statsmodels.robust.robust_linear_model.RLMResults

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

Class to contain RLM results

See also

statsmodels.base.model.LikelihoodModelResults

Attributes

bcov_scaledarray

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_unscaledarray

The usual p x p covariance matrix with scale set equal to 1. It is then just equivalent to normalized_cov_params.

bsearray

The standard errors of the parameter estimates.

chisqarray

An array of the chi-squared values of the paramter estimates.

df_model

See RLM.df_model

df_resid

See RLM.df_resid

fit_historydict

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_optionsdict

Contains the options given to fit.

fittedvaluesarray

The linear predicted values. dot(exog, params)

modelstatsmodels.rlm.RLM

A reference to the model instance

nobsfloat

The number of observations n

normalized_cov_paramsarray

See specific model class docstring

paramsarray

The coefficients of the fitted model

pinv_wexogarray

See RLM.pinv_wexog

pvaluesarray

The two-tailed p values for the t-stats of the params.

residarray

The residuals of the fitted model. endog - fittedvalues

scalefloat

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.

sresidarray

The scaled residuals.

tvaluesarray

Return the t-statistic for a given parameter estimate.

weightsarray

The reported weights are determined by passing the scaled residuals from the last weighted least squares fit in the IRLS algortihm.

Methods

bse()	The standard errors of the parameter estimates.
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)	Initialize (possibly re-initialize) a Results instance.
llf()	Log-likelihood of model
load(fname)	load a pickle, (class method)
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
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
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

bcov_scaled
bcov_unscaled
chisq
fittedvalues
resid
sresid
weights