Type: Package
Title: Translate Models from System Dynamics Software into 'R'
Version: 0.3.0
Description: The goal of 'readsdr' is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by 'R' libraries. The package accomplishes this goal by parsing 'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Suggests: testthat (≥ 2.1.0), igraph, knitr, rmarkdown, ggplot2, tidyr, truncnorm
Imports: stringr, xml2, purrr, dplyr, rlang, stringi, magrittr, stats, deSolve, utils, future, future.apply, progressr
BugReports: https://github.com/jandraor/readsdr/issues
VignetteBuilder: knitr
Depends: R (≥ 4.1.0)
NeedsCompilation: no
Packaged: 2024-04-05 15:40:16 UTC; jandraor
Author: Jair Andrade ORCID iD [aut, cre]
Maintainer: Jair Andrade <jair.albert.andrade@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-05 15:50:02 UTC

Influenza in Maryland during the 1918 pandemic

Description

Influenza in Maryland during the 1918 pandemic

Usage

Maryland

Format

A data frame with 91 rows and 6 columns:

Date

Date

Baltimore

Cases reported in the Baltimore

Cumberland

Cases reported in the Cumberland

Lonaconing

Cases reported in the Lonaconing

Frederick

Cases reported in the Frederick

Salisbury

Cases reported in the Salisbury

Source

<https://doi.org/10.2307/4575056>

Create a Stan's ODE function from an XMILE file

Description

create_stan_function returns a string with the code for a Stan's ODE function

Usage

create_stan_function(
  filepath,
  func_name,
  pars = NULL,
  override.consts = NULL,
  additional_funs = NULL
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

func_name

A string for naming the ODE function

pars

A character vector that indicates which constants will be considered as parameters in the ODE function

override.consts

A list in which each element is a name-value pair that replaces values of constants.

additional_funs

A vector of strings. Each string corresponds to a user-defined function.

Details

This function extracts the xml from the file specified via filepath to generate the code for an equivalent model in Stan.

Value

A string with the code containing the model's equations in the format required by Stan.

Examples

path <- system.file("models", "SIR.stmx", package = "readsdr")
create_stan_function(path, "my_model")

Expit transformation

Description

Expit transformation

Usage

expit(x)

Arguments

x

A real number

Value

A number in the range 0 to 1

Examples

expit(-3)

Extract the values over time of a stock from a Stan fit

Description

Extract the values over time of a stock from a Stan fit

Usage

extract_timeseries_stock(stock_name, posterior_df, all_stocks, ODE_output)

Arguments

stock_name

A string that indicates the stock's name for which the function will construct the timeseries.

posterior_df

A Stan fit object converted into a data frame

all_stocks

A vector of strings that contains the names of all the stocks in the model. This vector must have the same order as the differential equations in the Stan code.

ODE_output

A string that indicates the name of the variable where model's output in stored in Stan.

Value

A data frame

Examples

posterior_df <- data.frame(`yhat[1,2]` = rep(0, 2), `yhat[2,2]` = rep(1, 2),
                            check.names = FALSE)
stocks       <- c("S1", "S2")
extract_timeseries_stock("S2", posterior_df, stocks, "yhat")

Extract the values over time of a variable from a Stan fit

Description

Extract the values over time of a variable from a Stan fit

Usage

extract_timeseries_var(var_name, posterior_df)

Arguments

var_name

A string that indicates the variable's name for which the function will construct the timeseries.

posterior_df

A Stan fit object converted into a data frame

Value

A data frame

Examples

posterior_df <- data.frame(`var[1]` = rep(0, 2), `var[2]` = rep(1, 2),
                            check.names = FALSE)
extract_timeseries_var("var", posterior_df)

Inverse of a number

Description

Inverse of a number

Usage

inv(x)

Arguments

x

A real number

Value

A real number

Examples

inv(0.5) # Should return 2

Logit transformation

Description

Logit transformation

Usage

logit(p)

Arguments

p

A real number that represents a probability

Value

An unconstrained real number

Examples

logit(0.5)

Read an XMILE file into R

Description

read_xmile returns a list for constructing deSolve functions and graphs

Usage

read_xmile(filepath, stock_list = NULL, const_list = NULL, graph = FALSE)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

stock_list

A list in which each element's name is the name of the stock to override and the element's value correspond to the new init value.

const_list

A list in which each element's name is the name of the constant to override and the element's value correspond to the new value.

graph

A boolean parameter that indicates whether read_xmile returns a graph for the model.

Details

This function extracts the xml from the file specified via filepath to generate a list of objects. Such a list contains a summary of the model, the inputs for simulating through deSolve, and the inputs for creating a igraph object.

