s | R Documentation |
A symbolic routine to define that the systematic component \eta
is smooth in a predictor in a formula argument to cgam. This is the smooth version.
s(x, numknots = 0, knots = 0, space = "Q")
x |
A numeric predictor which has the same length as the response vector. |
numknots |
The number of knots used to constrain |
knots |
The knots used to constrain |
space |
A character specifying the method to create knots. It will not be used if the user specifies the knots argument. If space == "E", then equally spaced knots will be created; if space == "Q", then a vector of equal |
"s" returns the vector "x" and imposes on it five attributes: name, shape, numknots, knots and space.
The name attribute is used in the subroutine plotpersp; the numknots, knots and space attributes are the same as the numknots, knots and space arguments in "s"; the shape attribute is 17("smooth"). According to the value of the vector itself and its shape, numknots, knots and space attributes, the cone edges will be made by C-spline basis functions in Meyer (2008). The cone edges are a set of basis employed in the hinge algorithm.
Note that "s" does not make the corresponding cone edges itself. It sets things up to a subroutine called makedelta in cgam.
See references cited in this section for more details.
The vector x with five attributes, i.e., name: the name of x; shape: 17("smooth"); numknots: the numknots argument in "s"; knots: the knots argument in "s"; space: the space argument in "s".
Mary C. Meyer and Xiyue Liao
Meyer, M. C. (2013b) A simple new algorithm for quadratic programming with applications in statistics. Communications in Statistics 42(5), 1126–1139.
Meyer, M. C. (2008) Inference using shape-restricted regression splines. Annals of Applied Statistics 2(3), 1013–1033.
s.incr
, s.decr
, s.conc
, s.conv
, s.incr.conc
, s.incr.conv
, s.decr.conc
, s.decr.conv
# generate y
x <- seq(-1, 2, by = 0.1)
n <- length(x)
y <- - x^2 + rnorm(n, .3)
# regress y on x under the shape-restriction: "smooth"
ans <- cgam(y ~ s(x))
knots <- ans$knots[[1]]
# make a plot
plot(x, y)
lines(x, ans$muhat, col = 2)
legend("topleft", bty = "n", "smooth fit", col = 2, lty = 1)
legend(1.6, 1.8, bty = "o", "knots", pch = "X")
points(knots, 1:length(knots)*0+min(y), pch = "X")
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