Distribution of probabilities of detection
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.
p_dist_table(object, bins = seq(0, 1, by = 0.1), proportion = FALSE)
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
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:
# try out the tee data
data(book.tee.data)
egdata <- book.tee.data$book.tee.dataframe
# fit model with covariates
result <- ddf(dsmodel = ~mcds(key = "hn", formula = ~sex+size),
data = egdata[egdata$observer==1, ], method = "ds",
meta.data = list(width = 4))
# print table
p_dist_table(result)
# with proportions
p_dist_table(result, proportion=TRUE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.