ds.ns.RdThis function is based on the native R function ns from the
splines package. This function generate the B-spline basis matrix for a natural
cubic spline.
ds.ns(
x,
df = NULL,
knots = NULL,
intercept = FALSE,
Boundary.knots = NULL,
newobj = NULL,
datasources = NULL
)the predictor variable. Missing values are allowed.
degrees of freedom. One can supply df rather than knots; ns() then chooses df - 1 - intercept knots at suitably chosen quantiles of x (which will ignore missing values). The default, df = NULL, sets the number of inner knots as length(knots).
breakpoints that define the spline. The default is no knots; together with the natural boundary conditions this results in a basis for linear regression on x. Typical values are the mean or median for one knot, quantiles for more knots. See also Boundary.knots.
if TRUE, an intercept is included in the basis; default is FALSE.
boundary points at which to impose the natural boundary conditions and anchor the B-spline basis (default the range of the data). If both knots and Boundary.knots are supplied, the basis parameters do not depend on x. Data can extend beyond Boundary.knots.
a character string that provides the name for the output
variable that is stored on the data servers. Default ns.newobj.
a list of DSConnection-class
objects obtained after login. If the datasources argument is not specified
the default set of connections will be used: see datashield.connections_default.
A matrix of dimension length(x) * df where either df was supplied or if knots were supplied, df = length(knots) + 1 + intercept. Attributes are returned that correspond to the arguments to ns, and explicitly give the knots, Boundary.knots etc for use by predict.ns(). The object is assigned at each serverside.
ns is native R is based on the function splineDesign. It generates
a basis matrix for representing the family of piecewise-cubic splines with the specified
sequence of interior knots, and the natural boundary conditions. These enforce the constraint
that the function is linear beyond the boundary knots, which can either be supplied or default
to the extremes of the data.
A primary use is in modeling formula to directly specify a natural spline term in a model.