CovStep: Covariance Step

Description

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

Usage

CovStep()

Arguments

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

Kyun-Seop Bae <k@acr.kr>

Details

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

References

NONMEM Users Guide

Examples

Run this code

# Only after InitStep and EstStep
#CovStep()

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