In simisc/splintr: Natural Cubic SPLines with Interpretable INTercepts

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

splintr

Natural cubic splines with interpretable intercepts: 'centres' a basis generated using splines::ns() on a specified x-value. When used in a model formula, this allows the model intercept to be interpreted with respect to that central x-value, rather than with respect to the x-value of the first splines::ns() knot.

Installation

You can install splintr from github with:

# install.packages("devtools")
devtools::install_github("simisc/splintr")

Example

library(tidyverse)
library(splines)
library(splintr)
library(broom)
library(knitr)
library(ggthemes)
library(ggrepel)

women <- women %>%
  mutate(height_centred = height - mean(height))
women %>%
  head() %>%
  kable()

Different parametrisations of the same model:

fit0: using ns with raw heights
fit1: using ns with centred heights
fit2: using splintr with centred heights
fit3: using splintr with raw heights and explicit (arbitrary) centre

model_formulae <- list(
  fit0 = weight ~ ns(height, df = 3),
  fit1 = weight ~ ns(height_centred, df = 3),
  fit2 = weight ~ splintr(height_centred, df = 3),
  fit3 = weight ~ splintr(height, df = 3, centre = explicit_centre)
)

explicit_centre = 68.45
model_fits <- map(model_formulae, lm, data = women)

The models are identical:

model_fits %>%
  map_dfr(glance, .id = "model") %>%
  select(model, sigma, logLik, deviance) %>%
  kable()

But they have different intercepts:

model_fits %>%
  map_dfr(~tidy(.x, quick = TRUE), .id = "model") %>%
  filter(term == "(Intercept)") %>%
  select(model, estimate, std.error, statistic) %>%
  kable()

Using splines::ns(), the model intercept represents the estimated value of weight at the first boundary knot, i.e. when height takes its minimum value of 58. This is unchanged by centring the predictor, so fit0 and fit1 have identical intercepts.

Using splintr() instead, the intercept representes the estimated value of weight when the predictor height_centred takes a value of zero. An arbitrary "centre" can be specified directly in the splintr() call.

int_pts <- tibble(
  model = paste0("fit", 0:3),
  x = c(
    min(women$height),
    min(women$height),
    mean(women$height),
    explicit_centre
  ),
  y = map_dbl(model_fits, ~coef(.x)[1])
)

augment(model_fits[[1]], data = women) %>%
  ggplot(aes(x = height)) +
  geom_point(aes(y = weight)) +
  geom_line(aes(y = .fitted), col = "blue") +
  geom_point(data = int_pts, aes(x = x, y = y), col = "red") +
  geom_label_repel(data = int_pts, aes(x = x, y = y, label = model),
                   col = "red", nudge_y = 7) +
  theme_few()