augment.rspde_lme: Augment data with information from a 'rspde_lme' object

In rSPDE: Rational Approximations of Fractional Stochastic Partial Differential Equations

Augment data with information from a rspde_lme object

Description

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 '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.

See Also

glance.rspde_lme

rSPDE documentation built on Nov. 6, 2023, 1:06 a.m.