Usage
rsde3d(N, ...)
## S3 method for class 'default':
rsde3d(N = 1000, M = 100, x0 = 0, y0 = 0, z0 = 0, t0 = 0, T = 1, Dt, tau = 0.5,
driftx, diffx, drifty, diffy, driftz, diffz, alpha = 0.5, mu = 0.5,
type = c("ito", "str"), method = c("euler", "milstein", "predcorr",
"smilstein", "taylor", "heun", "rk1", "rk2", "rk3"), ...)
## S3 method for class 'rsde3d':
summary(object, \dots)
## S3 method for class 'rsde3d':
mean(x, \dots)
## S3 method for class 'rsde3d':
median(x, \dots)
## S3 method for class 'rsde3d':
quantile(x, \dots)
## S3 method for class 'rsde3d':
kurtosis(x, \dots)
## S3 method for class 'rsde3d':
skewness(x, \dots)
## S3 method for class 'rsde3d':
moment(x, order = 2, \dots)
## S3 method for class 'rsde3d':
bconfint(x, level=0.95, \dots)
## S3 method for class 'rsde3d':
plot(x, \dots)
Arguments
M
number of random numbers to be geneated.
x0, y0, z0
initial value of the process $X_{t}$, $Y_{t}$ and $Z_{t}$ at time t0
.
Dt
time step of the simulation (discretization). If it is missing
a default $\Delta t = \frac{T-t_{0}}{N}$. tau
moment (time) between t0
and T
. Random number generated at time=tau
.
driftx, drifty, driftz
drift coefficient: an expression
of four variables t
, x
, y
and z
for process $X_t$, $Y_t$ and $Z_t$. diffx, diffy, diffz
diffusion coefficient: an expression
of four variables t
, x
, y
and z
for process $X_t$, $Y_t$ and $Z_t$. alpha, mu
weight of the predictor-corrector scheme; the default alpha = 0.5
and mu = 0.5
.
type
sde of the type Ito or Stratonovich.
method
numerical methods of simulation, the default method = "euler"
; see snssde3d
. x, object
an object inheriting from class "rsde2d"
.
level
the confidence level required.
...
further arguments for (non-default) methods.