Value

This function returns a list with three elements. The first element, description, is a list that contains the simulation parameters, and the names and equations (including graphical functions) for each stock or level, variable and constant. The second element, deSolve_components, is a list that contains initial values, constants and the function for simulating via deSolve. The third element (optional), igraph contains the data.frames for creating a graph with igraph.

Examples

path <- system.file("models", "SIR.stmx", package = "readsdr")
read_xmile(path)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

deSolve

timestep

magrittr

%>%

Create Stan file for Bayesian inference

Description

Create Stan file for Bayesian inference

Usage

sd_Bayes(
  filepath,
  meas_mdl,
  estimated_params,
  data_params = NULL,
  data_inits = NULL,
  const_list = NULL,
  forecast = FALSE
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

meas_mdl

A list of strings. Each string corresponds to a sampling statement written in Stan language.

estimated_params

A list of lists. Each sublist describes each parameter that will be estimated in the inference stage. To construct this description, the user can avail of the function 'sd_prior'.

data_params

An optional string vector defining which model parameters will be configured through the Stan data block. That is, the user will provide fixed values for such parameters at every Stan run.

data_inits

An optional string vector defining which model parameters that only affect initial values (of stocks) will be configured through the Stan data block. That is, the user will provide fixed values for such parameters at every Stan run.

const_list

A list in which each element's name is the name of the constant to override and the element's value correspond to the new value.

forecast

An optional boolean that indicates whether the Stan file supports a forecast. If TRUE, the data block requires the user to supply an integer value for n_fcst. This variable corresponds to the number of periods that will be predicted.

Value

A string

Negative binomial measurement component

While this package aims to avoid making decisions for users whenever possible, I have taken the liberty to automate the transformation of phi (the concentration parameter) when using the Negative Binomial distribution (alternative parameterisation) as a measurement component. sd_Bayes() automatically creates an inverse phi parameter for computational efficiency, which will be subject to inference (instead of phi). Additionally, I have provided a default prior for this inv_phi but users can override it as needed.

Time

Simulation of the ordinary differential equation (ODE) model starts at time 0.

Examples

  filepath         <- system.file("models/", "SEIR.stmx", package = "readsdr")
  mm1              <- "y ~ neg_binomial_2(net_flow(C), phi)"
  meas_mdl         <- list(mm1)
  estimated_params <- list(
    sd_prior("par_beta", "lognormal", c(0, 1)),
    sd_prior("par_rho", "beta", c(2, 2)),
    sd_prior("I0", "lognormal", c(0, 1), "init"))
  sd_Bayes(filepath, meas_mdl, estimated_params)

Summarise the information of a model's constants in a data frame

Description

Summarise the information of a model's constants in a data frame

Usage

sd_constants(mdl)

Arguments

mdl

A list which is the output from read_xmile.

Value

A data frame.

Examples

path <- system.file("models", "SIR.stmx", package = "readsdr")
mdl  <- read_xmile(path)
sd_constants(mdl)

Function factory for SBC

Description

Function factory for SBC

Usage

sd_data_generator_fun(
  filepath,
  estimated_params,
  meas_mdl,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler"
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

estimated_params

A list of lists. Each sublist describes each parameter that will be estimated in the inference stage. To construct this description, the user can avail of the function 'sd_prior'.

meas_mdl

A list of strings. Each string corresponds to a sampling statement written in Stan language.

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

Value

A function.

Examples

  filepath <- system.file("models/", "SEIR.stmx", package = "readsdr")
  meas_mdl <- list("y ~ poisson(net_flow(C))")
  estimated_params <- list(
    sd_prior("par_beta", "lognormal", c(0, 1)),
    sd_prior("par_rho", "beta", c(2, 2)),
    sd_prior("I0", "lognormal", c(0, 1), "init"))
  sd_data_generator_fun(filepath, estimated_params, meas_mdl)

Fixed delay

Description

Fixed delay

Usage

sd_fixed_delay(var, time, delay, init, .memory)

Arguments

var

A string that indicates the delayed variable.

time

A number that indicates current simulation time.

delay

A number that indicates the delay time.

init

A number that indicates the function's output value of at the start of the simulation.

.memory

A data frame that keeps past values of delayed variables.

Value

A number.

