp.moment(rnorm(10000), order = 4, central = FALSE)
unif.moment(k1 = 0, k2 = 1, order = 4, central = FALSE)
emp.moment(runif(10000), order = 4, central = FALSE)

metinbulus/cosa documentation built on Sept. 9, 2021, 12:04 p.m.