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 |                          VARIANCE-COVARIANCE                    |
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 MEANING: Variance-covariance matrix
 CONTEXT: NONMEM output

 DISCUSSION:
 NONMEM  output  refers  to  "VARIANCE-COVARIANCE"  (or   "COVARIANCE")
 matrices in three contexts:

 OMEGA and SIGMA
      OMEGA is the variance-covariance matrix for the first level  ran-
      dom effects ETA.  SIGMA is the variance-covariance matrix for the
      second-level random effects EPSILON.

      Error  messages  referring  to  "VARIANCE-COVARIANCE  COMPONENTS"
      arise  from  difficulties  with  the  initial  estimates of OMEGA
      and/or SIGMA, either those supplied by the user, or when no esti-
      mates are supplied, with those obtained in NONMEM's Initial Esti-
      mates Step.  Initial estimates of both OMEGA and  SIGMA  must  be
      positive definite.

 VAR-COV
      Error messages referring to "VAR-COV" (in particular,  "ESTIMATED
      TO  BE SINGULAR" or "ESTIMATED TO BE 0") arise when the variance-
      covariance matrix for an individual's data is non-positive defin-
      ite.  For example, with the error model
      Y=F+F*EPS(1)
      predicted values for some observations (i.e. values of F) may  be
      zero or close to 0.  Then variances for these observations (which
      are proportional to F**2) are also zero, and this gives  rise  to
      such an error message.

 COVARIANCE MATRIX OF ESTIMATE
      This variance-covariance matrix refers  to  an  estimate  of  the
      variability  and covariability of the parameter estimates.  It is
      computed in the Covariance Step, from the R and  S  matrices.  An
      error message from the Covariance Step, stating that one of these
      two matrices is non-positive definite, indicates that the minimi-
      zation   procedure   did  not  find  a  true  or  unique  minimum
      (See covariance).

REFERENCES: Guide I Section C.3.5.2
REFERENCES: Guide IV Section III.B.10, III.B.11
REFERENCES: Guide V Section 5.4, 13.4.3


  
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