S3 methods to evaluate individual smooths

Usage

eval_smooth(smooth, ...)

# S3 method for class 'mgcv.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'soap.film'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  clip = TRUE,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'scam_smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'fs.interaction'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'sz.interaction'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'random.effect'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'mrf.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 't2.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  envir = NULL,
  back_transform = FALSE,
  ...
)

# S3 method for class 'tensor.smooth'
eval_smooth(
  smooth,
  model,
  n = 100,
  n_2d = NULL,
  n_3d = NULL,
  n_4d = NULL,
  data = NULL,
  unconditional = FALSE,
  frequentist = FALSE,
  overall_uncertainty = TRUE,
  dist = NULL,
  envir = NULL,
  back_transform = FALSE,
  ...
)

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 a univariate smooth.
n_2d: numeric; the number of points along each of the first two axes of a smooth surface, including surface panels of higher-dimensional smooths. The default is 50 in plotting and plot-preparation functions. If NULL, use n instead. Ignored when evaluation data are supplied. Factor levels are retained, and curves with only one continuous covariate use n.
n_3d, n_4d: numeric; the number of points along the third axis of a 3D smooth (n_3d, default 16), or each axis after the first two for smooths of dimension four or higher (n_4d, default 4). If NULL, use n for those axes. For factor conditioning dimensions, retain at most that many levels, taking the first in fitted order. The first two surface axes use n_2d. Supplied data are unchanged.
data: an optional data frame of values to evaluate smooth at.
unconditional: logical; if TRUE (and only if frequentist == FALSE) then the bayesian smoothing parameter uncertainty-corrected covariance matrix is returned, if available. Whether it is available depends on which smoothness selection method was used to fit the model.
frequentist: logical; if FALSE, the default, the bayesian covariance matrix is returned, otherwise the frequentist covariance matrix.
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.
envir: an optional environment supplying functions and constants used in model expressions. The available model formula environment is used when NULL. Covariate observations should be supplied in data.
back_transform: logical; generate automatic grids on the original covariate scale? The default, FALSE, preserves evenly spaced model-scale grids (e.g. in log(x)). With TRUE, recover raw fitting covariates and generate grids on their original scale, then evaluate the model expressions forwards. Each axis must depend on one distinct original model covariate. Functions such as foo(x) require no inverse. Fitted prediction parameters are retained where provided by the model, including for scale(x). When data are supplied, use those rows without generating a grid; TRUE requires raw coordinates for each axis. Returned model-coordinate columns keep their usual names and raw coordinates are retained in auxiliary .gratia_raw_* columns. Draw these estimates with draw(object, back_transform = TRUE) to display the raw coordinates. Estimates containing only transformed coordinates give an informative error if drawn on the original scale. This option does not change effect values or their intervals at the chosen evaluation points.
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.