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Type 'q()' to quit R. > library(EpiILMCT) Loading required package: coda Loading required package: parallel > set.seed(22) > ## distance-based SIR continuous-time ILMs ## > data(SpatialData) > ## performing the MCMC-tool for analyzing the fully observed spatial data > ## under the SIR distance-based continuous ILM: > suspar <- list(NULL) > suspar[[1]]<-list(2,c("gamma", 1, 0.01, 0.5)) > suspar[[2]]<- rep(1,length(SpatialData$epidat[,1])) > kernel1 <- list(2, c("gamma", 1, 0.01, 0.5)) > > mcmcres2 <- epictmcmc(object = SpatialData, + distancekernel = "powerlaw", datatype = "known epidemic", nsim = 50, + control.sus = suspar, + kernel.par = kernel1) ************************************************ Start performing MCMC for the known epidemic SIR ILM for 50 iterations ************************************************ > #plot(mcmcres2, plottype = "parameter") > print(mcmcres2) ********************************************************* Model: SIR distance -based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Spatial parameter [1,] 2.00000 2.00000 [2,] 2.00000 2.00000 [3,] 1.50826 2.00000 [4,] 1.03277 2.53348 [5,] 1.38699 2.53348 [6,] 1.38699 2.57478 [7,] 2.31436 2.44046 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -3.06312 -3.06312 1.14963 10.59471 13.58724 14.09541 10.90346 Available components: [1] "compart.framework" "kernel.type" "data.assumption" [4] "parameter.samples" "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" "number.chains" > summary(mcmcres2) ********************************************************* Model: SIR distance-based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic number.chains : 1 chains number.iteration : 49 iterations number.parameter : 2 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 1.89607 0.306726 0.0433776 0.0673176 Spatial parameter 2.96792 0.338620 0.0478882 0.1142946 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 1.38699 1.73027 1.95966 2.06507 2.43753 Spatial parameter 2.00000 2.84056 3.03490 3.22303 3.30094 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE 16.293955 4.939316 0.698525 1.504505 2.5% 25% 50% 75% 97.5% -2.11525 17.20890 18.01699 18.49467 18.74961 4. acceptance.rate : Alpha_s[1] Spatial parameter 0.612245 0.387755 > > suspar <- list(NULL) > suspar[[1]]<-list(2,c("gamma", 1, 0.1, 2.5)) > suspar[[2]]<- matrix(rep(1,length(SpatialData$epidat[,1])),ncol=1) > kernel1 <- list(0.2, c("gamma", 1, 0.1, 0.01)) > > mcmcres22 <- epictmcmc(object = SpatialData, + distancekernel = "Cauchy", datatype = "known epidemic", nsim = 50, + control.sus = suspar, + kernel.par = kernel1) ************************************************ Start performing MCMC for the known epidemic SIR ILM for 50 iterations ************************************************ > #plot(mcmcres22, plottype = "parameter") > print(mcmcres22) ********************************************************* Model: SIR distance -based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Spatial parameter [1,] 2.00000 0.200000 [2,] 7.20904 0.200000 [3,] 7.20904 0.192428 [4,] 6.61663 0.188696 [5,] 6.32853 0.188696 [6,] 6.32853 0.202810 [7,] 6.32853 0.208162 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -16.30159 -2.55599 -2.14931 -1.77451 -1.77184 -2.21233 -2.41781 Available components: [1] "compart.framework" "kernel.type" "data.assumption" [4] "parameter.samples" "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" "number.chains" > summary(mcmcres22) ********************************************************* Model: SIR distance-based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic number.chains : 1 chains number.iteration : 49 iterations number.parameter : 2 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 6.814363 1.730951 0.24479343 0.5314169 Spatial parameter 0.184182 0.015674 0.00221663 0.0063711 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 4.152365 5.891223 6.642539 7.348461 10.360924 Spatial parameter 0.160209 0.170313 