# Model differential dissolution rates between TGW and MSM
# Reference: https://rdrr.io/cran/EpiModel/man/dissolution_coefs.html
# Conceptual explanation -----------
## See model-differential-dissolution-rates.R for conceptual explanation.
rm(list=ls())
# Data and libraries -----------
library(EpiModel)
## Loading required package: deSolve
## Loading required package: networkDynamic
## 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.
##
## 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").
## Loading required package: tergm
## Loading required package: ergm
##
## 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.
##
## 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(file="initial-casual-network.RData")
# Add differential dissolution structure -----------
durs.cas <- c(casual.pt.duration, casual.pt.duration, 217, 217)
dissolution.het.cas <-
~offset(edges)+
offset(nodefactor("identity", levels=c("MSM", "TGW", "HRH")))
theta.casual.hetdiss.coefs <-
dissolution_coefs(dissolution = dissolution.het.cas,
duration = durs.cas,
d.rate = d.rate)
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 46.704% of 8192 proposed steps.
## SAN summary statistics:
## edges degree0
## 3179 1131
## degree1 mix.identity.msm.msm
## 2197 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
## 0 103
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 313 329
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 193 398
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 162 271
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 441 442
## mix.age_group.45-64.45-64 mix.race.White.White
## 527 550
## mix.race.White.Black mix.race.Black.Black
## 625 403
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 689 500
## mix.race.Hispanic.Hispanic
## 412
## 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] -146.000000 292.184210 42.394735 -2777.576884 -166.775180
## [6] -19.944564 0.000000 -184.539276 0.000000 -98.657287
## [11] 123.067018 -322.868860 193.000000 161.169977 -147.520453
## [16] 271.000000 441.000000 209.859639 -260.870254 8.371009
## [21] 379.260964 -454.579172 310.361256 409.728476 -84.493229
## New statistics scaling =
## [1] 0.02711156 0.05701316 0.01919618 0.01919618 0.01919618 0.01919618
## [7] 0.01919618 0.01919618 0.01919618 0.14874690 0.04913386 0.06308055
## [13] 0.05963009 0.04098345 0.11069226 0.04126743 0.03137965 0.03443314
## [19] 0.03698899 0.02355875 0.02162201 0.03904761 0.02026959 0.02347713
## [25] 0.03719065
## Scaled Mahalanobis distance = 210180.357691096
## #2 of 4:
## SAN Metropolis-Hastings accepted 28.211% of 17408 proposed steps.
## SAN summary statistics:
## edges degree0
## 3303 917
## degree1 mix.identity.msm.msm
## 1952 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
## 0 235
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 244 682
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 69 302
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 357 135
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 139 304
## mix.age_group.45-64.45-64 mix.race.White.White
## 836 613
## mix.race.White.Black mix.race.Black.Black
## 407 912
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 556 252
## mix.race.Hispanic.Hispanic
## 563
## 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] -22.00000 78.18421 -202.60527 -2777.57688 -166.77518 -19.94456
## [7] 0.00000 -184.53928 0.00000 33.34271 54.06702 30.13114
## [13] 69.00000 65.16998 47.47955 135.00000 139.00000 71.85964
## [19] 48.12975 71.37101 161.26096 54.42083 177.36126 161.72848
## [25] 66.50677
## New statistics scaling =
## [1] 0.01945416 0.04867684 0.01396712 0.01396712 0.01396712 0.01396712
## [7] 0.01396712 0.01396712 0.01396712 0.11764728 0.06316118 0.04184109
## [13] 0.10121790 0.05316420 0.07779761 0.06582862 0.04004395 0.04596360
## [19] 0.03564083 0.03216765 0.02880665 0.03491789 0.02548610 0.03151638
## [25] 0.03889824
## Scaled Mahalanobis distance = 110297.763030266
## #3 of 4:
## SAN Metropolis-Hastings accepted 20.896% of 34816 proposed steps.
## SAN summary statistics:
## edges degree0
## 3182 841
## 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
## 246 707
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## 57 294
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 367 59
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## 59 290
## mix.age_group.45-64.45-64 mix.race.White.White
## 845 637
## mix.race.White.Black mix.race.Black.Black
## 342 948
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 476 188
