bs {JointAI} | R Documentation |
B-Spline Basis for Polynomial Splines
Description
This function just calls bs()
from the
splines
package.
Usage
bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE,
Boundary.knots = range(x), warn.outside = TRUE)
Arguments
x |
the predictor variable. Missing values are allowed.
|
df |
degrees of freedom; one can specify df rather than
knots ; bs() then chooses df-degree (minus one
if there is an intercept) knots at suitable quantiles of x
(which will ignore missing values). The default, NULL ,
takes the number of inner knots as length(knots) . If that is
zero as per default, that corresponds to df = degree - intercept .
|
knots |
the internal breakpoints that define the
spline. The default is NULL , which results in a basis for
ordinary polynomial regression. Typical values are the mean or
median for one knot, quantiles for more knots. See also
Boundary.knots .
|
degree |
degree of the piecewise polynomial—default is 3 for
cubic splines.
|
intercept |
if TRUE , an intercept is included in the
basis; default is FALSE .
|
Boundary.knots |
boundary points at which to anchor the B-spline
basis (default the range of the non-NA data). If both
knots and Boundary.knots are supplied, the basis
parameters do not depend on x . Data can extend beyond
Boundary.knots .
|
warn.outside |
logical indicating if a
warning should be signalled in case some x values
are outside the boundary knots.
|
[Package
JointAI version 1.0.6
Index]