0.186134 0.196272 0.208162 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -2.660488 2.132398 0.301567 0.301567 2.5% 25% 50% 75% 97.5% -4.93207 -2.60576 -2.21981 -1.77649 -1.23782 4. acceptance.rate : Alpha_s[1] Spatial parameter 0.530612 0.897959 > > #plot(mcmcres2$log.likelihood) > #plot(mcmcres22$log.likelihood) > > ## performing the MCMC-tool for analyzing the partially observed spatial > ## data (unknown infection times) under the SIR distance-based > ## continuous ILM: > > suspar <- list(NULL) > suspar[[1]]<-list(2,c("gamma", 1, 0.01, 0.8)) > suspar[[2]]<- matrix(rep(1,length(SpatialData$epidat[,1])),ncol=1) > kernel1 <- list(2, c("gamma", 1, 0.01, 0.5)) > > mcmcres22 <- epictmcmc(object = SpatialData, distancekernel = "powerlaw", + datatype = "known removal", nsim = 50, + control.sus = suspar, kernel.par = kernel1, delta = list(1, 2, c(4, 2))) ************************************************ Start performing MCMC for the known removal SIR ILM for 50 iterations ************************************************ > > #plot(mcmcres22, plottype = "parameter") > print(mcmcres22) ********************************************************* Model: SIR distance -based continuous-time ILM Method: Data augmented Markov chain Monte Carlo (DA-MCMC) Data assumption: partially observed epidemic (unknown infection times) ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Spatial parameter Infectious period rate [1,] 2.000000 2.00000 2.00000 [2,] 2.000000 2.00000 1.68393 [3,] 1.468239 2.00000 1.74881 [4,] 0.769424 2.06189 1.69124 [5,] 0.769424 2.06189 1.63194 [6,] 1.058423 2.65603 2.00467 [7,] 1.979617 3.04849 2.14446 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -37.5869 -30.6891 -30.8283 -31.7703 -32.1660 -27.0975 -31.6516 infection.times.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0 0.00205424 0.170259 0.171529 0.229401 0.266345 0.296273 0.305499 [2,] 0 0.00205424 0.170259 0.171529 0.229401 0.266345 0.296273 0.305499 [3,] 0 0.00205424 0.170259 0.171529 0.229401 0.266345 0.296273 0.305499 [4,] 0 0.00205424 0.170259 0.129142 0.229401 0.266345 0.296273 0.305499 [5,] 0 0.00205424 0.170259 0.126579 0.574683 0.266345 0.296273 0.305499 [6,] 0 0.00205424 0.170259 0.173654 0.543768 0.384592 0.296273 0.305499 [7,] 0 0.00205424 0.170259 0.173654 0.118744 0.384592 0.296273 0.305499 [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] 0.318990 0.3376868 0.362637 0.403727 0.432961 0.591705 2.28403 2.32537 [2,] 0.318990 0.3376868 0.360203 0.441448 0.432961 0.591705 2.28403 2.32537 [3,] 0.318990 0.4806679 0.360203 0.407030 0.432961 0.591705 2.28403 2.32537 [4,] 0.318990 0.4806679 0.360203 0.407030 0.432961 0.591705 2.28403 2.36253 [5,] 0.350192 0.4806679 0.360203 0.407030 0.378509 0.591705 2.28403 2.36253 [6,] 0.350192 0.4405425 0.360203 0.407030 0.378509 0.591705 2.40133 2.28223 [7,] 0.350192 0.0437945 0.360203 0.368562 0.378509 0.591705 2.46112 2.39644 [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [1,] 2.34818 2.57043 2.57258 2.57268 2.57963 2.59514 2.60761 2.60862 2.6131 [2,] 2.34818 2.57043 2.57258 2.57268 2.57963 2.59514 2.60761 2.60862 2.6131 [3,] 2.34818 2.57043 2.57258 2.57268 2.57963 2.59514 2.60761 2.60862 2.6131 [4,] 2.34818 2.57043 2.57258 2.57268 2.57963 2.59514 2.60761 2.60862 2.6131 [5,] 2.34818 2.57043 2.57347 2.57268 2.57963 2.57608 2.60761 3.21554 2.6131 [6,] 2.51273 2.57043 2.57347 2.57268 2.57963 2.57608 2.60761 3.13657 2.6131 [7,] 2.50495 2.57043 2.57347 2.57268 2.57963 2.60976 2.60761 3.30307 2.6131 [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [1,] 2.61327 2.62189 2.75117 2.89072 2.93546 3.15278 3.72520 3.93308 [2,] 2.61327 2.62189 2.75117 2.89072 2.93546 3.15278 3.44762 3.93987 [3,] 2.61327 2.62189 2.75117 2.89072 2.63114 3.15278 3.44762 2.49516 [4,] 2.61327 2.62189 2.68837 2.40012 2.63114 2.85644 3.44762 2.49516 [5,] 2.61327 2.62189 2.53495 4.07396 2.63114 2.87806 3.44762 3.00887 [6,] 2.61327 2.62189 2.53495 3.18701 2.61734 2.88492 3.44762 4.01186 [7,] 2.61327 2.62189 2.53495 3.64901 2.61734 3.33963 