rm(list=ls())
library(ergm)
## Loading required package: network
## network: Classes for Relational Data
## Version 1.15 created on 2019-04-01.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.
##
## ergm: version 3.10.4, created on 2019-06-10
## Copyright (c) 2019, Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Carter T. Butts, University of California -- Irvine
## Steven M. Goodreau, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Martina Morris, University of Washington
## with contributions from
## Li Wang
## Kirk Li, University of Washington
## Skye Bender-deMoll, University of Washington
## Chad Klumb
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ergm").
## NOTE: Versions before 3.6.1 had a bug in the implementation of the
## bd() constriant which distorted the sampled distribution somewhat.
## In addition, Sampson's Monks datasets had mislabeled vertices. See
## the NEWS and the documentation for more details.
## NOTE: Some common term arguments pertaining to vertex attribute
## and level selection have changed in 3.10.0. See terms help for
## more details. Use 'options(ergm.term=list(version="3.9.4"))' to
## use old behavior.
library(network)
library(networkDynamic)
##
## networkDynamic: version 0.10.0, created on 2019-04-04
## Copyright (c) 2019, Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll, University of Washington
## with contributions from
## Zack Almquist, University of California -- Irvine
## David R. Hunter, Penn State University
## Li Wang
## Kirk Li, University of Washington
## Steven M. Goodreau, University of Washington
## Jeffrey Horner
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("networkDynamic").
library(tergm)
##
## tergm: version 3.6.1, created on 2019-06-12
## Copyright (c) 2019, Pavel N. Krivitsky, University of Wollongong
## Mark S. Handcock, University of California -- Los Angeles
## with contributions from
## David R. Hunter, Penn State University
## Steven M. Goodreau, University of Washington
## Martina Morris, University of Washington
## Nicole Bohme Carnegie, New York University
## Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Skye Bender-deMoll
## Li Wang
## Kirk Li, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("tergm").
load("init-pop-atts.RData")
formation <- ~edges+degree(0:1)+
nodemix("identity", levels=c("msm", "tgw", "hrh"))+
nodemix("age_group", levels = c("18-24", "25-34", "35-44", "45-64"))+
nodemix("race", levels=c("White", "Black", "Hispanic"))
dissolution <- ~offset(edges)
constraints <- ~.
formation.main.net <- update.formula(formation, main.net~.)
main.net <- net
main_mean_degree <- 0.453947358
main_mean_nedges <- n*main_mean_degree/2
main.degprop.0 <- 0.608552632
main.degprop.1 <- 0.335526316
main.degprop.2 <- 0.055921053
main.deg.prop <- c(main.degprop.0, main.degprop.1, main.degprop.2)
main.deg.nodes <- n*main.deg.prop
main.pt.duration <- 1303.457818 #by estimation
min.age <- 18; max.age <- 64 #by design
d.rate <- 1/((max.age - min.age)*365) #derived
pg.main <- (main.pt.duration - 1)/main.pt.duration #derived
ps2 <- (1 - d.rate)^2 #derived
theta.main.diss <- log(pg.main/(ps2-pg.main)) #derived
main.msm.msm.prop <- 0.841347695
main.tgw.tgw.prop <- 0.004420998
main.msm.tgw.prop <- (0.026310186+0.063390315)/2
main.msm.hrh.prop <- 0
main.tgw.hrh.prop <- 0.055500534
main.hrh.hrh.prop <- 0
main.18.24.18.24 <- 0.060648808
main.25.34.25.34 <- 0.196050785
main.35.44.35.44 <- 0.093088858
main.45.64.45.64 <- 0.23695346
main.25.34.18.24 <- (0.084626222+0.029619181)/2
main.35.44.18.24 <- 0
main.45.64.18.24 <- 0
main.35.44.25.34 <- (0.080394922+0.062059227)/2
main.45.64.25.34 <- 0
main.45.64.35.44 <- (0.050775745+0.088857555)/2
main.white.white <- 0.162895937
main.black.white <- (0.095022614 + 0.05279034)/2
main.hispanic.white <- (0.122171947 + 0.105580681)/2
main.black.black <- 0.257918548
main.hispanic.black <- mean(c(0.01809956, 0.036199101))
main.hispanic.hispanic <- 0.149321272
main.nodemix.identity <- main_mean_nedges*(c(main.msm.msm.prop,
main.msm.tgw.prop, main.tgw.tgw.prop,
main.msm.hrh.prop, main.tgw.hrh.prop, main.hrh.hrh.prop
))
main.nodemix.age <- main_mean_nedges*c(
main.18.24.18.24,
main.25.34.18.24, main.25.34.25.34,
main.35.44.18.24, main.35.44.25.34, main.35.44.35.44,
main.45.64.18.24, main.45.64.25.34, main.45.64.35.44, main.45.64.45.64
)
main.nodemix.race <- main_mean_nedges*c(main.white.white,
main.black.white, main.black.black,
main.hispanic.white, main.hispanic.black, main.hispanic.hispanic)
target.stats <- c(main_mean_nedges, main.deg.nodes[1:2], main.nodemix.identity,
main.nodemix.age, main.nodemix.race)
main.fit <- ergm(
formation.main.net,
target.stats=target.stats,
constraints=constraints,
eval.loglik=FALSE,
verbose=TRUE,
control=control.ergm(MCMLE.maxit=500)
)
