Description Usage Arguments Details Value See Also Examples

Generates simulations that are then used to evaluate the fitting and prediction of an IDE model.

1 |

`T` |
number of time points to simulate |

`nobs` |
number of observations randomly scattered in the domain and fixed for all time intervals |

`k_spat_invariant` |
flag indicating whether to simulate using a spatially-invariant kernel or a spatially-variant one |

`IDEmodel` |
object of class IDE to simulate form (optional) |

The domain considered is [0,1] x [0,1], and an IDE is simulated on top of a fixed effect comprising of an intercept, a linear horizontal effect, and a linear vertical effect (all with coefficients 0.2). The measurement-error variance and the variance of the additive disturbance are both 0.0001. When a spatially-invariant kernel is used, the following parameters are fixed: *θ_{p,1} = 150*, *θ_{p,2} = 0.002*, *θ_{p,3} = -0.1*, and *θ_{p,4} = 0.1*. See `IDE`

for details on these parameters. When a spatially-varying kernel is used, *θ_{p,1} = 200*, *θ_{p,2} = 0.002*, and *θ_{p,3}(s), θ_{p,4}(s)* are smooth spatial functions simulated on the domain.

A list containing the simulated process in `s_df`

, the simulated data in `z_df`

, the data as `STIDF`

in `z_STIDF`

, plots of the process and the observations in `g_truth`

and `g_obs`

, and the IDE model used to simulate the process and data in `IDEmodel`

.

`show_kernel`

for plotting the kernel and `IDE`

1 2 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.