Examples

.memory <- data.frame(time = 3, inflow = 3)
rownames(.memory) <- 3
sd_fixed_delay("inflow", 5, 2, 0, .memory)

Construct inputs for performing structural analysis via the impact method

Description

Construct inputs for performing structural analysis via the impact method

Usage

sd_impact_inputs(desc_list)

Arguments

desc_list

Element 'description' from the list returned by read_xmile()

Value

A list of three elements. The first element, flows, is a data frame that lists all the stock-flow links in the model. Further, this data frame describes the equation that governs the link and whether the link is an inflow (+) or an outflow (-). The second element, pathways, is a data frame that lists all the pathways among stocks. The third element, velocities, is a data frame in which each row corresponds to a stock. Each row consists of two columns (name & equation).

Examples

  filepath  <- system.file("models/", "SIR.stmx", package = "readsdr")
  mdl       <- read_xmile(filepath)
  desc_list <- mdl$description
  sd_impact_inputs(desc_list)

Interpret estimates

Description

Interpret estimates

Usage

sd_interpret_estimates(estimates, par_list)

Arguments

estimates

A list or data frame

par_list

A list

Value

A data frame

Examples

  estimates <- c(par_beta = 0,
                 par_rho  = 0.8472979,
                 I0       = 0,
                 inv_phi  = -2.302585)

  par_list <- list(list(par_name  = "par_beta",
                        par_trans = "exp"),
                   list(par_name  = "par_rho",
                        par_trans = "expit"),
                   list(par_name  = "I0",
                        par_trans = "exp"),
                   list(par_name  = "phi",
                        par_trans = c("exp", "inv")))
  sd_interpret_estimates(estimates, par_list)

Generate a log-likelihood function for an SD model

Description

Generate a log-likelihood function for an SD model

Usage

sd_loglik_fun(
  filepath,
  unknown_pars,
  meas_data_mdl,
  neg_log = FALSE,
  supplied_pars = NULL,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler",
  const_list = NULL
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

unknown_pars

A list of lists. Each second-level list contains at least the element name name, which corresponds to the parameter's name subject to estimation. In addition to the element name, users can incorporate in the sub-list the elements min and max. The value of min can only be 0, whereas the value of max can only be 1.

meas_data_mdl

A list of lists. Each second-level list corresponds to a sampling statement along with its measurements. Here is an example:
list(formula = "y ~ neg_binomial_2(net_flow(C), phi)", measurements = 1:10))

neg_log

A boolean that indicates whether the log-likelihood function returns a positive or negative value. If TRUE, the function returns a positive value (for minimisation optimisers). If FALSE, the function returns the original log-likelihood.

supplied_pars

A string vector indicating the name of parameters whose values will be supplied to the function. These values will not be subject to optimisation.

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

const_list

A list in which each element's name is the name of the constant to override and the element's value correspond to the new value.

Value

A list of three elements. The first element, fun, corresponds to the log likelihood function. The second element, par_names, indicates the order in which the unknowns are returned. The third element, sim_params, corresponds to the simulation parameters (start time, stop time, and the integration step or dt) employed by the solver function.

Examples

 filepath      <- system.file("models/", "SEIR.stmx", package = "readsdr")
 unknown_pars  <- list(list(par_name = "par_beta", min = 0))
 meas_data_mdl <- list(list(formula      = "y ~ neg_binomial_2(net_flow(C), phi)",
                            measurements = 1:10))
 fun_obj <- sd_loglik_fun(filepath, unknown_pars, meas_data_mdl, neg_log = FALSE,
                          start_time = 0, stop_time = 10, timestep = 1/32)

Generate measurements

Description

Generate measurements

Usage

sd_measurements(
  n_meas,
  meas_model,
  ds_inputs,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler"
)

Arguments

n_meas

Number of measurements. An integer.

meas_model

Measurement model. A list of strings, in which each string corresponds to sampling statement in Stan language.

ds_inputs

A list of deSolve inputs generated by read_xmile

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

Value

A data frame.

Examples

  filepath <- system.file("models/", "SEIR.stmx", package = "readsdr")
  mdl      <- read_xmile(filepath)

  mm1        <- "y ~ poisson(C)"
  meas_model <- list(mm1)

  sd_measurements(n_meas       = 2,
                  meas_model   = meas_model,
                  ds_inputs    = mdl$deSolve_components,
                  start_time   = 0,
                  stop_time    = 10,
                  timestep     = 1/16,
                  integ_method = "rk4")

Estimate the net change of a stock in discrete times

Description

Estimate the net change of a stock in discrete times

Usage

sd_net_change(sim_df, cumulative_var)

Arguments

sim_df

A data frame with the simulation output

cumulative_var

A string that indicates to which variable the discrete change will be estimated

Value

A dataframe.

