statsmodels.discrete.discrete_model.PoissonResults#
- class statsmodels.discrete.discrete_model.PoissonResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]#
- Attributes:
- aic
Akaike information criterion. -2*(llf - p) where p is the number of regressors including the intercept.
- bic
Bayesian information criterion. -2*llf + ln(nobs)*p where p is the number of regressors including the intercept.
- bse
The standard errors of the parameter estimates
- fittedvalues
Linear predictor XB.
- im_ratio
- llf
Log-likelihood of model
- llnull
Value of the constant-only log-likelihood.
- llr
Likelihood ratio chi-squared statistic; -2*(llnull - llf)
- llr_pvalue
The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom df_model.
- prsquared
McFadden’s pseudo-R-squared. 1 - (llf / llnull)
- pvalues
The two-tailed p values for the t-stats of the params
- resid
Residuals
The residuals for Count models are defined as
\[y - p\]where \(p = \exp(X\beta)\). Any exposure and offset variables are also handled.
resid_pearsonPearson residuals
- resid_response
Respnose residuals. The response residuals are defined as endog - fittedvalues
- tvalues
Return the t-statistic for a given parameter estimate
use_tFlag indicating to use the Student’s distribution in inference
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
get_diagnostic([y_max])Get instance of class with specification and diagnostic methods
Get an instance of MLEInfluence with influence and outlier measures
get_margeff([at, method, atexog, dummy, count])Get marginal effects of the fitted model
get_prediction([exog, transform, which, ...])Compute prediction results when endpoint transformation is valid.
info_criteria(crit[, dk_params])Return an information criterion for the model
initialize(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance
load(fname)Load a pickled results instance
See specific model class docstring
predict([exog, transform])Call self.model.predict with self.params as the first argument
predict_prob([n, exog, exposure, offset, ...])Return predicted probability of each count level for each observation
Remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])Save a pickle of this instance
score_test([exog_extra, params_constrained, ...])Score test for restrictions or for omitted variables
set_null_options([llnull, attach_results])Set the fit options for the Null (constant-only) model.
summary([yname, xname, title, alpha, yname_list])Summarize the Regression Results.
summary2([yname, xname, title, alpha, ...])Experimental function to summarize regression results.
t_test(r_matrix[, cov_p, use_t])Compute a t-test for 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
get_distribution
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
get_diagnostic([y_max])Get instance of class with specification and diagnostic methods
get_distribution([exog, transform])Get an instance of MLEInfluence with influence and outlier measures
get_margeff([at, method, atexog, dummy, count])Get marginal effects of the fitted model
get_prediction([exog, transform, which, ...])Compute prediction results when endpoint transformation is valid.
info_criteria(crit[, dk_params])Return an information criterion for the model
initialize(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance
load(fname)Load a pickled results instance
See specific model class docstring
predict([exog, transform])Call self.model.predict with self.params as the first argument
predict_prob([n, exog, exposure, offset, ...])Return predicted probability of each count level for each observation
Remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])Save a pickle of this instance
score_test([exog_extra, params_constrained, ...])Score test for restrictions or for omitted variables
set_null_options([llnull, attach_results])Set the fit options for the Null (constant-only) model.
summary([yname, xname, title, alpha, yname_list])Summarize the Regression Results.
summary2([yname, xname, title, alpha, ...])Experimental function to summarize regression results.
t_test(r_matrix[, cov_p, use_t])Compute a t-test for 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
Akaike information criterion.
Bayesian information criterion.
The standard errors of the parameter estimates
Linear predictor XB.
Log-likelihood of model
Value of the constant-only log-likelihood.
Likelihood ratio chi-squared statistic; -2*(llnull - llf)
The chi-squared probability of getting a log-likelihood ratio statistic greater than llr.
McFadden's pseudo-R-squared.
The two-tailed p values for the t-stats of the params
Residuals
Pearson residuals
Respnose residuals.
Return the t-statistic for a given parameter estimate
Flag indicating to use the Student's distribution in inference