add_splines: Add spline terms to a data frame

In ddsjoberg/bstfun: Functions for the MSK Biostatistics Department

Add spline terms to a data frame

Description

Adds spline terms calculated via Hmisc::rcspline.eval() to a data frame.

Usage

add_splines(data, variable, knots = NULL, nk = 5, norm = 2, new_names = NULL)

Arguments

data	a data frame
variable	name of column in data
knots	knot locations. If not given, knots will be estimated using default quantiles of x. For 3 knots, the outer quantiles used are 0.10 and 0.90. For 4-6 knots, the outer quantiles used are 0.05 and 0.95. For \code{nk}>6, the outer quantiles are 0.025 and 0.975. 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.
nk	number of knots. Default is 5. The minimum value is 3.
norm	‘⁠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 beon the x-scale.
new_names	Optionally specify names of new spline columns

Value

data frame

Knot Locations

Knot locations are returned in attr(data[[variable]], "knots")

Examples

trial %>%
  add_splines(age)

ddsjoberg/bstfun documentation built on June 1, 2024, 11:46 p.m.