__init__(model) |
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bse() |
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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. |
cov_random() |
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f_test(r_matrix[, cov_p, scale, invcov]) |
Compute the F-test for a joint linear hypothesis. |
initialize(model, params, **kwd) |
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llf() |
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load(fname) |
load a pickle, (class method) |
mean_random([idx]) |
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normalized_cov_params() |
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plot_random_univariate([bins, use_loc]) |
create plot of marginal distribution of random effects |
plot_scatter_all_pairs([title]) |
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plot_scatter_pairs(idx1, idx2[, title, ax]) |
create scatter plot of two random effects |
predict([exog, transform]) |
Call self.model.predict with self.params as the first argument. |
pvalues() |
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remove_data() |
remove data arrays, all nobs arrays from result and model |
save(fname[, remove_data]) |
save a pickle of this instance |
std_random() |
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t_test(r_matrix[, cov_p, scale, use_t]) |
Compute a t-test for a each linear hypothesis of the form Rb = q |
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. |