Document "orig_model" parameter in harmonizationLearn

[olfMombach]

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neuroHarmonize/neuroHarmonize/harmonizationLearn.py

Lines 10 to 67 in 478935b

	def harmonizationLearn(data, covars, eb=True, smooth_terms=[],
	smooth_term_bounds=(None, None), return_s_data=False,
	orig_model=None, seed=None):
	"""
	Wrapper for neuroCombat function that returns the harmonization model.
	Arguments
	---------
	data : a numpy array
	data to harmonize with ComBat, dimensions are N_samples x N_features
	covars : a pandas DataFrame
	contains covariates to control for during harmonization
	all covariates must be encoded numerically (no categorical variables)
	must contain a single column "SITE" with site labels for ComBat
	dimensions are N_samples x (N_covariates + 1)
	eb : bool, default True
	whether to use empirical Bayes estimates of site effects
	smooth_terms (Optional) : a list, default []
	names of columns in covars to include as smooth, nonlinear terms
	can be any or all columns in covars, except "SITE"
	if empty, ComBat is applied with a linear model of covariates
	if not empty, Generalized Additive Models (GAMs) are used
	will increase computation time due to search for optimal smoothing
	smooth_term_bounds (Optional) : tuple of float, default (None, None)
	feature to support custom boundaries of the smoothing terms
	useful when holdout data covers different range than
	specify the bounds as (minimum, maximum)
	currently not supported for models with mutliple smooth terms
	return_s_data (Optional) : bool, default False
	whether to return s_data, the standardized data array
	can be useful for diagnostics but will be costly to save/load if large
	seed (Optional) : int, default None
	By default, this function is non-deterministic. Setting the optional
	argument `seed` will make the function deterministic.
	Returns
	-------
	model : a dictionary of estimated model parameters
	design, var_pooled, B_hat, grand_mean,
	gamma_star, delta_star, info_dict (a neuroCombat invention),
	gamma_hat, delta_hat, gamma_bar, t2, a_prior, b_prior, smooth_model
	bayes_data : a numpy array
	harmonized data, corrected for effects of SITE
	dimensions are N_samples x N_features
	s_data (Optional) : a numpy array
	standardized residuals after accounting for `covars` other than `SITE`
	set return_s_data=True to output the variable
	"""