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

Class to contain RLM results


**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



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.
initialize(model, params, **kwd)
load(fname) load a pickle, (class method)
predict([exog, transform]) Call self.model.predict with self.params as the first argument.
remove_data() remove data arrays, all nobs arrays from result and model
save(fname[, remove_data]) save a pickle of this instance
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
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.


use_t bool(x) -> bool

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