sim_data_fe {AGPRIS}

Simulate space-time stochastic process with fixed-effect

Description

This function simulates a space-time stochastic process according to the defined spatial structure and input paramters. It simulates data of a dynamic spatial lag model. It includes one exogenous variable and a fixed-effect correlated with the exogenous variable.

Usage

sim_data_fe(
  dataset,
  N,
  TT,
  spatial = 100,
  Tau = -0.14,
  Rho = 0.67,
  Beta = 1,
  sdDev = 5,
  startingT = 11,
  LONGLAT = TRUE
)

Arguments

dataset

SpatialObject with the spatial units for which the data will be simulated

N

How many spatial units will be used

TT

Time dimension of the simulated process

spatial

Radius that defines the scope of spatial dependence

Tau

Autocorrelation parameter

Rho

Spatial dependence parameter

Beta

Coefficient associated to the exogenous variable

sdDev

Standard Deviation of the (gaussian) error term

startingT

The number of time periods after which the simulated data will be recorded

LONGLAT

Boolean. If the projection is longlat

Value

A list with two objects. The first object is the STFDF with the simulated data. The second object is the spatial weight matrix

Examples

library(spacetime)
library(sp)
library(spdep)


set.seed(123)
sd = sim_data_fe(dataset=regsamp,N=100,TT=8,
                 spatial = 80,Tau = -0.2,Rho = 0.4,
                 Beta = 2,sdDev = 2,startingT = 10,
                 LONGLAT = TRUE)
stplot(sd[[1]][,,'Y'])
dev.new()
plot(sel_regioni)
points(coordinates(sd[[1]]@sp))
plot(mat2listw(sd[[2]]),coordinates(sd[[1]]@sp),add=TRUE,col=2)