statsmodels.iolib.table.SimpleTable.extend SimpleTable.extend() L.extend(iterable) ? extend list by appending elements from the iterable

statsmodels.regression.linear_model.GLSAR.whiten GLSAR.whiten(X) [source] Whiten a series of columns according to an AR(p) covariance structure. This drops initial p observations. Parameters: X : array-like The data to be whitened, Returns: whitened array :

statsmodels.tsa.arima_process.arma_periodogram statsmodels.tsa.arima_process.arma_periodogram(ar, ma, worN=None, whole=0) [source] periodogram for ARMA process given by lag-polynomials ar and ma Parameters: ar : array_like autoregressive lag-polynomial with leading 1 and lhs sign ma : array_like moving average lag-polynomial with leading 1 worN : {None, int}, optional option for scipy.signal.freqz (read ?w or N?) If None, then compute at 512 frequencies around the unit circle. If a sin

statsmodels.sandbox.stats.multicomp.varcorrection_unequal statsmodels.sandbox.stats.multicomp.varcorrection_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes something is wrong Parameters: var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns: varjoint : float joint variance. dfjoint

statsmodels.genmod.families.links.inverse_squared.inverse_deriv inverse_squared.inverse_deriv(z) Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal statsmodels.sandbox.stats.multicomp.varcorrection_pairs_unequal(var_all, nobs_all, df_all) [source] return joint variance from samples with unequal variances and unequal sample sizes for all pairs something is wrong Parameters: var_all : array_like The variance for each sample nobs_all : array_like The number of observations for each sample df_all : array_like degrees of freedom for each sample Returns: varjoint : array

statsmodels.regression.linear_model.OLSResults.conf_int OLSResults.conf_int(alpha=0.05, cols=None) Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.tsa.vector_ar.var_model.VARProcess.mse VARProcess.mse(steps) [source] Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.stats.weightstats.ztost statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0) [source] Equivalence test based on normal distribution Parameters: x1 : array_like one sample or first sample for 2 independent samples low, upp : float equivalence interval low < m1 - m2 < upp x1 : array_like or None second sample for 2 independent samples test. If None, then a one-sample test is performed. usevar : string, ?pooled? If pooled, then the stan

statsmodels.discrete.discrete_model.MNLogit.score MNLogit.score(params) [source] Score matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: score : ndarray, (K * (J-1),) The 2-d score vector, i.e. the first derivative of the loglikelihood function, of the multinomial logit model evaluated at params. Notes for In the multinomial model the score matrix is K x J-1 but is returned as a flattened array to