sprinvgauss: Simulate a spatial inverse gaussian random variable

In spmodel: Spatial Statistical Modeling and Prediction

Simulate a spatial inverse gaussian random variable

Description

Simulate a spatial inverse gaussian random variable with a specific mean and covariance structure.

Usage

sprinvgauss(
  spcov_params,
  dispersion = 1,
  mean = 0,
  samples = 1,
  data,
  randcov_params,
  partition_factor,
  ...
)

Arguments

spcov_params	An spcov_params() object.
dispersion	The dispersion value.
mean	A numeric vector representing the mean. mean must have length 1 (in which case it is recycled) or length equal to the number of rows in data. The default is 0.
samples	The number of independent samples to generate. The default is 1.
data	A data frame or sf object containing spatial information.
randcov_params	A randcov_params() object.
partition_factor	A formula indicating the partition factor.
...	Additional arguments passed to sprnorm().

Details

The values of spcov_params, mean, and randcov_params are assumed to be on the link scale. They are used to simulate a latent normal (Gaussian) response variable using sprnorm(). This latent variable is the conditional mean used with dispersion to simulate a inverse gaussian random variable.

Value

If samples is 1, a vector of random variables for each row of data is returned. If samples is greater than one, a matrix of random variables is returned, where the rows correspond to each row of data and the columns correspond to independent samples.

Examples

spcov_params_val <- spcov_params("exponential", de = 0.2, ie = 0.1, range = 1)
sprinvgauss(spcov_params_val, data = caribou, xcoord = x, ycoord = y)
sprinvgauss(spcov_params_val, samples = 5, data = caribou, xcoord = x, ycoord = y)

spmodel documentation built on July 3, 2025, 9:08 a.m.