JointAI: bs – R documentation

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bs

Generate a Basis Matrix for Natural Cubic Splines

This function just calls bs() from the splines package.

bs(x, df = NULL, knots = NULL, degree = 3, intercept = FALSE,
  Boundary.knots = range(x))

`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`.

Joint Analysis and Imputation of Incomplete Data

v1.0.2

GPL (>= 2)

Authors

Nicole S. Erler [aut, cre] (<https://orcid.org/0000-0002-9370-6832>)

Initial release