rodeo-class {rodeo}

rodeo Class

Description

This documents the rodeo class to represent an ODE-based model. See the rodeo-package main page or type help(package="rodeo") for an introduction to the package of the same name.

Fields

prosTbl

A data frame with fields 'name', 'unit', 'description', and 'expression' defining the process rates.

stoiTbl

A data frame with fields 'variable', 'process', and 'expression' reprenting the stoichiometry matrix in data base format.

varsTbl

A data frame with fields 'name', 'unit', 'description' declaring the state variables of the model. The declared names become valid identifiers to be used in the expression fields of prosTbl or stoiTbl.

parsTbl

A data frame of the same structure as vars declaring the parameters of the model. The declared names become valid identifiers to be used in the expression fields of prosTbl or stoiTbl.

funsTbl

A data frame of the same structure as vars declaring any functions referenced in the expression fields of prosTbl or stoiTbl.

dim

Integer vector specifying the spatial dimensions.

vars

Numeric vector, holding values of state variables.

pars

Numeric vector, holding values of parameters.

Class methods

For most of the methods below, a separate help page is available describing its arguments and usage.

• initialize Initialization method for new objects.

• namesVars, namesPars, namesFuns, namesPros Functions returning the names of declared state variables, parameters, functions, and processes, respectively (character vectors). No arguments.

• lenVars, lenPars, lenFuns, lenPros Functions returning the number of declared state variables, parameters, functions and processes, respectively (integer). No arguments.

• getVarsTable, getParsTable, getFunsTable, getProsTable, getStoiTable Functions returning the data frames used to initialize the object. No arguments

• getDim Returns the spatial dimensions as an integer vector. No arguments.

• compile Compiles a Fortran library for use with numerical methods from packages deSolve or rootSolve.

• generate Translate the ODE-model specification into a function that computes process rates and the state variables' derivatives (either in R or Fortran). Consider to use the high-level method compile.

• setVars Assign values to state variables.

• setPars Assign values to parameters.

• getVars Returns the values of state variables.

• getPars Returns the values of parameters.

• stoichiometry Returns the stoichiometry matrix, either evaluated (numeric) or as text.

• plotStoichiometry Plots qualitative stoichiometry information.

See Also

See the rodeo-package main page or type help(package="rodeo") to find the documentation of any non-class methods contained in the rodeo package.

Examples

## Example using high-level functions: Epidemiological SEIR model

# Import model from workbook shipped with this package 
m <- buildFromWorkbook(system.file("models/SEIR.xlsx",
  package="rodeo"))

# Set parameters and initial state (defaults stored in workbook)
p <- m$getParsTable()
m$setPars(setNames(p$default, p$name))

v <- m$getVarsTable()
m$setVars(setNames(v$default, v$name))

# Run a dynamic simulations and print parts of the result
sim <- m$dynamics(time=1:30, fortran=FALSE)
print(head(sim))
print(tail(sim))


### Example using low-level functions: Bacterial growth in bioreactor

# Creation of model object (data frames shipped as package data)
data(vars, pars, funs, pros, stoi)
model <- rodeo$new(vars, pars, funs, pros, stoi, dim=c(1))

# Assignment of parameters and initial values
model$setPars(c(mu=0.8, half=0.1, yield= 0.1, vol=1000,
  flow=50, sub_in=1))
model$setVars(c(bac=0.01, sub=0))

# Implementation of functions declared in 'funs'
monod <- function(c,h) {c/(c+h)}

# Creation of derivatives function in a low-level way; calling
# the 'compile' method is a more convenient alternative
code <- model$generate(name="derivs", lang="r")
derivs <- eval(parse(text=code))

# Explicit call of an integrator from the deSolve package
times <- 0:96
out <- deSolve::ode(y=model$getVars(), times=times, func=derivs,
  parms=model$getPars())
colnames(out) <- c("time", model$namesVars(), model$namesPros())
plot(out)