Merges epidemiological data from two independent simulations of
stochastic individual contact models from icm.
Usage
# S3 method for class 'icm'
merge(x, y, ...)Value
An EpiModel object of class icm containing the
data from both x and y.
Details
This merge function combines the results of two independent simulations of
icm class models, simulated under separate function calls. The
model parameterization between the two calls must be exactly the same, except
for the number of simulations in each call. This allows for manual
parallelization of model simulations.
This merge function does not work the same as the default merge, which allows for a combined object where the structure differs between the input elements. Instead, the function checks that objects are identical in model parameterization in every respect (except number of simulations) and binds the results.
Examples
param <- param.icm(inf.prob = 0.2, act.rate = 0.8)
init <- init.icm(s.num = 1000, i.num = 100)
control <- control.icm(type = "SI", nsteps = 10,
nsims = 3, verbose = FALSE)
x <- icm(param, init, control)
control <- control.icm(type = "SI", nsteps = 10,
nsims = 1, verbose = FALSE)
y <- icm(param, init, control)
z <- merge(x, y)
# Examine separate and merged data
as.data.frame(x)
#> sim time s.num i.num num si.flow
#> 1 1 1 1000 100 1100 0
#> 2 1 2 992 108 1100 8
#> 3 1 3 976 124 1100 16
#> 4 1 4 963 137 1100 13
#> 5 1 5 946 154 1100 17
#> 6 1 6 919 181 1100 27
#> 7 1 7 894 206 1100 25
#> 8 1 8 864 236 1100 30
#> 9 1 9 834 266 1100 30
#> 10 1 10 804 296 1100 30
#> 11 2 1 1000 100 1100 0
#> 12 2 2 983 117 1100 17
#> 13 2 3 966 134 1100 17
#> 14 2 4 947 153 1100 19
#> 15 2 5 930 170 1100 17
#> 16 2 6 908 192 1100 22
#> 17 2 7 881 219 1100 27
#> 18 2 8 852 248 1100 29
#> 19 2 9 819 281 1100 33
#> 20 2 10 780 320 1100 39
#> 21 3 1 1000 100 1100 0
#> 22 3 2 984 116 1100 16
#> 23 3 3 967 133 1100 17
#> 24 3 4 951 149 1100 16
#> 25 3 5 933 167 1100 18
#> 26 3 6 917 183 1100 16
#> 27 3 7 889 211 1100 28
#> 28 3 8 861 239 1100 28
#> 29 3 9 832 268 1100 29
#> 30 3 10 805 295 1100 27
as.data.frame(y)
#> sim time s.num i.num num si.flow
#> 1 1 1 1000 100 1100 0
#> 2 1 2 985 115 1100 15
#> 3 1 3 960 140 1100 25
#> 4 1 4 941 159 1100 19
#> 5 1 5 921 179 1100 20
#> 6 1 6 895 205 1100 26
#> 7 1 7 868 232 1100 27
#> 8 1 8 839 261 1100 29
#> 9 1 9 814 286 1100 25
#> 10 1 10 788 312 1100 26
as.data.frame(z)
#> sim time s.num i.num num si.flow
#> 1 1 1 1000 100 1100 0
#> 2 1 2 992 108 1100 8
#> 3 1 3 976 124 1100 16
#> 4 1 4 963 137 1100 13
#> 5 1 5 946 154 1100 17
#> 6 1 6 919 181 1100 27
#> 7 1 7 894 206 1100 25
#> 8 1 8 864 236 1100 30
#> 9 1 9 834 266 1100 30
#> 10 1 10 804 296 1100 30
#> 11 2 1 1000 100 1100 0
#> 12 2 2 983 117 1100 17
#> 13 2 3 966 134 1100 17
#> 14 2 4 947 153 1100 19
#> 15 2 5 930 170 1100 17
#> 16 2 6 908 192 1100 22
#> 17 2 7 881 219 1100 27
#> 18 2 8 852 248 1100 29
#> 19 2 9 819 281 1100 33
#> 20 2 10 780 320 1100 39
#> 21 3 1 1000 100 1100 0
#> 22 3 2 984 116 1100 16
#> 23 3 3 967 133 1100 17
#> 24 3 4 951 149 1100 16
#> 25 3 5 933 167 1100 18
#> 26 3 6 917 183 1100 16
#> 27 3 7 889 211 1100 28
#> 28 3 8 861 239 1100 28
#> 29 3 9 832 268 1100 29
#> 30 3 10 805 295 1100 27
#> 31 4 1 1000 100 1100 0
#> 32 4 2 985 115 1100 15
#> 33 4 3 960 140 1100 25
#> 34 4 4 941 159 1100 19
#> 35 4 5 921 179 1100 20
#> 36 4 6 895 205 1100 26
#> 37 4 7 868 232 1100 27
#> 38 4 8 839 261 1100 29
#> 39 4 9 814 286 1100 25
#> 40 4 10 788 312 1100 26