## 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] -143.000000 2.184210 -1.605265 -2777.576884 -166.775180
## [6] -19.944564 0.000000 -184.539276 0.000000 56.342713
## [11] 56.067018 55.131140 57.000000 57.169977 57.479547
## [16] 59.000000 59.000000 57.859639 57.129746 95.371009
## [21] 96.260964 90.420828 97.361256 97.728476 94.506771
## New statistics scaling =
## [1] 0.02485258 0.06426584 0.01663728 0.01663728 0.01663728 0.01663728
## [7] 0.01663728 0.01663728 0.01663728 0.09766307 0.06255694 0.03806564
## [13] 0.10643064 0.04631475 0.06092895 0.07899323 0.04711507 0.04577161
## [19] 0.02829239 0.03051258 0.02815892 0.02870592 0.02524595 0.03748772
## [25] 0.03217721
## Scaled Mahalanobis distance = 129398.171082153
## #4 of 4:
## SAN Metropolis-Hastings accepted 0.059% of 69632 proposed steps.
## SAN summary statistics:
## edges degree0
## 3176 838
## degree1 mix.identity.msm.msm
## 2154 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
## 247 708
## 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
## 473 184
## 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] -149.000000 -0.815790 -0.605265 -2777.576884 -166.775180
## [6] -19.944564 0.000000 -184.539276 0.000000 56.342713
## [11] 57.067018 56.131140 57.000000 56.169977 56.479547
## [16] 57.000000 57.000000 56.859639 56.129746 94.371009
## [21] 94.260964 94.420828 94.361256 93.728476 94.506771
## New statistics scaling =
## [1] 0.02657542 0.06295376 0.01726501 0.01726501 0.01726501 0.01726501
## [7] 0.01726501 0.01726501 0.01726501 0.09433818 0.06071501 0.04198055
## [13] 0.10961473 0.04665773 0.06239862 0.07054211 0.05184223 0.04523866
## [19] 0.02877757 0.03059569 0.02831088 0.02692607 0.02593316 0.03360490
## [25] 0.03213967
## Scaled Mahalanobis distance = 134273.58301984
## 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 609 rows.
## Maximizing the pseudolikelihood.
## Finished MPLE.
## Starting Monte Carlo maximum likelihood estimation (MCMLE):
## Density guard set to 63792 from an initial count of 3176 edges.
##
## Iteration 1 of at most 500 with parameter:
## edges degree0
## -11.3154780 -5.0401271
## degree1 mix.identity.msm.msm
## -2.2222592 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 0.2621872
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -0.5715195 1.0068788
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -0.4876294
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## 0.5002263 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.0593519
## mix.age_group.45-64.45-64 mix.race.White.White
## 0.0000000 -0.4421731
## mix.race.White.Black mix.race.Black.Black
## -0.8122079 1.6938801
## mix.race.White.Hispanic mix.race.Black.Hispanic
## -1.1628621 -1.1712807
## mix.race.Hispanic.Hispanic
## 0.0000000
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -204.993164 45.705694
## degree1 mix.identity.msm.msm
## -3.386515 -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 85.737245
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 63.926393 66.772741
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 66.861384 70.717828
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 75.029561 81.103378
## mix.race.White.White mix.race.White.Black
## 88.800697 99.566628
## mix.race.Black.Black mix.race.White.Hispanic
## 51.013601 90.544850
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 99.895468 79.834896
## Starting MCMLE Optimization...
## Optimizing with step length 0.00504583622842381.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood did not improve.
##
## Iteration 2 of at most 500 with parameter:
## edges degree0
## 2.183209e+00 -4.989236e+00
## degree1 mix.identity.msm.msm
## -2.177554e+00 4.313363e-12
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 3.368542e-11 -9.767786e-12
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf -7.947813e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -1.541109e+01
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -1.626767e+01 -1.464320e+01
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -1.619437e+01
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -1.517124e+01 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.681680e+01
## mix.age_group.45-64.45-64 mix.race.White.White
## -1.566018e+01 1.771524e+00
## mix.race.White.Black mix.race.Black.Black
## 1.480995e+00 4.020775e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 1.041032e+00 1.080455e+00
## mix.race.Hispanic.Hispanic
## 2.209263e+00
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -187.539062 32.786749
## degree1 mix.identity.msm.msm
## 2.152547 -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 89.686463
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 63.862916 86.379187
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 53.824274 84.064508
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 49.349873 100.473496
## mix.race.White.White mix.race.White.Black
## 83.575111 113.703347
## mix.race.Black.Black mix.race.White.Hispanic
## 70.511648 85.450123
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 96.474570 77.395442
## Starting MCMLE Optimization...
## Optimizing with step length 0.42646177944529.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 2.569.
##
## Iteration 3 of at most 500 with parameter:
## edges degree0
## 2.018194e+00 -5.255862e+00
## degree1 mix.identity.msm.msm
## -2.304729e+00 4.313571e-12
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 3.368500e-11 -9.768123e-12
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf -7.946961e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -1.553071e+01
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -1.630178e+01 -1.465251e+01
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -1.613676e+01
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -1.517574e+01 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.678723e+01
