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Prepare data for RSF analyses for wild and semi-domesticated reindeer within SAM

data_prep_rsf_habitat_rein.Rd

This function takes in the already annotated data for RSF with either wild or semi-domesticated reindeer and further prepare columns and variables for the model fitting procedure. The function is suited for point resource selection functions (RSF) only.

Usage

data_prep_rsf_habitat_rein(
  dat,
  season,
  prediction = FALSE,
  fixwind = TRUE,
  land_cover_factor = FALSE,
  land_cover = c("norut_smd", "norut", "smd", "nmd")[1],
  ref_landcover = "heathland",
  include_zoi_nearest = FALSE,
  species = c("wrein", "trein")[1],
  prefix = "",
  reference_year = lubridate::year(lubridate::now()),
  use_binary_vars_as_categorical = FALSE
)

Arguments

dat

[data.frame]
Data set annotated with environmental covariates, almost ready for analysis.

season

[string]{"sum", "cal", "win"}
Season of interest. One of "sum", "cal", or "win".

prediction

[logical(1)=FALSE]
Additional changes that should only be done in the grid for prediction.

land_cover_factor

[logical(1)=FALSE]
Logical variable stating whether the land cover variable should be treated as a factor (one single column with land cover type names) or not (as multiple columns as dummy variables).

land_cover

[string(1)="norut_smd"]{"norut_smd", "norut", "smd", "nmd"}
Which land cover map should be used for analysis. It prepares the corresponding classes, using the ref_landcover as the reference class.

ref_landcover

[string="heathland"]
Reference class for land cover variables.

include_zoi_nearest

[logical(1)=FALSE]
If TRUE, variables representing the zone of influence (ZOI) of the nearest feature are also computed, based on the distance to the nearest features. Only relevant if prediction = TRUE.

species

[string="wrein"]{"wrein", "trein"}
The species/management system for which we should prepare the data for. Either "wrein" for wild reindeer or "trein" for semi-domesticated reindeer. It only implies some differences in data which are particular to each of the species/management systems.

prefix

[string=""]
Prefix to be added to the variable/column names of the dataset. Default is "", but it could be e.g. "startpt_", "endpt_", or "along_" for variables extracted at the starting or ending point or along steps.

use_binary_vars_as_categorical

[logical(1)=FALSE]
If TRUE, binary variables (e.g. lakes, reservoirs) should be treated as categorical. Otherwise, they are treated as numerical.