Function computes the inverse of a matrix.

internal

Optimal experimental designs for both population and individual
studies based on nonlinear mixed-effect models. Often this is based on a
computation of the Fisher Information Matrix. This package was developed
for pharmacometric problems, and examples and predefined models are available
for these types of systems. The methods are described in Nyberg et al.
(2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004)
<doi:10.1016/S0169-2607(03)00073-7>.

Andrew C. Hooker

PopED

Population (and Individual) Optimal Experimental Design

Marco Foracchia

Eric Stroemberg

Martin Fink

Giulia Lestini

Sebastian Ueckert

Joakim Nyberg

inv function

<dl><dt>mat</dt>
<dd>A matrix</dd>
<dt>method</dt>
<dd>Which method to use. 1 is Cholesky <code>chol2inv(chol(mat)</code>, 
2 is using <code>solve(mat)</code> and 3 is the Moore-Penrose generalized inverse (pseudoinverse).</dd>
<dt>tol</dt>
<dd>The tolerance at which we should identify a singular value as zero (used in pseudoinverse calculation).</dd>
<dt>pseudo_on_fail</dt>
<dd>If another method fails should the Moore-Penrose generalized inverse (pseudoinverse) be used?</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Compute the inverse of a matrix — inv

<dl>

<dt>mat</dt>
<dd>A matrix</dd>


<dt>method</dt>
<dd>Which method to use. 1 is Cholesky <code>chol2inv(chol(mat)</code>, 
2 is using <code>solve(mat)</code> and 3 is the Moore-Penrose generalized inverse (pseudoinverse).</dd>


<dt>tol</dt>
<dd>The tolerance at which we should identify a singular value as zero (used in pseudoinverse calculation).</dd>


<dt>pseudo_on_fail</dt>
<dd>If another method fails should the Moore-Penrose generalized inverse (pseudoinverse) be used?</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

inv: Compute the inverse of a matrix

Description

Usage

Value

Arguments