## mix.age_group.45-64.45-64 mix.race.White.White
## -1.574484e+01 1.708337e+00
## mix.race.White.Black mix.race.Black.Black
## 1.440536e+00 4.061665e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 9.828796e-01 1.050848e+00
## mix.race.Hispanic.Hispanic
## 2.194763e+00
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -180.162109 22.642218
## degree1 mix.identity.msm.msm
## -7.045695 -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 82.935487
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 68.112916 78.422156
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 71.190485 82.401422
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 63.990498 87.964706
## mix.race.White.White mix.race.White.Black
## 86.294838 102.844948
## mix.race.Black.Black mix.race.White.Hispanic
## 80.566336 78.263600
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 93.377890 93.139583
## Starting MCMLE Optimization...
## Optimizing with step length 0.892761437522729.
## Using lognormal metric (see control.ergm function).
## Using log-normal approx (no optim)
## The log-likelihood improved by 3.012.
##
## Iteration 4 of at most 500 with parameter:
## edges degree0
## 1.825790e+00 -5.568277e+00
## degree1 mix.identity.msm.msm
## -2.449548e+00 4.312988e-12
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 3.368552e-11 -9.768014e-12
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf -7.947382e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -1.565512e+01
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -1.629262e+01 -1.463588e+01
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -1.611640e+01
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -1.520153e+01 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.678247e+01
## mix.age_group.45-64.45-64 mix.race.White.White
## -1.583794e+01 1.629745e+00
## mix.race.White.Black mix.race.Black.Black
## 1.371653e+00 4.113583e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 9.622609e-01 1.009781e+00
## mix.race.Hispanic.Hispanic
## 2.159602e+00
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -161.114258 -3.223017
## degree1 mix.identity.msm.msm
## -4.981242 -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 78.624940
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 79.285768 80.705359
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 80.917048 74.586969
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 88.572529 71.372910
## mix.race.White.White mix.race.White.Black
## 86.287025 85.232644
## mix.race.Black.Black mix.race.White.Hispanic
## 101.337820 95.413990
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 84.989218 100.274349
## 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 1.019.
## Step length converged once. Increasing MCMC sample size.
##
## Iteration 5 of at most 500 with parameter:
## edges degree0
## 1.823996e+00 -5.560942e+00
## degree1 mix.identity.msm.msm
## -2.444630e+00 4.313010e-12
## mix.identity.msm.tgw mix.identity.tgw.tgw
## 3.368553e-11 -9.768009e-12
## mix.identity.msm.hrh mix.identity.tgw.hrh
## -Inf -7.947387e-12
## mix.identity.hrh.hrh mix.age_group.18-24.18-24
## -Inf -1.566710e+01
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## -1.631796e+01 -1.465749e+01
## mix.age_group.18-24.35-44 mix.age_group.25-34.35-44
## -Inf -1.612012e+01
## mix.age_group.35-44.35-44 mix.age_group.18-24.45-64
## -1.515284e+01 -Inf
## mix.age_group.25-34.45-64 mix.age_group.35-44.45-64
## -Inf -1.681036e+01
## mix.age_group.45-64.45-64 mix.race.White.White
## -1.579788e+01 1.672186e+00
## mix.race.White.Black mix.race.Black.Black
## 1.388800e+00 4.079808e+00
## mix.race.White.Hispanic mix.race.Black.Hispanic
## 9.605246e-01 1.025719e+00
## mix.race.Hispanic.Hispanic
## 2.117794e+00
## Starting unconstrained MCMC...
## Back from unconstrained MCMC.
## Average estimating function values:
## edges degree0
## -170.345459 4.120245
## degree1 mix.identity.msm.msm
## -4.099406 -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 75.017030
## mix.age_group.18-24.25-34 mix.age_group.25-34.25-34
## 75.299195 74.179724
## mix.age_group.25-34.35-44 mix.age_group.35-44.35-44
## 79.004206 85.861627
## mix.age_group.35-44.45-64 mix.age_group.45-64.45-64
## 70.523457 84.949081
## mix.race.White.White mix.race.White.Black
## 94.508461 91.655984
## mix.race.Black.Black mix.race.White.Hispanic
## 90.916922 94.268238
## mix.race.Black.Hispanic mix.race.Hispanic.Hispanic
## 92.333212 80.621028
## 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 0.6957.
## 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$coef
theta.casual.form[1] <- theta.casual.form[1] - theta.casual.hetdiss.coefs$coef.adj[1]
sim.test <- simulate(casual.net,
formation=formation.casual.net,
dissolution=dissolution.het.cas,
coef.form=theta.casual.form,
coef.diss=theta.casual.hetdiss.coefs$coef.adj,
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] 3073
degreedist(casual.net.xn)/network.size(casual.net.xn)
## degree0 degree1 degree2 degree3 degree4
## 902 2180 1794 118 6
## degree0 degree1 degree2 degree3 degree4
## 0.1804 0.4360 0.3588 0.0236 0.0012
mixingmatrix(casual.net.xn, "identity")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## HRH MSM TGW
## HRH 0 54 3
## MSM 54 2797 213
## TGW 3 213 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
## 18-24 275 254 0 0
## 25-34 254 704 338 0
## 35-44 0 338 366 304
## 45-64 0 0 304 832
mixingmatrix(casual.net.xn, "race")
## Note: Marginal totals can be misleading
## for undirected mixing matrices.
## Black Hispanic White
## Black 909 194 346
## Hispanic 194 539 470
## White 346 470 615
saveRDS(theta.casual.hetdiss.coefs, file="theta.casual.hetdiss.coefs.RDS")