# countDist: Compute Summary Statistics from Distance Sampling Data In AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c)

## Description

This function extracts various summary statistics from distance sampling data of various `unmarkedFrame` and `unmarkedFit` classes.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```countDist(object, plot.freq = TRUE, plot.distance = TRUE, ...) ## S3 method for class 'unmarkedFrameDS' countDist(object, plot.freq = TRUE, plot.distance = TRUE, ...) ## S3 method for class 'unmarkedFitDS' countDist(object, plot.freq = TRUE, plot.distance = TRUE, ...) ## S3 method for class 'unmarkedFrameGDS' countDist(object, plot.freq = TRUE, plot.distance = TRUE, ...) ## S3 method for class 'unmarkedFitGDS' countDist(object, plot.freq = TRUE, plot.distance = TRUE, ...) ```

## Arguments

 `object` an object of various `unmarkedFrame` or `unmarkedFit` classes containing distance sampling data. `plot.freq` logical. Specifies if the count data (pooled across seasons and distance classes) should be plotted. `plot.distance` logical. Specifies if the counts in each distance class should be plotted. `...` additional arguments passed to the function.

## Details

This function computes a number of summary statistics in data sets used for the distance sampling models of Royle et al. (2004) and Chandler et al. (2011).

`countDist` can take data frames of the `unmarkedFrameDS` and `unmarkedFrameGDS` classes as input. For convenience, the function can also extract the raw data from model objects of classes `unmarkedFitDS` and `unmarkedFitGDS`. Note that different model objects using the same data set will have identical values.

## Value

`countDist` returns a list with the following components:

 `count.table.full` a table with the frequency of each observed count pooled across distances classes. `count.table.seasons` a list of tables with the frequency of each season-specific count pooled across distance classes. `dist.sums.full` a table with the frequency of counts in each distance class across the entire sampling seasons. `hist.table.seasons` a list of tables with the frequency of counts in each distance class for each primary period. `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`). `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. `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.

## Author(s)

Marc J. Mazerolle

## References

Chandler, R. B., Royle, J. A., King, D. I. (2011) Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429–1435.

Royle, J. A., Dawson, D. K., Bates, S. (2004) Modeling abundance effects in distance sampling. Ecology 85, 1591–1597.

`covDiag`, `detHist`, `countHist`, `Nmix.chisq`, `Nmix.gof.test`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```##modified example from ?distsamp ## Not run: if(require(unmarked)){ data(linetran) ##format data ltUMF <- with(linetran, { unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4), siteCovs = data.frame(Length, area, habitat), dist.breaks = c(0, 5, 10, 15, 20), tlength = linetran\$Length * 1000, survey = "line", unitsIn = "m") }) ##compute descriptive stats from data object countDist(ltUMF) ##Half-normal detection function fm1 <- distsamp(~ 1 ~ 1, ltUMF) ##compute descriptive stats from model object countDist(fm1) } ## End(Not run) ```