# ngsim

##### Simulation by Non-Gaussian State Space Model

Simulation by non-Gaussian state space model.

- Keywords
- ts

##### Usage

```
ngsim(n = 200, trend = NULL, seasonal.order = 0, seasonal = NULL, arcoef = NULL,
ar = NULL, noisew = 1, wminmax = c(-1, 1), paramw = NULL, noisev = 1,
vminmax = c(-1, 1), paramv = NULL, seed = NULL, plot = TRUE, …)
```

##### Arguments

- n
the number of simulated data.

- trend
initial values of trend component of length at most 2.

- seasonal.order
seasonal order. (0 or 1)

- seasonal
if

`seasonal.order`

> 0, initial values of seasonal component of length \(p-1\), where \(p\) is the number of season in one period.- arcoef
AR coefficients.

- ar
initial values of AR component.

- noisew
type of the observational noise.

-1 : Cauchy random number (without an inverse function) -2 : exponential distribution (without an inverse function) -3 : double exponential distribution (without an inverse function) 0 : double exponential distribution (+ Euler's constant) 1 : normal distribution, 2 : Pearson distribution, - wminmax
lower and upper bound of observational noise.

- paramw
parameter of the observational noise density.

noisew = 1 : variance - noisev
type of the system noise.

-1 : Cauchy random number (without an inverse function) -2 : exponential distribution (without an inverse function) -3 : double exponential distribution (without an inverse function) 0 : double exponential distribution (+ Euler's constant) 1 : normal distribution 2 : Pearson distribution - vminmax
lower and upper bound of system noise.

- paramv
parameter of the system noise density.

noisev = 1 : variance - seed
arbitrary positive integer to generate a sequence of uniform random numbers. The default seed is based on the current time.

- plot
logical. If

`TRUE`

(default), simulated data are plotted.- …
further arguments to be passed to

`plot.simulate`

.

##### Value

An object of class `"simulate"`

, giving simulated data of non-Gaussian
state space model.

##### References

Kitagawa, G. (2010)
*Introduction to Time Series Modeling*. Chapman & Hall/CRC.

##### Examples

```
# NOT RUN {
ar1 <- ngsim(n = 400, arcoef = 0.95, noisew = 1, paramw = 1, noisev = 1,
paramv = 1, seed = 555)
plot(ar1, use = c(201, 400))
ar2 <- ngsim(n = 400, arcoef = c(1.3, -0.8), noisew = 1, paramw = 1, noisev = 1,
paramv = 1, seed = 555)
plot(ar2, use = c(201, 400))
# }
```

*Documentation reproduced from package TSSS, version 1.2.3, License: GPL (>= 2)*