This function computes the sample Fréchet mean from an observed sample of network-valued random variables according to a specified matrix representation. It currently only supports the Euclidean geometry i.e. the sample Fréchet mean is obtained as the argmin of the sum of squared Frobenius distances.
Arguments
- x
An
nvdobject.- weights
A numeric vector specifying weights for each observation (default: equally weighted).
- representation
A string specifying the graph representation to be used. Choices are adjacency, laplacian, modularity, graphon. Default is adjacency.
- ...
Other argument to be parsed to the
meanfunction.
Examples
gnp_params <- list(p = 1/3)
x <- nvd(model = "gnp", n = 10L, model_params = gnp_params)
mean(x)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 0.0 0.4 0.2 0.5 0.5 0.3 0.4 0.2 0.4 0.5 0.4 0.3 0.4
#> [2,] 0.4 0.0 0.4 0.2 0.4 0.3 0.3 0.0 0.3 0.5 0.2 0.5 0.5
#> [3,] 0.2 0.4 0.0 0.4 0.5 0.6 0.4 0.3 0.1 0.4 0.3 0.5 0.2
#> [4,] 0.5 0.2 0.4 0.0 0.4 0.5 0.1 0.7 0.3 0.3 0.3 0.2 0.3
#> [5,] 0.5 0.4 0.5 0.4 0.0 0.5 0.3 0.3 0.3 0.3 0.3 0.2 0.4
#> [6,] 0.3 0.3 0.6 0.5 0.5 0.0 0.1 0.3 0.3 0.4 0.4 0.6 0.3
#> [7,] 0.4 0.3 0.4 0.1 0.3 0.1 0.0 0.2 0.3 0.1 0.4 0.5 0.2
#> [8,] 0.2 0.0 0.3 0.7 0.3 0.3 0.2 0.0 0.3 0.1 0.5 0.3 0.4
#> [9,] 0.4 0.3 0.1 0.3 0.3 0.3 0.3 0.3 0.0 0.4 0.5 0.4 0.2
#> [10,] 0.5 0.5 0.4 0.3 0.3 0.4 0.1 0.1 0.4 0.0 0.3 0.3 0.5
#> [11,] 0.4 0.2 0.3 0.3 0.3 0.4 0.4 0.5 0.5 0.3 0.0 0.2 0.4
#> [12,] 0.3 0.5 0.5 0.2 0.2 0.6 0.5 0.3 0.4 0.3 0.2 0.0 0.2
#> [13,] 0.4 0.5 0.2 0.3 0.4 0.3 0.2 0.4 0.2 0.5 0.4 0.2 0.0
#> [14,] 0.3 0.4 0.2 0.5 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.3 0.3
#> [15,] 0.6 0.2 0.3 0.3 0.3 0.5 0.3 0.4 0.5 0.7 0.4 0.1 0.4
#> [16,] 0.5 0.2 0.2 0.5 0.3 0.2 0.0 0.4 0.1 0.3 0.2 0.5 0.2
#> [17,] 0.2 0.3 0.3 0.6 0.2 0.3 0.4 0.2 0.4 0.4 0.4 0.1 0.6
#> [18,] 0.5 0.2 0.2 0.4 0.3 0.5 0.1 0.5 0.1 0.6 0.5 0.3 0.4
