CovStep {nmw}

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()