3.44762 3.60033 Average.infectious.periods : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] 0.526360 0.533497 0.583209 0.609117 0.519299 0.509422 0.509078 Available components: [1] "compart.framework" "kernel.type" [3] "data.assumption" "parameter.samples" [5] "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" [9] "number.chains" "infection.times.samples" [11] "Average.infectious.periods" > summary(mcmcres22) ********************************************************* Model:SIR distance-based continuous-time ILM Method: Data augmented Markov chain Monte Carlo (DA-MCMC) Data assumption: partially observed epidemic (unknown infection times) number.chains : 1 chains number.iteration : 49 iterations number.parameter : 3 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 1.48846 0.340927 0.0482143 0.0798859 Spatial parameter 2.72630 0.304536 0.0430679 0.0873640 Infectious period rate 1.94589 0.300679 0.0425224 0.0425224 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 0.834449 1.26014 1.44916 1.69274 2.08718 Spatial parameter 2.000000 2.62407 2.75524 2.85487 3.39218 Infectious period rate 1.332744 1.72543 1.96393 2.13026 2.47657 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -25.33535 7.12539 1.00768 2.36127 2.5% 25% 50% 75% 97.5% -39.3472 -30.9354 -24.6569 -20.1263 -13.5805 4. Empirical mean, standard deviation, and quantiles for the average infectious periods, Mean SD Naive SE Time-series SE 0.52338927 0.03172609 0.00448675 0.00965965 2.5% 25% 50% 75% 97.5% 0.469662 0.498067 0.526398 0.544215 0.581773 5. acceptance.rate : Alpha_s[1] Spatial parameter Infectious period rate 0.387755 0.387755 1.000000 > > ## distance-based and network-based SIR ILMs ## > set.seed(22) > data(SpatialNetData) > ## performing the MCMC-tool for analyzing the fully observed spatial and > ## network data > ## under the SIR distance-based and network-based continuous-time ILM: > suspar <- list(NULL) > suspar[[1]]<-list(c(0.08,0.2),matrix(c("gamma", "gamma", 1, 1, 0.01, 0.01, 0.1, 0.5), + ncol = 4, nrow = 2)) > suspar[[2]]<- SpatialNetData[[2]] > kernel1 <- list(c(5, 0.5), matrix(c("gamma", "gamma", 1, 1, + 0.01, 0.01, 0.5, 1), ncol = 4, nrow = 2)) > > mcmcres3 <- epictmcmc(object = SpatialNetData[[1]], distancekernel = "powerlaw", + datatype = "known epidemic", nsim = 50, + control.sus = suspar, kernel.par = kernel1) ************************************************ Start performing MCMC for the known epidemic SIR ILM for 50 iterations ************************************************ > #plot(mcmcres3, plottype = "parameter") > print(mcmcres3) ********************************************************* Model: SIR both -based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Alpha_s[2] Spatial parameter Network parameter [1,] 0.08 0.2000000 5.00000 0.500000 [2,] 0.08 0.2000000 4.92086 0.500000 [3,] 0.08 0.2000000 4.38342 0.500000 [4,] 0.08 0.0656835 4.38342 0.500000 [5,] 0.08 0.0656835 4.38342 0.500000 [6,] 0.08 0.0656835 4.15239 0.500000 [7,] 0.08 0.0656835 4.15239 0.373708 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -1685.198 -1465.431 -596.536 -583.809 -583.809 -410.641 -410.011 Available components: [1] "compart.framework" "kernel.type" "data.assumption" [4] "parameter.samples" "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" "number.chains" > summary(mcmcres3) ********************************************************* Model: SIR both-based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic number.chains : 1 chains number.iteration : 49 iterations number.parameter : 4 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 0.0231832 0.0260979 0.00369080 0.0160319 Alpha_s[2] 0.1272768 0.0476480 0.00673845 0.0217451 Spatial parameter 2.1158366 1.1783617 0.16664551 0.7689205 Network parameter 1.1630018 0.6721603 0.09505782 0.2556254 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 0.00433566 0.00433566 0.0132278 0.0247037 0.08000 Alpha_s[2] 0.06568355 0.07981772 0.1185407 0.1796567 0.20000 Spatial parameter 0.98367282 