sprnbinom function

Simulate a spatial negative binomial random variable

Simulate a spatial negative binomial random variable with a specific mean and covariance structure.

sprnbinom(
  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().

Returns

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.

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 negative binomial random variable.

Examples

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