## Evaluating network in model.
## Initializing Metropolis-Hastings proposal(s): ergm:MH_TNT
## Initializing model.
## Warning: `set_attrs()` is deprecated as of rlang 0.3.0
## This warning is displayed once per session.
## Using initial method 'MPLE'.
## Constructing an approximate response network.
## Starting 4 MCMC iterations of 131072 steps each.
## #1 of 4:
## SAN Metropolis-Hastings accepted 41.150% of 8192 proposed steps.
## SAN summary statistics:
## edges degree0
## 1105 3067
## degree1 mix.identity.msm.msm
## 1684 0
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 0 0
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0 0
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0 67
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 88 219
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 38 106
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 104 46
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 49 109
## mix.age_group.45-64.45-64 mix.race.White.White
## 279 218
## mix.race.White.Black mix.race.Black.Black
## 129 304
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 177 84
## mix.race.Hispanic.Hispanic
## 193
## Meanstats Goal:
## edges degree0
## 1134.868395 3042.763160
## degree1 mix.identity.msm.msm
## 1677.631580 954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 50.899132 5.017251
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.000000 62.985802
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.000000 68.828415
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 64.826749 222.491840
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.000000 80.833356
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 105.643603 0.000000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.000000 79.232710
## mix.age_group.45-64.45-64 mix.race.White.White
## 268.910993 184.865451
## mix.race.White.Black mix.race.Black.Black
## 83.874125 292.703609
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 129.234630 30.810917
## mix.race.Hispanic.Hispanic
## 169.459992
## Difference: SAN target.stats - Goal target.stats =
## [1] -29.868395 24.236840 6.368420 -954.818908 -50.899132
## [6] -5.017251 0.000000 -62.985802 0.000000 -1.828415
## [11] 23.173251 -3.491840 38.000000 25.166644 -1.643603
## [16] 46.000000 49.000000 29.767290 10.089007 33.134549
## [21] 45.125875 11.296391 47.765370 53.189083 23.540008
## New statistics scaling =
## [1] 0.11040407 0.19164752 0.05385711 0.01758843 0.01758843 0.01758843
## [7] 0.01758843 0.01758843 0.01758843 0.09386251 0.03865531 0.02995053
## [13] 0.05396400 0.03076792 0.05683925 0.03011711 0.02188609 0.02746131
## [19] 0.02507601 0.02212653 0.01758843 0.02494171 0.01898450 0.02086558
## [25] 0.02547396
## Scaled Mahalanobis distance = 16733.5348711232
## #2 of 4:
## SAN Metropolis-Hastings accepted 44.342% of 17408 proposed steps.
## SAN summary statistics:
## edges degree0
## 1166 2986
## degree1 mix.identity.msm.msm
## 1696 0
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 0 0
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0 0
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0 87
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 90 240
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 27 106
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 125 35
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 47 112
## mix.age_group.45-64.45-64 mix.race.White.White
## 297 225
## mix.race.White.Black mix.race.Black.Black
## 147 326
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 178 86
## mix.race.Hispanic.Hispanic
## 204
## Meanstats Goal:
## edges degree0
## 1134.868395 3042.763160
## degree1 mix.identity.msm.msm
## 1677.631580 954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 50.899132 5.017251
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.000000 62.985802
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.000000 68.828415
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 64.826749 222.491840
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.000000 80.833356
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 105.643603 0.000000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.000000 79.232710
## mix.age_group.45-64.45-64 mix.race.White.White
## 268.910993 184.865451
## mix.race.White.Black mix.race.Black.Black
## 83.874125 292.703609
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 129.234630 30.810917
## mix.race.Hispanic.Hispanic
## 169.459992
## Difference: SAN target.stats - Goal target.stats =
## [1] 31.131605 -56.763160 18.368420 -954.818908 -50.899132
## [6] -5.017251 0.000000 -62.985802 0.000000 18.171585
## [11] 25.173251 17.508160 27.000000 25.166644 19.356397
## [16] 35.000000 47.000000 32.767290 28.089007 40.134549
## [21] 63.125875 33.296391 48.765370 55.189083 34.540008
## New statistics scaling =
## [1] 0.12527726 0.20893089 0.05584602 0.01853752 0.01853752 0.01853752
## [7] 0.01853752 0.01853752 0.01853752 0.05813524 0.03626135 0.02188732
## [13] 0.06700810 0.03123859 0.03946191 0.04222764 0.03013070 0.02689518
## [19] 0.01917693 0.02114623 0.01890109 0.01857471 0.01853752 0.02515435
## [25] 0.02398383
## Scaled Mahalanobis distance = 17311.2978067577
## #3 of 4:
## SAN Metropolis-Hastings accepted 38.445% of 34816 proposed steps.
## SAN summary statistics:
## edges degree0
## 1142 3013
## degree1 mix.identity.msm.msm
## 1690 0
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 0 0
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0 0
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0 91
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 91 247
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 24 104
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 124 26
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 30 105
## mix.age_group.45-64.45-64 mix.race.White.White
## 300 221
## mix.race.White.Black mix.race.Black.Black
## 131 340
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 171 72
## mix.race.Hispanic.Hispanic
## 207
## Meanstats Goal:
## edges degree0
## 1134.868395 3042.763160
## degree1 mix.identity.msm.msm
## 1677.631580 954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 50.899132 5.017251
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.000000 62.985802
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.000000 68.828415
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 64.826749 222.491840
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.000000 80.833356
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 105.643603 0.000000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.000000 79.232710
