statsmodels.discrete.discrete_model.Probit.cdf Probit.cdf(X) [source] Probit (Normal) cumulative distribution function Parameters: X : array-like The linear predictor of the model (XB). Returns: cdf : ndarray The cdf evaluated at X. Notes This function is just an alias for scipy.stats.norm.cdf

statsmodels.tsa.arima_process.arma2ma statsmodels.tsa.arima_process.arma2ma(ar, ma, nobs=100) get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By reversing the

statsmodels.discrete.discrete_model.MultinomialResults.aic static MultinomialResults.aic() [source]

statsmodels.discrete.discrete_model.DiscreteResults.load classmethod DiscreteResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.stats.multicomp.StepDown.iter_subsets StepDown.iter_subsets(indices) [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.pacf ArmaFft.pacf(nobs=None) 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 by ar, ma Notes solves yule-walker

statsmodels.sandbox.tsa.fftarma.ArmaFft.filter2 ArmaFft.filter2(x, pad=0) [source] filter a time series using fftconvolve3 with ARMA filter padding of x currently works only if x is 1d in example it produces same observations at beginning as lfilter even without padding. TODO: this returns 1 additional observation at the end

statsmodels.sandbox.tsa.fftarma.ArmaFft.from_coeffs classmethod ArmaFft.from_coeffs(arcoefs, macoefs, nobs=100) Create ArmaProcess instance from coefficients of the lag-polynomials Parameters: arcoefs : array-like Coefficient for autoregressive lag polynomial, not including zero lag. The sign is inverted to conform to the usual time series representation of an ARMA process in statistics. See the class docstring for more information. macoefs : array-like Coefficient for moving-average lag

statsmodels.tsa.ar_model.ARResults.fpe static ARResults.fpe() [source]

statsmodels.stats.proportion.proportions_chisquare_allpairs statsmodels.stats.proportion.proportions_chisquare_allpairs(count, nobs, multitest_method='hs') [source] chisquare test of proportions for all pairs of k samples Performs a chisquare test for proportions for all pairwise comparisons. The alternative is two-sided Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. prop : float, optional The proba