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Fitting linear splines

Source: vignettes/lspline.Rmd
lspline.Rmd

Here are some examples of using lspline(), qlspline(), and elspline().

Generate example data

We will use the following artificial data with knots at x=5 and x=10

set.seed(666)
n <- 200
d <- data.frame(
  x = scales::rescale(rchisq(n, 6), c(0, 20))
)
d$interval <- findInterval(d$x, c(5, 10), rightmost.closed = TRUE) + 1
d$slope <- c(2, -3, 0)[d$interval]
d$intercept <- c(0, 25, -5)[d$interval]
d$y <- with(d, intercept + slope * x + rnorm(n, 0, 1))

Plotting y against x:

library(ggplot2)
fig <- ggplot(d, aes(x=x, y=y)) + 
  geom_point(aes(color=as.character(slope))) +
  scale_color_discrete(name="Slope") +
  theme_bw()
fig

The slopes of the consecutive segments are 2, -3, and 0.

Setting knot locations manually

We can parametrize the spline with slopes of individual segments (default marginal=FALSE):

library(lspline)
m1 <- lm(y ~ lspline(x, c(5, 10)), data=d)
knitr::kable(broom::tidy(m1))
term estimate std.error statistic p.value
(Intercept) 0.1343204 0.2148116 0.6252941 0.5325054
lspline(x, c(5, 10))1 1.9435458 0.0597698 32.5171747 0.0000000
lspline(x, c(5, 10))2 -2.9666750 0.0503967 -58.8664832 0.0000000
lspline(x, c(5, 10))3 -0.0335289 0.0518601 -0.6465255 0.5186955

Or parametrize with coeficients measuring change in slope (with marginal=TRUE):

m2 <- lm(y ~ lspline(x, c(5,10), marginal=TRUE), data=d)
knitr::kable(broom::tidy(m2))
term estimate std.error statistic p.value
(Intercept) 0.1343204 0.2148116 0.6252941 0.5325054
lspline(x, c(5, 10), marginal = TRUE)1 1.9435458 0.0597698 32.5171747 0.0000000
lspline(x, c(5, 10), marginal = TRUE)2 -4.9102208 0.0975908 -50.3143597 0.0000000
lspline(x, c(5, 10), marginal = TRUE)3 2.9331462 0.0885445 33.1262479 0.0000000

The coefficients are

  • lspline(x, c(5, 10), marginal = TRUE)1 - the slope of the first segment
  • lspline(x, c(5, 10), marginal = TRUE)2 - the change in slope at knot \(x=5\); it is changing from 2 to -3, so by -5
  • lspline(x, c(5, 10), marginal = TRUE)3 - tha change in slope at knot \(x=10\); it is changing from -3 to 0, so by 3

The two parametrisations (obviously) give identical predicted values:

all.equal( fitted(m1), fitted(m2) )
## [1] TRUE

graphically

fig +
  geom_smooth(method="lm", formula=formula(m1), se=FALSE, color = "black") +
  geom_vline(xintercept = c(5, 10), linetype=2)

Knots at n equal-length intervals

Function elspline() sets the knots at points dividing the range of x into n equal length intervals.

m3 <- lm(y ~ elspline(x, 3), data=d)
knitr::kable(broom::tidy(m3))
term estimate std.error statistic p.value
(Intercept) 3.5484817 0.4603827 7.707678 0.00e+00
elspline(x, 3)1 0.4652507 0.1010200 4.605529 7.40e-06
elspline(x, 3)2 -2.4908385 0.1167867 -21.328105 0.00e+00
elspline(x, 3)3 0.9475630 0.2328691 4.069080 6.84e-05

Graphically

fig +
  geom_smooth(method="lm", formula=formula(m3), se=FALSE, n=200,
              color = "black")

Knots at quantiles of x

Function qlspline() sets the knots at points dividing the range of x into q equal-frequency intervals.

m4 <- lm(y ~ qlspline(x, 4), data=d)
knitr::kable(broom::tidy(m4))
term estimate std.error statistic p.value
(Intercept) 0.0782285 0.3948061 0.198144 0.8431388
qlspline(x, 4)1 2.0398804 0.1802724 11.315548 0.0000000
qlspline(x, 4)2 1.2675186 0.1471270 8.615132 0.0000000
qlspline(x, 4)3 -4.5846478 0.1476810 -31.044273 0.0000000
qlspline(x, 4)4 -0.4965858 0.0572115 -8.679818 0.0000000

Graphically

fig +
  geom_smooth(method="lm", formula=formula(m4), se=FALSE, n=200, 
              color = "black")