Description Usage Arguments Value Author(s)

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 | ```
## S3 method for class 'surrosurv'
convals(x, ...)
## S3 method for class 'surrosurv'
convergence(x, kkttol = 1e-2, kkt2tol = 1e-8, ...)
``` |

`x` |
The fitted models, an object of class |

`kkttol` |
The tolerance threshold for the assessing whether the maximum (absolute) scaled gradient is small enough. |

`kkt2tol` |
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) |

The function `convals()`

returns a matrix with one row per model and three columans,
reporting the values of the maximum scaled gradient (`maxSgrad`

),
of the minimum eigenvalue of the Hessian matrix (`minHev`

), and
of the minimum eigenvalue of the estimated variance-covariance matrix
of random treatment effects (`minREev`

).
The function `convergence()`

returns a matrix with the same structure as `convals()`

,
with `TRUE`

/`FALSE`

values for the test of the results of `convals()`

against the given thresholds `kkttol`

and `kkt2tol`

.

Federico Rotolo [aut, cre], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr]

surrosurv documentation built on Sept. 27, 2017, 9:04 a.m.

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