statsmodels.robust.robust_linear_model.RLMResults¶

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

Class to contain RLM results

bcov_scaled¶

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.

Type:¶

ndarray

bcov_unscaled¶

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

Type:¶

ndarray

bse¶

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.

Type:¶

ndarray

chisq¶

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

Type:¶

ndarray

df_model¶

See RLM.df_model

df_resid¶

See RLM.df_resid

fit_history¶

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.

Type:¶

dict

fit_options¶

Contains the options given to fit.

Type:¶

dict

fittedvalues¶

The linear predicted values. dot(exog, params)

Type:¶

ndarray

model¶

A reference to the model instance

Type:¶

statsmodels.rlm.RLM

nobs¶

The number of observations n

Type:¶

float

normalized_cov_params¶

See RLM.normalized_cov_params

Type:¶

ndarray

params¶

The coefficients of the fitted model

Type:¶

ndarray

pinv_wexog¶

See RLM.pinv_wexog

Type:¶

ndarray

pvalues¶

The p values associated with tvalues. Note that tvalues are assumed to be distributed standard normal rather than Student’s t.

Type:¶

ndarray

resid¶

The residuals of the fitted model. endog - fittedvalues

Type:¶

ndarray

scale¶

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.

Type:¶

float

sresid¶

The scaled residuals.

Type:¶

ndarray

tvalues¶

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.

Type:¶

ndarray

weights¶

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

Type:¶

ndarray

See also

statsmodels.base.model.LikelihoodModelResults

Methods

conf_int([alpha, cols])	Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, ...])	Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, invcov])	Compute the F-test for a joint linear hypothesis.
initialize(model, params, **kwargs)	Initialize (possibly re-initialize) a Results instance.
load(fname)	Load a pickled results instance
normalized_cov_params()	See specific model class docstring
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, 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.
wald_test(r_matrix[, cov_p, invcov, use_f, ...])	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.

Properties

bcov_scaled
bcov_unscaled
bse
chisq
fittedvalues
llf	Log-likelihood of model
pvalues
resid
sresid
tvalues	Return the t-statistic for a given parameter estimate.
use_t	Flag indicating to use the Student's distribution in inference.
weights