NiftiResponseFytter

class nideconv.nifti.NiftiResponseFitter(func_img, sample_rate, mask=None, oversample_design_matrix=20, add_intercept=True, detrend=False, standardize=False, confounds_for_extraction=None, memory=None, **kwargs)[source]

Methods

add_confounds(self, name, confound) Add a timeseries or set of timeseries to the general design matrix as a confound
add_event(self, event_name[, onsets, …]) create design matrix for a given event_type.
fit(self[, type, cv, alphas, store_residuals]) Regress a created design matrix on the input_data.
get_epochs(self, onsets, interval[, …]) Return a matrix corresponding to specific onsets, within a given interval.
get_rsq(self) calculate the rsq of a given fit.
predict_from_design_matrix(self[, X]) predict a signal given a design matrix.
ridge_regress(self, \*args, \*\*kwargs) run CV ridge regression instead of ols fit.
add_intercept  
get_basis_functions  
get_original_signal  
get_residuals  
get_standard_errors_timecourse  
get_t_value_timecourses  
get_time_to_peak  
get_timecourses  
plot_design_matrix  
plot_model_fit  
plot_timecourses  
add_confounds(self, name, confound)

Add a timeseries or set of timeseries to the general design matrix as a confound

Parameters:
confound : array

Confound of (n_timepoints) or (n_timepoints, n_confounds)

add_event(self, event_name, onsets=None, basis_set='fir', interval=[0, 10], n_regressors=None, durations=None, covariates=None, **kwargs)

create design matrix for a given event_type.

Parameters:
event_name : string

Name of the event_type, used as key to lookup this event_type’s characteristics

**kwargs : dict

keyward arguments to be internalized by the generated and internalized Event object. Needs to consist of the necessary arguments to create an Event object, see Event constructor method.

fit(self, type='ols', cv=20, alphas=None, store_residuals=False)

Regress a created design matrix on the input_data.

Creates internal variables betas, residuals, rank and s. The beta values are then injected into the event_type objects the ResponseFitter contains.

Parameters:
type : string, optional

the type of fit to be done. Options are ‘ols’ for np.linalg.lstsq, ‘ridge’ for CV ridge regression.

get_epochs(self, onsets, interval, remove_incomplete_epochs=True)

Return a matrix corresponding to specific onsets, within a given interval. Matrix size is (n_onsets, n_timepoints_within_interval).

Note that any events that are in the ResponseFitter-object will be regressed out before calculating the epochs.

get_rsq(self)[source]

calculate the rsq of a given fit. calls predict_from_design_matrix to predict the signal that has been fit

predict_from_design_matrix(self, X=None)[source]

predict a signal given a design matrix. Requires regression to have been run.

Parameters:
X : np.array, (timepoints, n_regressors)

the design matrix for which to predict data.

ridge_regress(self, *args, **kwargs)[source]

run CV ridge regression instead of ols fit. Uses sklearn’s RidgeCV class

Parameters:
cv : int

number of cross-validation folds

alphas : np.array

the alpha/lambda values to try out in the CV ridge regression