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 assed 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.