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 class 'graph_lme'
augment(
x,
newdata = NULL,
which_repl = NULL,
sd_post_re = FALSE,
se_fit = FALSE,
conf_int = FALSE,
pred_int = FALSE,
level = 0.95,
edge_number = "edge_number",
distance_on_edge = "distance_on_edge",
coord_x = "coord_x",
coord_y = "coord_y",
data_coords = c("PtE", "spatial"),
normalized = FALSE,
no_nugget = FALSE,
check_euclidean = FALSE,
...
)
```

## Arguments

- x
A

`graph_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.- which_repl
Which replicates to obtain the prediction. If

`NULL`

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

.- sd_post_re
Logical indicating whether or not a .sd_post_re column should be added to the augmented output containing the posterior standard deviations of the random effects.

- se_fit
Logical indicating whether or not a .se_fit column should be added to the augmented output containing the standard errors of the fitted values. If

`TRUE`

, the posterior standard deviations of the random effects will also be returned.- conf_int
Logical indicating whether or not confidence intervals for the posterior mean of the random effects should be built.

- pred_int
Logical indicating whether or not prediction intervals for the fitted values should be built. If

`TRUE`

, the confidence intervals for the posterior random effects will also be built.- level
Level of confidence and prediction intervals if they are constructed.

- 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`

.- coord_x
Column (or entry on the list) of the

`data`

that contains the x coordinate. If not supplied, the column with name "coord_x" will be chosen. Will not be used if`Spoints`

is not`NULL`

or if`data_coords`

is`PtE`

.- coord_y
Column (or entry on the list) of the

`data`

that contains the y coordinate. If not supplied, the column with name "coord_x" will be chosen. Will not be used if`Spoints`

is not`NULL`

or if`data_coords`

is`PtE`

.- data_coords
To be used only if

`Spoints`

is`NULL`

. It decides which coordinate system to use. If`PtE`

, the user must provide`edge_number`

and`distance_on_edge`

, otherwise if`spatial`

, the user must provide`coord_x`

and`coord_y`

.- normalized
Are the distances on edges normalized?

- no_nugget
Should the prediction be done without nugget?

- check_euclidean
Check if the graph used to compute the resistance distance has Euclidean edges? The graph used to compute the resistance distance has the observation locations as vertices.

- ...
Additional arguments.

## Value

A `tidyr::tibble()`

with columns:

`.fitted`

Fitted or predicted value.`.relwrconf`

Lower bound of the confidence interval of the random effects, if conf_int = TRUE`.reuprconf`

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

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

Upper bound of the prediction interval, if conf_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.`.std_resid`

The standardized residuals, that is, the ordinary residuals divided by the standard error of the fitted values (by the prediction standard error), if se_fit = TRUE or pred_int = TRUE.`.se_fit`

Standard errors of fitted values, if se_fit = TRUE.`.sd_post_re`

Standard deviation of the posterior mean of the random effects, if se_fit = TRUE.