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In this R package, a spatial dataset can be generated under the assumption that observations are collected from a two dimensional uniform grid consists of (m^2^) lattice points having unit distance between any two neighbouring points along the horizontal and vertical directions.
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generation of spatial coordinates of locations
The size of the population is N= m^2^. The spatial coordinates of the locations of observations can be computed by the following expressions
( Latitude~i~, Longitude~i~ )= ( mod(i-1,m), [(i-1)/m] ), i= 1,..., m^2^
*where, mod(i-1,m) is the remainder of (i-1) divided by m and [(i-1)/m] is the integer part of the number (i-1)/m *
generation of auxiliary variable from uniform distribution
X =runif(N,0,1)
error term drawn independently from normal distribution i.e. N(0,1)
e =rnorm(N, mean=0, sd=1)
generation of spatially varying regression coefficients
B~0~=(Latitude~i~+Longitude~i~)/6
B~1~=(Latitude~i~/3)
spatially varying regression model for generating the response variable
Y~i~ = B~0~( Latitude~i~,Longitude~i~ ) + B~1~( Latitude~i~,Longitude~i~ )*X~i~ + e~i~ ; i= 1,..., N
# Examples: generate an uniform two dimensional grid of lattice points library(SpatialPOP) coord_grid=spatial_grid(c(1:5),c(1:5)) coord_grid=as.data.frame(coord_grid) names(coord_grid)=cbind("x","y") coord_grid plot(coord_grid) # Examples: simulated data along with spatial coordinates and spatially varying model parameters library(SpatialPOP) coord_grid=spatial_grid(c(1:5),c(1:5)) coord_grid=as.data.frame(coord_grid) names(coord_grid)=cbind("x","y") coord_grid N<-nrow(coord_grid) N m<-sqrt(nrow(coord_grid)) m spatial_data<-spatialPOP(25,5,c(1:5),c(1:5)) spatial_data
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