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,
...
)

## Arguments

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).

## Value

The updated object.