1.33422426 1.5163194 2.4045377 4.79994 Network parameter 0.14692978 0.52418031 1.2013297 1.7832496 2.39566 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -175.8228 321.9825 45.5352 126.3105 2.5% 25% 50% 75% 97.5% -1269.9296 -71.7511 -62.1213 -60.9960 -59.5622 4. acceptance.rate : Alpha_s[1] Alpha_s[2] Spatial parameter Network parameter 0.0612245 0.1836735 0.4897959 0.4489796 > > ## network-based SIR ILMs ## > set.seed(22) > data(NetworkData) > ## performing the MCMC-tool for analyzing the fully observed network data > ## under the SIR network-based continuous-time ILM: > > suspar <- list(NULL) > suspar[[1]]<-list(c(0.08,0.5),matrix(c("gamma", "gamma", 1, 1, 1, 1, 0.1, 0.5), + ncol = 4, nrow = 2)) > suspar[[2]]<- NetworkData[[2]] > > mcmcres4 <- epictmcmc(object = NetworkData[[1]], datatype = "known epidemic", + nsim = 50, control.sus = suspar) ************************************************ Start performing MCMC for the known epidemic SIR ILM for 50 iterations ************************************************ > #plot(mcmcres4, plottype = "parameter") > print(mcmcres4) ********************************************************* Model: SIR network -based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Alpha_s[2] [1,] 0.0800000 0.500000 [2,] 0.0800000 0.500000 [3,] 0.0800000 0.500000 [4,] 0.0800000 0.500000 [5,] 0.0800000 0.541302 [6,] 0.0800000 0.406985 [7,] 0.0958326 0.406985 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -54.8056 -54.8056 -54.8056 -54.8056 -54.9080 -54.9925 -55.1539 Available components: [1] "compart.framework" "kernel.type" "data.assumption" [4] "parameter.samples" "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" "number.chains" > summary(mcmcres4) ********************************************************* Model: SIR network-based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic number.chains : 1 chains number.iteration : 49 iterations number.parameter : 2 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 0.0799349 0.0255693 0.00361605 0.00603613 Alpha_s[2] 0.5244400 0.1211571 0.01713420 0.02085079 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 0.0378314 0.0623953 0.078657 0.101391 0.123595 Alpha_s[2] 0.2927052 0.4208295 0.500000 0.645876 0.686824 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -55.7300414 0.6170862 0.0872692 0.1980547 2.5% 25% 50% 75% 97.5% -56.8526 -56.1493 -55.7465 -55.2689 -54.8056 4. acceptance.rate : Alpha_s[1] Alpha_s[2] 0.367347 0.306122 > > ## network-based SINR ILMs ## > set.seed(22) > data(NetworkDataSINR) > names(NetworkDataSINR) [1] "loc" "net" "cov" "epi" > > netSINR<-as.epidat(type = "SINR", kerneltype = "network", incub.time = NetworkDataSINR$epi[,4], inf.time = NetworkDataSINR$epi[,6], rem.time = NetworkDataSINR$epi[,2], id.individual = NetworkDataSINR$epi[,1], location = NetworkDataSINR$loc, network = NetworkDataSINR$net, network.type = "powerlaw") > > ## performing the MCMC-tool for analyzing the fully observed network data > ## under the SINR network-based continuous-time ILM: > suspar <- list(NULL) > suspar[[1]]<-list(c(0.08,0.2),matrix(c("gamma", "gamma", 1, 1, 0.01, 0.01, 0.05, 0.5), + ncol = 4, nrow = 2)) > suspar[[2]]<- NetworkDataSINR$cov > > mcmcres5 <- epictmcmc(object = netSINR, datatype = "known epidemic", + nsim = 500, control.sus = suspar) ************************************************ Start performing MCMC for the known epidemic SINR ILM for 500 iterations ************************************************ > > mcmcres5 ********************************************************* Model: SINR network -based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Alpha_s[2] [1,] 0.0800000 0.200000 [2,] 0.0921908 0.393152 [3,] 0.0842770 0.502432 [4,] 0.1428780 1.195509 [5,] 0.0891341 1.237414 [6,] 0.0891341 1.237414 [7,] 0.0915133 1.650862 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -57.9014 -48.5078 -45.7331 -36.8694 -35.9318 -35.9318 -34.1604 Available components: [1] "compart.framework" "kernel.type" "data.assumption" [4] "parameter.samples" "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" "number.chains" > #plot(mcmcres5, plottype = "parameter") > print(mcmcres5) ********************************************************* Model: SINR network -based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Alpha_s[2] [1,] 0.0800000 0.200000 [2,] 0.0921908 0.393152 [3,] 0.0842770 0.502432 [4,] 0.1428780 1.195509 [5,] 0.0891341 1.237414 [6,] 0.0891341 1.237414 [7,] 0.0915133 1.650862 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -57.9014 -48.5078 -45.7331 -36.8694 -35.9318 -35.9318 -34.1604 Available components: [1] "compart.framework" "kernel.type" "data.assumption" [4] "parameter.samples" "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" "number.chains" > summary(mcmcres5) ********************************************************* Model:SINR network-based continuous-time ILM Method: Markov chain Monte Carlo (MCMC) Data assumption: fully observed epidemic number.chains : 1 chains number.iteration : 499 iterations number.parameter : 2 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 0.104391 0.0268505 0.00120079 0.00241341 Alpha_s[2] 2.255763 0.5584145 0.02497306 0.07358180 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 0.0590052 0.0858674 0.0993167 0.121108 0.164175 Alpha_s[2] 1.1914624 1.9126262 2.2150284 2.664166 3.362149 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -34.6860101 1.6608976 0.0742776 0.1557291 2.5% 25% 50% 75% 97.5% -37.4167 -35.0181 -34.2680 -33.8277 -33.5738 4. acceptance.rate : Alpha_s[1] Alpha_s[2] 0.547094 0.699399 > > suspar <- list(NULL) > suspar[[1]]<-list(c(0.08,0.2),matrix(c("gamma", "gamma", 1, 1, 0.01, 0.01, 0.05, 0.5), + ncol = 4, nrow = 2)) > suspar[[2]]<- NetworkDataSINR$cov > delta1<-list(1,2,c(4,2)) > spark<-list(1,matrix(c("gamma", 1, 0.01, 0.05), ncol = 4, nrow = 1)) > > mcmcres5 <- epictmcmc(object = netSINR, datatype = "known removal", + nsim = 500, control.sus = suspar, spark.par = spark, delta = delta1) ************************************************ Start performing MCMC for the known removal SINR ILM for 500 iterations ************************************************ > > print(mcmcres5) ********************************************************* Model: SINR network -based continuous-time ILM Method: Data augmented Markov chain Monte Carlo (DA-MCMC) Data assumption: partially observed epidemic (unknown infection times) ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Alpha_s[2] Spark Incubation period rate [1,] 0.08000000 0.200000 1.000000 2.000000 [2,] 0.08000000 0.200000 1.000000 0.377508 [3,] 0.06970123 0.147506 1.000000 0.609031 [4,] 0.00265249 0.147506 0.996868 0.335893 [5,] 0.00265249 0.147506 0.996868 0.560736 [6,] 0.00265249 0.301711 0.996868 0.430007 [7,] 0.00265249 0.297776 0.945972 0.361570 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -424.604 -318.368 -333.921 -302.660 -318.866 -304.719 -284.737 infection.times.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0 0.798650 0.981407 1.150897 1.188763 1.404446 1.465774 1.55333 [2,] 0 0.499732 0.657686 0.480468 1.188763 2.530820 1.249633 1.55333 [3,] 0 0.499732 1.130589 0.480468 0.103574 2.530820 1.249633 1.55333 [4,] 0 0.499732 1.130589 0.480468 0.103574 2.530820 1.249633 1.55333 [5,] 0 0.499732 1.130589 0.480468 0.103574 2.722829 0.215008 1.55333 [6,] 0 0.223843 1.130589 0.748223 0.103574 0.427577 0.215008 1.55333 [7,] 0 0.223843 1.130589 1.241965 0.103574 0.427577 0.215008 1.55333 [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] 1.680805 1.69596 1.711758 1.836505 1.98264 2.342569 2.38468 2.507263 [2,] 0.697208 1.69596 1.711758 0.964088 2.65327 2.342569 2.38468 2.507263 [3,] 0.697208 3.80069 1.514523 0.964088 2.05445 0.566992 3.08232 0.796542 [4,] 2.267777 3.80069 1.851911 0.964088 2.05445 0.399615 3.08232 0.796542 [5,] 3.811607 3.77140 1.851911 0.964088 2.05445 0.399615 0.32994 0.796542 [6,] 1.619746 1.41366 1.283158 0.964088 2.05445 0.399615 0.32994 0.796542 [7,] 2.990378 1.41366 0.894828 0.964088 2.05445 0.399615 