statsmodels.duration.hazard_regression.PHRegResults

class statsmodels.duration.hazard_regression.PHRegResults(model, params, cov_params, scale=1.0, covariance_type='naive')[source]

Class to contain results of fitting a Cox proportional hazards survival model.

PHregResults inherits from statsmodels.LikelihoodModelResults

Parameters:See statsmodels.LikelihoodModelResults

See also

statsmodels.LikelihoodModelResults

Attributes

normalized_cov_params() See specific model class docstring
bse() Returns the standard errors of the parameter estimates.
model (class instance) PHreg model instance that called fit.
params (array) The coefficients of the fitted model. Each coefficient is the log hazard ratio corresponding to a 1 unit difference in a single covariate while holding the other covariates fixed.

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) Initialize (possibly re-initialize) a Results instance.
llf() Log-likelihood of model
load(fname) load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code.
martingale_residuals() The martingale residuals.
normalized_cov_params() See specific model class docstring
predict([endog, exog, strata, offset, …]) Returns predicted values from the proportional hazards regression model.
pvalues() The two-tailed p values for the t-stats of the params.
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
t_test_pairwise(term_name[, method, alpha, …]) perform pairwise t_test with multiple testing corrected p-values
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.