Extracts network statistics from a network epidemic model
simulated with netsim or a network diagnostics object
simulated with netdx. Statistics can be returned either
as a single data frame or as a list of matrices (one matrix
for each simulation).
Usage
get_nwstats(x, sim, network = 1, mode = c("data.frame", "list"))Arguments
Examples
# Two-group Bernoulli random graph TERGM
nw <- network_initialize(n = 100)
nw <- set_vertex_attribute(nw, "group", rep(1:2, each = 50))
formation <- ~edges
target.stats <- 50
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)
#> Starting maximum pseudolikelihood estimation (MPLE):
#> Obtaining the responsible dyads.
#> Evaluating the predictor and response matrix.
#> Maximizing the pseudolikelihood.
#> Finished MPLE.
dx <- netdx(est, nsim = 3, nsteps = 10, verbose = FALSE,
nwstats.formula = ~edges + isolates)
get_nwstats(dx)
#> time sim edges isolates
#> 1 1 1 44 45
#> 2 2 1 46 42
#> 3 3 1 49 40
#> 4 4 1 51 38
#> 5 5 1 48 39
#> 6 6 1 46 42
#> 7 7 1 48 39
#> 8 8 1 49 39
#> 9 9 1 51 39
#> 10 10 1 52 39
#> 11 1 2 50 38
#> 12 2 2 51 35
#> 13 3 2 49 37
#> 14 4 2 50 37
#> 15 5 2 48 37
#> 16 6 2 49 38
#> 17 7 2 48 38
#> 18 8 2 49 38
#> 19 9 2 48 38
#> 20 10 2 49 37
#> 21 1 3 54 26
#> 22 2 3 55 27
#> 23 3 3 57 25
#> 24 4 3 53 27
#> 25 5 3 55 28
#> 26 6 3 56 27
#> 27 7 3 58 27
#> 28 8 3 57 29
#> 29 9 3 58 28
#> 30 10 3 59 28
get_nwstats(dx, sim = 1)
#> time sim edges isolates
#> 1 1 1 44 45
#> 2 2 1 46 42
#> 3 3 1 49 40
#> 4 4 1 51 38
#> 5 5 1 48 39
#> 6 6 1 46 42
#> 7 7 1 48 39
#> 8 8 1 49 39
#> 9 9 1 51 39
#> 10 10 1 52 39
# SI epidemic model
param <- param.net(inf.prob = 0.3, inf.prob.g2 = 0.15)
init <- init.net(i.num = 10, i.num.g2 = 10)
control <- control.net(type = "SI", nsteps = 10, nsims = 3,
nwstats.formula = ~edges + meandeg + degree(0:5),
verbose = FALSE)
mod <- netsim(est, param, init, control)
# Extract the network statistics from all or sets of simulations
get_nwstats(mod)
#> time sim edges meandeg degree0 degree1 degree2 degree3 degree4 degree5
#> 1 1 1 47 0.94 40 33 22 4 0 1
#> 2 2 1 48 0.96 38 33 26 2 0 1
#> 3 3 1 46 0.92 38 37 22 2 0 1
#> 4 4 1 46 0.92 34 43 20 3 0 0
#> 5 5 1 48 0.96 34 41 20 5 0 0
#> 6 6 1 49 0.98 35 37 24 3 1 0
#> 7 7 1 51 1.02 33 38 24 4 1 0
#> 8 8 1 51 1.02 33 39 22 5 1 0
#> 9 9 1 50 1.00 32 42 21 4 1 0
#> 10 10 1 48 0.96 32 45 19 3 1 0
#> 11 1 2 52 1.04 30 43 22 3 2 0
#> 12 2 2 53 1.06 30 43 20 5 2 0
#> 13 3 2 51 1.02 32 43 17 7 1 0
#> 14 4 2 52 1.04 32 42 18 6 2 0
#> 15 5 2 51 1.02 33 41 18 7 1 0
#> 16 6 2 50 1.00 35 39 18 7 1 0
#> 17 7 2 50 1.00 36 37 19 7 1 0
#> 18 8 2 51 1.02 36 35 21 7 1 0
#> 19 9 2 54 1.08 32 37 23 7 1 0
