Evaluate a smooth at a grid of evenly spaced value over the range of the
covariate associated with the smooth. Alternatively, a set of points at which
the smooth should be evaluated can be supplied. `smooth_estimates()`

is a new
implementation of `evaluate_smooth()`

, and replaces that function, which has
been removed from the package.

## Usage

```
smooth_estimates(object, ...)
# S3 method for class 'gam'
smooth_estimates(
object,
select = NULL,
smooth = deprecated(),
n = 100,
n_3d = 16,
n_4d = 4,
data = NULL,
unconditional = FALSE,
overall_uncertainty = TRUE,
dist = NULL,
unnest = TRUE,
partial_match = FALSE,
...
)
```

## Arguments

- object
an object of class

`"gam"`

or`"gamm"`

.- ...
arguments passed to other methods.

- select
character; select which smooth's posterior to draw from. The default (

`NULL`

) means the posteriors of all smooths in`model`

wil be sampled from. If supplied, a character vector of requested terms.- smooth
- 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
a data frame of covariate values at which to evaluate the smooth.

- 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.- unnest
logical; unnest the smooth objects?

- partial_match
logical; in the case of character

`select`

, should`select`

match partially against`smooths`

? If`partial_match = TRUE`

,`select`

must only be a single string, a character vector of length 1.

## Examples

```
load_mgcv()
dat <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 2)
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")
## evaluate all smooths
smooth_estimates(m1)
#> # A tibble: 400 x 9
#> .smooth .type .by .estimate .se x0 x1 x2 x3
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 s(x0) TPRS NA -0.966542 0.316118 0.00710904 NA NA NA
#> 2 s(x0) TPRS NA -0.925391 0.297170 0.0171157 NA NA NA
#> 3 s(x0) TPRS NA -0.884233 0.279256 0.0271224 NA NA NA
#> 4 s(x0) TPRS NA -0.843050 0.262594 0.0371291 NA NA NA
#> 5 s(x0) TPRS NA -0.801824 0.247376 0.0471358 NA NA NA
#> 6 s(x0) TPRS NA -0.760536 0.233728 0.0571425 NA NA NA
#> 7 s(x0) TPRS NA -0.719175 0.221701 0.0671492 NA NA NA
#> 8 s(x0) TPRS NA -0.677736 0.211261 0.0771559 NA NA NA
#> 9 s(x0) TPRS NA -0.636220 0.202303 0.0871626 NA NA NA
#> 10 s(x0) TPRS NA -0.594641 0.194685 0.0971693 NA NA NA
#> # i 390 more rows
## or selected smooths
smooth_estimates(m1, select = c("s(x0)", "s(x1)"))
#> # A tibble: 200 x 7
#> .smooth .type .by .estimate .se x0 x1
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 s(x0) TPRS NA -0.966542 0.316118 0.00710904 NA
#> 2 s(x0) TPRS NA -0.925391 0.297170 0.0171157 NA
#> 3 s(x0) TPRS NA -0.884233 0.279256 0.0271224 NA
#> 4 s(x0) TPRS NA -0.843050 0.262594 0.0371291 NA
#> 5 s(x0) TPRS NA -0.801824 0.247376 0.0471358 NA
#> 6 s(x0) TPRS NA -0.760536 0.233728 0.0571425 NA
#> 7 s(x0) TPRS NA -0.719175 0.221701 0.0671492 NA
#> 8 s(x0) TPRS NA -0.677736 0.211261 0.0771559 NA
#> 9 s(x0) TPRS NA -0.636220 0.202303 0.0871626 NA
#> 10 s(x0) TPRS NA -0.594641 0.194685 0.0971693 NA
#> # i 190 more rows
```