This function extracts various summary statistics from distance sampling
data of various `unmarkedFrame`

and `unmarkedFit`

classes.

```
countDist(object, plot.freq = TRUE, plot.distance = TRUE, cex.axis = 1,
cex.lab = 1, cex.main = 1, ...)
```# S3 method for unmarkedFrameDS
countDist(object, plot.freq = TRUE,
plot.distance = TRUE, cex.axis = 1, cex.lab = 1,
cex.main = 1, ...)

# S3 method for unmarkedFitDS
countDist(object, plot.freq = TRUE,
plot.distance = TRUE, cex.axis = 1, cex.lab = 1,
cex.main = 1, ...)

# S3 method for unmarkedFrameGDS
countDist(object, plot.freq = TRUE,
plot.distance = TRUE, cex.axis = 1,
cex.lab = 1, cex.main = 1, ...)

# S3 method for unmarkedFitGDS
countDist(object, plot.freq = TRUE,
plot.distance = TRUE, cex.axis = 1,
cex.lab = 1, cex.main = 1, ...)

# S3 method for unmarkedFrameDSO
countDist(object, plot.freq = TRUE,
plot.distance = TRUE, cex.axis = 1,
cex.lab = 1, cex.main = 1, plot.seasons = FALSE,
...)

# S3 method for unmarkedFitDSO
countDist(object, plot.freq = TRUE,
plot.distance = TRUE, cex.axis = 1,
cex.lab = 1, cex.main = 1, plot.seasons = FALSE,
...)

`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.

- dist.table.seasons
a list of tables with the frequency of counts in each distance class for each primary period.

- dist.names
a character string of labels for the distance classes.

- n.dist.classes
the number of distance classes.

- 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.

- 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 (pooled across seasons) should be plotted.

- cex.axis
expansion factor influencing the size of axis annotations on plots produced by the function.

- cex.lab
expansion factor influencing the size of axis labels on plots produced by the function.

- cex.main
expansion factor influencing the size of the main title above plots produced by the function.

- plot.seasons
logical. Specifies if the count data should be plotted for each distance class and season separately. This argument is only relevant for data collected across more than a single season.

- ...
additional arguments passed to the function.

Marc J. Mazerolle

This function computes a number of summary statistics in data sets used for the distance sampling models of Royle et al. (2004), Chandler et al. (2011), and distance-sampling versions of models of Dail and Madsen (2011) and Hostetler and Chandler (2015) based on Sollmann et al. (2015).

`countDist`

can take data frames of the
`unmarkedFrameDS`

, `unmarkedFrameGDS`

,
`unmarkedFrameDSO`

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

, `unmarkedFitGDS`

, and
`unmarkedFitDSO`

. Note that different model objects using the
same data set will have identical values.

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

Dail, D., Madsen, L. (2011) Models for estimating abundance from
repeated counts of an open population. *Biometrics* **67**,
577--587.

Hostetler, J. A., Chandler, R. B. (2015) Improved state-space models
for inference about spatial and temporal variation in abundance from
count data. *Ecology* **96**, 1713--1723.

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

Sollmann, R., Gardner, B., Chandler, R. B., Royle, J. A., Sillett,
T. S. (2015) An open-population hierarchical distance sampling
model. **Ecology** **96**, 325--331.

`covDiag`

, `detHist`

, `detTime`

,
`countHist`

, `Nmix.chisq`

,
`Nmix.gof.test`

```
##modified example from ?distsamp
if (FALSE) {
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)
}
}
```

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