mlr2

Performs multiple linear regression and computes influence diagnostics
including DFFITS, DFBETAS, Cook's Distance, COVRATIO, and R-Student.

This shows how NONMEM(R) software works. NONMEM's classical
estimation methods like 'First Order(FO) approximation', 'First Order
Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
Additionally, provides functions for post-run processing of NONMEM output
files, generating comprehensive PDF diagnostic reports including objective
function value analysis, parameter estimates, prediction diagnostics,
residual diagnostics, empirical Bayes estimate (EBE) analysis, input data
summary, and individual pharmacokinetic parameter distributions.

Kyun-Seop Bae

Understanding Nonlinear Mixed Effects Modeling for Population
Pharmacokinetics

mlr2 function

<dl><dt>y</dt>
<dd>numeric vector of response</dd>
<dt>x.raw</dt>
<dd>data.frame of predictors</dd>
<dt>standardize</dt>
<dd>integer, standardization method (0=none, 1=center, 2=scale by mean, 3=z-score)</dd></dl>

Arguments

Multiple Linear Regression with Influence Diagnostics — mlr2

<dl>

<dt>y</dt>
<dd>numeric vector of response</dd>


<dt>x.raw</dt>
<dd>data.frame of predictors</dd>


<dt>standardize</dt>
<dd>integer, standardization method (0=none, 1=center, 2=scale by mean, 3=z-score)</dd>

</dl>

mlr2: Multiple Linear Regression with Influence Diagnostics

Description

Usage

Value

Arguments