glmmfields (version 0.1.7)

sim_glmmfields: Simulate a random field with a MVT distribution

Description

Simulate a random field with a MVT distribution

Usage

sim_glmmfields(
  n_knots = 15,
  n_draws = 10,
  gp_theta = 0.5,
  gp_sigma = 0.2,
  mvt = TRUE,
  df = 1e+06,
  seed = NULL,
  n_data_points = 100,
  sd_obs = 0.1,
  covariance = c("squared-exponential", "exponential", "matern"),
  matern_kappa = 0.5,
  obs_error = c("normal", "gamma", "poisson", "nb2", "binomial", "lognormal"),
  B = c(0),
  phi = 0,
  X = rep(1, n_draws * n_data_points),
  g = data.frame(lon = runif(n_data_points, 0, 10), lat = runif(n_data_points, 0, 10))
)

Arguments

n_knots

The number of knots

n_draws

The number of draws (for example, the number of years)

gp_theta

The Gaussian Process scale parameter

gp_sigma

The Gaussian Process variance parameter

mvt

Logical: MVT? (vs. MVN)

df

The degrees of freedom parameter for the MVT distribution

seed

The random seed value

n_data_points

The number of data points per draw

sd_obs

The observation process scale parameter

covariance

The covariance function of the Gaussian process ("squared-exponential", "exponential", "matern")

matern_kappa

The optional matern parameter. Can be 1.5 or 2.5. Values of 0.5 equivalent to exponential model.

obs_error

The observation error distribution

B

A vector of parameters. The first element is the intercept

phi

The auto regressive parameter on the mean of the random field knots

X

The model matrix

g

Grid of points

Examples

s <- sim_glmmfields(n_draws = 12, n_knots = 12, gp_theta = 1.5,
  gp_sigma = 0.2, sd_obs = 0.2)
names(s)