EpiModel (version 2.5.0)

as.data.frame.icm: Extract Model Data for Stochastic Models

Description

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 icm
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  out = "vals",
  sim = NULL,
  qval = NULL,
  ...
)
# S3 method for netsim
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  out = "vals",
  sim = NULL,
  ...
)

Value

A data frame containing the data from x.

Arguments

x

An EpiModel object of class icm or netsim.

row.names

See as.data.frame.default.

optional

See as.data.frame.default.

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 in qval, 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".

...

See as.data.frame.default.

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)
as.data.frame(mod, sim = 2)

# Time-specific means across simulations
as.data.frame(mod, out = "mean")

# Time-specific standard deviations across simulations
as.data.frame(mod, out = "sd")

# Time-specific quantile values across simulations
as.data.frame(mod, out = "qnt", qval = 0.25)
as.data.frame(mod, out = "qnt", qval = 0.75)

if (FALSE) {
## 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)
}