This function evaluates whether the fitted models for evaluating the surrogacy of a candidate endpoint have converged. Convergence is assessed by checking whether the maximum gradient is small enough, and whether the Hessian matrix and the variance-covariance matrix of random treatment effects are positive definite.
1 2 3 4
The fitted models, an object of class
The tolerance threshold for the assessing whether the maximum (absolute) scaled gradient is small enough.
The tolerance threshold for checking whether the Hessian matrix and the variance-covariance matrix of random treatment effects are positive definite. The threshold is for the minimum of the eigenvalues.
Further parameters (not implemented)
convals() returns a matrix with one row per model and three columans,
reporting the values of the maximum scaled gradient (
of the minimum eigenvalue of the Hessian matrix (
of the minimum eigenvalue of the estimated variance-covariance matrix
of random treatment effects (
convergence() returns a matrix with the same structure as
FALSE values for the test of the results of
against the given thresholds
Federico Rotolo [aut], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr, cre]
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