Class to contain RLM results
Attributes
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.
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() |