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algorithms.statistics.nlsmodel

Module: algorithms.statistics.nlsmodel

Inheritance diagram for nipy.algorithms.statistics.nlsmodel:

TODO

NLSModel

class nipy.algorithms.statistics.nlsmodel.NLSModel(Y, design, f, grad, theta, niter=10)

Bases: nipy.fixes.scipy.stats.models.model.Model

Class representing a simple nonlinear least squares model.

Methods

SSE
fit
getZ
getomega
initialize
next
predict
__init__(Y, design, f, grad, theta, niter=10)
Parameters :
Y : TODO

the data in the NLS model

design : TODO

the deisng matrix, X

f : TODO

the map between the linear parameters (in the design matrix) and the nonlinear parameters (theta)

grad : TODO

the gradient of f, this should be a function of an nxp design matrix X and qx1 vector theta that returns an nxq matrix df_i/dtheta_j where

f_i(theta) = f(X[i], theta)

is the nonlinear response function for the i-th instance in the model.

SSE()

Sum of squares error.

:Returns; TODO

fit()

Fit a model to data.

getZ()
Returns :None
getomega()
Returns :None
initialize()

Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

next()
Returns :None
predict(design=None)
Parameters :
design : TODO

TODO

Returns :

TODO