Simulate spatial data on unit square — sim

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)

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.

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

Author

Soumyakanti Pan span18@ucla.edu,
Sudipto Banerjee sudipto@ucla.edu

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")