spStack (version 1.0.1)

sim_spData: Simulate spatial data on unit square

Description

Generates synthetic spatial data of different types where the spatial co-ordinates are sampled uniformly on an unit square. Different types include point-referenced Gaussian, Poisson, binomial and binary data. The design includes an intercept and fixed covariates sampled from a standard normal distribution.

Usage

sim_spData(n, beta, cor.fn, spParams, spvar, deltasq, family, n_binom)

Value

a data.frame object containing the columns -

s1, s2

2D-coordinates in unit square

x1, x2, ...

covariates, not including intercept

y

response

n_trials

present only when binomial data is generated

z_true

true spatial effects with which the data is generated

Arguments

n

sample size.

beta

a \(p\)-dimensional vector of fixed effects.

cor.fn

a quoted keyword that specifies the correlation function used to model the spatial dependence structure among the observations. Supported covariance model key words are: 'exponential' and 'matern'.

spParams

a numeric vector containing spatial process parameters - e.g., spatial decay and smoothness.

spvar

value of spatial variance parameter.

deltasq

value of noise-to-spatial variance ratio.

family

a character specifying the distribution of the response as a member of the exponential family. Valid inputs are 'gaussian', 'poisson', 'binary', and 'binomial'.

n_binom

necessary only when family = 'binomial'. Must be a vector of length n that will specify the number of trials for each observation. If it is of length 1, then that value is considered to be the common value for the number of trials for all n observations.

Author

Soumyakanti Pan ,
Sudipto Banerjee

Examples

set.seed(1729)
n <- 10
beta <- c(2, 5)
phi0 <- 2
nu0 <- 0.5
spParams <- c(phi0, nu0)
spvar <- 0.4
deltasq <- 1
sim1 <- sim_spData(n = n, beta = beta, cor.fn = "matern",
                   spParams = spParams, spvar = spvar, deltasq = deltasq,
                   family = "gaussian")