smooth_data {gratia}

Generate regular data over the covariates of a smooth

Description

Generate regular data over the covariates of a smooth

Usage

smooth_data(
  model,
  id,
  n = 100,
  n_2d = NULL,
  n_3d = NULL,
  n_4d = NULL,
  offset = NULL,
  include_all = FALSE,
  var_order = NULL
)

Arguments

model

a fitted model

id

the number ID of the smooth within model to process.

n

numeric; the number of new observations to generate.

n_2d

numeric; the number of new observations to generate for the second dimension of a 2D smooth. Currently ignored.

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

offset

numeric; value of the model offset to use.

include_all

logical; include all covariates involved in the smooth? if FALSE, only the covariates involved in the smooth will be included in the returned data frame. If TRUE, a representative value will be included for all other covariates in the model that aren't actually used in the smooth. This can be useful if you want to pass the returned data frame on to mgcv::PredictMat().

var_order

character; the order in which the terms in the smooth should be processed. Only useful for tensor products with at least one 2d marginal smooth.

Examples

load_mgcv()
df <- data_sim("eg1", seed = 42)
m <- bam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df)

# generate data over range of x1 for smooth s(x1)
smooth_data(m, id = 2)

# generate data over range of x1 for smooth s(x1), with typical value for
# other covariates in the model
smooth_data(m, id = 2, include_all = TRUE)