labs.glm.glm¶
Classes¶
contrast
¶
-
class
nipy.labs.glm.glm.
contrast
(dim, type='t', tiny=1e-50, dofmax=10000000000.0)¶ Bases:
object
-
__init__
(dim, type='t', tiny=1e-50, dofmax=10000000000.0)¶ tiny is a numerical constant for computations.
-
pvalue
(baseline=0.0)¶ Return a parametric approximation of the p-value associated with the null hypothesis: (H0) ‘contrast equals baseline’
-
stat
(baseline=0.0)¶ Return the decision statistic associated with the test of the null hypothesis: (H0) ‘contrast equals baseline’
-
summary
()¶ Return a dictionary containing the estimated contrast effect, the associated ReML-based estimation variance, and the estimated degrees of freedom (variance of the variance).
-
zscore
(baseline=0.0)¶ Return a parametric approximation of the z-score associated with the null hypothesis: (H0) ‘contrast equals baseline’
-
glm
¶
-
class
nipy.labs.glm.glm.
glm
(Y=None, X=None, formula=None, axis=0, model='spherical', method=None, niter=2)¶ Bases:
object
-
__init__
(Y=None, X=None, formula=None, axis=0, model='spherical', method=None, niter=2)¶ Initialize self. See help(type(self)) for accurate signature.
-
contrast
(c, type='t', tiny=1e-50, dofmax=10000000000.0)¶ Specify and estimate a constrast
c must be a numpy.ndarray (or anything that numpy.asarray can cast to a ndarray). For a F contrast, c must be q x p where q is the number of contrast vectors and p is the total number of regressors.
-
fit
(Y, X, formula=None, axis=0, model='spherical', method=None, niter=2)¶
-
save
(file)¶ Save fit into a .npz file
-