sim_glmmfields: Simulate a random field with a MVT distribution

Description Usage Arguments Examples

Description

Simulate a random field with a MVT distribution

Usage

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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

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

glmmfields documentation built on May 18, 2019, 9 a.m.