`nilearn.glm`: Generalized Linear Models¶

Analyzing fMRI data using GLMs.

Classes¶

`Contrast`(effect, variance[, dim, dof, ...])	The contrast class handles the estimation of statistical contrasts on a given model: student (t) or Fisher (F).
`FContrastResults`(effect, covariance, F, df_num)	Results from an F contrast of coefficients in a parametric model.
`TContrastResults`(t, sd, effect[, df_den])	Results from a t contrast of coefficients in a parametric model.
`ARModel`(design, rho)	A regression model with an AR(p) covariance structure.
`OLSModel`(design)	A simple ordinary least squares model.
`LikelihoodModelResults`(theta, Y, model[, ...])	Class to contain results from likelihood models.
`RegressionResults`(theta, Y, model, ...[, ...])	Summarize the fit of a linear regression model.
`SimpleRegressionResults`(results)	Contain only information of the model fit necessary for contrast computation.

        classDiagram
  LikelihoodModelResults <|-- RegressionResults
  LikelihoodModelResults <|-- SimpleRegressionResults
  OLSModel <|-- ARModel

Functions¶

`compute_contrast`(labels, regression_result, ...)	Compute the specified contrast given an estimated glm.
`compute_fixed_effects`(contrast_imgs, ...[, ...])	Compute the fixed effects, given images of effects and variance.
`expression_to_contrast_vector`(expression, ...)	Convert a string describing a contrast to a contrast vector.
`fdr_threshold`(z_vals, alpha)	Return the Benjamini-Hochberg FDR threshold for the input z_vals.
`cluster_level_inference`(stat_img[, ...])	Report the proportion of active voxels for all clusters defined by the input threshold.
`threshold_stats_img`([stat_img, mask_img, ...])	Compute the required threshold level and return the thresholded map.
`save_glm_to_bids`(model, contrasts[, ...])	Save GLM results to BIDS-like files.

`nilearn.glm.first_level`¶

Classes¶

FirstLevelModel([t_r, slice_time_ref, ...])

Implement the General Linear Model for single run fMRI data.

Functions¶

`check_design_matrix`(design_matrix)	Check that the provided DataFrame is indeed a valid design matrix descriptor, and returns a triplet of fields.
`compute_regressor`(exp_condition, hrf_model, ...)	Convolve regressors with HRF model.
`first_level_from_bids`(dataset_path, task_label)	Create FirstLevelModel objects and fit arguments from a BIDS dataset.
`glover_dispersion_derivative`(t_r[, ...])	Implement the Glover dispersion derivative HRF model.
`glover_hrf`(t_r[, oversampling, time_length, ...])	Implement the Glover HRF model.
`glover_time_derivative`(t_r[, oversampling, ...])	Implement the Glover time derivative HRF (dhrf) model.
`make_first_level_design_matrix`(frame_times)	Generate a design matrix from the input parameters.
`mean_scaling`(Y[, axis])	Scaling of the data to have percent of baseline change along the specified axis.
`run_glm`(Y, X[, noise_model, bins, n_jobs, ...])	GLM fit for an fMRI data matrix.
`spm_dispersion_derivative`(t_r[, ...])	Implement the SPM dispersion derivative HRF model.
`spm_hrf`(t_r[, oversampling, time_length, onset])	Implement the SPM HRF model.
`spm_time_derivative`(t_r[, oversampling, ...])	Implement the SPM time derivative HRF (dhrf) model.

`nilearn.glm.second_level`¶

Classes¶

SecondLevelModel([mask_img, target_affine, ...])

Implement the General Linear Model for multiple subject fMRI data.

Functions¶

`make_second_level_design_matrix`(subjects_label)	Set up a second level design.
`non_parametric_inference`(second_level_input)	Generate p-values corresponding to the contrasts provided based on permutation testing.

        classDiagram
  BaseEstimator <|-- BaseGLM
  BaseGLM <|-- FirstLevelModel
  BaseGLM <|-- SecondLevelModel
  CacheMixin <|-- BaseGLM
  ReprHTMLMixin <|-- BaseEstimator
  _HTMLDocumentationLinkMixin <|-- BaseEstimator
  _MetadataRequester <|-- BaseEstimator