rm(list=ls())
library(ergm)
## Loading required package: network
## network: Classes for Relational Data
## Version 1.16.0 created on 2019-11-30.
## 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 0.10.1, created on 2020-01-16
## Copyright (c) 2020, 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.casual.net <- update.formula(formation, casual.net~.)
casual.net <- net
casual_mean_degree <- 1.33
casual_mean_nedges <- n*casual_mean_degree/2
casual.degprop.0 <- 0.167763158
casual.degprop.1 <- 0.430921053
casual.degprop.2 <- 0.401315789
casual.deg.prop <- c(casual.degprop.0, casual.degprop.1, casual.degprop.2)
casual.deg.nodes <- n*casual.deg.prop
casual.pt.duration <- 313.8824 #by estimation
min.age <- 18; max.age <- 64 #by design
d.rate <- 1/((max.age - min.age)*365) #derived
pg.casual <- (casual.pt.duration - 1)/casual.pt.duration #derived
ps2 <- (1 - d.rate)^2 #derived
theta.casual.diss <- log(pg.casual/(ps2-pg.casual)) #derived
casual.msm.msm.prop <- 0.835361469
casual.tgw.tgw.prop <- 0.005998365
casual.msm.tgw.prop <- (0.054821125+0.045494773)/2
casual.msm.hrh.prop <- 0
casual.tgw.hrh.prop <- 0.055500534
casual.hrh.hrh.prop <- 0
casual.18.24.18.24 <- 0.060648808
casual.25.34.25.34 <- 0.196050785
casual.35.44.35.44 <- 0.093088858
casual.45.64.45.64 <- 0.23695346
casual.25.34.18.24 <- (0.084626222+0.029619181)/2
casual.35.44.18.24 <- 0
casual.45.64.18.24 <- 0
casual.35.44.25.34 <- (0.080394922+0.062059227)/2
casual.45.64.25.34 <- 0
casual.45.64.35.44 <- (0.050775745+0.088857555)/2
casual.white.white <- 0.162895937
casual.black.white <- (0.095022614 + 0.05279034)/2
casual.hispanic.white <- (0.122171947 + 0.105580681)/2
casual.black.black <- 0.257918548
casual.hispanic.black <- mean(c(0.01809956, 0.036199101))
casual.hispanic.hispanic <- 0.149321272
casual.nodemix.identity <- casual_mean_nedges*(c(casual.msm.msm.prop,
casual.msm.tgw.prop, casual.tgw.tgw.prop,
casual.msm.hrh.prop, casual.tgw.hrh.prop, casual.hrh.hrh.prop
))
casual.nodemix.age <- casual_mean_nedges*c(
casual.18.24.18.24,
casual.25.34.18.24, casual.25.34.25.34,
casual.35.44.18.24, casual.35.44.25.34, casual.35.44.35.44,
casual.45.64.18.24, casual.45.64.25.34, casual.45.64.35.44, casual.45.64.45.64
)
casual.nodemix.race <- casual_mean_nedges*c(casual.white.white,
casual.black.white, casual.black.black,
casual.hispanic.white, casual.hispanic.black, casual.hispanic.hispanic)
target.stats <- c(casual_mean_nedges, casual.deg.nodes[1:2], casual.nodemix.identity,
casual.nodemix.age, casual.nodemix.race)
casual.fit <- ergm(
formation.casual.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 47.302% of 8192 proposed steps.
