Plots estimated smooths from a fitted GAM model in a similar way to
`mgcv::plot.gam()`

but instead of using base graphics, `ggplot2::ggplot()`

is used instead.

## Usage

```
# S3 method for class 'gam'
draw(
object,
data = NULL,
select = NULL,
parametric = FALSE,
terms = NULL,
residuals = FALSE,
scales = c("free", "fixed"),
ci_level = 0.95,
n = 100,
n_3d = 16,
n_4d = 4,
unconditional = FALSE,
overall_uncertainty = TRUE,
constant = NULL,
fun = NULL,
dist = 0.1,
rug = TRUE,
contour = TRUE,
grouped_by = FALSE,
ci_alpha = 0.2,
ci_col = "black",
smooth_col = "black",
resid_col = "steelblue3",
contour_col = "black",
n_contour = NULL,
partial_match = FALSE,
discrete_colour = NULL,
discrete_fill = NULL,
continuous_colour = NULL,
continuous_fill = NULL,
position = "identity",
angle = NULL,
ncol = NULL,
nrow = NULL,
guides = "keep",
widths = NULL,
heights = NULL,
crs = NULL,
default_crs = NULL,
lims_method = "cross",
wrap = TRUE,
envir = environment(formula(object)),
...
)
```

## Arguments

- object
a fitted GAM, the result of a call to

`mgcv::gam()`

.- data
a optional data frame that may or may not be used? FIXME!

- select
character, logical, or numeric; which smooths to plot. If

`NULL`

, the default, then all model smooths are drawn. Numeric`select`

indexes the smooths in the order they are specified in the formula and stored in`object`

. Character`select`

matches the labels for smooths as shown for example in the output from`summary(object)`

. Logical`select`

operates as per numeric`select`

in the order that smooths are stored.- parametric
logical; plot parametric terms also? Note that

`select`

is used for selecting which smooths to plot. The`terms`

argument is used to select which parametric effects are plotted. The default, as with`mgcv::plot.gam()`

, is to not draw parametric effects.- terms
character; which model parametric terms should be drawn? The Default of

`NULL`

will plot all parametric terms that can be drawn.- residuals
logical; should partial residuals for a smooth be drawn? Ignored for anything but a simple univariate smooth.

- scales
character; should all univariate smooths be plotted with the same y-axis scale? If

`scales = "free"`

, the default, each univariate smooth has its own y-axis scale. If`scales = "fixed"`

, a common y axis scale is used for all univariate smooths.Currently does not affect the y-axis scale of plots of the parametric terms.

- ci_level
numeric between 0 and 1; the coverage of credible interval.

- n
numeric; the number of points over the range of the covariate at which to evaluate the smooth.

- n_3d
numeric; the number of new observations to generate for the third dimension of a 3D smooth.

- n_4d
numeric; the number of new observations to generate for the dimensions higher than 2 (!) of a

*k*D smooth (*k*>= 4). For example, if the smooth is a 4D smooth, each of dimensions 3 and 4 will get`n_4d`

new observations.- 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?

- constant
numeric; a constant to add to the estimated values of the smooth.

`constant`

, if supplied, will be added to the estimated value before the confidence band is computed.- fun
function; a function that will be applied to the estimated values and confidence interval before plotting. Can be a function or the name of a function. Function

`fun`

will be applied after adding any`constant`

, if provided.- 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.- rug
logical; draw a rug plot at the bottom of each plot for 1-D smooths or plot locations of data for higher dimensions.

- contour
logical; should contours be draw on the plot using

`ggplot2::geom_contour()`

.- grouped_by
logical; should factor by smooths be drawn as one panel per level of the factor (

`FALSE`

, the default), or should the individual smooths be combined into a single panel containing all levels (`TRUE`

)?- ci_alpha
numeric; alpha transparency for confidence or simultaneous interval.

- ci_col
colour specification for the confidence/credible intervals band. Affects the fill of the interval.

- smooth_col
colour specification for the smooth line.

- resid_col
colour specification for the partial residuals.

- contour_col
colour specification for contour lines.

- n_contour
numeric; the number of contour bins. Will result in

`n_contour - 1`

contour lines being drawn. See`ggplot2::geom_contour()`

.- partial_match
logical; should smooths be selected by partial matches with

`select`

? If`TRUE`

,`select`

can only be a single string to match against.- discrete_colour
a suitable colour scale to be used when plotting discrete variables.

- discrete_fill
a suitable fill scale to be used when plotting discrete variables.

- continuous_colour
a suitable colour scale to be used when plotting continuous variables.

- continuous_fill
a suitable fill scale to be used when plotting continuous variables.

- position
Position adjustment, either as a string, or the result of a call to a position adjustment function.

- angle
numeric; the angle at which the x axis tick labels are to be drawn passed to the

`angle`

argument of`ggplot2::guide_axis()`

.- ncol, nrow
numeric; the numbers of rows and columns over which to spread the plots

- guides
character; one of

`"keep"`

(the default),`"collect"`

, or`"auto"`

. Passed to`patchwork::plot_layout()`

- widths, heights
The relative widths and heights of each column and row in the grid. Will get repeated to match the dimensions of the grid. If there is more than 1 plot and

`widths = NULL`

, the value of`widths`

will be set internally to`widths = 1`

to accommodate plots of smooths that use a fixed aspect ratio.- crs
the coordinate reference system (CRS) to use for the plot. All data will be projected into this CRS. See

`ggplot2::coord_sf()`

for details.- default_crs
the coordinate reference system (CRS) to use for the non-sf layers in the plot. If left at the default

`NULL`

, the CRS used is 4326 (WGS84), which is appropriate for spline-on-the-sphere smooths, which are parameterized in terms of latitude and longitude as coordinates. See`ggplot2::coord_sf()`

for more details.- lims_method
character; affects how the axis limits are determined. See

`ggplot2::coord_sf()`

. Be careful; in testing of some examples, changing this to`"orthogonal"`

for example with the chlorophyll-a example from Simon Wood's GAM book quickly used up all the RAM in my test system and the OS killed R. This could be incorrect usage on my part; right now the grid of points at which SOS smooths are evaluated (if not supplied by the user) can produce invalid coordinates for the corners of tiles as the grid is generated for tile centres without respect to the spacing of those tiles.- wrap
logical; wrap plots as a patchwork? If

`FALSE`

, a list of ggplot objects is returned, 1 per term plotted.- envir
an environment to look up the data within.

- ...
additional arguments passed to

`patchwork::wrap_plots()`

.

## Value

The object returned is created by `patchwork::wrap_plots()`

.

## Note

Internally, plots of each smooth are created using `ggplot2::ggplot()`

and composed into a single plot using `patchwork::wrap_plots()`

. As a
result, it is not possible to use `+`

to add to the plots in the way one
might typically work with `ggplot()`

plots. Instead, use the `&`

operator;
see the examples.

## Examples

```
load_mgcv()
# simulate some data
df1 <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 2)
# fit GAM
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df1, method = "REML")
# plot all smooths
draw(m1)
# can add partial residuals
draw(m1, residuals = TRUE)
df2 <- data_sim("eg2", n = 1000, dist = "normal", scale = 1, seed = 2)
m2 <- gam(y ~ s(x, z, k = 40), data = df2, method = "REML")
draw(m2, contour = FALSE, n = 50)
# See https://gavinsimpson.github.io/gratia/articles/custom-plotting.html
# for more examples and for details on how to modify the theme of all the
# plots produced by draw(). To modify all panels, for example to change the
# theme, use the & operator
```