s(x, df=4, spar=1)
gam.s(x, y, w, df, spar, xeval)
spar
below) is found
such that df=tr(S)-1
, where S
is the implicit smoother
matrix. Values for df
should be greater than 1
, with
df=1
implying a linear fit. If both df
and spar
are
supplied, the former takes precedence. Note that df
is not
necessarily an integer.(0,1]
. See smooth.spline
for more details.gam.s
during backfittinggam.s
produces a
prediction at xeval
.s
returns the vector x
, endowed with a number of
attributes. The vector itself is used in the construction of the model
matrix, while the attributes are needed for the backfitting algorithms
general.wam
(weighted additive model) or s.wam
.
Since smoothing splines reproduces linear fits, the linear
part will be efficiently computed with the other parametric linear parts
of the model. Note that s
itself does no smoothing; it simply sets things up
for gam
. One important attribute is named call
. For example, s(x)
has a call component
gam.s(data[["s(x)"]], z, w, spar = 1, df = 4)
.
This is an expression that gets evaluated repeatedly in general.wam
(the backfitting algorithm). gam.s
returns an object with components
x
), so these residual represent the
nonlinear part of the fit.gam.s
is evaluated with an xeval
argument, it returns a
vector of predictions.lo
, smooth.spline
, bs
, ns
, poly
# fit Start using a smoothing spline with 4 df.
y ~ Age + s(Start, 4)
# fit log(Start) using a smoothing spline with 5 df.
y ~ Age + s(log(Start), df=5)
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