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;
}
TransformationModelInterpolated::TransformationModelInterpolated(
const TransformationModel::DataPoints & data, const Param & params)
{
params_ = params;
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
// need monotonically increasing x values (can't have the same value twice):
map<DoubleReal, vector<DoubleReal> > mapping;
for (TransformationModel::DataPoints::const_iterator it = data.begin();
it != data.end(); ++it)
{
mapping[it->first].push_back(it->second);
}
size_ = mapping.size();
x_.resize(size_);
y_.resize(size_);
size_t i = 0;
for (map<DoubleReal, vector<DoubleReal> >::const_iterator it =
mapping.begin(); it != mapping.end(); ++it, ++i)
{
x_[i] = it->first;
// use average y value:
y_[i] = accumulate(it->second.begin(), it->second.end(), 0.0) /
it->second.size();
}
String interpolation_type = params_.getValue("interpolation_type");
const gsl_interp_type * type;
if (interpolation_type == "linear")
type = gsl_interp_linear;
else if (interpolation_type == "polynomial")
type = gsl_interp_polynomial;
else if (interpolation_type == "cspline")
type = gsl_interp_cspline;
else if (interpolation_type == "akima")
type = gsl_interp_akima;
else
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "unknown/unsupported interpolation type '" + interpolation_type + "'");
}
size_t min_size = type->min_size;
if (size_ < min_size)
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "'" + interpolation_type + "' interpolation model needs at least " + String(min_size) + " data points (with unique x values)");
}
interp_ = gsl_interp_alloc(type, size_);
acc_ = gsl_interp_accel_alloc();
double * x_start = &(x_[0]), * y_start = &(y_[0]);
gsl_interp_init(interp_, x_start, y_start, size_);
// linear model for extrapolation:
TransformationModel::DataPoints lm_data(2);
lm_data[0] = make_pair(x_[0], y_[0]);
lm_data[1] = make_pair(x_[size_ - 1], y_[size_ - 1]);
lm_ = new TransformationModelLinear(lm_data, Param());
}
TransformationModelLinear::TransformationModelLinear(
const TransformationModel::DataPoints & data, const Param & params)
{
params_ = params;
data_given_ = !data.empty();
if (!data_given_ && params.exists("slope") && (params.exists("intercept")))
{
// don't estimate parameters, use given values
slope_ = params.getValue("slope");
intercept_ = params.getValue("intercept");
}
else // estimate parameters from data
{
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
symmetric_ = params_.getValue("symmetric_regression") == "true";
size_t size = data.size();
if (size == 0) // no data
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__,
"no data points for 'linear' model");
}
else if (size == 1) // degenerate case, but we can still do something
{
slope_ = 1.0;
intercept_ = data[0].second - data[0].first;
}
else // compute least-squares fit
{
vector<double> x(size), y(size);
for (size_t i = 0; i < size; ++i)
{
if (symmetric_)
{
x[i] = data[i].second + data[i].first;
y[i] = data[i].second - data[i].first;
}
else
{
x[i] = data[i].first;
y[i] = data[i].second;
}
}
double cov00, cov01, cov11, sumsq; // covariance values, sum of squares
double * x_start = &(x[0]), * y_start = &(y[0]);
gsl_fit_linear(x_start, 1, y_start, 1, size, &intercept_, &slope_,
&cov00, &cov01, &cov11, &sumsq);
if (symmetric_) // undo coordinate transformation:
{
slope_ = (1.0 + slope_) / (1.0 - slope_);
intercept_ = intercept_ * 1.41421356237309504880; // 1.41... = sqrt(2)
}
}
}
}
TransformationModelLinear::TransformationModelLinear(const TransformationModel::DataPoints& data, const Param& params)
{
params_ = params;
data_given_ = !data.empty();
if (!data_given_ && params.exists("slope") && (params.exists("intercept")))
{
// don't estimate parameters, use given values
slope_ = params.getValue("slope");
intercept_ = params.getValue("intercept");
}
else // estimate parameters from data
{
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
symmetric_ = params_.getValue("symmetric_regression") == "true";
size_t size = data.size();
std::vector<Wm5::Vector2d> points;
if (size == 0) // no data
{
throw Exception::IllegalArgument(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION,
"no data points for 'linear' model");
}
else if (size == 1) // degenerate case, but we can still do something
{
slope_ = 1.0;
intercept_ = data[0].second - data[0].first;
}
else // compute least-squares fit
{
for (size_t i = 0; i < size; ++i)
{
points.push_back(Wm5::Vector2d(data[i].first, data[i].second));
}
if (!Wm5::HeightLineFit2<double>(static_cast<int>(size), &points.front(), slope_, intercept_))
{
throw Exception::UnableToFit(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "TransformationModelLinear", "Unable to fit linear transformation to data points.");
}
}
// update params
params_.setValue("slope", slope_);
params_.setValue("intercept", intercept_);
}
}
TransformationModelLowess::TransformationModelLowess(
const TransformationModel::DataPoints& data_,
const Param& params) : model_(0)
{
// parameter handling/checking:
params_ = params;
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
if (data_.size() < 2)
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__,
"'lowess' model requires more data");
}
// TODO copy ...
