neurostatx.cli.ExploratoryFA

Attributes

Name	Description
app	Cyclopts application for the ExploratoryFA command-line tool.

Functions

Name	Description
ExploratoryFA	Perform exploratory factor analysis with Horn's parallel analysis.

appattributemodule attribute#

View source

app = App(default_parameter=Parameter(negative=()))

Cyclopts application for the ExploratoryFA command-line tool.

ExploratoryFAfunction#

View source

def ExploratoryFA(in_dataset: Annotated[str, Parameter(show_default=False, group='Essential Files Options')], id_column: Annotated[str, Parameter(show_default=False, group='Essential Files Options')], desc_columns: Annotated[int, Parameter(show_default=False, group='Essential Files Options')], out_folder: Annotated[str, Parameter(group='Essential Files Options')] = './ResultsEFA/', rotation: Annotated[RotationTypes, Parameter(group='Factorial Analysis parameters')] = RotationTypes.promax, method: Annotated[MethodTypes, Parameter(group='Factorial Analysis parameters')] = MethodTypes.minres, nb_factors: Annotated[int, Parameter(--nb_factors, group='Factorial Analysis parameters')] = None, train_dataset_size: Annotated[float, Parameter(--train_dataset_size, group='Factorial Analysis parameters')] = 0.5, random_state: Annotated[int, Parameter(--random_state, group='Factorial Analysis parameters')] = 1234, verbose: Annotated[bool, Parameter(-v, --verbose, group='Optional parameters')] = False, save_parameters: Annotated[bool, Parameter(-s, --save_parameters, group='Optional parameters')] = False, overwrite: Annotated[bool, Parameter(-f, --overwrite, group='Optional parameters')] = False)

Perform exploratory factor analysis with Horn’s parallel analysis.

ExploratoryFA fits an EFA model using the provided rotation and method. When only an EFA is requested, Horn’s parallel analysis determines the optimal number of factors to extract. The script can also split the data into a training set for EFA and a test set for later ConfirmatoryFA. It writes the EFA model, loadings, communalities, and the transformed dataset.

Notes

Input specifications

The dataset can contain multiple descriptive columns before the variables of interest. Specify that count with –desc-columns. Rows with missing values are removed by default; use the mean or median option to impute missing data (be cautious when doing this). See [1], [2], and [3] for discussion of EFA vs CFA factor scores.

References

[1] Costa, V., & Sarmento, R. Confirmatory Factor Analysis

[2] Whether to use EFA or CFA to predict latent variable scores

[3] Comparison of factor score estimation methods

Parameters

Name	Type	Default	Description
in_dataset	str	required	Input dataset to use in the factorial analysis.
id_column	str	required	Name of the column containing the subject’s ID tag. Required for proper handling of IDs and merging multiple datasets.
desc_columns	int	required	Number of descriptive columns at the beginning of the dataset to exclude in statistics and descriptive tables.
out_folder	str	'./ResultsEFA/'	Path of the folder in which the results will be written. If not specified, current folder and default name will be used. Defaults to ./ResultsEFA/.
rotation	RotationTypes	RotationTypes.promax	Select the type of rotation to apply on your data. Defaults to promax.
method	MethodTypes	MethodTypes.minres	Select the method for fitting the data. Defaults to minres.
nb_factors	int	None	Specify the number of factors to extract from the data. If not specified, the script will use Horn’s parallel analysis to determine the optimal number of factors. Defaults to None.
train_dataset_size	float	0.5	Specify the proportion of the input dataset to use as training dataset in the EFA (value from 0 to 1). Defaults to 0.5.
random_state	int	1234	Random seed for reproducibility. Defaults to 1234.
verbose	bool	False	If true, produce verbose output. Defaults to False.
save_parameters	bool	False	If true, save the parameters used in the analysis in a text file. Defaults to False.
overwrite	bool	False	If true, force overwriting of existing output files. Defaults to False.

Examples

ExploratoryFA --in-dataset df --id-column IDs --out-folder results_FA/
--rotation promax --method ml --train_dataset_size 0.5 -v -f -s