R: Organize data for the distance sampling model of Royle et al....

unmarkedFrameDS {unmarked} | R Documentation

## Organize data for the distance sampling model of Royle et al. (2004) fit by distsamp

### Description

Organizes count data along with the covariates and metadata. This S4 class is required by the data argument of `distsamp`

### Usage

    unmarkedFrameDS(y, siteCovs=NULL, dist.breaks, tlength, survey,
        unitsIn, mapInfo)

### Arguments

`y` | An RxJ matrix of count data, where R is the number of sites (transects) and J is the number of distance classes.
`siteCovs` | A `data.frame` of covariates that vary at the site level. This should have R rows and one column per covariate
`dist.breaks` | vector of distance cut-points delimiting the distance classes. It must be of length J+1.
`tlength` | A vector of length R containing the trasect lengths. This is ignored when survey="point".
`survey` | Either "point" or "line" for point- and line-transects.
`unitsIn` | Either "m" or "km" defining the measurement units for both `dist.breaks` and `tlength`

.

`mapInfo` | Currently ignored

### Details

unmarkedFrameDS is the S4 class that holds data to be passed to the `distsamp` model-fitting function.

### Value

an object of class unmarkedFrameDS

### Note

If you have continuous distance data, they must be "binned" into discrete distance classes, which are delimited by dist.breaks.

### References

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

### See Also

`unmarkedFrame-class`, `unmarkedFrame`, `distsamp`

### Examples

    # Fake data
    R <- 4 # number of sites
    J <- 3 # number of distance classes

    db <- c(0, 10, 20, 30) # distance break points

    y <- matrix(c(
       5,4,3, # 5 detections in 0-10 distance class at this transect
       0,0,0,
       2,1,1,
       1,1,0), nrow=R, ncol=J, byrow=TRUE)
    y

    site.covs <- data.frame(x1=1:4, x2=factor(c('A','B','A','B')))
    site.covs

    umf <- unmarkedFrameDS(y=y, siteCovs=site.covs, dist.breaks=db, survey="point",
        unitsIn="m")            # organize data
    umf                         # look at data
    summary(umf)                # summarize
    fm <- distsamp(~1 ~1, umf)  # fit a model

[Package unmarked version 1.0.0 Index]