# labs.glm.glm

## Module: `labs.glm.glm`

Inheritance diagram for `nipy.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)
  contrast(c, type='t', tiny=1e-50, dofmax=10000000000.0)

    Specify and estimate a contrast

    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

## Functions

nipy.labs.glm.glm.load(file)

  Load a fitted glm

nipy.labs.glm.glm.ols(Y, X, axis=0)

  Essentially, compute pinv(X)*Y