Computes variance of \(Y\) at ego level
ego_variance(graph, Y, funname, all = FALSE)A numeric vector of length \(n\).
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).
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.
struct_test
Other statistics:
bass,
classify_adopters(),
cumulative_adopt_count(),
dgr(),
exposure(),
hazard_rate(),
infection(),
moran(),
struct_equiv(),
threshold(),
vertex_covariate_dist()