Sshaped (version 0.99)

cvxreg: Estimation of an increasing and convex function

Description

This function computes the the least squares inceasing and convex regression estimator by a sequential mixed primal-dual bases algorithm.

Usage

cvxreg(x,y)

Arguments

x

a numeric vector that contains all the design points. NB. for the current version, we require all values to be distinct (but not necessarily in ascending order).

y

a numeric vector that contains the values of the response with respect to the design points.

Value

An object of class sshaped, which contains the following fields:

x

covariates copied from input

y

response copied from input

fitted

the fitted values of the regression function with respect to the design points.

rss

the value of the minimised residual sum of squares of the fit

inflection

the location of the inflection point, which equals max(x) here

shape

the shape enforced in the fit, here equals "convex"

Details

For more details, see Sshaped.

Examples

# NOT RUN {
x<-seq(0,5,0.01)
y<-x^2 + rnorm(length(x))
output<-cvxreg(x,y)
plot(output)
xnew=rexp(5); predict(output,xnew)
# }