w.cov: weighted covariance

In Weighted.Desc.Stat: Weighted Descriptive Statistics

Description

Assume that x=(x_1, x_2, \cdots , x_n) is the observed value of a random sample from a fuzzy population. In classical and usual random sample, the degree of belonging x_i into the random sample is equal to 1, for 1 ≤q i ≤q n. But considering fuzzy population, we denote the degree of belonging x_i into the fuzzy population (or into the observed value of random sample) by μ_i which is a real-valued number from [0,1]. Therefore in such situations, it is more appropriate that we show the observed value of the random sample by notation \{ (x_1, μ_1), (x_2, μ_2), \cdots , (x_n, μ_n) \} which we called it real-valued fuzzy data. The goal of w.cov function is computing covariance (or, the weighted covariance) between two vector-valued data sets x_1, \cdots , x_n and y_1, \cdots , y_n based on real-valued fuzzy data \{ (x_1, μ_1), \cdots , (x_n, μ_n) \} and \{ (y_1, μ_1), \cdots , (y_n, μ_n) \} by considering their vector-valued weights, i.e.

s_{xy} = \frac{1}{∑_{i=1}^{n} μ_i} ∑_{i=1}^{n} μ_i (x_i-\bar{x})( y_i -\bar{y}).

Usage

w.cov(x, y, mu)

Arguments

x, y	Two vector-valued numeric data sets which you want to compute the weighted covariance between them.
mu	A vector of weights. The length of this vector must be equal to the length of data and each element of it is belongs to interval [0,1].

Value

The weighted covariance between two vectors x and y, by considering weights vector mu, is numeric or a vector of length one.

Warning

The length of x, y and mu must be equal. Also, each element of mu must be in interval [0,1].

Author(s)

Abbas Parchami

Examples

x <- c(1:10)
y <- c(10:1)
mu <- c(0.9, 0.7, 0.8, 0.7, 0.6, 0.4, 0.2, 0.3, 0.0, 0.1)
w.cov(x, y, mu)

## The function is currently defined as
function(x, y, mu)  (sum(mu*x*y)/sum(mu)) - (w.mean(x,mu) * w.mean(y,mu))

Weighted.Desc.Stat documentation built on May 1, 2019, 7:06 p.m.