Examples

test_output <- data.frame(time = seq(0, 2, by = 0.25),
                          C    = c(0, rep(5,4), rep(20, 4)))
sd_net_change(test_output, "C")

Posterior function

Description

Posterior function

Usage

sd_posterior_fun(
  filepath,
  meas_data_mdl,
  estimated_params,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler",
  const_list = NULL
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

meas_data_mdl

A list of lists. Each second-level list corresponds to a sampling statement along with its measurements. Here is an example:
list(formula = "y ~ neg_binomial_2(net_flow(C), phi)", measurements = 1:10))

estimated_params

A list of lists. Each sublist describes each parameter that will be estimated in the inference stage. To construct this description, the user can avail of the function 'sd_prior'.

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

const_list

A list in which each element's name is the name of the constant to override and the element's value correspond to the new value.

Value

A function

Examples

filepath         <- system.file("models/", "SEIR.stmx", package = "readsdr")
meas_data_mdl <- list(list(formula      = "y ~ neg_binomial_2(net_flow(C), phi)",
                            measurements = 1:10))
estimated_params <- list(
  sd_prior("par_beta", "lognormal", c(0, 1)),
  sd_prior("par_rho", "beta", c(2, 2)),
  sd_prior("I0", "lognormal", c(0, 1), "init"))
fun <- sd_posterior_fun(filepath, meas_data_mdl, estimated_params)

SD prior

Description

SD prior

Usage

sd_prior(par_name, dist, dist_pars, type = "constant", min_0 = FALSE)

Arguments

par_name

A string

dist

A string

dist_pars

A vector

type

A string. It can be either 'constant' or 'init'. It is 'constant' by default. 'init' refers to parameters that have only affect the model at time 0.

min_0

An optional boolean indicating whether the prior has a lower bound at zero. In the current implementation, this parameter only has an effect on normal priors.

Value

A list

Examples

sd_prior("par_beta", "lognormal", c(0, 1))
sd_prior("par_rho", "normal", c(0, 1), min_0 = TRUE)

Prior predictive checks

Description

Prior predictive checks

Usage

sd_prior_checks(
  filepath,
  meas_mdl,
  estimated_params,
  n_draws,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler"
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

meas_mdl

A list of strings. Each string corresponds to a sampling statement written in Stan language.

estimated_params

A list of lists. Each sublist describes each parameter that will be estimated in the inference stage. To construct this description, the user can avail of the function 'sd_prior'.

n_draws

An integer that indicates how many time-series will be returned.

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

Value

A list of two data frames.

Examples

  filepath <- system.file("models/", "SEIR.stmx", package = "readsdr")
  meas_mdl   <- list("y ~ neg_binomial_2(net_flow(C), phi)")
  estimated_params <- list(
    sd_prior("par_beta", "lognormal", c(0, 1)),
    sd_prior("par_rho", "beta", c(2, 2)),
    sd_prior("I0", "lognormal", c(0, 1), "init"))
  sd_prior_checks(filepath, meas_mdl, estimated_params, n_draws = 2,
   start_time = 0, stop_time = 5,
   integ_method = "rk4", timestep = 1/32)

Replicate the behaviour of the PULSE function from Stella

Description

This function must be placed inside the object that will be passed as the argument func to deSolve's ode function.

Usage

sd_pulse_s(time, volume, start_p, interval)

Arguments

time

A number

volume

A number

start_p

A number

interval

A number

Value

A number

Examples

timestep <- function() 0.25 # replicates timestep() from deSolve
sd_pulse_s(2, 1, 2, 0)

PULSE TRAIN

Description

PULSE TRAIN

Usage

sd_pulse_train(time, start_pulse, duration_pulse, repeat_pt, end_pulse)

Arguments

time

A numeric argument that indicates the current simulation time

start_pulse

A numeric argument that indicates the start of the pulse

duration_pulse

A numeric argument that indicates the width of the pulse

repeat_pt

A numeric argument that indicates the repetition pattern

end_pulse

A numeric argument that indicates the end of the sequence

Value

1 during the pulse, 0 otherwise.

Examples

sd_pulse_train(5, 5, 3, 10, 20)

Replicate the behaviour of the PULSE function from Vensim

Description

Replicate the behaviour of the PULSE function from Vensim

Usage

sd_pulse_v(time, startPulse, duration)

Arguments

time

A number

startPulse

A number

duration

A number

Value

A number

Examples

timestep <- function() 0.25 # replicates timestep() from deSolve
sd_pulse_v(1, 1, 2)

Perform a sensitivity run on a System Dynamics model

Description

sd_sensitivity_run returns a data frame with the simulation of a model for several iterations of different inputs.

