ANOVA fitted linear model comparison for statsmodels

anova_lm.py

from pandas import DataFrame
import numpy as np
from scipy import stats

def anova_lm(*args, **kwargs):
    """
    ANOVA table for one or more fitted linear models.

    Parmeters
    ---------
    args : fitted linear model results instance
        One or more fitted linear models

    **kwargs**

    scale : float
        Estimate of variance, If None, will be estimated from the largest
        model. Default is None.
    test : str {"F", "Chisq", "Cp"} or None
        Test statistics to provide (Why not just give all). Default is "F".

    Returns
    -------
    anova : DataFrame
        A DataFrame containing.

    Notes
    -----
    Model statistics are given in the order of args. Models must have
    a formula_str attribute.

    See Also
    --------
    model_results.compare_f_test, model_results.compare_lm_test
    """
    test = kwargs.get("test", "F")
    scale = kwargs.get("scale", None)
    n_models = len(args)

    model_formula = []
    pr_test = "PR(>%s)" % test
    names = ['df_resid', 'ssr', 'df_diff', 'ss_diff', test, pr_test]
    table = DataFrame(np.empty((n_models, 6)), columns = names)

    if not scale: # assume biggest model is last
        scale = args[-1].scale

    table["ssr"] = map(getattr, args, ["ssr"]*n_models)
    table["df_resid"] = map(getattr, args, ["df_resid"]*n_models)
    table.ix[1:]["df_diff"] = np.diff(map(getattr, args, ["df_model"]*n_models))
    table["ss_diff"] = -table["ssr"].diff()
    if test == "F":
        table["F"] = table["ss_diff"] / table["df_diff"] / scale
        table[pr_test] = stats.f.sf(table["F"], table["df_diff"],
                             table["df_resid"])

    return table