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

statsmodels.LikelihoodModelResults (See) –

Returns

  • **Attributes**

  • model (class instance) – PHreg model instance that called fit.

  • normalized_cov_params (array) – The sampling covariance matrix of the estimates

  • 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.

  • bse (array) – The standard errors of the fitted parameters.

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

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.

Attributes

use_t