convergence {surrosurv} | R Documentation |
Assesses the convergence of fitted models for surrogacy evaluation
Description
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.
Usage
## S3 method for class 'surrosurv'
convals(x, ...)
## S3 method for class 'surrosurv'
convergence(x, kkttol = 1e-2, kkt2tol = 1e-8, ...)
Arguments
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) |
Value
The function convals()
returns a matrix with one row per model and three columns,
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
.
Author(s)
NA