dshm_prep: Prepares data for Hurdle model fitting

In FilippoFranchini/dshm: Density surface modelling unsing Hurdle approach for zero inflation

Description

Prepares data for Hurdle model fitting

Usage

dshm_prep(det.fn, obsdata, segdata, group = FALSE, strip.width = NULL)

Arguments

det.fn	Detection function fitted by ds.
obsdata	Dataframe object containing 4 columns: (1) 'Sample.Label' (i.e. label for segments), (2) 'size' for cluster size, (3) 'distance' (in km) for perpendicular distance of sighting from the transect line, and (4) 'Effort' for segment length (in km).
segdata	Dataframe object with at least 3 columns: (1) 'Transect.Label' (i.e. label for transects), (2) 'Sample.Label' (i.e. label for segments), and (3) 'Effort' for segment length (in km). It may also contain additional columns with relevant habitat covariates specific to each segment that will be fed into the spatial model.
group	If TRUE group abundance is estimated (i.e. 'size' = 1).
strip.width	Transect strip width to calculate segment area if area is not provided.

Value

Input dataframe 'segdata' with an additional column for animal abundance ('abund') corrected by the detection probability estimated by ds. If they are not included, a column for presence-absence ('pa') is added together with one for segment area (Effort*w, i.e. the strip width estimated by ds).

Author(s)

Filippo Franchini [e-mail]

FilippoFranchini/dshm documentation built on April 25, 2020, 9:40 p.m.