ego_variance: Computes variance of Y at ego level

In netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

Computes variance of Y at ego level

Description

Computes variance of Y at ego level

Usage

ego_variance(graph, Y, funname, all = FALSE)

Arguments

graph	A matrix of size n\times n of class dgCMatrix.
Y	A numeric vector of length n.
funname	Character scalar. Comparison to make (see vertex_covariate_compare).
all	Logical scalar. When FALSE (default) f_i is mean at ego level. Otherwise is fix for all i (see details).

Details

For each vertex i the variance is computed as follows

% (\sum_j a_{ij})^{-1}\sum_j a_{ij} \left[f(y_i,y_j) - f_i\right]^2

Where a_{ij} is the ij-th element of graph, f is the function specified in funname, and, if all=FALSE f_i = \sum_j a_{ij}f(y_i,y_j)^2/\sum_ja_{ij}, otherwise f_i = f_j = \frac{1}{n^2}\sum_{i,j}f(y_i,y_j)

This is an auxiliary function for struct_test. The idea is to compute an adjusted measure of disimilarity between vertices, so the closest in terms of f is i to its neighbors, the smaller the relative variance.

Value

A numeric vector of length n.

See Also

struct_test

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), dgr(), exposure(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

netdiffuseR documentation built on Aug. 30, 2023, 5:07 p.m.