This function extracts model simulations for objects of classes
icm and netsim into a data frame using
the generic as.data.frame function.
Usage
# S3 method for class 'icm'
as.data.frame(
x,
row.names = NULL,
optional = FALSE,
out = "vals",
sim = NULL,
qval = NULL,
...
)
# S3 method for class 'netsim'
as.data.frame(
x,
row.names = NULL,
optional = FALSE,
out = "vals",
sim = NULL,
...
)Arguments
- x
An
EpiModelobject of classicmornetsim.- row.names
- optional
- out
Data output to data frame:
"mean"for row means across simulations,"sd"for row standard deviations across simulations,"qnt"for row quantiles at the level specified inqval, or"vals"for values from individual simulations.- sim
If
out="vals", the simulation number to output. If not specified, then data from all simulations will be output.- qval
Quantile value required when
out="qnt".- ...
Details
These methods work for both icm and netsim class models. The
available output includes time-specific means, standard deviations,
quantiles, and simulation values (compartment and flow sizes) from these
stochastic model classes. Means, standard deviations, and quantiles are
calculated by taking the row summary (i.e., each row of data is corresponds
to a time step) across all simulations in the model output.
Examples
## Stochastic ICM SIS model
param <- param.icm(inf.prob = 0.8, act.rate = 2, rec.rate = 0.1)
init <- init.icm(s.num = 500, i.num = 1)
control <- control.icm(type = "SIS", nsteps = 10,
nsims = 3, verbose = FALSE)
mod <- icm(param, init, control)
# Default output all simulation runs, default to all in stacked data.frame
as.data.frame(mod)
#> sim time s.num i.num num si.flow is.flow
#> 1 1 1 500 1 501 0 0
#> 2 1 2 500 1 501 0 0
#> 3 1 3 500 1 501 1 1
#> 4 1 4 497 4 501 3 0
#> 5 1 5 493 8 501 7 3
#> 6 1 6 485 16 501 10 2
#> 7 1 7 462 39 501 29 6
#> 8 1 8 418 83 501 53 9
#> 9 1 9 326 175 501 112 20
#> 10 1 10 208 293 501 146 28
#> 11 2 1 500 1 501 0 0
#> 12 2 2 500 1 501 2 2
#> 13 2 3 500 1 501 0 0
#> 14 2 4 498 3 501 2 0
#> 15 2 5 493 8 501 6 1
#> 16 2 6 485 16 501 9 1
#> 17 2 7 470 31 501 21 6
#> 18 2 8 428 73 501 47 5
#> 19 2 9 351 150 501 101 24
#> 20 2 10 253 248 501 128 30
#> 21 3 1 500 1 501 0 0
#> 22 3 2 498 3 501 3 1
#> 23 3 3 495 6 501 4 1
#> 24 3 4 489 12 501 7 1
#> 25 3 5 471 30 501 22 4
#> 26 3 6 433 68 501 46 8
#> 27 3 7 354 147 501 98 19
#> 28 3 8 255 246 501 132 33
#> 29 3 9 150 351 501 141 36
#> 30 3 10 82 419 501 108 40
as.data.frame(mod, sim = 2)
#> sim time s.num i.num num si.flow is.flow
#> 1 2 1 500 1 501 0 0
#> 2 2 2 500 1 501 2 2
#> 3 2 3 500 1 501 0 0
#> 4 2 4 498 3 501 2 0
#> 5 2 5 493 8 501 6 1
#> 6 2 6 485 16 501 9 1
#> 7 2 7 470 31 501 21 6
#> 8 2 8 428 73 501 47 5
#> 9 2 9 351 150 501 101 24
#> 10 2 10 253 248 501 128 30
# Time-specific means across simulations
as.data.frame(mod, out = "mean")
#> time s.num i.num num si.flow is.flow
#> 1 1 500.0000 1.000000 501 0.000000 0.0000000
#> 2 2 499.3333 1.666667 501 1.666667 1.0000000
#> 3 3 498.3333 2.666667 501 1.666667 0.6666667
#> 4 4 494.6667 6.333333 501 4.000000 0.3333333
#> 5 5 485.6667 15.333333 501 11.666667 2.6666667
#> 6 6 467.6667 33.333333 501 21.666667 3.6666667
#> 7 7 428.6667 72.333333 501 49.333333 10.3333333
#> 8 8 367.0000 134.000000 501 77.333333 15.6666667
#> 9 9 275.6667 225.333333 501 118.000000 26.6666667
#> 10 10 181.0000 320.000000 501 127.333333 32.6666667
# Time-specific standard deviations across simulations
as.data.frame(mod, out = "sd")
#> time s.num i.num num si.flow is.flow
#> 1 1 0.000000 0.000000 0 0.000000 0.0000000
#> 2 2 1.154701 1.154701 0 1.527525 1.0000000
#> 3 3 2.886751 2.886751 0 2.081666 0.5773503
#> 4 4 4.932883 4.932883 0 2.645751 0.5773503
#> 5 5 12.701706 12.701706 0 8.962886 1.5275252
#> 6 6 30.022214 30.022214 0 21.079216 3.7859389
#> 7 7 64.786830 64.786830 0 42.335958 7.5055535
#> 8 8 97.123633 97.123633 0 47.437678 15.1437556
#> 9 9 109.546033 109.546033 0 20.663978 8.3266640
#> 10 10 88.639720 88.639720 0 19.008770 6.4291005
# Time-specific quantile values across simulations
as.data.frame(mod, out = "qnt", qval = 0.25)
#> time s.num i.num num si.flow is.flow
#> 1 1 500.0 1.0 501 0.0 0.0
#> 2 2 499.0 1.0 501 1.0 0.5
#> 3 3 497.5 1.0 501 0.5 0.5
#> 4 4 493.0 3.5 501 2.5 0.0
#> 5 5 482.0 8.0 501 6.5 2.0
#> 6 6 459.0 16.0 501 9.5 1.5
#> 7 7 408.0 35.0 501 25.0 6.0
#> 8 8 336.5 78.0 501 50.0 7.0
#> 9 9 238.0 162.5 501 106.5 22.0
#> 10 10 145.0 270.5 501 118.0 29.0
as.data.frame(mod, out = "qnt", qval = 0.75)
#> time s.num i.num num si.flow is.flow
#> 1 1 500.0 1.0 501 0.0 0.0
#> 2 2 500.0 2.0 501 2.5 1.5
#> 3 3 500.0 3.5 501 2.5 1.0
#> 4 4 497.5 8.0 501 5.0 0.5
#> 5 5 493.0 19.0 501 14.5 3.5
#> 6 6 485.0 42.0 501 28.0 5.0
#> 7 7 466.0 93.0 501 63.5 12.5
#> 8 8 423.0 164.5 501 92.5 21.0
#> 9 9 338.5 263.0 501 126.5 30.0
#> 10 10 230.5 356.0 501 137.0 35.0
if (FALSE) { # \dontrun{
## Stochastic SI Network Model
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 50
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)
est <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)
param <- param.net(inf.prob = 0.5)
init <- init.net(i.num = 10)
control <- control.net(type = "SI", nsteps = 10, nsims = 3, verbose = FALSE)
mod <- netsim(est, param, init, control)
# Same data extraction methods as with ICMs
as.data.frame(mod)
as.data.frame(mod, sim = 2)
as.data.frame(mod, out = "mean")
as.data.frame(mod, out = "sd")
as.data.frame(mod, out = "qnt", qval = 0.25)
as.data.frame(mod, out = "qnt", qval = 0.75)
} # }