simIDE: Simulate datasets from the IDE model

Description Usage Arguments Details Value See Also Examples

Description

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

Usage

1
simIDE(T = 9, nobs = 100, k_spat_invariant = 1, IDEmodel = NULL)

Arguments

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)

Details

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.

Value

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.

See Also

show_kernel for plotting the kernel and IDE

Examples

1
2
SIM1 <- simIDE(T = 5, nobs = 100, k_spat_invariant = 1)
SIM2 <- simIDE(T = 5, nobs = 100, k_spat_invariant = 0)

IDE documentation built on June 24, 2019, 9:03 a.m.