GeneralizedParetoValidation

class GeneralizedParetoValidation(*args)

Validation of GeneralizedExtremeValue inference.

Warning

This class is experimental and likely to be modified in future releases. To use it, import the openturns.experimental submodule.

Parameters:
resultDistributionFactoryResult

Inference result to validate.

sample2-d sequence of float

Data on which the inference was performed.

See also

openturns.GeneralizedExtremeValueFactory

Methods

drawDiagnosticPlot()

Draw the 4 usual diagnostic plots.

drawPDF()

Draw the estimated density and the data histogram.

drawReturnLevel()

Draw the return level with confidence interval.

getClassName()

Accessor to the object's name.

getConfidenceLevel()

Confidence level accessor.

getName()

Accessor to the object's name.

hasName()

Test if the object is named.

setConfidenceLevel(confidenceLevel)

Confidence level accessor.

setName(name)

Accessor to the object's name.
__init__(*args)
drawDiagnosticPlot()

Draw the 4 usual diagnostic plots.

Returns:
gridGridLayout
Returns a grid of 4 graphs:

  • the QQ-plot,

  • the PP-plot,

  • the return level graph (with confidence lines),

  • the density graph.

Notes

The 4 graphs are the probability-probability plot, the quantile-quantile plot, the return level plot, the data histogram with the fitted model density.

If (z_{(1)} \leq z_{(2)} \leq \dots \leq z_{(n)}) denotes the ordered block maximum data and \hat{G} the cumulative distribution function of the GPD distribution fitted on the data, the graphs are defined as follows.

The probability-probability plot consists of the points:

\left\{ \left( i/(n+1), \hat{G}(z_{(i)}) \right), i=1, \dots , m\right\}

The quantile-quantile plot consists of the points:

\left\{ \left( z_{(i)}, \hat{G}^{-1}(i/(n+1)) \right), i=1, \dots , n\right\}

The return level plot consists of the points:

\left\{ \left( m, \hat{z}_m\right), m> 0\right\}

and the points:

\left\{ \left( m, z_{m}^{emp}\right), m> 0\right\}

where z_{m}^{emp} is the empirical m-observation return level and \hat{z}_{m} the m-observation return level calculated with the fitted GPD.

drawPDF()

Draw the estimated density and the data histogram.

Returns:
graphGraph

The estimated density and the data histogram.

drawReturnLevel()

Draw the return level with confidence interval.

Returns:
graphGraph

The return level graph.

Notes

The return level plot consists of the points:

\left\{ \left( m, \hat{z}_m\right), m >0 \right\}

and the points:

\left\{ \left( m, z_{m}^{emp}\right), m> 0\right\}

where z_{m}^{emp} is the empirical m-observation return level and \hat{z}_{m} the m-observation return level calculated with the fitted GPD.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getConfidenceLevel()

Confidence level accessor.

Returns:
levelfloat

Confidence level for the confidence lines.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

setConfidenceLevel(confidenceLevel)

Confidence level accessor.

Parameters:
levelfloat

Confidence level for the confidence lines.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

Examples using the class

Estimate a GPD on the Wooster temperature data

Estimate a GPD on the Wooster temperature data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the daily rainfall data

Estimate a GPD on the daily rainfall data