## mix.age_group.45-64.45-64 mix.race.White.White
## 268.910993 184.865451
## mix.race.White.Black mix.race.Black.Black
## 83.874125 292.703609
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 129.234630 30.810917
## mix.race.Hispanic.Hispanic
## 169.459992
## Difference: SAN target.stats - Goal target.stats =
## [1] 7.131605 -29.763160 12.368420 -954.818908 -50.899132
## [6] -5.017251 0.000000 -62.985802 0.000000 22.171585
## [11] 26.173251 24.508160 24.000000 23.166644 18.356397
## [16] 26.000000 30.000000 25.767290 31.089007 36.134549
## [21] 47.125875 47.296391 41.765370 41.189083 37.540008
## New statistics scaling =
## [1] 0.13245284 0.22435805 0.05996417 0.01680060 0.01680060 0.01680060
## [7] 0.01680060 0.01680060 0.01680060 0.05918920 0.03380300 0.02246296
## [13] 0.05580900 0.02907355 0.04050898 0.04198943 0.02922202 0.02596659
## [19] 0.02010495 0.02073484 0.01836913 0.01923969 0.01680060 0.02538577
## [25] 0.02376162
## Scaled Mahalanobis distance = 15682.2885552589
## #4 of 4:
## SAN Metropolis-Hastings accepted 0.014% of 69632 proposed steps.
## SAN summary statistics:
## edges degree0
## 1158 2989
## degree1 mix.identity.msm.msm
## 1706 0
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 0 0
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0 0
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0 91
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 88 250
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 21 113
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 129 23
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 35 111
## mix.age_group.45-64.45-64 mix.race.White.White
## 297 230
## mix.race.White.Black mix.race.Black.Black
## 132 336
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 182 69
## mix.race.Hispanic.Hispanic
## 209
## Meanstats Goal:
## edges degree0
## 1134.868395 3042.763160
## degree1 mix.identity.msm.msm
## 1677.631580 954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 50.899132 5.017251
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.000000 62.985802
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.000000 68.828415
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 64.826749 222.491840
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.000000 80.833356
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 105.643603 0.000000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.000000 79.232710
## mix.age_group.45-64.45-64 mix.race.White.White
## 268.910993 184.865451
## mix.race.White.Black mix.race.Black.Black
## 83.874125 292.703609
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 129.234630 30.810917
## mix.race.Hispanic.Hispanic
## 169.459992
## Difference: SAN target.stats - Goal target.stats =
## [1] 23.131605 -53.763160 28.368420 -954.818908 -50.899132
## [6] -5.017251 0.000000 -62.985802 0.000000 22.171585
## [11] 23.173251 27.508160 21.000000 32.166644 23.356397
## [16] 23.000000 35.000000 31.767290 28.089007 45.134549
## [21] 48.125875 43.296391 52.765370 38.189083 39.540008
## New statistics scaling =
## [1] 0.12657416 0.21467785 0.05802342 0.01749891 0.01749891 0.01749891
## [7] 0.01749891 0.01749891 0.01749891 0.05660119 0.03721288 0.02445139
## [13] 0.06345884 0.03049870 0.03946175 0.04869060 0.02857828 0.02651336
## [19] 0.02034864 0.02039561 0.01749891 0.01829971 0.01796302 0.02175843
## [25] 0.02399978
## Scaled Mahalanobis distance = 16312.2641313
## Finished SAN run.
## Observed statistic(s) mix.identity.msm.hrh, mix.identity.hrh.hrh, mix.age_group.18-24.35-44, mix.age_group.18-24.45-64, and mix.age_group.25-34.45-64 are at their smallest attainable values. Their coefficients will be fixed at -Inf.
## Fitting initial model.
## Unable to match target stats. Using MCMLE estimation.
## Starting maximum pseudolikelihood estimation (MPLE):
## Evaluating the predictor and response matrix.
## MPLE covariate matrix has 503 rows.
## Maximizing the pseudolikelihood.
## Warning in ergm.mple(nw, fd, m, MPLEtype = MPLEtype, init = init, control =
## control, : glm.fit: fitted probabilities numerically 0 or 1 occurred
## Finished MPLE.
## Starting Monte Carlo maximum likelihood estimation (MCMLE):
## Density guard set to 23259 from an initial count of 1158 edges.
##
## Iteration 1 of at most 500 with parameter:
## edges degree0
## -38.61306926 -30.52349509
## degree1 mix.identity.msm.msm
## -15.00224019 0.00000000
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 0.00000000 0.00000000
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf 0.00000000
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf 0.08655923
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -0.90767435 0.49193510
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -0.80512762
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 0.20811676 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.13245071
