survival_to_cumulative_hazard

Helper function to transform between survival function and CHF

Survival analysis models are commonly used in medicine and other areas. Many of them
are too complex to be interpreted by human. Exploration and explanation is needed, but
standard methods do not give a broad enough picture. 'survex' provides easy-to-apply
methods for explaining survival models, both complex black-boxes and simpler statistical models.
They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023)
<doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as
extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.

Mikołaj Spytek

survex

Explainable Machine Learning in Survival Analysis

Mateusz Krzyziński

Sophie Langbein

Hubert Baniecki

Lorenz A. Kapsner

Przemyslaw Biecek

survival_to_cumulative_hazard function

<dl><dt>survival_functions</dt>
<dd>matrix or vector, with each row representing a survival function</dd>
<dt>epsilon</dt>
<dd>a positive numeric number to add, so that the logarithm can be taken</dd></dl>

Arguments

Transform Survival to Cumulative Hazard — survival_to_cumulative_hazard

<dl>

<dt>survival_functions</dt>
<dd>matrix or vector, with each row representing a survival function</dd>


<dt>epsilon</dt>
<dd>a positive numeric number to add, so that the logarithm can be taken</dd>

</dl>

survival_to_cumulative_hazard: Transform Survival to Cumulative Hazard

Description

Usage

Value

Arguments

Examples