Takes a fitted `FDboost`

-object produced by `FDboost()`

and
returns estimated coefficient functions/surfaces \(\beta(t), \beta(s,t)\) and
estimated smooth effects \(f(z), f(x,z)\) or \(f(x, z, t)\).
Not implemented for smooths in more than 3 dimensions.

```
# S3 method for FDboost
coef(
object,
raw = FALSE,
which = NULL,
computeCoef = TRUE,
returnData = FALSE,
n1 = 40,
n2 = 40,
n3 = 20,
n4 = 10,
...
)
```

object

a fitted `FDboost`

-object

raw

logical defaults to `FALSE`

.
If `raw = FALSE`

for each effect the estimated function/surface is calculated.
If `raw = TRUE`

the coefficients of the model are returned.

which

a subset of base-learners for which the coefficients
should be computed (numeric vector),
defaults to NULL which is the same as `which=1:length(object$baselearner)`

.
In the special case of `which=0`

, only the coefficients of the offset are returned.

computeCoef

defaults to `TRUE`

, if `FALSE`

only the names of the terms are returned

returnData

return the dataset which is used to get the coefficient estimates as predictions, see Details.

n1

see below

n2

see below

n3

n1, n2, n3 give the number of grid-points for 1-/2-/3-dimensional smooth terms used in the marginal equidistant grids over the range of the covariates at which the estimated effects are evaluated.

n4

gives the number of points for the third dimension in a 3-dimensional smooth term

...

other arguments, not used.

If `raw = FALSE`

, a list containing

`offset`

a list with plot information for the offset.`smterms`

a named list with one entry for each smooth term in the model. Each entry contains`x, y, z`

the unique grid-points used to evaluate the smooth/coefficient function/coefficient surface`xlim, ylim, zlim`

the extent of the x/y/z-axes`xlab, ylab, zlab`

the names of the covariates for the x/y/z-axes`value`

a vector/matrix/list of matrices containing the coefficient values`dim`

the dimensionality of the effect`main`

the label of the smooth term (a short label)

If `raw = TRUE`

, a list containing the estimated spline coefficients.

If `raw = FALSE`

the function `coef.FDboost`

generates adequate dummy data
and uses the function `predict.FDboost`

to
compute the estimated coefficient functions.