sim_glmmfields: Simulate a random field with a MVT distribution In glmmfields: Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling

Description

Simulate a random field with a MVT distribution

Usage

 ```1 2 3 4 5 6 7 8``` ```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

 ```1 2 3``` ```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.