Prediction of a mixed effects regression model on a metric graph.
Source:R/rspde_lme.R
predict.rspde_lme.Rd
Prediction of a mixed effects regression model on a metric graph.
Usage
# S3 method for rspde_lme
predict(
object,
newdata = NULL,
loc = NULL,
mesh = FALSE,
which_repl = NULL,
compute_variances = FALSE,
posterior_samples = FALSE,
n_samples = 100,
sample_latent = FALSE,
edge_number = "edge_number",
distance_on_edge = "distance_on_edge",
normalized = FALSE,
return_as_list = FALSE,
return_original_order = TRUE,
...,
data = deprecated()
)
Arguments
- object
The fitted object with the
rspde_lme()
function- newdata
A
data.frame
or alist
containing the covariates, the edge number and the distance on edge for the locations to obtain the prediction.- loc
Prediction locations. Can either be a
data.frame
, amatrix
or a character vector, that contains the names of the columns of the coordinates of the locations. For models usingmetric_graph
objects, plase useedge_number
anddistance_on_edge
instead.- mesh
Obtain predictions for mesh nodes? The graph must have a mesh, and either
only_latent
is set to TRUE or the model does not have covariates.- which_repl
Which replicates to use? If
NULL
all replicates will be used.- compute_variances
Set to also TRUE to compute the kriging variances.
- posterior_samples
If
TRUE
, posterior samples will be returned.- n_samples
Number of samples to be returned. Will only be used if
sampling
isTRUE
.- sample_latent
Do posterior samples only for the random effects?
- edge_number
Name of the variable that contains the edge number, the default is
edge_number
.- distance_on_edge
Name of the variable that contains the distance on edge, the default is
distance_on_edge
.- normalized
Are the distances on edges normalized?
- return_as_list
Should the means of the predictions and the posterior samples be returned as a list, with each replicate being an element?
- return_original_order
Should the results be return in the original (input) order or in the order inside the graph?
- ...
Not used.
- data
Value
A list with elements mean
, which contains the means of the
predictions, fe_mean
, which is the prediction for the fixed effects, re_mean
, which is the prediction for the random effects, variance
(if compute_variance
is TRUE
), which contains the
variances of the predictions, samples
(if posterior_samples
is TRUE
),
which contains the posterior samples.