sim_data_fe: Simulate space-time stochastic process with fixed-effect

In edoardoBaldoni/AGPRIS: AGricultural PRoductivity in Space

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,
  T,
  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
T	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

## Not run: 
library(spacetime)
library(sp)
library(spdep)
set.seed(123)
sd = sim_data_fe(dataset=regsamp,N=100,T=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)

## End(Not run)

edoardoBaldoni/AGPRIS documentation built on Sept. 27, 2020, 12:04 a.m.