Number of Iterations

Extracts the number of IRLS iterations performed for a VGLM object.

niters(object, ...)
niters.vlm(object, history = FALSE, ...)

Arguments

object

A vglm object. Currently a vgam object is accepted but the correct value is not returned.

history

Logical, if TRUE it returns the convergence history with respect to the criterion, e.g., vglm.control()[["criterion"]].

...

Currently unused.

Details

The number of iteratively reweighted least squares (IRLS) iterations needed for convergence (or non-convergence) does say something about the model. Since Fisher scoring has a linear convergence rate in general, it should take no more than 10 iterations, say, for successful convergence. Much more indicates potential problems, e.g., a large disagreement between data and the specified model.

Value

A non-negative integer by default. If history = TRUE then a matrix.

Note

Step-halving may or may not affect the answer.

See also

vglm, vglm.control.

Examples

fit <- vglm(rpois(9, 2) ~ 1, poissonff, crit = "c")
niters(fit)
#> [1] 5
niters(fit, history = TRUE)
#>      (Intercept)
#> [1,]   0.9495500
#> [2,]   0.7663708
#> [3,]   0.7473967
#> [4,]   0.7472144
#> [5,]   0.7472144