CPM Regression

CPMRegression

This class handles the process of performing CPM Regression with cross-validation and permutation testing.

__init__(results_directory, cv=KFold(n_splits=10, shuffle=True, random_state=42), inner_cv=None, edge_selection=UnivariateEdgeSelection(edge_statistic='pearson', edge_selection=[PThreshold(threshold=[0.05], correction=[None])]), select_stable_edges=False, stability_threshold=0.8, impute_missing_values=True, n_permutations=0, atlas_labels=None)

Initialize the CPMRegression object.

Parameters:

Name	Type	Description	Default
results_directory	str	Directory to save results.	required
cv	Union[BaseCrossValidator, BaseShuffleSplit]	Outer cross-validation strategy.	KFold(n_splits=10, shuffle=True, random_state=42)
inner_cv	Union[BaseCrossValidator, BaseShuffleSplit]	Inner cross-validation strategy for edge selection.	None
edge_selection	UnivariateEdgeSelection	Method for edge selection.	UnivariateEdgeSelection(edge_statistic='pearson', edge_selection=[PThreshold(threshold=[0.05], correction=[None])])
impute_missing_values	bool	Whether to impute missing values.	True
n_permutations	int	Number of permutations to run for permutation testing.	0
atlas_labels	str	CSV file containing atlas and regions labels.	None

run(X, y, covariates)

Estimates a model using the provided data and conducts permutation testing. This method first fits the model to the actual data and subsequently performs estimation on permuted data for a specified number of permutations. Finally, it calculates permutation results.

Parameters:

Name	Type	Description	Default
X	Union[DataFrame, ndarray]		required
y	Union[Series, DataFrame, ndarray]		required
covariates	Union[Series, DataFrame, ndarray]		required