neurostatx.cli.ConfirmatoryFA

Attributes

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

Functions

Name	Description
ConfirmatoryFA	Perform confirmatory factor analysis from loadings or a

appattributemodule attribute#

View source

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

Cyclopts application for the ConfirmatoryFA command-line tool.

ConfirmatoryFAfunction#

View source

def ConfirmatoryFA(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')] = './ResultsCFA/', loadings_df: Annotated[str, Parameter(show_default=False, group='Factorial Analysis parameters')] = None, model: Annotated[List[str], Parameter(show_default=False, group='Factorial Analysis parameters')] = None, threshold: Annotated[float, Parameter(--threshold, group='Factorial Analysis parameters')] = 0.4, iterations: Annotated[int, Parameter(--iterations, group='Factorial Analysis parameters')] = None, 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 confirmatory factor analysis from loadings or a lavaan-style model.

ConfirmatoryFA tests a hypothesized model of the relationships between observed variables and latent constructs. It writes factor scores, goodness of fit statistics (Chi-square, RMSEA, CFI, TLI), and an HTML report.

Notes

EFA vs CFA scores

Both EFA and CFA scores can be used to derive factor scores. There is no clear consensus on which is preferred [2], so the script outputs both. The two methods are highly correlated [3]; the choice is a matter of preference. A useful reference for the fit metrics is [1].

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).

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.
desc_columns	int	required	Number of descriptive columns at the beginning of the dataset to exclude in statistics and descriptive tables.
out_folder	str	'./ResultsCFA/'	Path of the folder in which the results will be written. If not specified, current folder and default name will be used. Defaults to ./ResultsCFA/.
loadings_df	str	None	Filename of the dataframe containing the loadings of the EFA analysis. Columns must be factors and rows variables. Defaults to None.
model	List[str]	None	Model specification for the CFA analysis. Must be provided within brackets (ex: –model “factor1 =~ var1 + var2 + var3” –model “factor2 =~ var4 + var5”). Defaults to None.
threshold	float	0.4	Threshold to use to determine variables to include for each factor in CFA analysis (ex: if set to 0.40, only variables with loadings higher than 0.40 will be assigned to a factor in the CFA model). Defaults to 0.40.
iterations	int	None	Number of iterations to perform the bootstrapping of the model. Defaults to None.
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

ConfirmatoryFA --in-dataset dataset.csv --id-column ID --desc-columns 1
--out-folder ./output/ --loadings-df loadings.csv --threshold 0.40
--mean -v -f