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# scikits.statsmodels.rlm.RLMResults

class `scikits.statsmodels.rlm.``RLMResults`(model, params, normalized_cov_params, scale)

  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
  fittedvalues : array
    The linear predicted values. dot(exog, params)
  model : scikits.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
  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.

  See also

  `scikits.statsmodels.model.LikelihoodModelResults`

  Methods

  `conf_int`([alpha, cols]) | Returns the confidence interval of the fitted parameters.
  `cov_params`([r_matrix, column, scale, other]) | Returns the variance/covariance matrix.
  `f_test`(r_matrix[, scale, invcov]) | Compute an Fcontrast/F-test for a contrast matrix.
  `initialize`(model, params, **kwd) |
  `normalized_cov_params`() |
  `t`([column]) | Return the t-statistic for a given parameter estimate.
  `t_test`(r_matrix[, scale]) | Compute a tcontrast/t-test for a row vector array.

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