Organize data for the combined distance and removal point-count model of Amundson et al. (2014) fit by gdistremoval

Organize data for the combined distance and removal point-count model of Amundson et al. (2014) fit by gdistremoval

unmarkedFrameGDR(yDistance, yRemoval, numPrimary=1, siteCovs=NULL, obsCovs=NULL, 
                   yearlySiteCovs=NULL, dist.breaks, unitsIn, period.lengths=NULL)

Arguments

yDistance	An MxTJ matrix of count data, where M is the number of sites (points), T is the number of primary periods (can be 1) and J is the number of distance classes
yRemoval	An MxTJ matrix of count data, where M is the number of sites (points), T is the number of primary periods (can be 1) and J is the number of time removal periods
numPrimary	Number of primary periods in the dataset
siteCovs	A `data.frame` of covariates that vary at the site level. This should have M rows and one column per covariate
obsCovs	A `data.frame` of covariates that vary at the site level. This should have MxTJ rows and one column per covariate. These covariates are used only by the removal part of the model
yearlySiteCovs	A `data.frame` of covariates that vary by site and primary period. This should have MxT rows and one column per covariate
dist.breaks	vector of distance cut-points delimiting the distance classes. It must be of length J+1
unitsIn	Either "m" or "km" defining the measurement units for `dist.breaks`
period.lengths	Optional vector of time lengths of each removal period. Each value in the vector must be a positive integer, and the total length of the vector must be equal to the number of removal periods J. If this is not provided (the default), then all periods are assumed to have an equal length of 1 time unit

Details

unmarkedFrameGDR is the S4 class that holds data to be passed to the gdistremoval model-fitting function.

Value

an object of class unmarkedFrameGDR

Note

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

References

Amundson, C.L., Royle, J.A. and Handel, C.M., 2014. A hierarchical model combining distance sampling and time removal to estimate detection probability during avian point counts. The Auk 131: 476-494.

Author

Ken Kellner contact@kenkellner.com