statsmodels.robust.robust_linear_model.RLMResults

class statsmodels.robust.robust_linear_model.RLMResults(model, params, normalized_cov_params, scale) [source]

Class to contain RLM results

Returns:

**Attributes** bcov_scaled (array) – p x p scaled covariance matrix specified in the model fit method. The default is H1. H1 is defined as k**2 * (1/df_resid*sum(M.psi(sresid)**2)*scale**2)/ ((1/nobs*sum(M.psi_deriv(sresid)))**2) * (X.T X)^(-1) where k = 1 + (df_model +1)/nobs * var_psiprime/m**2 where m = mean(M.psi_deriv(sresid)) and var_psiprime = var(M.psi_deriv(sresid)) H2 is defined as k * (1/df_resid) * sum(M.psi(sresid)**2) *scale**2/ ((1/nobs)*sum(M.psi_deriv(sresid)))*W_inv H3 is defined as 1/k * (1/df_resid * sum(M.psi(sresid)**2)*scale**2 * (W_inv X.T X W_inv)) where k is defined as above and W_inv = (M.psi_deriv(sresid) exog.T exog)^(-1) See the technical documentation for cleaner formulae. bcov_unscaled (array) – The usual p x p covariance matrix with scale set equal to 1. It is then just equivalent to normalized_cov_params. bse (array) – An array of the standard errors of the parameters. The standard errors are taken from the robust covariance matrix specified in the argument to fit. chisq (array) – An array of the chi-squared values of the paramter estimates. df_model – See RLM.df_model df_resid – See RLM.df_resid fit_history (dict) – Contains information about the iterations. Its keys are deviance, params, iteration and the convergence criteria specified in RLM.fit, if different from deviance or params. fit_options (dict) – Contains the options given to fit. fittedvalues (array) – The linear predicted values. dot(exog, params) model (statsmodels.rlm.RLM) – A reference to the model instance nobs (float) – The number of observations n normalized_cov_params (array) – See RLM.normalized_cov_params params (array) – The coefficients of the fitted model pinv_wexog (array) – See RLM.pinv_wexog pvalues (array) – The p values associated with tvalues. Note that tvalues are assumed to be distributed standard normal rather than Student’s t. resid (array) – The residuals of the fitted model. endog - fittedvalues scale (float) – The type of scale is determined in the arguments to the fit method in RLM. The reported scale is taken from the residuals of the weighted least squares in the last IRLS iteration if update_scale is True. If update_scale is False, then it is the scale given by the first OLS fit before the IRLS iterations. sresid (array) – The scaled residuals. tvalues (array) – The “t-statistics” of params. These are defined as params/bse where bse are taken from the robust covariance matrix specified in the argument to fit. weights (array) – The reported weights are determined by passing the scaled residuals from the last weighted least squares fit in the IRLS algortihm.

See also

statsmodels.base.model.LikelihoodModelResults

Methods

bcov_scaled()
bcov_unscaled()
bse()
chisq()
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Returns the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()
initialize(model, params, **kwd)
llf()
load(fname)	load a pickle, (class method)
normalized_cov_params()
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pvalues()
remove_data()	remove data arrays, all nobs arrays from result and model
resid()
save(fname[, remove_data])	save a pickle of this instance
sresid()
summary([yname, xname, title, alpha, return_fmt])	This is for testing the new summary setup
summary2([xname, yname, title, alpha, …])	Experimental summary function for regression results
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q
t_test_pairwise(term_name[, method, alpha, …])	perform pairwise t_test with multiple testing corrected p-values
tvalues()	Return the t-statistic for a given parameter estimate.
wald_test(r_matrix[, cov_p, scale, invcov, …])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns
weights()

Attributes

use_t