statsmodels.genmod.families.links.probit.deriv2 probit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.regression.quantile_regression.QuantRegResults.wresid static QuantRegResults.wresid()

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional class statsmodels.nonparametric.kernel_density.KDEMultivariateConditional(endog, exog, dep_type, indep_type, bw, defaults=) [source] Conditional multivariate kernel density estimator. Calculates P(Y_1,Y_2,...Y_n | X_1,X_2...X_m) = P(X_1, X_2,...X_n, Y_1, Y_2,..., Y_m)/P(X_1, X_2,..., X_m). The conditional density is by definition the ratio of the two densities, see [R8]. Parameters: endog: list of ndarrays or 2-D ndarray :

statsmodels.sandbox.stats.runs.mcnemar statsmodels.sandbox.stats.runs.mcnemar(x, y=None, exact=True, correction=True) [source] McNemar test Parameters: x, y : array_like two paired data samples. If y is None, then x can be a 2 by 2 contingency table. x and y can have more than one dimension, then the results are calculated under the assumption that axis zero contains the observation for the samples. exact : bool If exact is true, then the binomial distribution will be used. If exact is f

statsmodels.sandbox.sysreg.SUR class statsmodels.sandbox.sysreg.SUR(sys, sigma=None, dfk=None) [source] Seemingly Unrelated Regression Parameters: sys : list [endog1, exog1, endog2, exog2,...] It will be of length 2 x M, where M is the number of equations endog = exog. sigma : array-like M x M array where sigma[i,j] is the covariance between equation i and j dfk : None, ?dfk1?, or ?dfk2? Default is None. Correction for the degrees of freedom should be specified for small samples. See t

statsmodels.tsa.arima_model.ARMA.hessian ARMA.hessian(params) [source] Compute the Hessian at params, Notes This is a numerical approximation.

Generalized Linear Models Generalized linear models currently supports estimation using the one-parameter exponential families See Module Reference for commands and arguments. Examples # Load modules and data import statsmodels.api as sm data = sm.datasets.scotland.load() data.exog = sm.add_constant(data.exog) # Instantiate a gamma family model with the default link function. gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma()) gamma_results = gamma_model.fit() Detailed ex

statsmodels.regression.linear_model.OLS.whiten OLS.whiten(Y) [source] OLS model whitener does nothing: returns Y.

Regression with Discrete Dependent Variable Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson) data. See Module Reference for commands and arguments. Examples # Load the data from Spector and Mazzeo (1980) spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog) # Logit Model logit_mod = sm.Logit(spector_data.endog, spe

statsmodels.robust.robust_linear_model.RLM.deviance RLM.deviance(tmp_results) [source] Returns the (unnormalized) log-likelihood from the M estimator.