deviance_o: Deviance of the orthogonal residuals

In onls: Orthogonal Nonlinear Least-Squares Regression

Deviance of the orthogonal residuals

Description

Returns the deviance (residual sum of squares) \sum_{i=1}^n \hat d_i^2 of the fitted, precision-weighted orthogonal distances from the fitted onls model – the same quantity that onls itself minimizes. See 'Details' for what \hat d_i represents in general; a single-predictor, unweighted fit is a special case where this reduces to plain squared Euclidean distance.

Usage

deviance_o(object) 

Arguments

object

an object returned from onls.

Details

\hat d_i is the fitted, precision-weighted orthogonal distance of observation i (see 'Details' in onls for its full definition and the construction of Qyy_i, Qx_i from weights, sigma_x, and sigma_y); it reduces to plain Euclidean distance only for a single-predictor, unweighted fit. deviance_o returns \sum_{i=1}^n \hat d_i^2, numerically identical to sum(residuals_o(object)^2) and to the value onls itself minimized (object$objective).

Value

The deviance of the fitted orthogonal (precision-weighted) distances.

Author(s)

Andrej-Nikolai Spiess

Examples

## See 'onls'.

onls documentation built on Sept. 21, 2026, 9:06 a.m.