statsmodels.regression.quantile_regression.QuantReg.predict QuantReg.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.

statsmodels.stats.weightstats.DescrStatsW.tconfint_mean DescrStatsW.tconfint_mean(alpha=0.05, alternative='two-sided') [source] two-sided confidence interval for weighted mean of data If the data is 2d, then these are separate confidence intervals for each column. Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternat

statsmodels.tsa.arima_process.arma_pacf statsmodels.tsa.arima_process.arma_pacf(ar, ma, nobs=10) [source] partial autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned pacf Returns: pacf : array partial autocorrelation of ARMA process given

statsmodels.sandbox.tsa.fftarma.ArmaFft.acf ArmaFft.acf(nobs=None) theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, ma See also arma_acovf, acf, acovf

statsmodels.duration.hazard_regression.PHReg.score_residuals PHReg.score_residuals(params) [source] Returns the score residuals calculated at a given vector of parameters. Parameters: params : ndarray The parameter vector at which the score residuals are calculated. Returns: The score residuals, returned as a ndarray having the same : shape as `exog`. : Notes Observations in a stratum with no observed events have undefined score residuals, and contain NaN in the returned matrix.

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects static MixedLMResults.random_effects() [source] Returns the conditional means of all random effects given the data. Returns: random_effects : DataFrame A DataFrame with the distinct group values as the index and the conditional means of the random effects in the columns.

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

statsmodels.stats.weightstats.DescrStatsW.demeaned static DescrStatsW.demeaned() [source] data with weighted mean subtracted

statsmodels.sandbox.regression.gmm.IVGMM.gmmobjective_cu IVGMM.gmmobjective_cu(params, weights_method='cov', wargs=()) objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.genmod.cov_struct.Independence.summary Independence.summary() [source]