Adds spline terms calculated via Hmisc::rcspline.eval() to a data frame.
add_splines(data, variable, knots = NULL, nk = 5, norm = 2, new_names = NULL)a data frame
name of column in data
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.
number of knots. Default is 5. The minimum value is 3.
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.
Optionally specify names of new spline columns
data frame
Knot locations are returned in attr(data[[variable]], "knots")
trial %>%
add_splines(age)
#> # A tibble: 200 × 11
#> trt age spage1 spage2 spage3 marker stage grade respo…¹ death ttdeath
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <int> <int> <dbl>
#> 1 Drug A 23 0 0 0 0.16 T1 II 0 0 24
#> 2 Drug B 9 0 0 0 1.11 T2 I 1 0 24
#> 3 Drug A 31 0.178 0 0 0.277 T1 II 0 0 24
#> 4 Drug A NA NA NA NA 2.07 T3 III 1 1 17.6
#> 5 Drug A 51 9.59 1.05e+0 0.0305 2.77 T4 III 1 1 16.4
#> 6 Drug B 39 1.67 4.77e-4 0 0.613 T4 I 0 1 15.6
#> 7 Drug A 37 1.10 0 0 0.354 T1 II 0 0 24
#> 8 Drug A 32 0.263 0 0 1.74 T1 I 0 1 18.4
#> 9 Drug A 31 0.178 0 0 0.144 T1 II 0 0 24
#> 10 Drug B 34 0.506 0 0 0.205 T3 I 0 1 10.5
#> # … with 190 more rows, and abbreviated variable name ¹response