## mix.age_group.45-64.45-64 mix.race.White.White
## 0.00000000 -0.37837207
## mix.race.White.Black mix.race.Black.Black
## -1.19990330 0.77339863
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -1.07652404 -1.70836751
## mix.race.Hispanic.Hispanic
## 0.00000000
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -32.520739 36.173363
## degree1 mix.identity.msm.msm
## -40.199939 -954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -50.899132 -5.017251
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -62.985802 24.403030
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 25.744540 32.473981
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 35.920551 24.055616
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 35.299517 33.682757
## mix.race.White.White mix.race.White.Black
## 35.550565 44.285055
## mix.race.Black.Black mix.race.White.Hispanic
## 27.161626 42.512441
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 31.645138 30.244109
## Starting MCMLE Optimization...
## Optimizing with step length 0.804254898619941.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 2.96.
##
## Iteration 2 of at most 500 with parameter:
## edges degree0
## -3.867261e+01 -3.050661e+01
## degree1 mix.identity.msm.msm
## -1.491694e+01 -5.700756e-18
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -3.796334e-17 1.271376e-17
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf -6.115015e-18
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf 9.917056e-02
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -8.653248e-01 5.005248e-01
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -8.641688e-01
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 2.429071e-01 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.207096e+00
## mix.age_group.45-64.45-64 mix.race.White.White
## -2.419334e-02 -4.139466e-01
## mix.race.White.Black mix.race.Black.Black
## -1.256023e+00 8.202344e-01
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -1.120522e+00 -1.679528e+00
## mix.race.Hispanic.Hispanic
## 4.784169e-04
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -38.437731 20.559106
## degree1 mix.identity.msm.msm
## 2.862561 -954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -50.899132 -5.017251
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -62.985802 28.316116
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 28.781650 32.014996
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 26.204730 29.785108
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 26.366900 34.193499
## mix.race.White.White mix.race.White.Black
## 31.455838 36.628805
## mix.race.Black.Black mix.race.White.Hispanic
## 37.365727 38.506581
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 32.282833 29.242156
## Starting MCMLE Optimization...
## Optimizing with step length 1.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 0.5203.
## Step length converged once. Increasing MCMC sample size.
##
## Iteration 3 of at most 500 with parameter:
## edges degree0
## -3.868600e+01 -3.050297e+01
## degree1 mix.identity.msm.msm
## -1.489742e+01 -6.694784e-18
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -4.731440e-17 1.720988e-17
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf -2.104258e-17
