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

In EpiModel: Mathematical Modeling of Infectious Disease Dynamics

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 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 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.

Value

A data frame containing the data from x.

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)

## Not run: 
## 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)

## End(Not run)

EpiModel documentation built on Oct. 12, 2024, 1:06 a.m.