This function is used to extract a coefficient from a fitted `pfr` model, in
particular smooth functions resulting from including functional terms specified
with `lf`

, `af`

, etc. It can also be used to extract smooths
genereated using `mgcv`

's `s`

, `te`

, or `t2`

.

```
# S3 method for pfr
coefficients(
object,
select = 1,
coords = NULL,
n = NULL,
se = ifelse(length(object$smooth) & select, TRUE, FALSE),
seWithMean = FALSE,
useVc = TRUE,
Qtransform = FALSE,
...
)
```# S3 method for pfr
coef(
object,
select = 1,
coords = NULL,
n = NULL,
se = ifelse(length(object$smooth) & select, TRUE, FALSE),
seWithMean = FALSE,
useVc = TRUE,
Qtransform = FALSE,
...
)

object

return object from `pfr`

select

integer indicating the index of the desired smooth term
in `object$smooth`

. Enter 0 to request the raw coefficients
(i.e., `object$coefficients`

) and standard errors (if `se==TRUE`

).

coords

named list indicating the desired coordinates where the
coefficient function is to be evaluated. Names must match the argument names
in `object$smooth[[select]]$term`

. If `NULL`

, uses `n`

to generate equally-spaced coordinates.

n

integer vector indicating the number of equally spaced coordinates
for each argument. If length 1, the same number is used for each argument.
Otherwise, the length must match `object$smooth[[select]]$dim`

.

se

if `TRUE`

, returns pointwise standard error estimates. Defaults
to `FALSE`

if raw coefficients are being returned; otherwise `TRUE`

.

seWithMean

if `TRUE`

the standard errors include uncertainty about
the overall mean; if `FALSE`

, they relate purely to the centered
smooth itself. Marra and Wood (2012) suggests that `TRUE`

results in
better coverage performance for GAMs.

useVc

if `TRUE`

, standard errors are calculated using a covariance
matrix that has been corrected for smoothing parameter uncertainty. This
matrix will only be available under ML or REML smoothing.

Qtransform

For additive functional terms, `TRUE`

indicates the
coefficient should be extracted on the quantile-transformed scale, whereas
`FALSE`

indicates the scale of the original data. Note this is
different from the `Qtransform`

arguemnt of `af`

, which specifies
the scale on which the term is fit.

...

these arguments are ignored

a data frame containing the evaluation points,
coefficient function values and optionally the SE's for the term indicated
by `select`

.

Marra, G and S.N. Wood (2012) Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics.