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.

Methods

bcov_scaled()
bcov_unscaled()
bse()
chisq()
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.
fittedvalues()
initialize(model, params, **kwd)
normalized_cov_params()
resid()
sresid()
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.
weights()

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