## SAN summary statistics:
## edges degree0
## 3173 1154
## degree1 mix.identity.msm.msm
## 2167 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 96
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 304 322
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 200 391
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 165 304
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 453 430
## mix.age_group.45-64.45-64 mix.race.White.White
## 508 560
## mix.race.White.Black mix.race.Black.Black
## 605 397
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 692 490
## mix.race.Hispanic.Hispanic
## 429
## Meanstats Goal:
## edges degree0
## 3325.00000 838.81579
## degree1 mix.identity.msm.msm
## 2154.60527 2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 166.77518 19.94456
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.00000 184.53928
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.00000 201.65729
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 189.93298 651.86886
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.00000 236.83002
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 309.52045 0.00000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.00000 232.14036
## mix.age_group.45-64.45-64 mix.race.White.White
## 787.87025 541.62899
## mix.race.White.Black mix.race.Black.Black
## 245.73904 857.57917
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 378.63874 90.27152
## mix.race.Hispanic.Hispanic
## 496.49323
## Difference: SAN target.stats - Goal target.stats =
## [1] -152.00000 315.18421 12.39473 -2777.57688 -166.77518 -19.94456
## [7] 0.00000 -184.53928 0.00000 -105.65729 114.06702 -329.86886
## [13] 200.00000 154.16998 -144.52045 304.00000 453.00000 197.85964
## [19] -279.87025 18.37101 359.26096 -460.57917 313.36126 399.72848
## [25] -67.49323
## New statistics scaling =
## [1] 0.02795049 0.06022544 0.02069044 0.01930735 0.01930735 0.01930735
## [7] 0.01930735 0.01930735 0.01930735 0.14246354 0.04290364 0.06238788
## [13] 0.06049941 0.04172072 0.10385497 0.03993485 0.02521813 0.03357416
## [19] 0.04517837 0.03019194 0.02001020 0.04743611 0.01930735 0.02539464
## [25] 0.03521364
## Scaled Mahalanobis distance = 210709.326380314
## #2 of 4:
## SAN Metropolis-Hastings accepted 28.125% of 17408 proposed steps.
## SAN summary statistics:
## edges degree0
## 3295 879
## degree1 mix.identity.msm.msm
## 2016 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
## 0 248
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 264 682
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 78 295
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 357 97
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 151 317
## mix.age_group.45-64.45-64 mix.race.White.White
## 806 658
## mix.race.White.Black mix.race.Black.Black
## 400 907
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 509 235
## mix.race.Hispanic.Hispanic
## 586
## Meanstats Goal:
## edges degree0
## 3325.00000 838.81579
## degree1 mix.identity.msm.msm
## 2154.60527 2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 166.77518 19.94456
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.00000 184.53928
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.00000 201.65729
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 189.93298 651.86886
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.00000 236.83002
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 309.52045 0.00000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.00000 232.14036
