This function extracts various summary statistics from detection history
data of various unmarkedFrame and unmarkedFit classes.
detHist(object, …)# S3 method for unmarkedFitColExt
detHist(object, …)
# S3 method for unmarkedFitOccu
detHist(object, …)
# S3 method for unmarkedFitOccuFP
detHist(object, …)
# S3 method for unmarkedFitOccuRN
detHist(object, …)
# S3 method for unmarkedFitOccuMulti
detHist(object, …)
# S3 method for unmarkedFrameOccu
detHist(object, …)
# S3 method for unmarkedFrameOccuFP
detHist(object, …)
# S3 method for unmarkedMultFrame
detHist(object, …)
# S3 method for unmarkedFrameOccuMulti
detHist(object, …)
an object of various unmarkedFrame or unmarkedFit
classes containing detection history data.
additional arguments passed to the function.
For objects of classes unmarkedFitOccu, unmarkedFitOccuRN,
unmarkedFitOccuRN, unmarkedFitColExt,
unmarkedFrameOccu, unmarkedFrameOccuFP, and
unmarkedMultFrame, detHist returns a list with the
following components:
a table with the frequency of each observed detection history.
a list of tables with the frequency of each season-specific detection history.
a matrix where the rows correspond to each sampling
season and where columns consist of the number of sites sampled in
season \(t\) (sampled) and the number of sites with at
least one detection in season \(t\) (detected). For
multiseason data, the matrix includes the number of sites sampled in
season \(t - 1\) with colonizations observed in season \(t\)
(colonized), the number of sites sampled in season \(t -
1\) with extinctions observed in season \(t\) (extinct), the
number of sites sampled in season \(t - 1\) without changes
observed in season \(t\) (static), and the number of sites
sampled in season \(t\) that were also sampled in season \(t -
1\) (common). For multispecies data, out.freqs
presents for each species the number of sites sampled and the number
of sites with at least one detection.
a matrix where the rows correspond to each sampling
season and where columns consist of the proportion of sites in
season t with at least one detection (naive.occ). For
multiseason data, the matrix includes the proportion of sites
sampled in season \(t - 1\) with colonizations observed in season
\(t\) (naive.colonization), the proportion of sites
sampled in season \(t - 1\) with extinctions observed in season
\(t\) (naive.extinction), and the proportion of sites
sampled in season \(t - 1\) with no changes observed in season
\(t\). For multispecies data, out.props presents the
proportion of sites with a least one detection for each species.
the number of seasons (primary periods) in the data set.
the maximum number of visits per season in the data set.
the number of species in the data set.
For objects of classes unmarkedFitOccuMulti and unmarkedFrameOccuMulti, detHist returns a list with the following components:
a table with the frequency of each observed detection history. The species are coded with letters and follow the same order of presentation as in the other parts of the output.
a list of tables with the frequency of
each species-specific detection history. The last element of
hist.table.species features the number of sites with
co-occurrence of the different species (coOcc).
a matrix where the rows correspond to each species
and where columns consist of the number of sites sampled during the
season (sampled) and the number of sites with at least one
detection (detected).
a matrix where the rows correspond to each species
and where columns consist of the proportion of sites with at least
one detection during the season (naive.occ).
the number of seasons (primary periods) in the data set.
the maximum number of visits per season in the data set.
the number of species in the data set.
This function computes a number of summary statistics in data sets used for single-season occupancy models (MacKenzie et al. 2002), dynamic occupancy models (MacKenzie et al. 2003), Royle-Nichols models (Royle and Nichols 2003), false-positive occupancy models (Royle and Link 2006, Miller et al. 2011), and multispecies occupancy models (Rota et al. 2016.
detHist can take data frames of the unmarkedFrameOccu,
unmarkedFrameOccuFP, unmarkedMultFrame, and
unmarkedFrameOccuMulti classes as input. For convenience, the
function can also extract the raw data from model objects of classes
unmarkedFitColExt, unmarkedFitOccu,
unmarkedFitOccuFP, unmarkedFitOccuRN, and
unmarkedFrameOccuMulti. Note that different model objects
using the same data set will have identical values.
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A., Langtimm, C. A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248--2255.
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G., Franklin, A. B. (2003) Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200--2207.
Mazerolle, M. J. (2015) Estimating detectability and biological parameters of interest with the use of the R environment. Journal of Herpetology 49, 541--559.
Miller, D. A. W., Nichols, J. D., McClintock, B. T., Campbell Grant, E. H., Bailey, L. L. (2011) Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification. Ecology 92, 1422--1428.
Rota, C. T., Ferreira, M. A. R., Kays, R. W., Forrester, T. D., Kalies, E. L., McShea, W. J., Parsons, A. W., Millspaugh, J. J. (2016) A multispecies occupancy model for two or more interacting species. Methods in Ecology and Evolution 7, 1164--1173.
Royle, J. A., Link, W. A. (2006) Generalized site occupancy models allowing for false positive and false negative errors. Ecology 87, 835--841.
Royle, J. A., Nichols, J. D. (2003) Estimating abundance from repeated presence-absence data or point counts. Ecology 84, 777--790.
# NOT RUN {
##data from Mazerolle (2015)
# }
# NOT RUN {
data(bullfrog)
##detection data
detections <- bullfrog[, 3:9]
##load unmarked package
if(require(unmarked)){
##assemble in unmarkedFrameOccu
bfrog <- unmarkedFrameOccu(y = detections)
##compute descriptive stats from data object
detHist(bfrog)
##run model
fm <- occu(~ 1 ~ 1, data = bfrog)
##compute descriptive stats from model object
detHist(fm)
}
# }
Run the code above in your browser using DataLab