# 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 a`list`

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`

, a`matrix`

or a character vector, that contains the names of the columns of the coordinates of the locations. For models using`metric_graph`

objects, plase use`edge_number`

and`distance_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`

is`TRUE`

.- 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.