lfSolver: General Least Squares Loss Function

In isotone: Active Set and Generalized PAVA for Isotone Optimization

General Least Squares Loss Function

Description

Solver for the general least squares monotone regression problem of the form (y-x)'W(y-x).

Usage

lfSolver(z, a, extra)

Arguments

z	Vector containing observed response
a	Matrix with active constraints
extra	List with element y containing the observed response vector and weights as weight matrix W which is not necessarily positive definite.

Details

This function is called internally in activeSet by setting mySolver = lfSolver.

Value

x	Vector containing the fitted values
lbd	Vector with Lagrange multipliers
f	Value of the target function
gx	Gradient at point x

See Also

activeSet

Examples

##Fitting isotone regression 
set.seed(12345)
y <- rnorm(9)              ##response values
w <- diag(rep(1,9))        ##unit weight matrix
btota <- cbind(1:8, 2:9)   ##Matrix defining isotonicity (total order)
#fit.lf <- activeSet(btota, lfSolver, weights = w, y = y)

isotone documentation built on March 7, 2023, 3:17 p.m.