statsmodels.stats.anova.anova_lm¶

statsmodels.stats.anova.anova_lm(*args, **kwargs)[source]¶

Anova table for one or more fitted linear models.

Parameters:¶

argsfitted linear model results instance

One or more fitted linear models

scalefloat

Estimate of variance, If None, will be estimated from the largest model. Default is None.

teststr {“F”, “Chisq”, “Cp”} or None

Test statistics to provide. Default is “F”.

typstr or int {“I”,”II”,”III”} or {1,2,3}

The type of Anova test to perform. See notes.

robust{None, “hc0”, “hc1”, “hc2”, “hc3”}

Use heteroscedasticity-corrected coefficient covariance matrix. If robust covariance is desired, it is recommended to use hc3.

Returns:¶

anovaDataFrame

When args is a single model, return is DataFrame with columns:

sum_sqfloat64

Sum of squares for model terms.

dffloat64

Degrees of freedom for model terms.

Ffloat64

F statistic value for significance of adding model terms.

PR(>F)float64

P-value for significance of adding model terms.

When args is multiple models, return is DataFrame with columns:

df_residfloat64

Degrees of freedom of residuals in models.

ssrfloat64

Sum of squares of residuals in models.

df_difffloat64

Degrees of freedom difference from previous model in args

ss_dfffloat64

Difference in ssr from previous model in args

Ffloat64

F statistic comparing to previous model in args

PR(>F): float64

P-value for significance comparing to previous model in args

See also

model_results.compare_f_test, model_results.compare_lm_test

Notes

Model statistics are given in the order of args. Models must have been fit using the formula api.

Examples

>>> import statsmodels.api as sm
>>> from statsmodels.formula.api import ols
>>> moore = sm.datasets.get_rdataset("Moore", "carData", cache=True) # load
>>> data = moore.data
>>> data = data.rename(columns={"partner.status" :
...                             "partner_status"}) # make name pythonic
>>> moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)',
...                 data=data).fit()
>>> table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 Anova DataFrame
>>> print(table)