39 lines
2.0 KiB
Matlab
39 lines
2.0 KiB
Matlab
function [LoadingRate, StandardError, ConfidenceInterval] = bootstrapErrorEstimation(this, ovenObj, NumberOfLoadedAtoms)
|
|
n = this.NumberOfAtoms;
|
|
NumberOfTimeSteps = int64(this.SimulationTime/this.TimeStep);
|
|
|
|
Autocorrelation = autocorr(NumberOfLoadedAtoms,'NumLags', double(NumberOfTimeSteps - 1));
|
|
|
|
if Autocorrelation(1)~=0
|
|
CorrelationFactor = table(Helper.findAllZeroCrossings(linspace(1, double(NumberOfTimeSteps), double(NumberOfTimeSteps)), Autocorrelation)).Var1(1);
|
|
if ~isnan(CorrelationFactor)
|
|
SampleLength = floor(CorrelationFactor);
|
|
NumberOfBootsrapSamples = 1000;
|
|
MeanCaptureRatioInEachSample = zeros(1,NumberOfBootsrapSamples);
|
|
for SampleNumber = 1:NumberOfBootsrapSamples
|
|
BoostrapSample = datasample(NumberOfLoadedAtoms, SampleLength); % Sample with replacement
|
|
MeanCaptureRatioInEachSample(SampleNumber) = mean(BoostrapSample) / n; % Empirical bootstrap distribution of sample means
|
|
end
|
|
|
|
LoadingRate = mean(MeanCaptureRatioInEachSample) * ovenObj.ReducedFlux;
|
|
|
|
Variance = 0; % Bootstrap Estimate of Variance
|
|
for SampleNumber = 1:NumberOfBootsrapSamples
|
|
Variance = Variance + (MeanCaptureRatioInEachSample(SampleNumber) - mean(MeanCaptureRatioInEachSample))^2;
|
|
end
|
|
|
|
StandardError = sqrt((1 / (NumberOfBootsrapSamples-1)) * Variance) * ovenObj.ReducedFlux;
|
|
|
|
ts = tinv([0.025 0.975],NumberOfBootsrapSamples-1); % T-Score
|
|
ConfidenceInterval = LoadingRate + ts*StandardError; % 95% Confidence Intervals
|
|
else
|
|
LoadingRate = nan;
|
|
StandardError = nan;
|
|
ConfidenceInterval = [nan nan];
|
|
end
|
|
else
|
|
LoadingRate = nan;
|
|
StandardError = nan;
|
|
ConfidenceInterval = [nan nan];
|
|
end
|
|
end |