rspde_lme: rSPDE linear mixed effects models

In rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations

rSPDE linear mixed effects models

Description

Fitting linear mixed effects model with latent Whittle-Matern models.

Usage

rspde_lme(
  formula,
  loc,
  data,
  model = NULL,
  repl = NULL,
  which_repl = NULL,
  optim_method = "L-BFGS-B",
  use_data_from_graph = TRUE,
  starting_values_latent = NULL,
  start_sigma_e = NULL,
  start_alpha = NULL,
  alpha = NULL,
  start_nu = NULL,
  nu = NULL,
  nu_upper_bound = 4,
  rspde_order = NULL,
  parallel = FALSE,
  n_cores = parallel::detectCores() - 1,
  optim_controls = list(),
  improve_hessian = FALSE,
  hessian_args = list()
)

Arguments

formula	Formula object describing the relation between the response variables and the fixed effects. If the response variable is a matrix, each column of the matrix will be treated as a replicate.
loc	A vector with the names of the columns in data that contain the observation locations, or a matrix or a data.frame containing the observation locations. If the model is of class metric_graph, the locations must be either a matrix or a data.frame with two columns, or a character vector with the names of the two columns. The first column being the number of the edge, and the second column being the normalized position on the edge. If the model is a 2d model, loc must be either a matrix or data.frame with two columns or a character vector with the name of the two columns that contain the location, the first entry corresponding to the x entry and the second corresponding to the y entry.
data	A data.frame containing the data to be used.
model	Either an object generated by matern.operators() or spde.matern.operators(). If NULL, a simple linear regression will be performed.
repl	Vector indicating the replicate of each observation. If NULL it will assume there is only one replicate.
which_repl	Which replicates to use? If NULL all replicates will be used.
optim_method	The method to be used with optim function.
use_data_from_graph	Logical. Only for models generated from graphs from metric_graph class. In this case, should the data, the locations and the replicates be obtained from the graph object?
starting_values_latent	A vector containing the starting values for the latent model. If the latent model was generated by matern.operators(), then the starting values should be provided as a vector of the form c(tau,kappa). If the model was generated by spde.matern.operators(), then the starting values should be provided as a vector containing the nonstationary parameters.
start_sigma_e	Starting value for the standard deviation of the measurament error.
start_alpha	Starting value for the smoothness parameter. Will be used if start_nu is not given.
alpha	If NULL, the smoothness parameter will be estimated, otherwise the smoothness parameter will be kept fixed at the provided value. Will be used if nu is not given.
start_nu	Starting value for the smoothness parameter.
nu	If NULL, the smoothness parameter will be estimated, otherwise the smoothness parameter will be kept fixed at the provided value.
nu_upper_bound	A parameter that limits the maximum value that nu can assume.
rspde_order	The order of the rational approximation to be used while fitting the model. If not given, the order from the model object will be used.
parallel	logical. Indicating whether to use optimParallel or not.
n_cores	Number of cores to be used if parallel is true.
optim_controls	Additional controls to be passed to optim or optimParallel.
improve_hessian	Should a more precise estimate of the hessian be obtained? Turning on might increase the overall time.
hessian_args	List of controls to be used if improve_hessian is TRUE. The list can contain the arguments to be passed to the method.args argument in the numDeriv::hessian function. See the help of the hessian function in numDeriv package for details. Observe that it only accepts the "Richardson" method for now, the method "complex" is not supported.

Value

A list containing the fitted model.

rSPDE documentation built on Nov. 6, 2023, 1:06 a.m.