bootsum.p: Summarise detection probability bootstrap results.

In david-borchers/hmltm: Line transect estimation with hidden Markov availability models

Summarise detection probability bootstrap results.

Description

Uses bootstrap results from bootstrap.p.with.Et or bootstrap.p.with.hmm to work out bootstrap means, variance estimates, CVs and confidence intervals.

Usage

bootsum.p(bs, probs = c(0.025, 0.975), pcut = 0)

Arguments

bs	output from bootstrap.p.with.Et or bootstrap.p.with.hmm.
probs	lower and upper percentile points for confidence interval reporting.
pcut	minimum estimated detection probability to use. This is a quick and dirty method to robustify against small detection probability estimates skewing the distribution of 1/phat badly for small samples. It is ad-hoc. If you use it, do histogram of $bs$phat to see if there is a reasonable cutpoint.

Value

Returns a list with elements

• nboot: number of bootstrap estimates used in constructing bootstrap statistics.

• nbad: number of bad estimates excluded from results.

• parcov: parameter estimate variance-covariance matrix.

• parcorr: parameter estimate correlation matrix.

• bests: bootstrap estimate statistics, comprising meand, standard error, percentage CV and confidence interval limits for: (a) estimated mean detection probability, (a) 1/(estimated mean detection probability), (c) estimated p(0), ad (d) detection hazard function parameters.

david-borchers/hmltm documentation built on Oct. 29, 2023, 9:07 p.m.