1d:T769,# Simulation by Gaussian State Space Model Simulate time series by Gaussian State Space Model. ```r simssm(n = 200, trend = NULL, seasonal.order = 0, seasonal = NULL, arcoef = NULL, ar = NULL, tau1 = NULL, tau2 = NULL, tau3 = NULL, sigma2 = 1.0, seed = NULL, plot = TRUE, ...) ``` ## Arguments - `n`: the number of data generated by simulation. - `trend`: initial values of trend component of length $m1$, where $m1$ is trend order (1, 2). If `NULL` (default), trend order is 0. - `seasonal.order`: order of seasonal component model (0, 1, 2). - `seasonal`: if `seasonal.order` > 0, initial values of seasonal component of length $p-1$, where $p$ is period of one season. - `arcoef`: AR coefficients. - `ar`: initial values of AR component. - `tau1`: variance of trend component model. - `tau2`: variance of AR component model. - `tau3`: variance of seasonal component model. - `sigma2`: variance of the observation noise. - `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. - ``...``: graphical arguments passed to `plot.simulate`. ## Returns An object of class `"simulate"`, giving simulated data of Gaussian state space model. ## References Kitagawa, G. (2020) **Introduction to Time Series Modeling with Applications in R**. Chapman & Hall/CRC. ## Examples ```r # BLSALLFOOD data data(BLSALLFOOD) m1 <- 2; m2 <- 1; m3 <- 2 z <- season(BLSALLFOOD, trend.order = m1, seasonal.order = m2, ar.order = m3) nl <- length(BLSALLFOOD) trend <- z$trend[m1:1] arcoef <- z$arcoef period <- 12 seasonal <- z$seasonal[(period-1):1] ar <- z$ar[m3:1] tau1 <- z$tau2[1] tau2 <- z$tau2[2] tau3 <- z$tau2[3] simssm(n = nl, trend, seasonal.order = m2, seasonal, arcoef, ar, tau1, tau2, tau3, sigma2 = z$sigma2, seed = 333) ```

simssm function