Class to contain results of fitting a Cox proportional hazards survival model.
PHregResults inherits from statsmodels.LikelihoodModelResults
Parameters: | See statsmodels.LikelihoodModelResults : |
---|---|
Returns: | **Attributes** : model : class instance
normalized_cov_params : array
params : array
bse : array
|
See also
statsmodels.LikelihoodModelResults
Methods
baseline_cumulative_hazard() | A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points. |
baseline_cumulative_hazard_function() | A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function. |
bse() | Returns the standard errors of the parameter estimates. |
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. |
f_test(r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
get_distribution() | Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. |
initialize(model, params, **kwd) | |
llf() | |
load(fname) | load a pickle, (class method) |
martingale_residuals() | The martingale residuals. |
normalized_cov_params() | |
predict([endog, exog, strata, offset, ...]) | Returns predicted values from the proportional hazards regression model. |
pvalues() | |
remove_data() | remove data arrays, all nobs arrays from result and model |
save(fname[, remove_data]) | save a pickle of this instance |
schoenfeld_residuals() | A matrix containing the Schoenfeld residuals. |
score_residuals() | A matrix containing the score residuals. |
standard_errors() | Returns the standard errors of the parameter estimates. |
summary([yname, xname, title, alpha]) | Summarize the proportional hazards regression results. |
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. |
wald_test_terms([skip_single, ...]) | Compute a sequence of Wald tests for terms over multiple columns |
weighted_covariate_averages() | The average covariate values within the at-risk set at each event time point, weighted by hazard. |
Methods
baseline_cumulative_hazard() | A list (corresponding to the strata) containing the baseline cumulative hazard function evaluated at the event points. |
baseline_cumulative_hazard_function() | A list (corresponding to the strata) containing function objects that calculate the cumulative hazard function. |
bse() | Returns the standard errors of the parameter estimates. |
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. |
f_test(r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
get_distribution() | Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. |
initialize(model, params, **kwd) | |
llf() | |
load(fname) | load a pickle, (class method) |
martingale_residuals() | The martingale residuals. |
normalized_cov_params() | |
predict([endog, exog, strata, offset, ...]) | Returns predicted values from the proportional hazards regression model. |
pvalues() | |
remove_data() | remove data arrays, all nobs arrays from result and model |
save(fname[, remove_data]) | save a pickle of this instance |
schoenfeld_residuals() | A matrix containing the Schoenfeld residuals. |
score_residuals() | A matrix containing the score residuals. |
standard_errors() | Returns the standard errors of the parameter estimates. |
summary([yname, xname, title, alpha]) | Summarize the proportional hazards regression results. |
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. |
wald_test_terms([skip_single, ...]) | Compute a sequence of Wald tests for terms over multiple columns |
weighted_covariate_averages() | The average covariate values within the at-risk set at each event time point, weighted by hazard. |
Attributes
use_t | bool(x) -> bool |