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 2 985 115 1100 15
#> 3 1 3 961 139 1100 24
#> 4 1 4 940 160 1100 21
#> 5 1 5 909 191 1100 31
#> 6 1 6 884 216 1100 25
#> 7 1 7 860 240 1100 24
#> 8 1 8 839 261 1100 21
#> 9 1 9 813 287 1100 26
#> 10 1 10 779 321 1100 34
#> 11 2 1 1000 100 1100 0
#> 12 2 2 983 117 1100 17
#> 13 2 3 972 128 1100 11
#> 14 2 4 947 153 1100 25
#> 15 2 5 925 175 1100 22
#> 16 2 6 902 198 1100 23
#> 17 2 7 875 225 1100 27
#> 18 2 8 850 250 1100 25
#> 19 2 9 814 286 1100 36
#> 20 2 10 780 320 1100 34
#> 21 3 1 1000 100 1100 0
#> 22 3 2 986 114 1100 14
#> 23 3 3 967 133 1100 19
#> 24 3 4 941 159 1100 26
#> 25 3 5 918 182 1100 23
#> 26 3 6 901 199 1100 17
#> 27 3 7 875 225 1100 26
#> 28 3 8 850 250 1100 25
#> 29 3 9 821 279 1100 29
#> 30 3 10 792 308 1100 29
as.data.frame(y)
#> sim time s.num i.num num si.flow
#> 1 1 1 1000 100 1100 0
#> 2 1 2 989 111 1100 11
#> 3 1 3 968 132 1100 21
#> 4 1 4 948 152 1100 20
#> 5 1 5 919 181 1100 29
#> 6 1 6 884 216 1100 35
#> 7 1 7 848 252 1100 36
#> 8 1 8 826 274 1100 22
#> 9 1 9 799 301 1100 27
#> 10 1 10 758 342 1100 41
as.data.frame(z)
#> 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 3 961 139 1100 24
#> 4 1 4 940 160 1100 21
#> 5 1 5 909 191 1100 31
#> 6 1 6 884 216 1100 25
#> 7 1 7 860 240 1100 24
#> 8 1 8 839 261 1100 21
#> 9 1 9 813 287 1100 26
#> 10 1 10 779 321 1100 34
#> 11 2 1 1000 100 1100 0
#> 12 2 2 983 117 1100 17
#> 13 2 3 972 128 1100 11
#> 14 2 4 947 153 1100 25
#> 15 2 5 925 175 1100 22
#> 16 2 6 902 198 1100 23
#> 17 2 7 875 225 1100 27
#> 18 2 8 850 250 1100 25
#> 19 2 9 814 286 1100 36
#> 20 2 10 780 320 1100 34
#> 21 3 1 1000 100 1100 0
#> 22 3 2 986 114 1100 14
#> 23 3 3 967 133 1100 19
#> 24 3 4 941 159 1100 26
#> 25 3 5 918 182 1100 23
#> 26 3 6 901 199 1100 17
#> 27 3 7 875 225 1100 26
#> 28 3 8 850 250 1100 25
#> 29 3 9 821 279 1100 29
#> 30 3 10 792 308 1100 29
#> 31 4 1 1000 100 1100 0
#> 32 4 2 989 111 1100 11
#> 33 4 3 968 132 1100 21
#> 34 4 4 948 152 1100 20
#> 35 4 5 919 181 1100 29
#> 36 4 6 884 216 1100 35
#> 37 4 7 848 252 1100 36
#> 38 4 8 826 274 1100 22
#> 39 4 9 799 301 1100 27
#> 40 4 10 758 342 1100 41