cgam (version 1.4)

s.conv: Specify a Smooth and Convex Shape-Restriction in a CGAM Formula

Description

A symbolic routine to define that the systematic component $\eta$ is smooth and convex in a predictor in a formula argument to cgam. This is the smooth version.

Usage

s.conv(x, numknots = 0, knots = 0, space = "Q")

Arguments

x

A numeric predictor which has the same length as the response vector.

numknots

The number of knots used to constrain $x$. It will not be used if the user specifies the knots argument. The default is numknots = $0$.

knots

The knots used to constrain $x$. User-defined knots will be used when given. Otherwise, numknots and space will be used to create knots. The default is knots = $0$.

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 $x$ quantiles will be created bas

Value

• The vector x with five attributes, i.e., name: the name of x; shape: 11("smooth and convex"); numknots: the numknots argument in "s.conv"; knots: the knots argument in "s.conv"; space: the space argument in "s.conv".

Details

"s.conv" 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.conv"; the shape attribute is 11("smooth and convex"). 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.conv" 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.

References

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.

See Also

conv

Examples

# 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 and convex"
  ans <- cgam(y ~ s.conv(x))
  knots <- ans$knots[[1]]

  # make a plot
  plot(x, y)
  lines(x, ans$muhat, col = 2)
  legend("topleft", bty = "n", "smooth and convex fit", col = 2, lty = 1)
  legend(1.6, -1, bty = "o", "knots", pch = "X")
  points(knots, 1:length(knots)*0+min(y), pch = "X")