rSPDE linear mixed effects models — rspde

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

Usage

rspde_lme(
  formula,
  loc,
  loc_time = NULL,
  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.
loc_time: For spatio-temporal models, the name of the column in data that is the time variable, or a matrix or vector containing the observation time points.
data: A data.frame containing the data to be used.
model: Object generated by matern.operators(), spde.matern.operators() or spacetime.operators(). If NULL, simple linear regression will be performed.
repl: Vector indicating the replicate of each observation. If NULL it will assume there is only one replicate. If the model is generated from graphs from metric_graph class and use_data_from_graph is TRUE, repl needs to be the name of the column inside the metric graph data that contains the replicate. 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 is generated by matern.operators(), then the vector should be on the form c(tau,kappa). If the model is generated by spde.matern.operators(), the vector should contain the nonstationary parameters. If the model is generated by spacetime.operators(), the vector should be on the form c(kappa,sigma,gamma,rho).
start_sigma_e: Starting value for the standard deviation of the measurement error.
start_alpha: Starting value for the smoothness parameter of spatial models. Will be used if start_nu is not given. Not used for spatio-temporal models.
alpha: If NULL, the smoothness parameter will be estimated for spatial models, otherwise it is kept fixed at the provided value. Will be used if nu is not given. Not used for spatio-temporal models. returned as component of the returned value.
start_nu: Starting value for the smoothness parameter of spatial models. Not used for spatio-temporal models.
nu: If NULL, the smoothness parameter will be estimated for spatial models, otherwise the smoothness parameter will be kept fixed at the provided value. Not used for spatio-temporal models.
nu_upper_bound: A parameter that limits the maximum value that nu can assume. Not used for spatio-temporal models.
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. Not used for spatio-temporal models.
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.