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

statsmodels.robust.robust_linear_model.RLM.initialize RLM.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.sandbox.regression.gmm.GMM.fitgmm GMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) [source] estimate parameters using GMM Parameters: start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.regression.linear_model.OLSResults.mse_total static OLSResults.mse_total()

statsmodels.stats.proportion.binom_tost statsmodels.stats.proportion.binom_tost(count, nobs, low, upp) [source] exact TOST test for one proportion using binomial distribution Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns: pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-

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

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

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.DiscreteModel.information DiscreteModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

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]: