Options for the importance-sampling covariance in setCov()

Used by setCov(fit, "imp"), which runs frozen importance-sampling EM iterations at the fit's estimates.

Usage

impCovControl(nIter = 1L, isample = 300L, impSeed = 42L)

Arguments

nIter

frozen EM iterations (0 is an E-step-only evaluation)

isample

Number of importance samples drawn per subject per iteration (NONMEM ISAMPLE). Either a single count used for every subject, or a vector of length `nsub` giving a count **per subject**.

Per-subject counts are the NM7 Technical Guide's own remedy for poor coverage (its derivation is Gaussian throughout and never mentions a t proposal): a subject whose weights are badly behaved can be given more samples without charging every other subject for them. Note this treats the symptom rather than the cause – more draws from a proposal whose tails are too light still gives weights with infinite variance, which `fit$env$impPsisK` will show. See `df` for the shape-based remedy.

impSeed

Base seed for the per-subject thread-safe (threefry) RNG streams; results are reproducible and independent of the thread count.

Value

impCovControl object

Author

Matt Fidler

Examples

impCovControl(isample = 1000)
#> $nIter
#> [1] 1
#> 
#> $isample
#> [1] 1000
#> 
#> $impSeed
#> [1] 42
#> 
#> attr(,"class")
#> [1] "impCovControl"