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Methods for epiworldR objects

Source: R/model-methods.R
epiworld-methods.Rd

The functions described in this section are methods for objects of class epiworld_model. Besides of printing and plotting, other methods provide access to manipulate model parameters, getting information about the model and running the simulation.

Usage

queuing_on(x)

queuing_off(x)

verbose_off(x)

verbose_on(x)

run(model, ndays, seed = NULL)

# S3 method for class 'epiworld_model'
summary(object, ...)

get_states(x)

get_param(x, pname)

add_param(x, pname, pval)

# S3 method for class 'epiworld_model'
add_param(x, pname, pval)

set_param(x, pname, pval)

set_name(x, mname)

get_name(x)

get_n_viruses(x)

get_n_tools(x)

get_ndays(x)

today(x)

get_n_replicates(x)

size(x)

set_agents_data(model, data)

get_agents_data_ncols(model)

get_virus(model, virus_pos)

get_tool(model, tool_pos)

initial_states(model, proportions)

clone_model(model)

Arguments

x

An object of class epiworld_model.

model

Model object.

ndays

Number of days (steps) of the simulation.

seed

Seed to set for initializing random number generator (passed to set.seed()).

object

Object of class epiworld_model.

...

Additional arguments.

pname

String. Name of the parameter.

pval

Numeric. Value of the parameter.

mname

String. Name of the model.

data

A numeric matrix.

virus_pos

Integer. Relative location (starting from 0) of the virus in the model

tool_pos

Integer. Relative location (starting from 0) of the tool in the model

proportions

Numeric vector. Proportions in which agents will be distributed (see details).

Value

  • The verbose_on and verbose_off functions return the same model, however verbose_off returns the model with no progress bar.

  • The run function returns the simulated model of class epiworld_model.

  • The summary function prints a more detailed view of the model, and returns the same model invisibly.

  • The get_states function returns the unique states found in a model.

  • The get_param function returns a selected parameter from the model object of class epiworld_model.

  • add_param returns the model with the added parameter invisibly.

  • The set_param function does not return a value but instead alters a parameter value.

  • The set_name function does not return a value but instead alters an object of epiworld_model.

  • get_name returns the name of the model.

  • get_n_viruses returns the number of viruses of the model.

  • get_n_tools returns the number of tools of the model.

  • get_ndays returns the number of days of the model.

  • today returns the current model day

  • get_n_replicates returns the number of replicates of the model.

  • size.epiworld_model returns the number of agents in the model.

  • The 'set_agents_data' function returns an object of class DataFrame.

  • 'get_agents_data_ncols' returns the number of columns in the model dataframe.

  • 'get_virus' returns a virus.

  • get_tool returns a tool.

  • inital_states returns the model with an updated initial state.

  • clone_model returns a copy of the model.

Details

The verbose_on and verbose_off functions activate and deactivate printing progress on screen, respectively. Both functions return the model (x) invisibly.

epiworld_model objects are pointers to an underlying C++ class in epiworld. To generate a copy of a model, use clone_model, otherwise, the assignment operator will only copy the pointer.

Examples


model_sirconn <- ModelSIRCONN(
  name                = "COVID-19",
  n                   = 10000,
  prevalence          = 0.01,
  contact_rate        = 5,
  transmission_rate   = 0.4,
  recovery_rate       = 0.95
)

# Queuing - If you wish to implement the queuing function, declare whether
# you would like it "on" or "off", if any.
queuing_on(model_sirconn)
#> Warning: SIR Connected models do not have queue.
queuing_off(model_sirconn)
#> Warning: SIR Connected models do not have queue.
run(model_sirconn, ndays = 100, seed = 1912)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#>  done.

# Verbose - "on" prints the progress bar on the screen while "off"
# deactivates the progress bar. Declare which function you want to implement,
# if any.
verbose_on(model_sirconn)
verbose_off(model_sirconn)
run(model_sirconn, ndays = 100, seed = 1912)

get_states(model_sirconn) # Returns all unique states found within the model.
#> [1] "Susceptible" "Infected"    "Recovered"  

get_param(model_sirconn, "Contact rate") # Returns the value of the selected
#> [1] 5
# parameter within the model object.
# In order to view the parameters,
# run the model object and find the
# "Model parameters" section.

set_param(model_sirconn, "Contact rate", 2) # Allows for adjustment of model
# parameters within the model
# object. In this example, the
# Contact rate parameter is
# changed to 2. You can now rerun
# the model to observe any
# differences.

set_name(model_sirconn, "My Epi-Model") # This function allows for setting
# a name for the model. Running the
# model object, the name of the model
# is now reflected next to "Name of
# the model".

get_name(model_sirconn) # Returns the set name of the model.
#> [1] "My Epi-Model"

get_n_viruses(model_sirconn) # Returns the number of viruses in the model.
#> [1] 1
# In this case, there is only one virus:
# "COVID-19".

get_n_tools(model_sirconn) # Returns the number of tools in the model. In
#> [1] 0
# this case, there are zero tools.

get_ndays(model_sirconn) # Returns the length of the simulation in days. This
#> [1] 100
# will match "ndays" within the "run" function.

today(model_sirconn) # Returns the current day of the simulation. This will
#> [1] 100
# match "get_ndays()" if run at the end of a simulation, but will differ if run
# during a simulation

get_n_replicates(model_sirconn) # Returns the number of replicates of the
#> [1] 2
# model.

size(model_sirconn) # Returns the population size in the model. In this case,
#> [1] 10000
# there are 10,000 agents in the model.
# Set Agents Data
# First, your data matrix must have the same number of rows as agents in the
# model. Below is a generated matrix which will be passed into the
# "set_agents_data" function.
data <- matrix(data = runif(20000, min = 0, max = 100), nrow = 10000, ncol = 2)
set_agents_data(model_sirconn, data)
get_agents_data_ncols(model_sirconn) # Returns number of columns
#> [1] 2

get_virus(model_sirconn, 0) # Returns information about the first virus in
#> Virus         : COVID-19
#> Id            : 0
#> state_init    : 1
#> state_post    : 2
#> state_removed : 2
#> queue_init    : 2
#> queue_post    : -2
#> queue_removed : -99
# the model (index begins at 0).

add_tool(model_sirconn, tool("Vaccine", .9, .9, .5, 1, prevalence = 0.5, as_prop = TRUE))
get_tool(model_sirconn, 0) # Returns information about the first tool in the
#> Tool       : Vaccine
#> Id         : 0
#> state_init : -99
#> state_post : -99
#> queue_init : 0
#> queue_post : 0
# model. In this case, there are no tools so an
# error message will occur.