cloned.fit {openCR}

Cloning to Evaluate Identifiability

Description

The identifiability of parameters may be examined by refitting a model with cloned data (each capture history replicated nclone times). For identifiable parameters the estimated variances are proportional to 1/nclone.

Usage

cloned.fit(object, nclone = 100, newdata = NULL, linkscale = FALSE)

Arguments

object

previously fitted openCR object

nclone

integer number of times to replicate each capture history

newdata

optional dataframe of values at which to evaluate model

linkscale

logical; if TRUE then comparison uses SE of linear predictors

Details

The key output is the ratio of SE for estimates from the uncloned and cloned datasets, adjusted for the level of cloning (nclone). For identifiable parameters the ratio is expected to be 1.0.

Cloning is not implemented for spatial models.

The comparison may be done either on the untransformed scale (using approximate SE) or on the link scale.

Value

Dataframe with columns* –

estimate

original estimate

SE.estimate

original SE

estimate.xxx

cloned estimate (xxx = nclone)

SE.estimate.xxx

cloned SE

SE.ratio

SE.estimate / SE.estimate.xxx / sqrt(nclone)

* ‘estimate’ becomes ‘beta’ when linkscale = TRUE.

References

Lele, S.R., Nadeem, K. and Schmuland, B. (2010) Estimability and likelihood inference for generalized linear mixed models using data cloning. Journal of the American Statistical Association 105, 1617–1625.

See Also

openCR.fit

Examples

## Not run: 
fit <- openCR.fit(dipperCH)
cloned.fit(fit)

## End(Not run)