statsmodels.discrete.discrete_model.CountResults.llr_pvalue static CountResults.llr_pvalue()

Time Series Filters Link to Notebook GitHub In [1]: from __future__ import print_function import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm In [2]: dta = sm.datasets.macrodata.load_pandas().data In [3]: index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')) print(index) <class 'pandas.tseries.index.DatetimeIndex'> [1959-03-31, ..., 2009-09-30] Length: 203, Freq: None, Timezone: None In [4]:

statsmodels.genmod.families.family.InverseGaussian.fitted InverseGaussian.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.discrete.discrete_model.NegativeBinomialResults.llnull static NegativeBinomialResults.llnull()

statsmodels.tsa.arima_model.ARMAResults.hqic static ARMAResults.hqic() [source]

statsmodels.sandbox.regression.gmm.GMM.score GMM.score(params, weights, epsilon=None, centered=True) [source]

statsmodels.discrete.discrete_model.BinaryModel.loglike BinaryModel.loglike(params) Log-likelihood of model.

statsmodels.sandbox.regression.try_catdata.groupsstats_1d statsmodels.sandbox.regression.try_catdata.groupsstats_1d(y, x, labelsunique) [source] use ndimage to get fast mean and variance

statsmodels.tools.tools.monotone_fn_inverter statsmodels.tools.tools.monotone_fn_inverter(fn, x, vectorized=True, **keywords) Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x.

statsmodels.stats.weightstats._zstat_generic2 statsmodels.stats.weightstats._zstat_generic2(value, std_diff, alternative) [source] generic (normal) z-test to save typing can be used as ztest based on summary statistics