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

Attributes

bse() The standard errors of the parameter estimates.
normalized_cov_params() See specific model class docstring
pvalues() The two-tailed p values for the t-stats of the params.
tvalues() Return the t-statistic for a given parameter estimate.
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.
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
params (array) The coefficients of the fitted model
pinv_wexog (array) See RLM.pinv_wexog
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.
weights (array) The reported weights are determined by passing the scaled residuals from the last weighted least squares fit in the IRLS algortihm.

Methods

bcov_scaled()
bcov_unscaled()
bse() The standard errors of the parameter estimates.
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) Initialize (possibly re-initialize) a Results instance.
llf() Log-likelihood of model
load(fname) load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code.
normalized_cov_params() See specific model class docstring
predict([exog, transform]) Call self.model.predict with self.params as the first argument.
pvalues() The two-tailed p values for the t-stats of the params.
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()