rsde2d: Approximate transitional densities and random generation for...

In Sim.DiffProc: Simulation of Diffusion Processes

Approximate transitional densities and random generation for 2-D SDE's

Description

Transition density and random generation for the joint and marginal of (X(t-s),Y(t-s) | X(s)=x0,Y(s)=y0) of the SDE's 2-d.

Usage

rsde2d(object, ...)
dsde2d(object, ...)

## Default S3 method:
rsde2d(object, at, ...)

## Default S3 method:
dsde2d(object, pdf=c("Joint","Marginal"), at, ...)
## S3 method for class 'dsde2d'
plot(x,display=c("persp","rgl","image","contour"),hist=FALSE,...)

Arguments

object	an object inheriting from class snssde2d and bridgesde2d.
at	time between s=t0 and t=T. The default at = T.
pdf	probability density function Joint or Marginal.
x	an object inheriting from class dsde2d.
display	display plots.
hist	if hist=TRUE plot histogram. Based on truehist function.
...	potentially potentially arguments to be passed to methods, such as density for marginal density and kde2d fro joint density.

Details

The function rsde2d returns a M random variable x_{t=at},y_{t=at} realize at time t=at.

And dsde2d returns a bivariate density approximation for (X(t-s),Y(t-s) | X(s)=x0,Y(s)=y0). with t=at is a fixed time between t0 and T.

An overview of this package, see browseVignettes('Sim.DiffProc') for more informations.

Value

dsde2d()	gives the bivariate density approximation for (X(t-s),Y(t-s) | X(s)=x0,Y(s)=y0).
rsde2d()	generates random of the couple (X(t-s),Y(t-s) | X(s)=x0,Y(s)=y0).

Author(s)

A.C. Guidoum, K. Boukhetala.

See Also

kde2d Two-dimensional kernel density estimation in "MASS" package.

kde Kernel density estimate for 1- to 6-dimensional data in "ks" package.

sm.density Nonparametric density estimation in one, two or three dimensions in "sm" package.

rng random number generators in "yuima" package.

BiGQD.density Generate the transition density of a bivariate generalized quadratic diffusion model (2D GQD) in "DiffusionRgqd" package.

Examples

## Example:1
set.seed(1234)

# SDE's 2d
fx <- expression(3*(2-y),2*x)
gx <- expression(1,y)
mod2d <- snssde2d(drift=fx,diffusion=gx,x0=c(1,2),M=1000)

# random 
r2d <- rsde2d(mod2d,at=0.5)
summary(r2d)

# Marginal density 

denM <- dsde2d(mod2d,pdf="M", at=0.5)
denM
plot(denM)

# Joint density
denJ <- dsde2d(mod2d,pdf="J",n=200, at= 0.5,lims=c(-3,4,0,6))
denJ
plot(denJ)
plot(denJ,display="contour")

## Example 2: Bivariate Transition Density of 2 Brownian motion (W1(t),W2(t)) in [0,1]

## Not run: 
B2d <- snssde2d(drift=rep(expression(0),2),diffusion=rep(expression(1),2),
       M=10000)
for (i in seq(B2d$Dt,B2d$T,by=B2d$Dt)){
plot(dsde2d(B2d, at = i,lims=c(-3,3,-3,3),n=100),
   display="contour",main=paste0('Transition Density \n t = ',i))
}

## End(Not run)

## Example 3: 

## Not run: 
fx <- expression(4*(-1-x)*y , 4*(1-y)*x )
gx <- expression(0.25*y,0.2*x)
mod2d1 <- snssde2d(drift=fx,diffusion=gx,x0=c(x0=1,y0=-1),
      M=5000,type="str")

# Marginal transition density
for (i in seq(mod2d1$Dt,mod2d1$T,by=mod2d1$Dt)){
plot(dsde2d(mod2d1,pdf="M", at = i),main=
      paste0('Marginal Transition Density \n t = ',i))
}

# Bivariate transition density
for (i in seq(mod2d1$Dt,mod2d1$T,by=mod2d1$Dt)){
plot(dsde2d(mod2d1, at = i,lims=c(-1,2,-1,1),n=100),
    display="contour",main=paste0('Transition Density \n t = ',i))
}

## End(Not run)

## Example 4: Bivariate Transition Density of 2 bridge Brownian motion (W1(t),W2(t)) in [0,1]

## Not run: 
B2d <- bridgesde2d(drift=rep(expression(0),2),
  diffusion=rep(expression(1),2),M=5000)
for (i in seq(0.01,0.99,by=B2d$Dt)){ 
plot(dsde2d(B2d, at = i,lims=c(-3,3,-3,3),
 n=100),display="contour",main=
 paste0('Transition Density \n t = ',i))
}

## End(Not run)

## Example 5: Bivariate Transition Density of bridge 
## Ornstein-Uhlenbeck process and its integral in [0,5]
## dX(t) = 4*(-1-X(t)) dt + 0.2 dW1(t)
## dY(t) = X(t) dt + 0 dW2(t)
## x01 = 0 , y01 = 0
## x02 = 0, y02 = 0 
## Not run: 
fx <- expression(4*(-1-x) , x)
gx <- expression(0.2 , 0)
OUI <- bridgesde2d(drift=fx,diffusion=gx,Dt=0.005,M=1000)
for (i in seq(0.01,4.99,by=OUI$Dt)){
plot(dsde2d(OUI, at = i,lims=c(-1.2,0.2,-2.5,0.2),n=100),
 display="contour",main=paste0('Transition Density \n t = ',i))
}

## End(Not run)

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