algorithms.statistics.models.glm

Module: algorithms.statistics.models.glm

Inheritance diagram for nipy.algorithms.statistics.models.glm:

Inheritance diagram of nipy.algorithms.statistics.models.glm

General linear models

Model

class nipy.algorithms.statistics.models.glm.Model(design, family=<nipy.algorithms.statistics.models.family.family.Gaussian object>)

Bases: nipy.algorithms.statistics.models.regression.WLSModel, nipy.externals.six.Iterator

__init__(design, family=<nipy.algorithms.statistics.models.family.family.Gaussian object>)
cont(tol=1e-05)

Continue iterating, or has convergence been obtained?

deviance(Y=None, results=None, scale=1.0)

Return (unnormalized) log-likelihood for GLM.

Note that self.scale is interpreted as a variance in old_model, so we divide the residuals by its sqrt.

estimate_scale(Y=None, results=None)

Return Pearson’s X^2 estimate of scale.

fit(Y)
niter = 10