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 unmarkedFitOccuMS
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, ...)

# S3 method for unmarkedFrameOccuMS
detHist(object, ...)

For objects of classes `unmarkedFitOccu`

, `unmarkedFitOccuRN`

,

`unmarkedFitOccuFP`

, `unmarkedFitColExt`

,

`unmarkedFitOccuMS`

, `unmarkedFrameOccu`

,

`unmarkedFrameOccuFP`

, `unmarkedMultFrame`

, and

`unmarkedFrameOccuMS`

, `detHist`

returns a list with the
following components:

- hist.table.full
a table with the frequency of each observed detection history.

- hist.table.seasons
a list of tables with the frequency of each season-specific detection history.

- out.freqs
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.- out.props
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.- n.seasons
the number of seasons (primary periods) in the data set.

- n.visits.season
the maximum number of visits per season in the data set.

- n.species
the number of species in the data set.

For objects of classes `unmarkedFitOccuMulti`

and

`unmarkedFrameOccuMulti`

, `detHist`

returns a list with the
following components:

- hist.table.full
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.

- hist.table.species
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`

).- out.freqs
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`

).- out.props
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`

).- n.seasons
the number of seasons (primary periods) in the data set.

- n.visits.season
the maximum number of visits per season in the data set.

- n.species
the number of species in the data set.

- object
an object of various

`unmarkedFrame`

or`unmarkedFit`

classes containing detection history data.- ...
additional arguments passed to the function.

Marc J. Mazerolle

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), multispecies occupancy models (Rota et al. 2016), and multistate occupancy models (Nichols et al. 2007, MacKenzie et al. 2009).

`detHist`

can take data frames of the `unmarkedFrameOccu`

,
`unmarkedFrameOccuFP`

, `unmarkedMultFrame`

,
`unmarkedFrameOccuMulti`

, `unmarkedFrameOccuMS`

classes as
input. For convenience, the function can also extract the raw data
from model objects of classes `unmarkedFitColExt`

,
`unmarkedFitOccu`

, `unmarkedFitOccuFP`

,
`unmarkedFitOccuRN`

, `unmarkedFrameOccuMulti`

, and
`unmarkedFrameOccuMS`

. 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.

MacKenzie, D. I., Nichols, J. D., Seamans, M. E., Gutierrez,
R. J. (2009) Modeling species occurrence dynamics with multiple states
and imperfect detection. *Ecology* **90**, 823--835.

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.

Nichols, J. D., Hines, J. E., Mackenzie, D. I., Seamans, M. E.,
Gutierrez, R. J. (2007) Occupancy estimation and modeling with
multiple states and state uncertainty. *Ecology* **88**,
1395--1400.

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.

`covDiag`

, `countHist`

, `countDist`

,
`detTime`

, `mb.chisq`

, `mb.gof.test`

```
##data from Mazerolle (2015)
if (FALSE) {
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)
}
}
```

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