eval_smooth: S3 methods to evaluate individual smooths

In gratia: Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

S3 methods to evaluate individual smooths

Description

S3 methods to evaluate individual smooths

Usage

eval_smooth(smooth, ...)

## S3 method for class 'mgcv.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  ...
)

## S3 method for class 'soap.film'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  clip = TRUE,
  ...
)

## S3 method for class 'scam_smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  ...
)

## S3 method for class 'fs.interaction'
eval_smooth(
  smooth,
  model,
  n = 100,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  ...
)

## S3 method for class 'sz.interaction'
eval_smooth(
  smooth,
  model,
  n = 100,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  ...
)

## S3 method for class 'random.effect'
eval_smooth(
  smooth,
  model,
  n = 100,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  ...
)

## S3 method for class 'mrf.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  ...
)

## S3 method for class 't2.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  ...
)

## S3 method for class 'tensor.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  ...
)

Arguments

smooth	currently an object that inherits from class mgcv.smooth.
...	arguments passed to other methods
model	a fitted model; currently only mgcv::gam() and mgcv::bam() models are supported.
n	numeric; the number of points over the range of the covariate at which to evaluate the smooth.
n_3d, n_4d	numeric; the number of points over the range of last covariate in a 3D or 4D smooth. The default is NULL which achieves the standard behaviour of using n points over the range of all covariate, resulting in n^d evaluation points, where d is the dimension of the smooth. For d > 2 this can result in very many evaluation points and slow performance. For smooths of d > 4, the value of n_4d will be used for all dimensions ⁠> 4⁠, unless this is NULL, in which case the default behaviour (using n for all dimensions) will be observed.
data	an optional data frame of values to evaluate smooth at.
unconditional	logical; should confidence intervals include the uncertainty due to smoothness selection? If TRUE, the corrected Bayesian covariance matrix will be used.
overall_uncertainty	logical; should the uncertainty in the model constant term be included in the standard error of the evaluate values of the smooth?
dist	numeric; if greater than 0, this is used to determine when a location is too far from data to be plotted when plotting 2-D smooths. The data are scaled into the unit square before deciding what to exclude, and dist is a distance within the unit square. See mgcv::exclude.too.far() for further details.
clip	logical; should evaluation points be clipped to the boundary of a soap film smooth? The default is FALSE, which will return NA for any point that is deemed to lie outside the boundary of the soap film.

gratia documentation built on Feb. 7, 2026, 9:06 a.m.