Usage
## S3 method for class 'pfr':
coefficients(object, select = 1, coords = NULL, n = NULL,
se = ifelse(length(object$smooth) & select, TRUE, FALSE),
seWithMean = FALSE, useVc = TRUE, Qtransform = FALSE, ...)
## S3 method for class 'pfr':
coef(object, select = 1, coords = NULL, n = NULL,
se = ifelse(length(object$smooth) & select, TRUE, FALSE),
seWithMean = FALSE, useVc = TRUE, Qtransform = FALSE, ...)
Arguments
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 coor
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 f
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
...
these arguments are ignored