CovStep: Covariance Step

In nmw: Understanding Nonlinear Mixed Effects Modeling for Population Pharmacokinetics

Covariance Step

Description

It calculates standard errors and various variance matrices with the e$FinalPara after estimation step.

Usage

CovStep()

Details

Because EstStep uses nonlinear optimization, covariance step is separated from estimation step. It calculates variance-covariance matrix of estimates in the original scale.

Value

Time	consumed time
Standard Error	standard error of the estimates in the order of theta, omega, and sigma
Covariance Matrix of Estimates	covariance matrix of estimates in the order of theta, omega, and sigma. This is inverse(R) x S x inverse(R) by default.
Correlation Matrix of Estimates	correlation matrix of estimates in the order of theta, omega, and sigma
Inverse Covariance Matrix of Estimates	inverse covariance matrix of estimates in the order of theta, omega, and sigma
Eigen Values	eigen values of covariance matrix
R Matrix	R matrix of NONMEM, the second derivative of log likelihood function with respect to estimation parameters
S Matrix	S matrix of NONMEM, sum of individual cross-product of the first derivative of log likelihood function with respect to estimation parameters

Author(s)

Kyun-Seop Bae <[e-mail]>

References

NONMEM Users Guide

See Also

EstStep, InitStep

Examples

# Only after InitStep and EstStep
#CovStep()

nmw documentation built on May 31, 2023, 6:24 p.m.