#> 20 10 2 55 1.10 31 38 22 8 1 0
#> 21 1 3 50 1.00 35 40 17 6 2 0
#> 22 2 3 50 1.00 35 39 18 7 1 0
#> 23 3 3 51 1.02 34 40 17 8 1 0
#> 24 4 3 52 1.04 33 43 13 9 2 0
#> 25 5 3 54 1.08 33 39 17 9 2 0
#> 26 6 3 54 1.08 35 37 17 7 4 0
#> 27 7 3 54 1.08 34 41 14 5 6 0
#> 28 8 3 51 1.02 37 39 13 7 4 0
#> 29 9 3 46 0.92 42 35 15 5 3 0
#> 30 10 3 50 1.00 38 38 14 6 4 0
get_nwstats(mod, sim = 2)
#> time sim edges meandeg degree0 degree1 degree2 degree3 degree4 degree5
#> 1 1 2 52 1.04 30 43 22 3 2 0
#> 2 2 2 53 1.06 30 43 20 5 2 0
#> 3 3 2 51 1.02 32 43 17 7 1 0
#> 4 4 2 52 1.04 32 42 18 6 2 0
#> 5 5 2 51 1.02 33 41 18 7 1 0
#> 6 6 2 50 1.00 35 39 18 7 1 0
#> 7 7 2 50 1.00 36 37 19 7 1 0
#> 8 8 2 51 1.02 36 35 21 7 1 0
#> 9 9 2 54 1.08 32 37 23 7 1 0
#> 10 10 2 55 1.10 31 38 22 8 1 0
get_nwstats(mod, sim = c(1, 3))
#> time sim edges meandeg degree0 degree1 degree2 degree3 degree4 degree5
#> 1 1 1 47 0.94 40 33 22 4 0 1
#> 2 2 1 48 0.96 38 33 26 2 0 1
#> 3 3 1 46 0.92 38 37 22 2 0 1
#> 4 4 1 46 0.92 34 43 20 3 0 0
#> 5 5 1 48 0.96 34 41 20 5 0 0
#> 6 6 1 49 0.98 35 37 24 3 1 0
#> 7 7 1 51 1.02 33 38 24 4 1 0
#> 8 8 1 51 1.02 33 39 22 5 1 0
#> 9 9 1 50 1.00 32 42 21 4 1 0
#> 10 10 1 48 0.96 32 45 19 3 1 0
#> 11 1 3 50 1.00 35 40 17 6 2 0
#> 12 2 3 50 1.00 35 39 18 7 1 0
#> 13 3 3 51 1.02 34 40 17 8 1 0
#> 14 4 3 52 1.04 33 43 13 9 2 0
#> 15 5 3 54 1.08 33 39 17 9 2 0
#> 16 6 3 54 1.08 35 37 17 7 4 0
#> 17 7 3 54 1.08 34 41 14 5 6 0
#> 18 8 3 51 1.02 37 39 13 7 4 0
#> 19 9 3 46 0.92 42 35 15 5 3 0
#> 20 10 3 50 1.00 38 38 14 6 4 0
# On the fly summary stats
summary(get_nwstats(mod))
#> time sim edges meandeg degree0
#> Min. : 1.0 Min. :1 Min. :46.00 Min. :0.920 Min. :30.00
#> 1st Qu.: 3.0 1st Qu.:1 1st Qu.:49.25 1st Qu.:0.985 1st Qu.:32.25
#> Median : 5.5 Median :2 Median :51.00 Median :1.020 Median :34.00
#> Mean : 5.5 Mean :2 Mean :50.50 Mean :1.010 Mean :34.40
#> 3rd Qu.: 8.0 3rd Qu.:3 3rd Qu.:52.00 3rd Qu.:1.040 3rd Qu.:35.75
#> Max. :10.0 Max. :3 Max. :55.00 Max. :1.100 Max. :42.00
#> degree1 degree2 degree3 degree4 degree5
#> Min. :33.00 Min. :13.0 Min. :2.0 Min. :0.000 Min. :0.0
#> 1st Qu.:37.00 1st Qu.:17.0 1st Qu.:4.0 1st Qu.:1.000 1st Qu.:0.0
#> Median :39.00 Median :19.0 Median :6.0 Median :1.000 Median :0.0
#> Mean :39.23 Mean :19.1 Mean :5.6 Mean :1.567 Mean :0.1
#> 3rd Qu.:41.75 3rd Qu.:22.0 3rd Qu.:7.0 3rd Qu.:2.000 3rd Qu.:0.0
#> Max. :45.00 Max. :26.0 Max. :9.0 Max. :6.000 Max. :1.0
colMeans(get_nwstats(mod))
#> time sim edges meandeg degree0 degree1 degree2 degree3
#> 5.500000 2.000000 50.500000 1.010000 34.400000 39.233333 19.100000 5.600000
#> degree4 degree5
#> 1.566667 0.100000