data_simul_KDE

Simulates data based on the kernel density estimation (KDE) of given data.
KDE is a non-parametric way to estimate the probability density function of a random variable.
This function applies the accept-reject method to generate values that follow
the estimated density of the original dataset.

Provides tools for simulating synthetic survival data using a variety of
methods, including kernel density estimation, parametric distribution fitting,
and bootstrap resampling techniques for a desired sample size.

Maria Thurow

RealSurvSim

Simulate Survival Data

Manasi Butee

Ina Dormuth

Christina Sauer

Marc Ditzhaus

Markus Pauly

data_simul_KDE function

<dl><dt>orig_vals</dt>
<dd>Numeric vector of values from the original dataset.</dd>
<dt>n</dt>
<dd>Integer, number of observations to simulate. If <code>NULL</code>, the function
simulates the same number of observations as in the original dataset. Defaults to <code>NULL</code>.</dd>
<dt>kernel</dt>
<dd>Character, specifying the kernel to be used for KDE. Defaults to "gaussian".</dd></dl>

Arguments

Kernel Density Estimation-based Data Simulation — data_simul_KDE

<dl>

<dt>orig_vals</dt>
<dd>Numeric vector of values from the original dataset.</dd>


<dt>n</dt>
<dd>Integer, number of observations to simulate. If <code>NULL</code>, the function
simulates the same number of observations as in the original dataset. Defaults to <code>NULL</code>.</dd>


<dt>kernel</dt>
<dd>Character, specifying the kernel to be used for KDE. Defaults to "gaussian".</dd>

</dl>

data_simul_KDE: Kernel Density Estimation-based Data Simulation

Description

Usage

Value

Arguments

Examples