Map model formula onto a sspm_dataset object

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 class 'sf,ANY,formula'
map_formula(data_frame, boundaries, formula, time, ...)

# S4 method for class 'ANY,missing,ANY'
spm_smooth(
  sspm_object,
  formula,
  boundaries,
  keep_fit = TRUE,
  predict = TRUE,
  ...
)

# S4 method for class 'ANY,ANY,missing'
spm_smooth(
  sspm_object,
  formula,
  boundaries,
  keep_fit = TRUE,
  predict = TRUE,
  ...
)

# S4 method for class '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.

Examples

if (FALSE) { # \dontrun{
map_formula(data_frame = all_data, boundaries = boundaries,
            formula = formula, time = time, ...)
} # }