R version 4.4.0 RC (2024-04-16 r86468 ucrt) -- "Puppy Cup"
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> ## This file is part of SimInf, a framework for stochastic
> ## disease spread simulations.
> ##
> ## Copyright (C) 2015 -- 2022 Stefan Widgren
> ##
> ## SimInf is free software: you can redistribute it and/or modify
> ## it under the terms of the GNU General Public License as published by
> ## the Free Software Foundation, either version 3 of the License, or
> ## (at your option) any later version.
> ##
> ## SimInf is distributed in the hope that it will be useful,
> ## but WITHOUT ANY WARRANTY; without even the implied warranty of
> ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
> ## GNU General Public License for more details.
> ##
> ## You should have received a copy of the GNU General Public License
> ## along with this program.  If not, see <https://www.gnu.org/licenses/>.
> 
> library(SimInf)
> library(tools)
> source("util/check.R")
> 
> ## Specify the number of threads to use.
> set_num_threads(1)
> 
> ##
> ## Create a model with parameters in gdata
> ##
> model <- mparse(transitions = c("S -> beta*S*I/(S+I+R) -> I",
+                                 "I -> gamma*I -> R"),
+                 compartments = c("S", "I", "R"),
+                 gdata = c(beta = 1, gamma = 0.5),
+                 u0 = data.frame(S = rep(9999, 2), I = 1, R = 0),
+                 tspan = 1:50)
> 
> distance_fn_gdata <- function(result, ...) {
+     p <- c(2e-04, 0.00015, 5e-05, 5e-05, 2e-04, 0.00025, 0.00025,
+            0.00025, 0.00025, 0.00015, 0.00035, 6e-04, 0.001, 0.0022,
+            0.00395, 0.00655, 0.0102, 0.01755, 0.02795, 0.04235,
+            0.05925, 0.07135, 0.08025, 0.08205, 0.0744, 0.0657,
+            0.05785, 0.04775, 0.03735, 0.02855, 0.02265, 0.01775,
+            0.0128, 0.01005, 0.00745, 0.00545, 0.0038, 0.0027, 0.00205,
+            0.00145, 0.0012, 8e-04, 7e-04, 3e-04, 2e-04, 0.00015,
+            5e-05, 0, 0, 0)
+ 
+     sum((prevalence(result, I ~ .)$prevalence - p)^2)
+ }
> 
> set.seed(123)
> fit <- abc(model = model,
+            priors = c(beta ~ uniform(0.5, 1.5),
+                       gamma ~ uniform(0.3, 0.7)),
+            npart = 10,
+            distance = distance_fn_gdata,
+            tolerance = c(0.1, 0.05))
> fit
Number of particles per generation: 10
Number of generations: 2

Generation 2:
-------------
 Accrate: 7.14e-01
 ESS: 9.56e+00

        Min. 1st Qu. Median  Mean 3rd Qu.  Max.
 beta  0.513   0.553  0.725 0.715   0.840 0.944
 gamma 0.396   0.502  0.546 0.555   0.623 0.683
> summary(fit)
Number of particles per generation: 10
Number of generations: 2

Generation 1:
-------------
 Accrate: 7.14e-01
 ESS: 1.00e+01

        Min. 1st Qu. Median  Mean 3rd Qu.  Max.
 beta  0.542   0.626  0.682 0.798   0.946 1.209
 gamma 0.393   0.440  0.514 0.519   0.548 0.685

Generation 2:
-------------
 Accrate: 7.14e-01
 ESS: 9.56e+00

        Min. 1st Qu. Median  Mean 3rd Qu.  Max.
 beta  0.513   0.553  0.725 0.715   0.840 0.944
 gamma 0.396   0.502  0.546 0.555   0.623 0.683
> 
> proc.time()
   user  system elapsed 
   1.12    0.10    2.98