find.moments: Compute Statistical Moments

In mpascariu/MortalityForecast: Standard Tools to Compare and Evaluate Various Mortality Forecasting Methods

Description

Compute raw and central statistical moments of a distribution of deaths.

Usage

find.moments(data, x, y = NULL, n, na.rm = TRUE)

Arguments

data	A data.frame or matrix containing 3 columns: Year, Age, Dx.
x	Vector of ages.
y	Numerical vector indicating the years in input data. Optional. Default: NULL.
n	The maximum order of the moments to be computed. The order should be at least 2.
na.rm	Logical value. If TRUE, remove NA values. Otherwise, keep NA values.

Details

If f(x) is a probability density function, then μ(n) is called the n-th moment of the probability distribution, where:

μ(n) = \int (x-c)^n f(x)dx.

Value

An object containing:

• central.moments — Moments about the mean (c = mean). The zeroth moment is the total probability (i.e. one), the first moment is zero, the second central moment is the variance, the third central moment is the skewness (with normalization), and the fourth central moment is the kurtosis (with normalization).

• raw.moments — Moments about zero (c = 0). The first moment is the mean.

• normalized.moments — Normalized moments.

See Also

convert.moments all.moments raw2central

Examples

x  <- 0:110
y  <- 1960:2016
dx <- HMD_male$dx$GBRTENW[paste(x), paste(y)]
find.moments(data = dx, x = x, y = y, n = 4)

mpascariu/MortalityForecast documentation built on Sept. 28, 2020, 2:40 p.m.