Two data.frames must be provided to dsm. They are referred to as observation.data and segment.data.
If multiple detection functions are to be used, then a column named ddfobj must be included in observation.data and segment.data. This lets the model know which detection function each observation is from. These are numeric and ordered as the ddf.obj argument to dsm, e.g., dddf.obj=list(ship_ddf, aerial_ddf) means ship detections have ddfobj=1 and aerial detections have ddfobj=2 in the observation data.
When using mrds models that include mark-recapture components (currently independent observer and trial modes are supported) then the format of the observation data needs to be checked to ensure that observations are not duplicated. The observer column is also required in the observation.data frame.
| Independent observer mode | only unique observations (unique object IDs) are required. |
The segment.data table has the sample identifiers which define the segments, the corresponding effort (line length) expended and the environmental covariates that will be used to model abundance/density. observation.data provides a link table between the observations used in the detection function and the samples (segments), so that we can aggregate the observations to the segments (i.e. observation.data is a "look-up table" between the observations and the segments).
observation.data - the observation data.frame must have (at least) the following columns:
object |
unique object identifier |
Sample.Label |
the identifier for the segment that the observation occurred in |
size |
the size of each observed group (e.g 1 if all animals occurred individually) |
One can often also use observation.data to fit a detection function (so additional columns for detection function covariates are allowed in this table).
segment.data: the segment data.frame must have (at least) the following columns:
Effort |
the effort (in terms of length of the segment) |
Sample.Label |
identifier for the segment (unique!) |