This functions is now used internally to map a formula onto a `sspm_dataset`

or `sspm`

object.

```
map_formula(data_frame, boundaries, formula, time, ...)
# S4 method for sf,ANY,formula
map_formula(data_frame, boundaries, formula, time, ...)
# S4 method for ANY,missing,ANY
spm_smooth(
sspm_object,
formula,
boundaries,
keep_fit = TRUE,
predict = TRUE,
...
)
# S4 method for ANY,ANY,missing
spm_smooth(
sspm_object,
formula,
boundaries,
keep_fit = TRUE,
predict = TRUE,
...
)
# S4 method for ANY,ANY,sspm_boundary
spm_smooth(
sspm_object,
formula,
boundaries,
keep_fit = TRUE,
predict = TRUE,
...
)
```

- data_frame
**[sf data.frame]**The data.- boundaries
**[sspm_boundary]**An object of class sspm_discrete_boundary.- formula
**[formula]**A formula definition of the form response ~ smoothing_terms + ...- time
**[character]**The time column.- ...
a list of variables that are the covariates that this smooth is a function of. Transformations whose form depends on the values of the data are best avoided here: e.g.

`s(log(x))`

is fine, but`s(I(x/sd(x)))`

is not (see`predict.gam`

).- sspm_object
**[sspm_dataset]**An object of class sspm_dataset.- keep_fit
**[logical]**Whether or not to keep the fitted values and model (default to TRUE, set to FALSE to reduce memory footprint).- predict
**[logical]**Whether or not to generate the smoothed predictions (necessary to fit the final SPM model, default to TRUE).

The updated object.