smooth.construct.unc_pcre.smooth.spec {MJMbamlss} | R Documentation |
Sets up design matrix for functional random effects based on the PC scores
of the covariance operator of the random effect process. Note that there is
no constraint on the smoother.
See smooth.construct.re.smooth.spec
for more details on
mgcv
-style smoother specification
and smooth.construct.pcre.smooth.spec
for the
corresponding refund
implementation.
## S3 method for class 'unc_pcre.smooth.spec'
smooth.construct(object, data, knots, ...)
object |
a smooth specification object, see
|
data |
see |
knots |
see |
... |
see |
This is an internal function as the corresponding smooth object and its predict method is primarily used within the bamlss call.
An object of class "random.effect"
. See
smooth.construct
for the elements that this object will contain.
Alexander Volkmann; adapted from 'pcre' constructor by F. Scheipl (adapted from 're' constructor by S.N. Wood).
data(pbc_subset)
mfpca <- preproc_MFPCA(pbc_subset, uni_mean = paste0(
"logy ~ 1 + sex + drug + s(obstime, k = 5, bs = 'ps') + ",
"s(age, k = 5, bs = 'ps')"),
pve_uni = 0.99, nbasis = 5, weights = TRUE, save_uniFPCA = TRUE)
pbc_subset <- attach_wfpc(mfpca, pbc_subset, n = 2)
mfpca_list <- list(
list(functions = funData::extractObs(mfpca$functions, 1),
values = mfpca$values[1]),
list(functions = funData::extractObs(mfpca$functions, 2),
values = mfpca$values[2]))
sm <- smoothCon(s(id, fpc.1, bs = "unc_pcre",
xt = list("mfpc" = mfpca_list[[1]], scale = "FALSE")), pbc_subset)