## mix.age_group.45-64.45-64 mix.race.White.White
## 787.87025 541.62899
## mix.race.White.Black mix.race.Black.Black
## 245.73904 857.57917
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 378.63874 90.27152
## mix.race.Hispanic.Hispanic
## 496.49323
## Difference: SAN target.stats - Goal target.stats =
## [1] -30.00000 40.18421 -138.60527 -2777.57688 -166.77518 -19.94456
## [7] 0.00000 -184.53928 0.00000 46.34271 74.06702 30.13114
## [13] 78.00000 58.16998 47.47955 97.00000 151.00000 84.85964
## [19] 18.12975 116.37101 154.26096 49.42083 130.36126 144.72848
## [25] 89.50677
## New statistics scaling =
## [1] 0.01942945 0.05266556 0.01461267 0.01461267 0.01461267 0.01461267
## [7] 0.01461267 0.01461267 0.01461267 0.12435834 0.05830459 0.04429824
## [13] 0.10601657 0.04935205 0.07746370 0.06640937 0.04093393 0.04277005
## [19] 0.03292543 0.03173893 0.02476718 0.03172956 0.02349641 0.03490258
## [25] 0.03614939
## Scaled Mahalanobis distance = 114905.569095983
## #3 of 4:
## SAN Metropolis-Hastings accepted 22.418% of 34816 proposed steps.
## SAN summary statistics:
## edges degree0
## 3185 843
## degree1 mix.identity.msm.msm
## 2147 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
## 0 259
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 247 706
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 58 296
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 365 57
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 61 292
## mix.age_group.45-64.45-64 mix.race.White.White
## 844 636
## mix.race.White.Black mix.race.Black.Black
## 344 952
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 477 186
## mix.race.Hispanic.Hispanic
## 590
## Meanstats Goal:
## edges degree0
## 3325.00000 838.81579
## degree1 mix.identity.msm.msm
## 2154.60527 2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 166.77518 19.94456
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.00000 184.53928
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.00000 201.65729
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 189.93298 651.86886
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.00000 236.83002
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 309.52045 0.00000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.00000 232.14036
## mix.age_group.45-64.45-64 mix.race.White.White
## 787.87025 541.62899
## mix.race.White.Black mix.race.Black.Black
## 245.73904 857.57917
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 378.63874 90.27152
## mix.race.Hispanic.Hispanic
## 496.49323
## Difference: SAN target.stats - Goal target.stats =
## [1] -140.000000 4.184210 -7.605265 -2777.576884 -166.775180
## [6] -19.944564 0.000000 -184.539276 0.000000 57.342713
## [11] 57.067018 54.131140 58.000000 59.169977 55.479547
## [16] 57.000000 61.000000 59.859639 56.129746 94.371009
## [21] 98.260964 94.420828 98.361256 95.728476 93.506771
## New statistics scaling =
## [1] 0.02478205 0.06637811 0.01715840 0.01715840 0.01715840 0.01715840
## [7] 0.01715840 0.01715840 0.01715840 0.08960174 0.05580076 0.04123883
## [13] 0.10733328 0.04803185 0.06621328 0.07287780 0.04731961 0.04644026
## [19] 0.02906566 0.02933677 0.02958717 0.02589605 0.02708918 0.03740103
## [25] 0.03549775
## Scaled Mahalanobis distance = 133452.145232984
## #4 of 4:
## SAN Metropolis-Hastings accepted 0.080% of 69632 proposed steps.
## SAN summary statistics:
## edges degree0
## 3177 838
## degree1 mix.identity.msm.msm
## 2153 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
## 0 258
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 247 709
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 57 293
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 366 57
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 57 289
## mix.age_group.45-64.45-64 mix.race.White.White
## 844 636
## mix.race.White.Black mix.race.Black.Black
## 340 952
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 473 185
## mix.race.Hispanic.Hispanic
## 591
## Meanstats Goal:
## edges degree0
## 3325.00000 838.81579
## degree1 mix.identity.msm.msm
## 2154.60527 2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 166.77518 19.94456
## mix.identity.msm.hrh mix.identity.tgw.hrh
## 0.00000 184.53928
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## 0.00000 201.65729
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 189.93298 651.86886
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 0.00000 236.83002
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 309.52045 0.00000
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 0.00000 232.14036
## mix.age_group.45-64.45-64 mix.race.White.White
## 787.87025 541.62899
## mix.race.White.Black mix.race.Black.Black
## 245.73904 857.57917
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 378.63874 90.27152
## mix.race.Hispanic.Hispanic
## 496.49323
## Difference: SAN target.stats - Goal target.stats =
## [1] -148.000000 -0.815790 -1.605265 -2777.576884 -166.775180
## [6] -19.944564 0.000000 -184.539276 0.000000 56.342713
## [11] 57.067018 57.131140 57.000000 56.169977 56.479547
## [16] 57.000000 57.000000 56.859639 56.129746 94.371009
## [21] 94.260964 94.420828 94.361256 94.728476 94.506771
## New statistics scaling =
## [1] 0.02424008 0.05765795 0.01568054 0.01568054 0.01568054 0.01568054
## [7] 0.01568054 0.01568054 0.01568054 0.09812965 0.05404560 0.03628285
## [13] 0.11823285 0.04831788 0.06335544 0.08639861 0.04983349 0.04685578
## [19] 0.03006900 0.02884023 0.02641301 0.02530161 0.02698596 0.03638204
## [25] 0.03289421
## Scaled Mahalanobis distance = 121950.818834369
## 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 616 rows.
## Maximizing the pseudolikelihood.
## Finished MPLE.
## Starting Monte Carlo maximum likelihood estimation (MCMLE):
## Density guard set to 63812 from an initial count of 3177 edges.
##
## Iteration 1 of at most 500 with parameter:
## edges degree0
## -11.2444274 -4.9804771
## degree1 mix.identity.msm.msm
## -2.1769630 0.0000000
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 0.0000000 0.0000000
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf 0.0000000
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf 0.2797174
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -0.5410597 1.0423672
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -0.4750810
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 0.4965671 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.0729202
## mix.age_group.45-64.45-64 mix.race.White.White
## 0.0000000 -0.4850644
## mix.race.White.Black mix.race.Black.Black
## -0.8173001 1.6945548
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -1.1861896 -1.1841609
## mix.race.Hispanic.Hispanic
## 0.0000000
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -194.64355 32.30628
## degree1 mix.identity.msm.msm
## 10.22970 -2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -166.77518 -19.94456
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -184.53928 80.57709
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 80.01624 69.61649
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 67.66802 73.51861
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 68.66237 80.41978
## mix.race.White.White mix.race.White.Black
## 86.38566 110.77757
## mix.race.Black.Black mix.race.White.Hispanic
## 49.79387 86.30657
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 100.40328 86.33880
## Starting MCMLE Optimization...
## Optimizing with step length 0.00291778672914711.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 6.305.
##
## Iteration 2 of at most 500 with parameter:
## edges degree0
## -5.238568e+00 -5.055319e+00
## degree1 mix.identity.msm.msm
## -2.213793e+00 1.645232e-11
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -4.155624e-12 -1.403690e-11
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf 2.299613e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -6.750397e+00
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -7.595490e+00 -5.991722e+00
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -7.532948e+00
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -6.539588e+00 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -8.148936e+00
## mix.age_group.45-64.45-64 mix.race.White.White
## -7.061978e+00 4.948895e-01
## mix.race.White.Black mix.race.Black.Black
## 1.505691e-01 2.733270e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -2.121935e-01 -1.342766e-01
## mix.race.Hispanic.Hispanic
## 9.954401e-01
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -184.952148 29.263312
## degree1 mix.identity.msm.msm
## -2.477335 -2777.576884
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -166.775180 -19.944564
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -184.539276 86.892518
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 75.848268 78.299109
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 60.044977 78.396539
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 66.735615 84.010605
## mix.race.White.White mix.race.White.Black
## 88.103431 97.419167
## mix.race.Black.Black mix.race.White.Hispanic
## 63.275320 84.559498
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 109.041953 87.297786
## Starting MCMLE Optimization...
## Optimizing with step length 0.541391739794384.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 2.871.
##
## Iteration 3 of at most 500 with parameter:
## edges degree0
## -5.424469e+00 -5.361894e+00
## degree1 mix.identity.msm.msm
## -2.351425e+00 1.645247e-11
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -4.156796e-12 -1.403741e-11
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf 2.299567e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -6.830755e+00
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -7.644278e+00 -5.993680e+00
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -7.495168e+00
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -6.571939e+00 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -8.143465e+00
