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)