0.32994 0.796542 [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [1,] 2.57691 2.59159 2.67964 2.70830 2.78291 2.88363 3.00249 3.17423 3.56576 [2,] 2.57691 2.98036 2.67964 3.72241 1.60874 2.88363 3.00249 3.17423 3.56576 [3,] 1.84042 2.69132 1.44105 3.72241 1.34286 2.95763 3.00249 3.17423 3.56576 [4,] 1.84042 2.69132 1.44105 3.72241 1.43536 2.95763 2.10878 3.17423 3.56576 [5,] 1.84042 2.69132 1.44105 3.43700 1.05330 2.88220 2.10878 2.50835 3.70938 [6,] 1.84042 1.65894 1.44105 3.40244 3.01211 3.08337 2.10878 3.48962 4.79620 [7,] 2.94762 1.65894 3.87147 3.40244 3.01211 2.26047 2.10878 2.41854 5.38670 [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [1,] 3.859196 4.03189 5.14651 5.74088 6.20128 6.39138 6.90974 7.88329 8.46363 [2,] 2.381537 4.03189 5.14651 5.74088 4.98871 6.39138 7.57937 7.51476 8.46363 [3,] 2.381537 4.03189 5.14651 5.74088 7.50633 1.79678 6.54359 6.35593 8.46363 [4,] 1.415022 2.97226 5.14651 5.74088 7.86739 1.79678 6.54359 6.35593 8.46363 [5,] 1.415022 2.97226 5.14651 5.74088 5.84400 1.79678 6.54359 8.15245 8.46363 [6,] 0.818126 3.42523 5.14651 4.33559 7.29166 1.79678 6.52771 3.94196 8.46363 [7,] 0.818126 2.28125 5.14651 4.33559 5.76639 1.79678 5.48353 3.94196 7.14591 Average.incubation.periods : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] 2.07371 2.00740 2.44279 2.28815 2.56920 2.64582 2.86066 Available components: [1] "compart.framework" "kernel.type" [3] "data.assumption" "parameter.samples" [5] "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" [9] "number.chains" "infection.times.samples" [11] "Average.incubation.periods" > summary(mcmcres5) ********************************************************* Model: SINR network -based continuous-time ILM Method: Data augmented Markov chain Monte Carlo (DA-MCMC) Data assumption: partially observed epidemic (unknown infection times) number.chains : 1 chains number.iteration : 499 iterations number.parameter : 4 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 0.0901432 0.0422878 0.00189117 0.00905739 Alpha_s[2] 1.3426357 0.6300286 0.02817573 0.15872823 Spark 0.0690621 0.1865990 0.00834496 0.07118797 Incubation period rate 0.6516658 0.1765595 0.00789598 0.04514386 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 0.002244012 0.06257701 0.0962865 0.1181139 0.166238 Alpha_s[2] 0.200000000 0.87965838 1.2523315 1.7607854 2.671738 Spark 0.000442772 0.00765814 0.0156120 0.0272933 0.834310 Incubation period rate 0.274160713 0.54931130 0.6589894 0.7488295 0.978796 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -98.25200 39.60211 1.77106 10.46456 2.5% 25% 50% 75% 97.5% -235.8996 -98.0788 -89.0247 -81.4148 -70.1543 4. Empirical mean, standard deviation, and quantiles for the average incubation periods, Mean SD Naive SE Time-series SE 1.7626573 0.5845258 0.0261408 0.1583242 2.5% 25% 50% 75% 97.5% 1.21330 1.45875 1.60276 1.80631 3.82202 5. acceptance.rate : Alpha_s[1] Alpha_s[2] Spark 0.507014 0.575150 0.306613 Incubation period rate 1.000000 > #plot(mcmcres5, plottype = "parameter") > #plot(mcmcres5, plottype = "inf.times") > > suspar <- list(NULL) > suspar[[1]]<-list(c(0.08,0.2),matrix(c("gamma", "gamma", 1, 1, 0.01, 0.01, 0.05, 0.5), + ncol = 4, nrow = 2)) > suspar[[2]]<- NetworkDataSINR$cov > delta1<-list(NULL) > delta1[[1]]<-c(1,1) > delta1[[2]]<-c(2,2) > delta1[[3]]<-matrix(c(4,4,2,2),ncol=2,nrow=2) > spark<-list(1,matrix(c("gamma", 1, 0.01, 0.05), ncol = 4, nrow = 1)) > > mcmcres5 <- epictmcmc(object = netSINR, datatype = "unknown removal", + nsim = 500, control.sus = suspar, spark.par = spark, delta = delta1) ************************************************ Start performing MCMC for the unknown removal SINR ILM for 500 iterations ************************************************ > > print(mcmcres5) ********************************************************* Model: SIR network -based continuous-time ILM Method: Data augmented Markov chain Monte Carlo (DA-MCMC) Data assumption: partially observed epidemic (unknown infection & removal times) ********************************************************* Output: parameter.