# -*- perl -*- # Test unweighted line fit, three points (an equilateral triangle), 1-D arrays use strict; use Test::More tests => 19; my $epsilon = 1.0e-12; my @x = (1, 1.5, 2); my @y = (1, 1.866025403784438, 1); eval { use Statistics::LineFit; my $lineFit = Statistics::LineFit->new(); is($lineFit->setData(\@x, \@y), 1, 'setData(\@x, \@y)'); my @coefficients = $lineFit->coefficients(); cmp_ok(abs($coefficients[0] - 1.28867513459481), "<", $epsilon, 'coefficients[0]'); is($coefficients[1], 0, 'coefficients[1]'); is($lineFit->durbinWatson(), 3, 'durbinWatson()'); cmp_ok(abs($lineFit->meanSqError() - 0.166666666666666), "<", $epsilon, 'meanSqError()'); my @predictedY = $lineFit->predictedYs(); my @results = (1.28867513459481, 1.28867513459481, 1.28867513459481); for (my $i = 0; $i < @predictedY; ++$i) { cmp_ok(abs($predictedY[$i] - $results[$i]), "<", $epsilon, 'predictedY()'); } my @residuals = $lineFit->residuals(); @results = (-0.288675134594813, 0.577350269189625, -0.288675134594813); for (my $i = 0; $i < @residuals; ++$i) { cmp_ok(abs($residuals[$i] - $results[$i]), "<", $epsilon, 'residuals()'); } is($lineFit->rSquared(), 0, 'rSquared()'); cmp_ok(abs($lineFit->sigma() - 0.707106781186547), "<", $epsilon, 'sigma()'); my @tStatistics = $lineFit->tStatistics(); cmp_ok(abs($tStatistics[0] - 0.828962859079729), "<", $epsilon, 'tStatistics[0]'); is($tStatistics[1], 0, 'tStatistics[1]'); my @varianceOfEstimates = $lineFit->varianceOfEstimates(); cmp_ok(abs($varianceOfEstimates[0] - 0.412037037037036), "<", $epsilon, 'varianceOfEstimates[0]'); cmp_ok(abs($varianceOfEstimates[1] - 0.166666666666666), "<", $epsilon, 'varianceOfEstimates[1]'); my $sumSqErrors = 0; foreach my $residual (@residuals) { $sumSqErrors += $residual ** 2 } cmp_ok(abs($sumSqErrors - $lineFit->sumSqErrors()), "<", $epsilon, 'sumSqErrors()'); }; is($@, '', 'eval error trap');