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,
  ...
)

# 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 suported.
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.