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
modelclass instance

PHreg model instance that called fit.

normalized_cov_paramsndarray

See specific model class docstring

paramsndarray

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.

bsendarray

The standard errors of the fitted parameters.

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_distribution()

Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case.

initialize(model, params, **kwargs)

Initialize (possibly re-initialize) a Results instance.

load(fname)

Load a pickled results instance

normalized_cov_params()

See specific model class docstring

predict([endog, exog, strata, offset, ...])

Returns predicted values from the proportional hazards regression model.

remove_data()

Remove data arrays, all nobs arrays from result and model.

save(fname[, remove_data])

Save a pickle of this instance.

summary([yname, xname, title, alpha])

Summarize the proportional hazards regression results.

t_test(r_matrix[, cov_p, 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.

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

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.

llf

Log-likelihood of model

martingale_residuals

The martingale residuals.

pvalues

The two-tailed p values for the t-stats of the params.

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.

tvalues

Return the t-statistic for a given parameter estimate.

use_t

Flag indicating to use the Student's distribution in inference.

weighted_covariate_averages

The average covariate values within the at-risk set at each event time point, weighted by hazard.