statsmodels.sandbox.regression.gmm.IVRegressionResults.mse_resid static IVRegressionResults.mse_resid()

statsmodels.discrete.discrete_model.LogitResults.pred_table LogitResults.pred_table(threshold=0.5) Prediction table Parameters: threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes pred_table[i,j] refers to the number of times ?i? was observed and the model predicted ?j?. Correct predictions are along the diagonal.

statsmodels.genmod.generalized_linear_model.GLM.initialize GLM.initialize() [source] Initialize a generalized linear model.

statsmodels.genmod.families.links.NegativeBinomial.inverse NegativeBinomial.inverse(z) [source] Inverse of the negative binomial transform Parameters: z : array-like The value of the inverse of the negative binomial link at p. Returns : ??- : p : array Mean parameters Notes g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))

statsmodels.regression.linear_model.OLS.score OLS.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.duration.hazard_regression.PHReg.weighted_covariate_averages PHReg.weighted_covariate_averages(params) [source] Returns the hazard-weighted average of covariate values for subjects who are at-risk at a particular time. Parameters: params : ndarray Parameter vector Returns: averages : list of ndarrays averages[stx][i,:] is a row vector containing the weighted average values (for all the covariates) of at-risk subjects a the i^th largest observed failure time in stratum stx,

statsmodels.stats.proportion.proportions_ztest statsmodels.stats.proportion.proportions_ztest(count, nobs, value=None, alternative='two-sided', prop_var=False) [source] test for proportions based on normal (z) test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as

statsmodels.tsa.stattools.pacf statsmodels.tsa.stattools.pacf(x, nlags=40, method='ywunbiased', alpha=None) [source] Partial autocorrelation estimated Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : ?ywunbiased? (default) or ?ywmle? or ?ols? specifies which method for the calculations to use: yw or ywunbiased : yule walker with bias correction in denominator for acovf ywm or ywmle : yule walke

statsmodels.stats.power.GofChisquarePower class statsmodels.stats.power.GofChisquarePower(**kwds) [source] Statistical Power calculations for one sample chisquare test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha, n_bins[, ddof]) Calculate the power of a chisquare test for one sample solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample ch

statsmodels.stats.moment_helpers.cov2corr statsmodels.stats.moment_helpers.cov2corr(cov, return_std=False) [source] convert covariance matrix to correlation matrix Parameters: cov : array_like, 2d covariance matrix, see Notes Returns: corr : ndarray (subclass) correlation matrix return_std : bool If this is true then the standard deviation is also returned. By default only the correlation matrix is returned. Notes This function does not convert subclasses of ndarrays. This requires