x
.
rcspline.eval(x, knots, nk=5, inclx=FALSE, knots.only=FALSE, type="ordinary", norm=2, rpm=NULL)
x
. For 3-5 knots, the outer quantiles used are .05 and .95.
For
nk>5
, the outer quantiles are .025 and .975. The knots are
equally spaced between these on the quantile scale. For fewer than 100
non-missing values of
x
, the outer knots are the 5th smallest and
largest
x
.
TRUE
to add
x
as the first column of the returned matrix
"ordinary"
to fit the function,
"integral"
to fit its anti-derivative.
0
to use the terms as originally given by Devlin and Weeks (1986),
1
to normalize non-linear terms by the cube of the spacing between the last two
knots,
2
to normalize by the square of the spacing between the first
and last knots (the default).
norm=2
has the advantage of making all
nonlinear terms be on the
x
-scale.
x
will be replaced with the value
rpm
after
estimating any knot locations.
knots.only=TRUE
, returns a vector of knot locations. Otherwise returns
a matrix with
x
(if
inclx=TRUE
) followed by
nk-2
nonlinear terms.
The matrix has an attribute
knots
which is the vector of knots used.
Devlin TF and Weeks BJ (1986): Spline functions for logistic regression modeling. Proc 11th Annual SAS Users Group Intnl Conf, p. 646–651. Cary NC: SAS Institute, Inc.
x <- 1:100 rcspline.eval(x, nk=4, inclx=TRUE) #lrm.fit(rcspline.eval(age,nk=4,inclx=TRUE), death)