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WeightedLeastSquares

Weighted least squares with the inference table on top of it, at statsmodels.api.WLS parity.

public static class WeightedLeastSquares

Example — six group means, the first over forty observations and the last over three.

using Lodestar.Stats.Regression;

double[] dose = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
double[] meanResponse = [2.3, 3.8, 6.4, 7.7, 11.6, 10.9];
double[] groupSize = [40.0, 35.0, 30.0, 12.0, 5.0, 3.0];

OlsSummary weighted = WeightedLeastSquares.Fit(dose, meanResponse, groupSize, featureCount: 1);

double slope = Math.Round(weighted.Coefficients[1], 4);      // => 1.9882
double error = Math.Round(weighted.StandardErrors[1], 4);    // => 0.1767

Remarks — the same rows fitted by OrdinaryLeastSquares.Fit give a slope of 1.9343 with a standard error of 0.2388: the two smallest groups, which pull the line hardest, are the ones measured least precisely, and weighting hands the say back to the forty.

Applies to — net10.0, netstandard2.0.

See also — OlsSummary, OlsOptions, the weighted least squares index.

Members

Member	What it does
WeightedLeastSquares.Fit	Fits a linear model with one weight per row and reports what a summary table holds.