discreteVPCTTE

interpol.locf

interpol.lin

This function provides VPC plots for time-to-event data models (work in progress)

plot

The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Emmanuelle Comets

saemix

Stochastic Approximation Expectation Maximization (SAEM)
Algorithm

Audrey Lavenu

Marc Lavielle

Belhal Karimi

Maud Delattre

Marilou Chanel

Johannes Ranke

Sofia Kaisaridi

Lucie Fayette

discreteVPCTTE function

<dl><dt>object</dt>
<dd>an saemixObject object returned by the <code>saemix</code> function.
The object must include simulated data under the empirical design,
using the model and estimated parameters from a fit, produced via the
<code>simulateDiscreteSaemix</code> function</dd>
<dt>ngrid</dt>
<dd>number of grid points on the X-axis to extrapolate the KM-VPC</dd>
<dt>interpolation.method</dt>
<dd>method to use for the interpolation of the KM for the simulated datasets. Available methods are
"step": the value of the survival function for a given grid point is set to the value of the last time
"lin": a linear approximation is used between two consecutive times (defaults to "step")</dd>
<dt>verbose</dt>
<dd>whether to print messages (defaults to FALSE)</dd>
<dt>...</dt>
<dd>additional arguments, used to pass graphical options (to be implemented, currently not available)</dd></dl>

Arguments

Emmanuelle Comets <a href="/link/emmanuelle.comets%40inserm.fr?package=saemix&version=3.3" data-mini-rdoc="saemix::emmanuelle.comets@inserm.fr">emmanuelle.comets@inserm.fr</a>

Author

VPC for time-to-event models — discreteVPCTTE

<dl>

<dt>object</dt>
<dd>an saemixObject object returned by the <code>saemix</code> function.
The object must include simulated data under the empirical design,
using the model and estimated parameters from a fit, produced via the
<code>simulateDiscreteSaemix</code> function</dd>


<dt>ngrid</dt>
<dd>number of grid points on the X-axis to extrapolate the KM-VPC</dd>


<dt>interpolation.method</dt>
<dd>method to use for the interpolation of the KM for the simulated datasets. Available methods are
"step": the value of the survival function for a given grid point is set to the value of the last time
"lin": a linear approximation is used between two consecutive times (defaults to "step")</dd>


<dt>verbose</dt>
<dd>whether to print messages (defaults to FALSE)</dd>


<dt>...</dt>
<dd>additional arguments, used to pass graphical options (to be implemented, currently not available)</dd>

</dl>

Emmanuelle Comets <a href='mailto:emmanuelle.comets@inserm.fr'>emmanuelle.comets@inserm.fr</a>

discreteVPCTTE: VPC for time-to-event models

Description

Usage

Arguments

Author

Details

See Also