lsSolver: Least Squares Loss Function

In isotone: Active Set and Generalized PAVA for Isotone Optimization

Least Squares Loss Function

Description

Solver for the least squares monotone regression problem with optional weights.

Usage

lsSolver(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 with optional observation weights

Details

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

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 using active set
set.seed(12345)
y <- rnorm(9)              ##response values
w <- rep(1,9)              ##unit weights
btota <- cbind(1:8, 2:9)   ##Matrix defining isotonicity (total order)
fit.ls <- activeSet(btota, lsSolver, weights = w, y = y)

isotone documentation built on March 7, 2023, 6:58 p.m.