statsmodels.genmod.families.family.Family.fitted Family.fitted(lin_pred) [source] 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.sandbox.stats.multicomp.set_remove_subs statsmodels.sandbox.stats.multicomp.set_remove_subs(ssli) [source] remove sets that are subsets of another set from a list of tuples Parameters: ssli : list of tuples each tuple is considered as a set Returns: part : list of tuples new list with subset tuples removed, it is sorted by set-length of tuples. The list contains original tuples, duplicate elements are not removed. Examples >>> set_remove_subs([(0, 1), (1, 2), (1,

statsmodels.regression.quantile_regression.QuantRegResults.remove_data QuantRegResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

statsmodels.regression.mixed_linear_model.MixedLMResults.bse_re static MixedLMResults.bse_re() [source] Returns the standard errors of the variance parameters. Note that the sampling distribution of variance parameters is strongly skewed unless the sample size is large, so these standard errors may not give meaningful confidence intervals of p-values if used in the usual way.

statsmodels.sandbox.regression.try_ols_anova.data2groupcont statsmodels.sandbox.regression.try_ols_anova.data2groupcont(x1, x2) [source] create dummy continuous variable Parameters: x1 : 1d array label or group array x2 : 1d array (float) continuous variable Notes useful for group specific slope coefficients in regression

statsmodels.tools.eval_measures.stde statsmodels.tools.eval_measures.stde(x1, x2, ddof=0, axis=0) [source] standard deviation of error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: stde : ndarray or float standard deviation of difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarr

statsmodels.tsa.arima_process.lpol2index statsmodels.tsa.arima_process.lpol2index(ar) [source] remove zeros from lagpolynomial, squeezed representation with index Parameters: ar : array_like coefficients of lag polynomial Returns: coeffs : array non-zero coefficients of lag polynomial index : array index (lags) of lagpolynomial with non-zero elements

statsmodels.tsa.arima_process.arma_acf statsmodels.tsa.arima_process.arma_acf(ar, ma, nobs=10) [source] 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, m

statsmodels.stats.proportion.binom_tost_reject_interval statsmodels.stats.proportion.binom_tost_reject_interval(low, upp, nobs, alpha=0.05) [source] rejection region for binomial TOST The interval includes the end points, reject if and only if r_low <= x <= r_upp. The interval might be empty with r_upp < r_low. Parameters: low, upp : floats lower and upper limit of equivalence region nobs : integer the number of trials or observations. Returns: x_low, x_upp : float lower and

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