CovStep | R Documentation |
It calculates standard errors and various variance matrices with the e$FinalPara
after estimation step.
CovStep()
Because EstStep
uses nonlinear optimization, covariance step is separated from estimation step.
It calculates variance-covariance matrix of estimates in the original scale.
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 |
Kyun-Seop Bae <k@acr.kr>
NONMEM Users Guide
EstStep
, InitStep
# Only after InitStep and EstStep
#CovStep()
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