Distance (version 1.0.9)

p_dist_table: Distribution of probabilities of detection

Description

Generate a table of frequencies of probability of detection from a detection function model. This is particularly useful when employing covariates, as it can indicate if there are detections with very small detection probabilities that can be unduly influential when calculating abundance estimates.

Value

a data.frame with probability bins, counts and (optionally) proportions. The object has an attribute p_range which contains the range of estimated detection probabilities

Arguments

object

fitted detection function

bins

how the results should be binned

proportion

should proportions be returned as well as counts?

Author

David L Miller

Details

Because dht uses a Horvitz-Thompson-like estimator, abundance estimates can be sensitive to errors in the estimated probabilities. The estimator is based on \(\sum 1/ \hat{P}_a(z_i)\), which means that the sensitivity is greater for smaller detection probabilities. As a rough guide, we recommend that the method be not used if more than say 5% of the \(\hat{P}_a(z_i)\) are less than 0.2, or if any are less than 0.1. If these conditions are violated, the truncation distance w can be reduced. This causes some loss of precision relative to standard distance sampling without covariates.

References

Marques, F.F.C. and S.T. Buckland. 2004. Covariate models for the detection function. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.

Examples

if (FALSE) {
# example using a model for the minke data
data(minke)
# fit a model
result <- ds(minke, formula=~Region.Label)
# print table
p_dist_table(result)
# with proportions
p_dist_table(result, proportion=TRUE)
}