This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.


class nilearn.glm.LikelihoodModelResults(theta, Y, model, cov=None, dispersion=1.0, nuisance=None)[source]

Class to contain results from likelihood models.

This is the class in which things like AIC, BIC, llf can be implemented as methods, not computed in, say, the fit method of OLSModel.

__init__(theta, Y, model, cov=None, dispersion=1.0, nuisance=None)[source]

Set up results structure.


Parameter estimates from estimated model.



modelLikelihoodModel instance

Model used to generate fit.

covNone or ndarray, optional

Covariance of thetas.

dispersionscalar, default=1

Multiplicative factor in front of cov.

nuisanceNone of ndarray, optional

Parameter estimates needed to compute logL.


The covariance of thetas is given by:

dispersion * cov

For (some subset of models) dispersion will typically be the mean square error from the estimated model (sigma^2)


Return the maximized log-likelihood.


Return the (Wald) t-statistic for a given parameter estimate.

Use Tcontrast for more complicated (Wald) t-statistics.

vcov(matrix=None, column=None, dispersion=None, other=None)[source]

Return Variance/covariance matrix of linear contrast.

matrix(dim, self.theta.shape[0]) array, optional

Numerical contrast specification, where dim refers to the ‘dimension’ of the contrast i.e. 1 for t contrasts, 1 or more for F contrasts.

columnint, optional

Alternative way of specifying contrasts (column index).

dispersionfloat or (n_voxels,) array, optional

Value(s) for the dispersion parameters.

other(dim, self.theta.shape[0]) array, optional

Alternative contrast specification (?).

cov(dim, dim) or (n_voxels, dim, dim) array

The estimated covariance matrix/matrices.

Returns the variance/covariance matrix of a linear contrast of the
estimates of theta, multiplied by dispersion which will often be an
estimate of dispersion, like, sigma^2.
The covariance of interest is either specified as a (set of) column(s)
or a matrix.
Tcontrast(matrix, store=('t', 'effect', 'sd'), dispersion=None)[source]

Compute a Tcontrast for a row vector matrix.

To get the t-statistic for a single column, use the ‘t’ method.

matrix1D array-like

Contrast matrix.

storesequence, default=(‘t’, ‘effect’, ‘sd’)

Components of t to store in results output object.

dispersionNone or float, optional
resTContrastResults object
Fcontrast(matrix, dispersion=None, invcov=None)[source]

Compute an F contrast for a contrast matrix matrix.

Here, matrix M is assumed to be non-singular. More precisely

M pX pX' M'

is assumed invertible. Here, pX is the generalized inverse of the design matrix of the model. There can be problems in non-OLS models where the rank of the covariance of the noise is not full.

See the contrasts module to see how to specify contrasts. In particular, the matrices from these contrasts will always be non-singular in the sense above.

matrix1D array-like

Contrast matrix.

dispersionNone or float, optional

If None, use self.dispersion.

invcovNone or array, optional

Known inverse of variance covariance matrix. If None, calculate this matrix.

f_resFContrastResults instance

with attributes F, df_den, df_num


For F contrasts, we now specify an effect and covariance.

conf_int(alpha=0.05, cols=None, dispersion=None)[source]

Return the confidence interval of the specified theta estimates.

alphafloat, default=0.05

The alpha level for the confidence interval. ie., alpha = .05 returns a 95% confidence interval.

colstuple, optional

cols specifies which confidence intervals to return.

dispersionNone or scalar, optional

Scale factor for the variance / covariance (see class docstring and vcov method docstring).


cis is shape (len(cols), 2) where each row contains [lower, upper] for the given entry in cols


Confidence intervals are two-tailed.

tailsstring, optional

Possible values: ‘two’ | ‘upper’ | ‘lower’


>>> from numpy.random import standard_normal as stan
>>> from nilearn.glm import OLSModel
>>> x = np.hstack((stan((30,1)),stan((30,1)),stan((30,1))))
>>> beta=np.array([3.25, 1.5, 7.0])
>>> y =,beta) + stan((30))
>>> model = OLSModel(x).fit(y)
>>> confidence_intervals = model.conf_int(cols=(1,2))