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.robust.norms.TukeyBiweight.psi_deriv TukeyBiweight.psi_deriv(z) [source] The derivative of Tukey?s biweight psi function Notes Used to estimate the robust covariance matrix.

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.descriptivestats.sign_test statsmodels.stats.descriptivestats.sign_test(samp, mu0=0) [source] Signs test. Parameters: samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns: M, p-value : See also scipy.stats.wilcoxon Notes The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above m

statsmodels.discrete.discrete_model.MultinomialResults.llr static MultinomialResults.llr()

statsmodels.iolib.foreign.StataReader.file_timestamp StataReader.file_timestamp() [source] Returns the date and time Stata recorded on last file save. Returns: out : str

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdroots ArmaFft.spdroots(w) [source] spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies)

statsmodels.genmod.cov_struct.GlobalOddsRatio.observed_crude_oddsratio GlobalOddsRatio.observed_crude_oddsratio() [source] To obtain the crude (global) odds ratio, first pool all binary indicators corresponding to a given pair of cut points (c,c?), then calculate the odds ratio for this 2x2 table. The crude odds ratio is the inverse variance weighted average of these odds ratios. Since the covariate effects are ignored, this OR will generally be greater than the stratified OR.

statsmodels.genmod.families.links.inverse_power.inverse inverse_power.inverse(z) Inverse of the power transform link function Parameters: `z` : array-like Value of the transformed mean parameters at p Returns: `p` : array Mean parameters Notes g^(-1)(z`) = z`**(1/`power)

statsmodels.robust.norms.TrimmedMean.rho TrimmedMean.rho(z) [source] The robust criterion function for least trimmed mean. Parameters: z : array-like 1d array Returns: rho : array rho(z) = (1/2.)*z**2 for |z| <= c rho(z) = 0 for |z| > c