7.4.3.2.1. statsmodels.robust.robust_linear_model.RLMResults¶
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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**2wherem = mean(M.psi_deriv(sresid))andvar_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_invH3 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.model.LikelihoodModelResultsMethods
__init__(model, params, ...)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 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. weights()Attributes
use_t