convergence {surrosurv}

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 surrosurv.

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