class_docstr: Class to contain RLM results
  
  Attributes
  ----------
  
  bcov_scaled : ndarray
      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 : ndarray
      The usual p x p covariance matrix with scale set equal to 1.  It
      is then just equivalent to normalized_cov_params.
  bse : ndarray
      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 : ndarray
      An array of the chi-squared values of the parameter 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 : ndarray
      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 : ndarray
      See RLM.normalized_cov_params
  params : ndarray
      The coefficients of the fitted model
  pinv_wexog : ndarray
      See RLM.pinv_wexog
  pvalues : ndarray
      The p values associated with `tvalues`. Note that `tvalues` are assumed
      to be distributed standard normal rather than Student's t.
  resid : ndarray
      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 : ndarray
      The scaled residuals.
  tvalues : ndarray
      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 : ndarray
      The reported weights are determined by passing the scaled residuals
      from the last weighted least squares fit in the IRLS algorithm.
  
  See Also
  --------
  statsmodels.base.model.LikelihoodModelResults
code: def resid(self):
      return self.model.endog - self.fittedvalues
  
imports: import numpy
  import scipy.stats
  import statsmodels.base.model
  import statsmodels.base.wrapper
  import statsmodels.regression._tools
  import statsmodels.regression.linear_model
  import statsmodels.robust.norms
  import statsmodels.robust.scale
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
label_desc: Use image node_id 3 for calling the RLMResults obj's underlying member method code with example usage: obj.resid() and returns: unknown