moment: Monte-Carlo statistics of SDE's

In Sim.DiffProc: Simulation of Diffusion Processes

Monte-Carlo statistics of SDE's

Description

Generic function for compute the kurtosis, skewness, median, mode and coefficient of variation (relative variability), moment and confidence interval of class "sde".

Usage

## Default S3 method:
bconfint(x, level = 0.95, ...)
## Default S3 method:
kurtosis(x, ...)
## Default S3 method:
moment(x, order = 1,center = TRUE, ...)
## Default S3 method:
cv(x, ...)
## Default S3 method:
skewness(x, ...)
## Default S3 method:
Median(x, ...)
## Default S3 method:
Mode(x, ...)

Arguments

x	an object inheriting from class "sde".
order	order of moment.
center	if TRUE is a central moment.
level	the confidence level required.
...	potentially further arguments for (non-default) methods.

Author(s)

A.C. Guidoum, K. Boukhetala.

Examples

## Example 1:
## dX(t) = 2*(3-X(t)) *dt + dW(t)
set.seed(1234)

f <- expression( 2*(3-x) )
g <- expression( 1 )
mod <- snssde1d(drift=f,diffusion=g,M=10000,T=5)
## Monte-Carlo statistics of 5000 trajectory of X(t) at final time T of 'mod'
summary(mod)
kurtosis(mod)
skewness(mod)
mean(mod)
Median(mod)
Mode(mod)
moment(mod,order=4)
cv(mod)
bconfint(mod,level = 0.95) ## of mean

Sim.DiffProc documentation built on May 29, 2024, 8:09 a.m.