Usage

sd_sensitivity_run(
  ds_inputs,
  consts_df = NULL,
  stocks_df = NULL,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler",
  multicore = FALSE,
  n_cores = NULL,
  reporting_interval = 1
)

Arguments

ds_inputs

A list of deSolve inputs generated by read_xmile

consts_df

A data frame that contains the values of constants to simulate. Each column corresponds to a constant and each row to an iteration. If stocks_df is also supplied, both data frames must have the same number of rows.

stocks_df

A data frame that containts the initial value of stocks to be explored. Each column corresponds to a stock and each row to an iteration. If consts_df is also supplied, both data frames must have the same number of rows.

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

multicore

A boolean value that indicates whether the process is parallelised.

n_cores

An integer indicating the number of cores for the parallel run.

reporting_interval

A real number indicating the interval at which the simulation results are returned. The default is set to 1. For instance, if the simulation runs from 0 to 10. This function returns the results at times 0, 1, 2, ..., 10.

Value

A data frame

Examples

path      <- system.file("models", "SIR.stmx", package = "readsdr")
ds_inputs <- xmile_to_deSolve(path)
consts_df <- data.frame(i = c(0.25, 0.30))
sd_sensitivity_run(ds_inputs, consts_df)

Simulate a System Dynamics model

Description

Simulate a System Dynamics model

Usage

sd_simulate(
  ds_inputs,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler"
)

Arguments

ds_inputs

A list of deSolve inputs generated by read_xmile

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

Value

a data frame

Examples

path      <- system.file("models", "SIR.stmx", package = "readsdr")
ds_inputs <- xmile_to_deSolve(path)
sd_simulate(ds_inputs, 0, 1, 0.25, "rk4")

Summarise the information of a model's stocks in a data frame

Description

Summarise the information of a model's stocks in a data frame

Usage

sd_stocks(mdl)

Arguments

mdl

A list which is the output from read_xmile.

Value

A data frame.

Examples

path <- system.file("models", "SIR.stmx", package = "readsdr")
mdl  <- read_xmile(path)
sd_stocks(mdl)

What if from time t we change the value of some parameters

Description

What if from time t we change the value of some parameters

Usage

sd_what_if_from_time(
  time,
  up_to_time = Inf,
  par_list,
  ds_inputs,
  start_time = NULL,
  stop_time = NULL,
  timestep = NULL,
  integ_method = "euler"
)

Arguments

time

Time at which the parameter values change

up_to_time

Time from which the original values are restored.

par_list

A list that indicates which parameters change from time t. For instance, if you wanted to change the value of parameter c to 4, you would provide the list(c = 4)

ds_inputs

A list of deSolve inputs generated by read_xmile

start_time

A number indicating the time at which the simulation begins.

stop_time

A number indicating the time at which the simulation ends.

timestep

A number indicating the time interval for the simulation. Also known as dt.

integ_method

A string indicating the integration method. It can be either "euler" or "rk4"

Value

A data frame

Examples

  filepath       <- system.file("models/", "SIR.stmx", package = "readsdr")
  mdl            <- read_xmile(filepath)
  ds_components  <- mdl$deSolve_components
  output         <- sd_what_if_from_time(3, Inf, list(c = 4), ds_components)

Create Stan ODE function

Description

Create Stan ODE function

Usage

stan_ode_function(
  filepath,
  func_name,
  pars = NULL,
  const_list = NULL,
  extra_funs = NULL,
  XMILE_structure
)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

func_name

A string for naming the ODE function

pars

A character vector that indicates which constants will be considered as parameters in the ODE function

const_list

A list in which each element's name is the name of the constant to override and the element's value correspond to the new value.

extra_funs

A vector of strings. Each string corresponds to a user-defined function.

XMILE_structure

A list.

Value

A string with the code containing a function with the model's equations in the format required by cmdstan 2.24+.

Examples

path <- system.file("models", "SIR.stmx", package = "readsdr")
stan_ode_function(path, "my_model")

Parse XMILE to deSolve components

Description

xmile_to_deSolve returns a list that serves as an input for deSolve's ODE function.

Usage

xmile_to_deSolve(filepath)

Arguments

filepath

A string that indicates a path to a file with extension .stmx or .xmile. Vensim files (.mdl) are not xmile files. They must be exported from Vensim with extension .xmile

Details

This function extracts the xml from the file specified via filepath to generate a list with the necessary elements to simulate with deSolve.

Value

This function returns a list with at least four elements. stocks, a numeric vector that contains initial values. consts, a numeric vector with the model's constants. func, the function that wraps the model's equations. sim_params, a list with control parameters. If the model includes a table or graphical function, this function returns the element graph_funs, a list with these functions.

Examples

path <- system.file("models", "SIR.stmx", package = "readsdr")
xmile_to_deSolve(path)