p_dist_table {Distance} | R Documentation |
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.
object |
fitted detection function |
bins |
how the results should be binned |
proportion |
should proportions be returned as well as counts? |
Because dht
uses a Horvitz-Thompson-like estimator, abundance
estimates can be sensitive to errors in the estimated probabilities. The
estimator is based on ∑ 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.
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
This function is located in the mrds
package but the documentation
is provided here for easy access.
David L Miller
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.
## Not run: # 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) ## End(Not run)