sim_glmmfields | R Documentation |
Simulate a random field with a MVT distribution
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))
)
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 |
s <- sim_glmmfields(n_draws = 12, n_knots = 12, gp_theta = 1.5,
gp_sigma = 0.2, sd_obs = 0.2)
names(s)
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