22 lines
1.3 KiB
Matlab
22 lines
1.3 KiB
Matlab
function [LoadingRate, StandardError, ConfidenceInterval] = bootstrapErrorEstimation(this, ovenObj, NumberOfLoadedAtoms)
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n = this.NumberOfAtoms;
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SampleLength = this.BootstrapSampleLength;
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NumberOfBootsrapSamples = this.BootstrapSampleNumber;
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MeanCaptureRatioInEachSample = zeros(1,NumberOfBootsrapSamples);
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for SampleNumber = 1:NumberOfBootsrapSamples
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BoostrapSample = datasample(NumberOfLoadedAtoms, SampleLength); % Sample with replacement
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MeanCaptureRatioInEachSample(SampleNumber) = mean(BoostrapSample) / n; % Empirical bootstrap distribution of sample means
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end
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LoadingRate = mean(MeanCaptureRatioInEachSample) * ovenObj.ReducedFlux;
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Variance = 0; % Bootstrap Estimate of Variance
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for SampleNumber = 1:NumberOfBootsrapSamples
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Variance = Variance + (MeanCaptureRatioInEachSample(SampleNumber) - mean(MeanCaptureRatioInEachSample))^2;
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end
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StandardError = sqrt((1 / (NumberOfBootsrapSamples-1)) * Variance) * ovenObj.ReducedFlux;
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ts = tinv([0.025 0.975],NumberOfBootsrapSamples-1); % T-Score
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ConfidenceInterval = LoadingRate + ts*StandardError; % 95% Confidence Intervals
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end |