Computes variance of \(Y\) at ego level
ego_variance(graph, Y, funname, all = FALSE)A matrix of size \(n\times n\) of class dgCMatrix.
A numeric vector of length \(n\).
Character scalar. Comparison to make (see vertex_covariate_compare).
Logical scalar. When FALSE (default) \(f_i\) is mean at
ego level. Otherwise is fix for all i (see details).
A numeric vector of length \(n\).
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.
Other statistics:
bass,
classify_adopters(),
cumulative_adopt_count(),
dgr(),
exposure(),
hazard_rate(),
infection(),
moran(),
struct_equiv(),
threshold(),
vertex_covariate_dist()