statsmodels.regression.linear_model.RegressionResults.compare_lm_test RegressionResults.compare_lm_test(restricted, demean=True, use_lr=False) [source] Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bo

statsmodels.regression.quantile_regression.QuantRegResults class statsmodels.regression.quantile_regression.QuantRegResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] Results instance for the QuantReg model Methods HC0_se() HC1_se() HC2_se() HC3_se() aic() bic() bse() centered_tss() compare_f_test(restricted) use F test to test whether restricted model is correct compare_lm_test(restricted[, demean, use_lr

statsmodels.regression.quantile_regression.QuantReg class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs) [source] Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters: endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit

statsmodels.regression.mixed_linear_model.MixedLMResults class statsmodels.regression.mixed_linear_model.MixedLMResults(model, params, cov_params) [source] Class to contain results of fitting a linear mixed effects model. MixedLMResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance Pointer to PHreg model instance that called fit. normalized_cov_params : array The sampling covariance

statsmodels.regression.mixed_linear_model.MixedLM class statsmodels.regression.mixed_linear_model.MixedLM(endog, exog, groups, exog_re=None, use_sqrt=True, missing='none', **kwargs) [source] An object specifying a linear mixed effects model. Use the fit method to fit the model and obtain a results object. Parameters: endog : 1d array-like The dependent variable exog : 2d array-like A matrix of covariates used to determine the mean structure (the ?fixed effects? covariates). groups : 1d

statsmodels.regression.linear_model.WLS class statsmodels.regression.linear_model.WLS(endog, exog, weights=1.0, missing='none', hasconst=None, **kwargs) [source] A regression model with diagonal but non-identity covariance structure. The weights are presumed to be (proportional to) the inverse of the variance of the observations. That is, if the variables are to be transformed by 1/sqrt(W) you must supply weights = 1/W. Parameters: endog : array-like 1-d endogenous response variable. The d

statsmodels.regression.linear_model.yule_walker statsmodels.regression.linear_model.yule_walker(X, order=1, method='unbiased', df=None, inv=False, demean=True) [source] Estimate AR(p) parameters from a sequence X using Yule-Walker equation. Unbiased or maximum-likelihood estimator (mle) See, for example: http://en.wikipedia.org/wiki/Autoregressive_moving_average_model Parameters: X : array-like 1d array order : integer, optional The order of the autoregressive process. Default is 1. met

statsmodels.regression.linear_model.RegressionResults class statsmodels.regression.linear_model.RegressionResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] This class summarizes the fit of a linear regression model. It handles the output of contrasts, estimates of covariance, etc. Returns: **Attributes** : aic : Aikake?s information criteria. For a model with a constant . For a model without a constant . bic : Bayes? i

statsmodels.regression.linear_model.OLSResults class statsmodels.regression.linear_model.OLSResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] Results class for for an OLS model. Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are: get_influence outlier_test el_test conf_int_el See also RegressionResults Methods HC0_se() See statsmodels.Regressi

statsmodels.regression.linear_model.OLS class statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) [source] A simple ordinary least squares model. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_