void AbsoluteQuantitation::calculateBiasAndR(
const std::vector<AbsoluteQuantitationStandards::featureConcentration> & component_concentrations,
const String & feature_name,
const String & transformation_model,
const Param & transformation_model_params,
std::vector<double> & biases,
double & correlation_coefficient)
{
// reset biases
biases.clear();
// extract out the calibration points
std::vector<double> concentration_ratios, feature_amounts_ratios;
TransformationModel::DataPoints data;
TransformationModel::DataPoint point;
for (size_t i = 0; i < component_concentrations.size(); ++i)
{
// calculate the actual and calculated concentration ratios
double calculated_concentration_ratio = applyCalibration(component_concentrations[i].feature,
component_concentrations[i].IS_feature,
feature_name,
transformation_model,
transformation_model_params);
double actual_concentration_ratio = component_concentrations[i].actual_concentration/
component_concentrations[i].IS_actual_concentration;
concentration_ratios.push_back(component_concentrations[i].actual_concentration);
// extract out the feature amount ratios
double feature_amount_ratio = calculateRatio(component_concentrations[i].feature,
component_concentrations[i].IS_feature,
feature_name)/component_concentrations[i].dilution_factor;
feature_amounts_ratios.push_back(feature_amount_ratio);
// calculate the bias
double bias = calculateBias(actual_concentration_ratio, calculated_concentration_ratio);
biases.push_back(bias);
point.first = actual_concentration_ratio;
point.second = feature_amount_ratio;
data.push_back(point);
}
// apply weighting to the feature amounts and actual concentration ratios
TransformationModel tm(data, transformation_model_params);
tm.weightData(data);
std::vector<double> concentration_ratios_weighted, feature_amounts_ratios_weighted;
for (size_t i = 0; i < data.size(); ++i)
{
concentration_ratios_weighted.push_back(data[i].first);
feature_amounts_ratios_weighted.push_back(data[i].second);
}
// calculate the R2 (R2 = Pearson_R^2)
correlation_coefficient = Math::pearsonCorrelationCoefficient(
concentration_ratios_weighted.begin(), concentration_ratios_weighted.begin() + concentration_ratios_weighted.size(),
feature_amounts_ratios_weighted.begin(), feature_amounts_ratios_weighted.begin() + feature_amounts_ratios_weighted.size()
);
}
Param AbsoluteQuantitation::fitCalibration(
const std::vector<AbsoluteQuantitationStandards::featureConcentration> & component_concentrations,
const String & feature_name,
const String & transformation_model,
const Param & transformation_model_params)
{
// extract out the calibration points
TransformationModel::DataPoints data;
TransformationModel::DataPoint point;
for (size_t i = 0; i < component_concentrations.size(); i++)
{
point.first = component_concentrations[i].actual_concentration/component_concentrations[i].IS_actual_concentration;
double ratio = calculateRatio(component_concentrations[i].feature, component_concentrations[i].IS_feature,feature_name);
point.second = ratio/component_concentrations[i].dilution_factor; // adjust based on the dilution factor
data.push_back(point);
}
// fit the data to the model
TransformationDescription tmd(data);
// tmd.setDataPoints(data);
tmd.fitModel(transformation_model, transformation_model_params);
Param params = tmd.getModelParameters();
// AbsoluteQuantitationMethod aqm;
// Param params = aqm.fitTransformationModel(transformation_model, data, transformation_model_params);
// store the information about the fit
return params;
}
