| vcov.riley {metamisc} | R Documentation |
Calculate Variance-Covariance Matrix for a Fitted Riley Model Object
Description
Returns the variance-covariance matrix of the main parameters of a fitted model object.
Usage
## S3 method for class 'riley'
vcov(object, ...)Arguments
object |
a |
... |
arguments to be passed on to other functions |
Details
The variance-covariance matrix is obtained from the inverse Hessian as provided by optim.
Value
A matrix of the estimated covariances between the parameter estimates in the Riley model: logit of sensitivity (mu1), logit of false positive rate (mu2), additional variation of mu1 beyond sampling error (psi1), additional variation of mu2 beyond sampling error (psi2) and a transformation of the correlation between psi1 and psi2 (rhoT). The original correlation is given as inv.logit(rhoT)*2-1.
Note
A warning message is casted when the Hessian matrix contains negative eigenvalues. This implies that the identified minimum for the (restricted) negative log-likelihood is a saddle point, and that the solution is therefore not optimal.
Author(s)
Thomas Debray <thomas.debray@gmail.com>
References
Riley, RD., Thompson, JR., & Abrams, KR. (2008). “An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown.” Biostatistics, 9, 172–186.