# `nilearn.glm`: Generalized Linear Models#

Analysing 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.

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.

## `nilearn.glm.first_level`#

Classes:

 `FirstLevelModel`([t_r, slice_time_ref, ...]) Implement the General Linear Model for single session 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`(tr[, ...]) Implement the Glover dispersion derivative HRF model. `glover_hrf`(tr[, oversampling, time_length, ...]) Implement the Glover HRF model. `glover_time_derivative`(tr[, 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`(tr[, ...]) Implement the SPM dispersion derivative HRF model. `spm_hrf`(tr[, oversampling, time_length, onset]) Implement the SPM HRF model. `spm_time_derivative`(tr[, 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.