This function uses the generative functions in the
random.params list to create values for the parameters.
random.params
The random.params argument to the param.net function
must be a named list of functions that each return a value that can be used
as the argument with the same name. In the example below, param_random
is a function factory provided by EpiModel for act.rate and
for tx.halt.part.prob we provide bespoke functions. A function factory
is a function that returns a new function
(see https://adv-r.hadley.nz/function-factories.html).
Generator Functions
The functions used inside random_params must be 0 argument functions
returning a valid value for the parameter with the same name.
param_random_set
The random_params list can optionally contain a
param_random_set element. It must be a data.frame of possible
values to be used as parameters.
The column names must correspond either to:
the name of one parameter, if this parameter is of size 1; or the name of one
parameter with "_1", "2", etc. appended, with the number representing the
position of the value, if this parameter is of size > 1. This means that the
parameter names cannot contain any underscores "" if you intend to use
param_random_set.
The point of the param.random.set data.frame is to allow the
random parameters to be correlated. To achieve this, a whole row of the
data.frame is selected for each simulation.
Examples
if (FALSE) { # \dontrun{
## Example with only the generator function
# Define random parameter list
my_randoms <- list(
act.rate = param_random(c(0.25, 0.5, 0.75)),
tx.prob = function() rbeta(1, 1, 2),
stratified.test.rate = function() c(
rnorm(1, 0.05, 0.01),
rnorm(1, 0.15, 0.03),
rnorm(1, 0.25, 0.05)
)
)
# Parameter model with fixed and random parameters
param <- param.net(inf.prob = 0.3, random.params = my_randoms)
# Below, `tx.prob` is set first to 0.3 then assigned a random value using
# the function from `my_randoms`. A warning notifying of this overwrite is
# therefore produced.
param <- param.net(tx.prob = 0.3, random.params = my_randoms)
# Parameters are drawn automatically in netsim by calling the function
# within netsim_loop. Demonstrating draws here but this is not used by
# end user.
paramDraw <- generate_random_params(param, verbose = TRUE)
paramDraw
## Addition of the `param.random.set` `data.frame`
# This function will generate sets of correlated parameters
generate_correlated_params <- function() {
param.unique <- runif(1)
param.set.1 <- param.unique + runif(2)
param.set.2 <- param.unique * rnorm(3)
return(list(param.unique, param.set.1, param.set.2))
}
# Data.frame set of random parameters :
correlated_params <- t(replicate(10, unlist(generate_correlated_params())))
correlated_params <- as.data.frame(correlated_params)
colnames(correlated_params) <- c(
"param.unique",
"param.set.1_1", "param.set.1_2",
"param.set.2_1", "param.set.2_2", "param.set.2_3"
)
# Define random parameter list with the `param.random.set` element
my_randoms <- list(
act.rate = param_random(c(0.25, 0.5, 0.75)),
param.random.set = correlated_params
)
# Parameter model with fixed and random parameters
param <- param.net(inf.prob = 0.3, random.params = my_randoms)
# Parameters are drawn automatically in netsim by calling the function
# within netsim_loop. Demonstrating draws here but this is not used by
# end user.
paramDraw <- generate_random_params(param, verbose = TRUE)
paramDraw
} # }