diffusion: Diffusion processes

In GaDiFPT: First Passage Time Simulation for Gaussian Diffusion Processes

Description

diffusion creates an object of class “diffusion” from the set of provided values. is.diffusionchecks if its argument is an object of class “diffusion”. print shows an object of class “diffusion”.

Usage

diffusion(text)
is.diffusion(obj)
## S3 method for class 'diffusion'
print(x, ...)

Arguments

text	a character vector of length two, containing the infinitesimal mean and infinitesimal variance of the process
obj	an R object to be tested
x	an object of class “diffusion”
...	additional arguments potentially passed (currently none is considered).

Value

diffusion returns an object of class “diffusion” that defines a family of diffusion processes. It is a two-component list:

mean	character of length 1 with the mathematical expression of the infinitesimal mean of the process;
var	character of length 1 with the mathematical expression of the infinitesimal variance of the process.

is.diffusion returns TRUE or FALSE depending on whether its argument is an object of class “diffusion” or not. print.diffusion shows a brief description of the process reporting the functional form of its infinitesimal mean and variance.

Author(s)

A. Buonocore, M.F. Carfora

Examples

## Creating a "diffusion" object representing a Wiener process 
Wiener <- diffusion(c("mu","sigma2"))

## Creating a "diffusion" object representing 
## an Ornstein-Uhlenbeck process with an injected current

OrnUhlCur <- diffusion(c("-x/theta + mu + i0*exp(-(t-t0)/theta1)","sigma2"))

## testing diffusion objects

is.diffusion(Wiener)
is.diffusion(OrnUhlCur)

Example output

[1] TRUE
[1] TRUE

GaDiFPT documentation built on May 2, 2019, 1:18 p.m.