conv {cgam}

Specify a Convex Shape-Restriction in a CGAM Formula

Description

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

Usage

conv(x, numknots = 0, knots = 0, space = "E")

Arguments

x

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

numknots

The number of knots used to smoothly constrain a predictor. The value should be 0 for a shape-restricted predictor without smoothing. The default value is 0.

knots

The knots used to smoothly constrain a predictor. The value should be 0 for a shape-restricted predictor without smoothing. The default value is 0.

space

A character specifying the method to create knots. It will not be used for a shape-restricted predictor without smoothing. The default value is "E".

Details

"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 "conv"; the shape attribute is 3("convex"), and according to the value of the vector itself and its shape attribute, the cone edges of the cone generated by the constraint matrix, which constrains the relationship between the systematic component \eta and "x" to be convex, will be made. The cone edges are a set of basis employed in the hinge algorithm.

Note that "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.

Value

The vector x with five attributes, i.e., name: the name of x; shape: 3("convex"); numknots: the numknots argument in "conv"; knots: the knots argument in "conv" ; space: the space argument in "conv".

Author(s)

Mary C. Meyer and Xiyue Liao

References

Meyer, M. C. (2013b) A simple new algorithm for quadratic programming with applications in statistics. Communications in Statistics 42(5), 1126–1139.

See Also

conc

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: "convex"
  ans <- cgam(y ~ conv(x))

  # make a plot
  plot(x, y)
  lines(x, ans$muhat, col = 2)
  legend("topleft", bty = "n", "convex fit", col = 2, lty = 1)