#> [19,] 0.3 0.3 0.3 0.1 0.3 0.4 0.0 0.4 0.4 0.2 0.5 0.4 0.4
#> [20,] 0.4 0.4 0.2 0.3 0.6 0.3 0.5 0.5 0.6 0.2 0.4 0.5 0.3
#> [21,] 0.4 0.5 0.6 0.3 0.0 0.2 0.6 0.1 0.3 0.0 0.4 0.3 0.3
#> [22,] 0.4 0.3 0.3 0.2 0.3 0.2 0.3 0.5 0.3 0.3 0.5 0.6 0.0
#> [23,] 0.5 0.2 0.3 0.4 0.2 0.6 0.3 0.5 0.3 0.4 0.2 0.3 0.1
#> [24,] 0.2 0.1 0.2 0.6 0.5 0.3 0.5 0.3 0.5 0.3 0.2 0.4 0.1
#> [25,] 0.1 0.6 0.3 0.2 0.4 0.3 0.4 0.3 0.3 0.3 0.4 0.2 0.3
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 0.3 0.6 0.5 0.2 0.5 0.3 0.4 0.4 0.4 0.5 0.2 0.1
#> [2,] 0.4 0.2 0.2 0.3 0.2 0.3 0.4 0.5 0.3 0.2 0.1 0.6
#> [3,] 0.2 0.3 0.2 0.3 0.2 0.3 0.2 0.6 0.3 0.3 0.2 0.3
#> [4,] 0.5 0.3 0.5 0.6 0.4 0.1 0.3 0.3 0.2 0.4 0.6 0.2
#> [5,] 0.3 0.3 0.3 0.2 0.3 0.3 0.6 0.0 0.3 0.2 0.5 0.4
#> [6,] 0.4 0.5 0.2 0.3 0.5 0.4 0.3 0.2 0.2 0.6 0.3 0.3
#> [7,] 0.3 0.3 0.0 0.4 0.1 0.0 0.5 0.6 0.3 0.3 0.5 0.4
#> [8,] 0.3 0.4 0.4 0.2 0.5 0.4 0.5 0.1 0.5 0.5 0.3 0.3
#> [9,] 0.4 0.5 0.1 0.4 0.1 0.4 0.6 0.3 0.3 0.3 0.5 0.3
#> [10,] 0.2 0.7 0.3 0.4 0.6 0.2 0.2 0.0 0.3 0.4 0.3 0.3
#> [11,] 0.2 0.4 0.2 0.4 0.5 0.5 0.4 0.4 0.5 0.2 0.2 0.4
#> [12,] 0.3 0.1 0.5 0.1 0.3 0.4 0.5 0.3 0.6 0.3 0.4 0.2
#> [13,] 0.3 0.4 0.2 0.6 0.4 0.4 0.3 0.3 0.0 0.1 0.1 0.3
#> [14,] 0.0 0.4 0.1 0.2 0.2 0.1 0.2 0.1 0.4 0.4 0.2 0.4
#> [15,] 0.4 0.0 0.2 0.5 0.3 0.5 0.6 0.3 0.3 0.5 0.1 0.2
#> [16,] 0.1 0.2 0.0 0.4 0.5 0.5 0.3 0.5 0.3 0.2 0.4 0.1
#> [17,] 0.2 0.5 0.4 0.0 0.4 0.2 0.1 0.1 0.6 0.5 0.4 0.3
#> [18,] 0.2 0.3 0.5 0.4 0.0 0.3 0.2 0.3 0.4 0.2 0.2 0.3
#> [19,] 0.1 0.5 0.5 0.2 0.3 0.0 0.5 0.4 0.5 0.1 0.4 0.1
#> [20,] 0.2 0.6 0.3 0.1 0.2 0.5 0.0 0.5 0.4 0.2 0.5 0.1
#> [21,] 0.1 0.3 0.5 0.1 0.3 0.4 0.5 0.0 0.4 0.4 0.4 0.4
#> [22,] 0.4 0.3 0.3 0.6 0.4 0.5 0.4 0.4 0.0 0.2 0.1 0.5
#> [23,] 0.4 0.5 0.2 0.5 0.2 0.1 0.2 0.4 0.2 0.0 0.2 0.3
#> [24,] 0.2 0.1 0.4 0.4 0.2 0.4 0.5 0.4 0.1 0.2 0.0 0.3
#> [25,] 0.4 0.2 0.1 0.3 0.3 0.1 0.1 0.4 0.5 0.3 0.3 0.0
#> attr(,"representation")
#> [1] "adjacency"