SpatGC (version 0.1.0)

rGCgeo: Generate Data from GC Spatial Regression Model with Geospatial Dependency

Description

This function generates spatially dependent count data based on the Gamma-Count (GC) spatial regression model. It uses a specified geospatial dependency model with parameters such as `sigma` for variance and `range` for spatial range. The function returns a list containing the generated data and relevant information about the simulation.

Usage

rGCgeo(
  n = n,
  alpha,
  beta0,
  beta,
  V = NULL,
  rho = 1,
  sigma = NULL,
  range = NULL
)

Value

A list containing the following components:

covariate

A matrix of covariates with the specified number of knots (`n`) and columns based on the length of `beta`.

phi

A vector of spatial random effects generated from the Matern covariance model.

eta

A vector representing the linear predictor, calculated as the dot product of the covariates and coefficients plus the spatial effects (`phi`).

mu

A vector of mean response values calculated as the product of `alpha` and the exponential of `eta`.

y

A vector of simulated count data based on the GC model and the mean response values (`mu`).

ID

A vector of knot IDs from 1 to `n`.

Arguments

n

Integer. The number of knots (or spatial units) for which the data should be generated.

alpha

Numeric. The dispersion parameter of the Gamma-Count model.

beta0

Numeric. The intercept term for the model.

beta

Numeric vector. The regression coefficients (fixed effects) for the model.

V

Optional numeric. The variance of the spatial random effects for lattice data.

rho

Optional numeric. The spatial correlation coefficient for the CAR model. Default is 1.

sigma

Optional numeric. The variance of the spatial random effects for geospatial data with Matern covariance.

range

Optional numeric. The range parameter for geospatial data with Matern covariance.

Examples

# \donttest{
# Generate data from the GC spatial regression model with geospatial dependency

data <- rGCgeo(n = 100, alpha = 1, beta0 = 0.3, beta = c(-0.5, 0.5),
sigma = 1, range = 2)

# View the generated data
print(data)
# }