gmisclib.gpk_rlsq.robust_linear

Class robust_linear_fit

Instance Methods

__init__(self, a, y, sigma_y=None, minsv=None, minsvr=None, copy=True)
This does bounded influence regression, with a robust M-estimator in the y-direction.

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float

eff_n(self)
Returns something like the number of data, except that it looks at their weighting and the structure of the problem. (Inherited from gmisclib.gpk_lsq.lls_base)

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float

eff_rank(self)
Returns something like the rank of the solution, but rather than counting how many dimensions can be solved at all, it reports how many dimensions can be solved with a relatively good accuracy. (Inherited from gmisclib.gpk_lsq.linear_least_squares)

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fit(self, copy=False) (Inherited from gmisclib.gpk_rlsq.w_linear_least_squares)

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hat(self, copy=True)
Hat Matrix Diagonal Data points that are far from the centroid of the X-space are potentially influential. (Inherited from gmisclib.gpk_lsq.linear_least_squares)

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rank(self) (Inherited from gmisclib.gpk_lsq.linear_least_squares)

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residual(self) (Inherited from gmisclib.gpk_rlsq.w_linear_least_squares)

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set_y(self, y, copy=True) (Inherited from gmisclib.gpk_rlsq.w_linear_least_squares)

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sv(self) (Inherited from gmisclib.gpk_lsq.linear_least_squares)

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variance_about_fit(self)
Returns the estimator of the standard deviation of the data about the fit. (Inherited from gmisclib.gpk_lsq.lls_base)

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x(self, y=None, copy=True) (Inherited from gmisclib.gpk_lsq.lls_base)

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x_variances(self)
Estimated standard deviations of the solution. (Inherited from gmisclib.gpk_lsq.linear_least_squares)

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y(self, copy=True) (Inherited from gmisclib.gpk_rlsq.w_linear_least_squares)

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Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Class Variables

HTMIN = 3.0

STMIN = 1.5

Properties

Inherited from object: __class__

Method Details

init(self, a, y, sigma_y=None, minsv=None, minsvr=None, copy=True)
(Constructor)

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This does bounded influence regression, with a robust M-estimator in the y-direction.

Overrides: object.__init__

Class robust_linear_fit

__init__(self, a, y, sigma_y=None, minsv=None, minsvr=None, copy=True) (Constructor)

init(self, a, y, sigma_y=None, minsv=None, minsvr=None, copy=True)
(Constructor)