ResponseFitter¶
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class
nideconv.
ResponseFitter
(input_signal, sample_rate, oversample_design_matrix=20, add_intercept=True, **kwargs)[source]¶ ResponseFitter takes an input signal and performs deconvolution on it. To do this, it requires event times, and possible covariates. ResponseFitter can, for each event type, use different basis function sets, see Event.
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, melt])predict a signal given a design matrix. ridge_regress
(self[, cv, alphas, …])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)[source]¶ 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)
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add_event
(self, event_name, onsets=None, basis_set='fir', interval=[0, 10], n_regressors=None, durations=None, covariates=None, **kwargs)[source]¶ 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.
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fit
(self, type='ols', cv=20, alphas=None, store_residuals=False)[source]¶ 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.
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get_epochs
(self, onsets, interval, remove_incomplete_epochs=True)[source]¶ 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.
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get_rsq
(self)[source]¶ calculate the rsq of a given fit. calls predict_from_design_matrix to predict the signal that has been fit
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