gdistremovalunmarkedFrameGDR.RdOrganize 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)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
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
Number of primary periods in the dataset
A data.frame of covariates that vary at the
site level. This should have M rows and one column per covariate
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
A data.frame of covariates that vary
by site and primary period. This should have MxT rows and one column per covariate
vector of distance cut-points delimiting the distance classes. It must be of length J+1
Either "m" or "km" defining the measurement units for
dist.breaks
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
unmarkedFrameGDR is the S4 class that holds data to be passed
to the gdistremoval model-fitting function.
an object of class unmarkedFrameGDR
If you have continuous distance data, they must be "binned" into discrete distance classes, which are delimited by dist.breaks.
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.