Detailed DescriptionNamespace "npsi" (nonparametric statistics interface) is used for classes and functions in the NPStat package which rely on the Minuit function minimization package. See http://www.cern.ch/minuit/ Function Documentation◆ fitCompositeJohnson()
template<typename InputData , typename OutputData >
Density estimation by the transformation method using the following sequence of steps:
Function arguments are as follows: input, nInput – Array of input data points (typically floats or doubles) and the number of points in this array. nBins – Number of bins for the histogram which will be used for fitting parameters of the Johnson system. xmin, xmax – Range (support) of the estimated density. qmin, qmax, minlog – Parameters passed to the JohnsonFit class. filters, nFilters – A collection of smoothers to try on the transformed density. All of them will be used and the smoother with the best cross-validation pseudo-likelihood will be chosen to build the final result. smoothedCurve, lenCurve – The array in which the smoothed values will be stored. The coordinates correspond to the bin centers of a histogram with "lenCurve" bins between "xmin" and "xmax". intitialFitConverged – Can be used to find out whether the initial Johnson system fit converged successfully. This parameter can also be NULL. filterUsed – On output, will contain the number of the best filter from "filters" (or can be NULL). ◆ minuitLocalQuantileRegression1D()
template<class Numeric1 , class Numeric2 >
High-level driver functions for performing local 1-d quantile regression fits using Minuit2 as a minimization engine The arguments are as follows: inputPoints – are the points for which the regression should be performed. Predictor is the first member of the pair and response is the second. As a side effect of this function, the input points will be sorted in the increasing order. This is why the vector of input points is non-const. symbetaPower – the power parameter for "SymmetricBeta1D". 3 and 4 are good values to try. bandwidthInCDFSpace – Approximate fraction of sample points which will participate in each fit. Due to robustness requirements (obtaining limited bandwidth in coordinate space), the bandwidth in the CDF space must be less than 0.5 (and, of course, positive). polyDegree – this defines the degree of the polynomial that will be fitted to the quantile curve. It does not make much sense to go beyond 3 here. cdfValue – which quantile to use in the regression xmin, xmax – the result will be calculated between xmin and xmax in equidistant steps result – array where the result will be stored nResultPoints – number of coordinate points to use to build the result. The interval (xmin, xmax) will be split into "nResultPoints" bins. The coordinates at which the fits are performed are taken from the middle of those bins (as in a histogram). Naturally, array "result" must have at least "nResultPoints" elements. verbose – this switch can be turned on for debugging purposes ◆ minuitQuantileRegression()
template<typename Numeric , typename Num2 , unsigned StackLen2, unsigned StackDim2>
High-level driver function for performing local quantile regression fits using Minuit2 as a minimization engine. The weight function is assumed to be symmetric in each dimension. Function arguments are as follows: qrb – Naturally, an instance of the npstat::QuantileRegressionBase template. Carries the information about the dataset, the kernel, the bandwidth, and the quantile to fit for. For more details, look at the LocalQuantileRegression.hh header. polyDegree – Degree of the local polynomial to fit. Can be 0, 1 (local linear regression), or 2 (local quadratic regression). result – Grid which will hold the results on exit. It defines the number of points in each dimension and provides the storage space. resultBox – Coordinates of the grid boundaries. The points for which the regression is performed will be positioned inside this box just like histogram bin centers. reportProgressEvery – Print out a message about the number of grid points processed to the standard output every "reportProgressEvery" points. The default value of 0 means that such printouts are disabled. upFactor – A factor for the Minuit UP parameter, to multiply by the value estimated internally. Don't change the default unless you really understand what you are doing. For this function, it is assumed that the constant bandwidth is set up already, with the weight function which was used to create the orthogonal polynomials. ◆ minuitQuantileRegressionIncrBW()
template<typename Numeric , typename Num2 , unsigned StackLen2, unsigned StackDim2, typename NumHisto >
High-level driver function for performing local quantile regression fits using Minuit2 as a minimization engine. The weight function is assumed to be symmetric in each dimension. This function is similar to minuitQuantileRegression. However, it sometimes automatically increases the bandwidth: it makes sure that the regression box has at least the minimal fraction of points inside it, as specified by the "minimalSampleFraction" parameter. The fraction is calculated from the "predictorHisto" histogram whose dimensionality and axis order should coincide with the regression predictors. It is expected that this histogram will contain the predictor variables for the sample actually used in the regression. "minimalSampleFraction" must be <= 1.0. 0 or negative values will result in the constant bandwidth use, just like in the minuitQuantileRegression function. ◆ minuitUnbinnedLogisticRegression()
template<class Point , class Numeric , class BooleanFunctor , typename Num2 , unsigned StackLen2, unsigned StackDim2>
High-level driver function for performing local logistic regression fits using Minuit2 as a minimization engine. It is assumed that the constant bandwidth is set up already, with the weight function which was used to create the orthogonal polynomials. The weight function is assumed to be symmetric in each dimension. ◆ weightedLocalQuantileRegression1D()
template<class Numeric1 , class Numeric2 >
High-level driver functions for performing local 1-d quantile regression fits for weighted points using Minuit2 as a minimization engine The arguments are as follows: inputPoints – are the points for which the regression should be performed. Predictor is the first member of the triple, response is the second, and weight is the third. As a side effect of this function, the input points will be sorted in the increasing order of the predictor. This is why the vector of input points is non-const. symbetaPower – the power parameter for "SymmetricBeta1D". 3 and 4 are good values to try. bandwidthInCDFSpace – Approximate fraction of sample points which will participate in each fit. Due to robustness requirements (obtaining limited bandwidth in coordinate space), the bandwidth in the CDF space must be less than 0.5 (and, of course, positive). polyDegree – this defines the degree of the polynomial that will be fitted to the quantile curve. It does not make much sense to go beyond 3 here. cdfValue – which quantile to use in the regression xmin, xmax – the result will be calculated between xmin and xmax in equidistant steps result – array where the result will be stored nResultPoints – number of coordinate points to use to build the result. The interval (xmin, xmax) will be split into "nResultPoints" bins. The coordinates at which the fits are performed are taken from the middle of those bins (as in a histogram). Naturally, array "result" must have at least "nResultPoints" elements. verbose – this switch can be turned on for debugging purposes Generated by  1.9.1
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