## mix.age_group.45-64.45-64 mix.race.White.White
## -7.127675e+00 4.255018e-01
## mix.race.White.Black mix.race.Black.Black
## 1.357972e-01 2.827302e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -2.763322e-01 -2.070847e-01
## mix.race.Hispanic.Hispanic
## 9.366145e-01
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -169.51562 9.18128
## degree1 mix.identity.msm.msm
## -2.65507 -2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -166.77518 -19.94456
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -184.53928 93.60639
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 65.44104 80.01981
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 78.17193 72.80670
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 78.11940 77.49889
## mix.race.White.White mix.race.White.Black
## 83.44035 88.79905
## mix.race.Black.Black mix.race.White.Hispanic
## 90.69329 92.66497
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 94.92184 94.61419
## Starting MCMLE Optimization...
## Optimizing with step length 0.89004800392375.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 2.344.
##
## Iteration 4 of at most 500 with parameter:
## edges degree0
## -5.613076e+00 -5.644588e+00
## degree1 mix.identity.msm.msm
## -2.487381e+00 1.645146e-11
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -4.156485e-12 -1.403749e-11
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf 2.299319e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -6.983313e+00
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -7.617396e+00 -5.990856e+00
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -7.514274e+00
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -6.540646e+00 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -8.179536e+00
## mix.age_group.45-64.45-64 mix.race.White.White
## -7.169547e+00 3.942995e-01
## mix.race.White.Black mix.race.Black.Black
## 1.244117e-01 2.846921e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -3.218882e-01 -2.507479e-01
## mix.race.Hispanic.Hispanic
## 8.601945e-01
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -153.24414 -11.16638
## degree1 mix.identity.msm.msm
## -10.29765 -2777.57688
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -166.77518 -19.94456
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -184.53928 77.88764
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 90.84827 78.38016
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 66.10259 86.62213
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 75.54519 86.54967
## mix.race.White.White mix.race.White.Black
## 101.43839 85.08225
## mix.race.Black.Black mix.race.White.Hispanic
## 105.69036 96.71770
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 84.37301 88.10345
## 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 1.867.
## Step length converged once. Increasing MCMC sample size.
##
## Iteration 5 of at most 500 with parameter:
## edges degree0
## -5.549398e+00 -5.538510e+00
## degree1 mix.identity.msm.msm
## -2.423052e+00 1.645103e-11
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -4.156437e-12 -1.403809e-11
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf 2.298949e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -7.003308e+00
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -7.661142e+00 -5.963969e+00
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -7.452498e+00
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -6.557898e+00 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -8.118675e+00
## mix.age_group.45-64.45-64 mix.race.White.White
## -7.174400e+00 3.748003e-01
## mix.race.White.Black mix.race.Black.Black
## 1.501285e-01 2.797430e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -2.907448e-01 -2.125147e-01
## mix.race.Hispanic.Hispanic
## 8.977704e-01
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -178.008545 5.554815
## degree1 mix.identity.msm.msm
## 8.687215 -2777.576884
## mix.identity.msm.tgw mix.identity.tgw.tgw
## -166.775180 -19.944564
## mix.identity.tgw.hrh mix.age_group.18-24.18-24
## -184.539276 75.907167
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 71.607301 81.162390
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 79.853327 70.762262
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 87.711201 70.167587
## mix.race.White.White mix.race.White.Black
## 77.796058 107.645974
## mix.race.Black.Black mix.race.White.Hispanic
## 74.488943 93.871998
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 93.278281 89.559505
## 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.
## The log-likelihood improved by 1.541.
## 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.casual.form <- casual.fit$coef
theta.casual.form[1] <- theta.casual.form[1] - theta.casual.diss
sim.test <- simulate(casual.net,
formation=formation.casual.net,
dissolution=dissolution,
coef.form=theta.casual.form,
coef.diss=theta.casual.diss,
time.slices=2e4,
#time.slices=1e2,
constraints=constraints,
monitor=~edges+degree(0:5)
)
casual.net.xn <- network.collapse(sim.test, at=20000)
network.size(casual.net.xn)
## [1] 5000
network.edgecount(casual.net.xn)
## [1] 3130
degreedist(casual.net.xn) /network.size(casual.net.xn)
## degree0 degree1 degree2 degree3 degree4
## 842 2196 1828 128 6
## degree0 degree1 degree2 degree3 degree4
## 0.1684 0.4392 0.3656 0.0256 0.0012
mixingmatrix(casual.net.xn, "identity")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## HRH MSM TGW
## HRH 1 61 5
## MSM 61 2813 244
## TGW 5 244 6
mixingmatrix(casual.net.xn, "age_group")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## 18-24 25-34 35-44 45-64
## 18-24 254 276 0 0
## 25-34 276 734 290 0
## 35-44 0 290 391 319
## 45-64 0 0 319 866
mixingmatrix(casual.net.xn, "race")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## Black Hispanic White
## Black 931 175 328
## Hispanic 175 592 474
## White 328 474 630
save.image(file="initial-casual-network.RData")