TransformationModel::DataPoints data(data_);
// sort data
std::sort(data.begin(), data.end(), cmpFirstDimension);
vector<double> x(data.size()), y(data.size()), result(data.size());
double xmin_ = data[0].first;
double xmax_ = xmin_;
for (Size i = 0; i < data.size(); ++i)
{
x[i] = data[i].first;
y[i] = data[i].second;
if (x[i] < xmin_)
{
xmin_ = x[i];
}
else if (x[i] > xmax_)
{
xmax_ = x[i];
}
}
double span = params_.getValue("span");
int nsteps = params_.getValue("num_iterations");
double delta = params_.getValue("delta");
if (delta < 0.0)
{
delta = (xmax_ - xmin_) * 0.01; // automatically determine delta
}
FastLowessSmoothing::lowess(x, y, span, nsteps, delta, result);
TransformationModel::DataPoints data_out;
for (Size i = 0; i < result.size(); ++i)
{
data_out.push_back( std::make_pair(x[i], result[i]) );
}
// TODO thin out data here ? we may not need that many points here to interpolate ... it is enough if we store a few datapoints
Param p;
TransformationModelInterpolated::getDefaultParameters(p);
/// p.setValue("interpolation_type", "cspline"); // linear interpolation between lowess pts
/// p.setValue("extrapolation_type", "four-point-linear");
p.setValue("interpolation_type", params_.getValue("interpolation_type"));
p.setValue("extrapolation_type", params_.getValue("extrapolation_type"));
// create new interpolation model based on the lowess data
model_ = new TransformationModelInterpolated(data_out, p);
}
TransformationModelBSpline::TransformationModelBSpline(
const TransformationModel::DataPoints & data, const Param & params)
{
params_ = params;
Param defaults;
getDefaultParameters(defaults);
params_.setDefaults(defaults);
if (data.size() < 4) // TODO: check number
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "'b_spline' model needs at least four data points");
}
Size num_breakpoints = params_.getValue("num_breakpoints");
String break_positions = params_.getValue("break_positions");
if ((break_positions != "uniform") && (break_positions != "quantiles"))
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "parameter 'break_positions' for 'b_spline' model must be 'uniform' or 'quantiles'");
}
size_ = data.size();
x_ = gsl_vector_alloc(size_);
y_ = gsl_vector_alloc(size_);
w_ = gsl_vector_alloc(size_);
for (size_t i = 0; i < size_; ++i)
{
gsl_vector_set(x_, i, data[i].first);
gsl_vector_set(y_, i, data[i].second);
gsl_vector_set(w_, i, 1.0); // TODO: non-uniform weights
}
gsl_vector_minmax(x_, &xmin_, &xmax_);
// set up cubic (k = 4) spline workspace:
if (num_breakpoints < 2)
{
num_breakpoints = 2;
LOG_WARN << "Warning: Increased parameter 'num_breakpoints' to 2 (minimum)." << endl;
}
else if (num_breakpoints > size_ - 2)
{
num_breakpoints = size_ - 2;
LOG_WARN << "Warning: Decreased parameter 'num_breakpoints' to " + String(num_breakpoints) + " (maximum for this number of data points)." << endl;
}
workspace_ = gsl_bspline_alloc(4, num_breakpoints);
if (break_positions == "uniform")
{
gsl_bspline_knots_uniform(xmin_, xmax_, workspace_);
}
else
{
vector<double> quantiles(num_breakpoints, 1.0);
double step = 1.0 / (num_breakpoints - 1);
for (Size i = 0; i < num_breakpoints - 1; ++i)
{
quantiles[i] = i * step;
}
gsl_vector * breakpoints;
breakpoints = gsl_vector_alloc(num_breakpoints);
getQuantiles_(x_, quantiles, breakpoints);
gsl_bspline_knots(breakpoints, workspace_);
gsl_vector_free(breakpoints);
}
ncoeffs_ = gsl_bspline_ncoeffs(workspace_);
gsl_vector_minmax(workspace_->knots, &xmin_, &xmax_);
computeFit_();
}