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 Alpha_s[1] Alpha_s[2] Spark Incubation period rate Delay period rate [1,] 0.0800000 0.2000000 1.000000 2.000000 2.00000 [2,] 0.0631093 0.2000000 1.000000 0.463754 2.11551 [3,] 0.0631093 0.2000000 0.926837 0.508486 2.91697 [4,] 0.0631093 0.2000000 0.926837 0.465738 2.81587 [5,] 0.0631093 0.2000000 0.926837 0.425564 3.04722 [6,] 0.0617764 0.0863188 0.926837 0.487198 2.34059 [7,] 0.0100152 0.1809111 0.926837 0.407987 1.96173 log.likelihood : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] -462.735 -346.151 -339.682 -331.641 -339.933 -333.702 -319.883 infection.times.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0 0.798650 0.981407 1.15090 1.18876 1.404446 1.46577 1.553328 1.680805 [2,] 0 0.798650 0.609762 1.15090 1.69215 1.404446 1.25781 1.931474 1.644585 [3,] 0 0.962407 0.609762 1.67894 1.69215 1.404446 2.25760 0.082044 1.644585 [4,] 0 1.031230 0.446182 1.27479 1.69215 1.404446 2.20787 2.721110 1.091716 [5,] 0 1.031230 0.446182 1.09108 1.69215 1.404446 2.20787 2.721110 3.579887 [6,] 0 1.337644 2.381303 1.09108 1.69215 1.404446 2.20787 2.721110 2.931319 [7,] 0 1.337644 2.656687 2.18727 3.32539 0.921639 2.83233 3.134041 0.870639 [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [1,] 1.695957 1.71176 1.83651 1.98264 2.34257 2.384676 2.507263 2.57691 [2,] 1.953689 1.71176 1.83651 3.15612 2.28773 2.384676 2.507263 2.57691 [3,] 1.953689 1.72029 3.36629 3.15612 2.47253 1.402250 2.507263 3.71029 [4,] 0.956114 2.72288 3.36629 3.15612 1.07158 0.539254 1.684692 3.83622 [5,] 0.956114 2.72288 3.22510 3.22896 1.07158 3.465129 0.239833 3.83622 [6,] 0.956114 1.61113 3.22637 2.55302 1.07158 3.465129 0.239833 5.19614 [7,] 1.421447 1.89511 3.16870 2.84106 1.17156 3.465129 0.239833 5.19614 [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 2.591592 2.67964 2.70830 2.782910 2.88363 3.00249 3.17423 3.56576 3.85920 [2,] 2.591592 2.67964 3.03162 2.084053 2.30065 2.41378 2.86030 3.56576 3.90135 [3,] 0.824271 2.33634 3.03162 1.426486 2.50337 2.36822 4.02787 2.57131 3.39042 [4,] 0.824271 1.34319 3.03162 1.020939 2.24953 2.36822 4.02787 2.57131 3.39042 [5,] 0.824271 2.69053 3.03162 1.020939 2.24953 3.50079 4.02787 2.57131 2.02577 [6,] 0.201719 1.30044 3.03162 1.020939 2.63805 3.71934 2.19472 2.57131 2.02577 [7,] 0.201719 1.30044 2.57715 0.262596 2.63805 3.90713 2.19472 2.57131 2.02577 [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [1,] 4.03189 5.14651 5.74088 6.20128 6.39138 6.909738 7.88329 8.46363 [2,] 3.57070 5.14651 5.74088 5.16583 6.39138 0.706856 7.46777 8.46363 [3,] 3.57070 4.51889 5.74088 5.16583 1.69278 0.706856 6.30382 8.46363 [4,] 3.57070 4.51889 5.74088 5.16583 1.69278 0.706856 7.90897 8.46363 [5,] 3.57070 8.45256 5.74088 5.16583 1.69278 0.706856 4.66720 8.46363 [6,] 3.57070 8.14775 5.74088 5.60772 1.69278 0.706856 2.06022 8.46363 [7,] 3.57070 8.32478 5.74088 5.60772 1.69278 0.706856 2.06022 8.46363 Average.incubation.periods : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] 2.07371 2.14161 2.54474 2.41185 2.42536 2.38297 2.50805 removal.times.samples : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 4.50490 2.29471 3.70100 2.27570 6.94112 3.09950 4.35899 4.00825 4.70701 [2,] 2.41519 2.29471 3.88444 2.27570 5.88941 3.09950 4.95810 3.93696 4.44331 [3,] 2.21610 3.34211 3.88444 2.36352 5.88941 3.09950 5.21841 4.12833 4.44331 [4,] 2.03101 2.32819 3.55542 2.56197 5.88941 3.09950 4.88689 3.85544 4.72153 [5,] 2.70881 2.32819 3.55542 3.03617 5.88941 3.09950 4.88689 3.85544 5.04278 [6,] 1.90641 2.67777 3.62883 3.03617 5.88941 3.09950 4.88689 3.85544 4.58645 [7,] 1.77673 2.67777 5.19275 2.68625 6.34543 2.90739 4.15495 4.20396 5.48281 [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [1,] 4.74021 3.15956 3.53135 5.08073 4.36785 4.60618 3.63237 6.09532 3.23545 [2,] 5.53037 3.15956 3.53135 4.64449 4.67583 4.60618 3.63237 6.09532 3.23545 [3,] 5.53037 3.49333 3.37837 4.64449 3.71486 4.56704 3.63237 6.08600 