SmoothingCubicSplineBase Properties |
The SmoothingCubicSplineBase type exposes the following members.
Properties| | Name | Description |
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 | CalculateStandardErrorEstimates |
If true, standard error estimates are calculated and provided in ErrorEstimate.
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| CheckArguments |
If true, the array given as arguments are checked.
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| Coefficient0 |
Returns the spline coefficient of order 0. This are the splined y values at the positions given by x.
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| Coefficient1 |
Returns the spline coefficient of order 1 (linear term).
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| Coefficient2 |
Returns the spline coefficient of order 2 (quadratic term).
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| Coefficient3 |
Returns the spline coefficient of order 2 (cubic term).
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| CombineNeighbouringPoints |
If true, points with x values that are very similar are combined into one point, which helds the average value of x and y
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| ErrorEstimate |
Returns the error estimates of the y points.
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| ErrorStandardDeviation |
If the error standard deviation of the provided points is unknown, set this value to -1. Then a cross validating cubic spline is fitted to the data.
If the error standard deviation is known and is equal for all points, set this value to the error standard deviation of the points.
If the error standard deviation is known and different for each point, set this value to 1, and provide the error standard deviation for each point
by calling Interpolate(IReadOnlyListDouble, IReadOnlyListDouble, Double, IReadOnlyListDouble).
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| EstimatedDegreesOfFreedom |
Estimate of the number of degrees of
freedom of the residual sum of squares
which reduces to the usual value of n-2
when a least squares regression line
was calculated.
|
| EstimatedErrorVariance |
Estimate of the error variance.
The value coincides with the output value of var if var is negative on input.
It is calculated with the unscaled values of the df[i] to facilitate
comparisons with a priori variance estimates.
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| EstimatedTrueMeanSquareError |
Estimate of the true mean square error at the data points.
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| GeneralizedCrossValidation |
Generalized cross validation.
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| MeanSquareOfInputStandardDeviation |
Mean square value of the standard deviations in dy[i] (if they were provided).
The values of GeneralizedCrossValidation, MeanSquareResidual and EstimatedTrueMeanSquareError
are calculated with the dy[i] scaled to have a mean square value 1.
The unscaled values of GeneralizedCrossValidation, MeanSquareResidual and EstimatedTrueMeanSquareError
may be calculated by dividing by this value.
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| MeanSquareResidual |
Mean square residual.
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| SmoothingParameter |
Smoothing parameter = rho/(rho+1), that varies between 0 (not smoothed) and 1 (full smoothed).
If the value is 0 (rho=0) an interpolating natural cubic spline has been calculated.
If the value is 1 (rho=infinite) a least squares regression line has been calculated.
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