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf 1.017160e-01
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -8.617807e-01 4.894919e-01
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -8.414678e-01
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 2.236171e-01 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.189303e+00
## mix.age_group.45-64.45-64 mix.race.White.White
## -5.384762e-02 -4.038720e-01
## mix.race.White.Black mix.race.Black.Black
## -1.286936e+00 8.040664e-01
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -1.156221e+00 -1.654046e+00
## mix.race.Hispanic.Hispanic
## 3.430845e-02
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -45.480700 26.184838
## degree1 mix.identity.msm.msm
## 5.697033 -954.818908
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -50.899132 -5.017251
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -62.985802 27.995803
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 29.956943 27.656842
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 29.513080 27.768751
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 29.299517 26.429095
## mix.race.White.White mix.race.White.Black
## 34.049833 33.352926
## mix.race.Black.Black mix.race.White.Hispanic
## 30.799565 30.643056
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 33.294796 36.298797
## Starting MCMLE Optimization...
## Optimizing with step length 1.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## Starting MCMC s.e. computation.
## Error in solve.default(H, tol = 1e-20) :
## Lapack routine dgesv: system is exactly singular: U[16,16] = 0
## Warning in ergm.MCMCse.lognormal(theta = theta, init = init, statsmatrix =
## statsmatrix0, : Approximate Hessian matrix is singular. Standard errors due
## to MCMC approximation of the likelihood cannot be evaluated. This is likely
## due to insufficient MCMC sample size or highly correlated model terms.
## The log-likelihood improved by 0.09839.
## Step length converged twice. Stopping.
## Finished MCMLE.
## This model was fit using MCMC. To examine model diagnostics and
## check for degeneracy, use the mcmc.diagnostics() function.
theta.main.form <- main.fit$coef
theta.main.form[1] <- theta.main.form[1] - theta.main.diss
sim.test <- simulate(main.net,
formation=formation.main.net,
dissolution=dissolution,
coef.form=theta.main.form,
coef.diss=theta.main.diss,
time.slices=2e4,
#time.slices=1e2,
constraints=constraints,
monitor=~edges+degree(0:5)
)
main.net.xn <- network.collapse(sim.test, at=20000)
network.size(main.net.xn)
## [1] 5000
network.edgecount(main.net.xn)
## [1] 1072
degreedist(main.net.xn)/network.size(main.net.xn)
## degree0 degree1 degree2
## 3078 1700 222
## degree0 degree1 degree2
## 0.6156 0.3400 0.0444
mixingmatrix(main.net.xn, "identity")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## HRH MSM TGW
## HRH 0 16 1
## MSM 16 951 99
## TGW 1 99 5
mixingmatrix(main.net.xn, "age_group")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## 18-24 25-34 35-44 45-64
## 18-24 109 97 0 0
## 25-34 97 229 101 0
## 35-44 0 101 137 107
## 45-64 0 0 107 292
mixingmatrix(main.net.xn, "race")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## Black Hispanic White
## Black 330 62 121
## Hispanic 62 206 159
## White 121 159 194
save.image(file="initial-main-network.RData")