Augment accepts a model object and a dataset and adds information about each observation in the dataset. It includes
predicted values in the `.fitted`

column, residuals in the `.resid`

column, and standard errors for the fitted values in a `.se.fit`

column.
It also contains the New columns always begin with a . prefix to avoid overwriting columns in the original dataset.

## Usage

```
# S3 method for rspde_lme
augment(
x,
newdata = NULL,
loc = NULL,
mesh = FALSE,
which_repl = NULL,
se_fit = FALSE,
conf_int = FALSE,
pred_int = FALSE,
level = 0.95,
n_samples = 100,
edge_number = "edge_number",
distance_on_edge = "distance_on_edge",
normalized = FALSE,
...
)
```

## Arguments

- x
A

`rspde_lme`

object.- 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. If`NULL`

, the fitted values will be given for the original locations where the model was fitted.- 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 obtain the prediction. If

`NULL`

predictions will be obtained for all replicates. Default is`NULL`

.- se_fit
Logical indicating whether or not a .se.fit column should be added to the augmented output. If TRUE, it only returns a non-NA value if type of prediction is 'link'.

- conf_int
Logical indicating whether or not confidence intervals for the fitted variable should be built.

- pred_int
Logical indicating whether or not prediction intervals for future observations should be built.

- level
Level of confidence and prediction intervals if they are constructed.

- n_samples
Number of samples when computing prediction intervals.

- 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?

- ...
Additional arguments.

## Value

A `tidyr::tibble()`

with columns:

`.fitted`

Fitted or predicted value.`.fittedlwrconf`

Lower bound of the confidence interval, if conf_int = TRUE`.fitteduprconf`

Upper bound of the confidence interval, if conf_int = TRUE`.fittedlwrpred`

Lower bound of the prediction interval, if pred_int = TRUE`.fitteduprpred`

Upper bound of the prediction interval, if pred_int = TRUE`.fixed`

Prediction of the fixed effects.`.random`

Prediction of the random effects.`.resid`

The ordinary residuals, that is, the difference between observed and fitted values.`.se_fit`

Standard errors of fitted values, if se_fit = TRUE.