clarite.analyze.interaction_test

clarite.analyze.interaction_test(outcome_variable: str, covariates: List[str], data: pandas.core.frame.DataFrame, min_n: int = 200, interactions: Union[List[Tuple[str, str]], str, None] = None, report_betas: bool = False)

Perform LRT tests comparing a model with interaction terms to one without.

An intercept, covariates, and main effects of the variables used in the interactiona are included in both the full and restricted models. All variables in data other than the outcome and covariates are potential interaction variables. All pairwise interactions are tested unless specific Results are sorted in order of increasing pvalue

Parameters:

data: pd.DataFrame

The data to be analyzed, including the outcome, covariates, and any variables to be regressed.

outcome_variable: string

The variable to be used as the output (y) of the regression

covariates: list (strings),

The variables to be used as covariates. Any variables in the DataFrames not listed as covariates are regressed.

min_n: int or None

Minimum number of complete-case observations (no NA values for outcome, covariates, or variable) Defaults to 200

interactions: list(tuple(strings)), str, or None

Valid variables are those in the data that are not the outcome variable or a covariate. None: Test all pairwise interactions between valid variables String: Test all interactions of this valid variable with other valid variables List of tuples: Test specific interactions of valid variables

report_betas: boolean

False by default.

If True, the results will contain one row for each interaction term and will include the beta value, standard error (SE), and beta pvalue for that specific interaction. The number of terms increases with the number of categories in each interacting variable.

Returns:

df: pd.DataFrame

DataFrame with these columns: [‘Test_Number’, ‘Converged’, ‘N’, ‘Beta’, ‘SE’, ‘Beta_pvalue’, ‘LRT_pvalue’] indexed by the interaction terms (“Term1”, “Term2”) and the outcome variable (“Outcome”)

Examples

>>> ewas_discovery = clarite.analyze.interaction_test("logBMI", covariates, nhanes_discovery)