3.71692 [4,] 5.60290 3.42949 3.37837 4.64449 3.33725 4.83103 4.06271 6.02694 3.71692 [5,] 5.60290 3.42949 3.55667 4.57998 3.33725 4.87355 3.88993 6.02694 3.71692 [6,] 5.60290 3.38442 3.51111 4.87619 3.33725 4.87355 3.88993 6.48035 3.72279 [7,] 4.73409 3.86058 3.38885 5.22635 3.77899 4.87355 3.88993 6.48035 3.72279 [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [1,] 4.91063 4.00842 4.16219 5.97581 4.14633 5.40700 6.47184 6.24091 7.59606 [2,] 4.91063 4.23561 4.57990 4.30346 4.19099 5.29137 6.47184 6.14466 6.99496 [3,] 4.07798 4.23561 4.24590 4.14833 4.54416 4.78023 6.49301 6.23139 6.99496 [4,] 4.11839 4.23561 4.28167 4.36789 4.54416 4.78023 6.49301 6.23139 6.99496 [5,] 4.22924 4.23561 4.28167 4.36789 4.39038 4.78023 6.49301 6.24538 6.99496 [6,] 4.31540 4.23561 4.28167 4.11968 4.97525 4.81242 6.49301 6.24538 6.99496 [7,] 4.31540 3.98216 4.71975 4.11968 4.24019 4.81242 6.49301 6.24538 6.99496 [,28] [,29] [,30] [,31] [,32] [,33] [,34] [1,] 9.85260 9.79913 11.43192 8.28133 9.17592 8.61186 11.4464 [2,] 9.85260 9.79913 8.20512 8.28133 8.77855 9.19164 11.4464 [3,] 9.56259 9.79913 8.20512 8.51514 8.77855 8.81332 11.4464 [4,] 9.56259 9.79913 8.20512 8.51514 8.77855 8.72112 11.4464 [5,] 10.24497 9.79913 8.20512 8.51514 8.77855 9.03137 11.4464 [6,] 9.67143 9.79913 8.40749 8.51514 8.77855 9.10841 11.4464 [7,] 9.79480 9.79913 8.40749 8.51514 8.77855 9.10841 11.4464 Average.delay.periods : Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 7 Thinning interval = 1 [1] 0.472753 0.446615 0.248111 0.389215 0.284422 0.460111 0.334455 Available components: [1] "compart.framework" "kernel.type" [3] "data.assumption" "parameter.samples" [5] "log.likelihood" "acceptance.rate" [7] "number.iteration" "number.parameter" [9] "number.chains" "infection.times.samples" [11] "Average.incubation.periods" "removal.times.samples" [13] "Average.delay.periods" > summary(mcmcres5) ********************************************************* Model:SINR network-based continuous-time ILM Method: Data augmented Markov chain Monte Carlo (DA-MCMC) Data assumption: partially observed epidemic (unknown infection & removal times) number.chains : 1 chains number.iteration : 499 iterations number.parameter : 5 parameters ********************************************************* 1. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean SD Naive SE Time-series SE Alpha_s[1] 0.0906850 0.035796 0.00160084 0.00552482 Alpha_s[2] 1.9264352 0.770483 0.03445704 0.25045562 Spark 0.0475757 0.161995 0.00724462 0.05230526 Incubation period rate 0.8131876 0.203314 0.00909250 0.02769249 Delay period rate 1.1895168 0.634408 0.02837160 0.27874133 2. Quantiles for each variable: 2.5% 25% 50% 75% 97.5% Alpha_s[1] 0.002478839 0.06816303 0.09021533 0.1142395 0.151443 Alpha_s[2] 0.217856373 1.44259630 1.91260662 2.4290290 3.431349 Spark 0.000565167 0.00343653 0.00661801 0.0138649 0.674608 Incubation period rate 0.398069175 0.69175877 0.79999615 0.9435593 1.211758 Delay period rate 0.399093948 0.71843162 1.02470640 1.4876172 2.697205 3. Empirical mean, standard deviation, and quantiles for the log likelihood, Mean SD Naive SE Time-series SE -122.50923 39.82169 1.78088 10.83528 2.5% 25% 50% 75% 97.5% -258.3478 -121.9456 -114.2149 -106.1493 -94.0826 4. Empirical mean, standard deviation, and quantiles for the average incubation periods, Mean SD Naive SE Time-series SE 1.3811755 0.4054302 0.0181314 0.0951797 2.5% 25% 50% 75% 97.5% 0.969978 1.155928 1.294316 1.453665 2.881035 5. Empirical mean, standard deviation, and quantiles for the average delay periods, Mean SD Naive SE Time-series SE 1.1222098 0.5685896 0.0254281 0.2430022 2.5% 25% 50% 75% 97.5% 0.311357 0.674145 1.050419 1.420156 2.385745 6. acceptance.rate : Alpha_s[1] Alpha_s[2] Spark 0.521042 0.653307 0.252505 Incubation period rate Delay period rate 1.000000 1.000000 > #plot(mcmcres5, plottype = "parameter") > #plot(mcmcres5, plottype = "inf.times") > #plot(mcmcres5, plottype = "rem.times") > > proc.time() user system elapsed 2.89 0.10 2.98