Simulating SCR data — sim.capt • acre

Simulates SCR capture histories and associated additional information in the correct format for use with the function fit.acre. If fit is provided then no other arguments are required. Otherwise, at least traps, mask, and pars are needed.

Usage

sim.capt(
  fit,
  detfn,
  param,
  model = NULL,
  traps,
  control.mask = list(),
  session.cov = NULL,
  trap.cov = NULL,
  loc.cov = NULL,
  dist.cov = NULL,
  time.loc.cov = NULL,
  convert.loc2mask = list(),
  survey.length = NULL,
  ss.opts = NULL,
  cue.rates = NULL,
  n.sessions = NULL,
  n.rand = 1,
  random.location = FALSE,
  sound.speed = 331
)

Arguments

fit: an object generated from the model fitting function "fit.acre()" or the bootstrap process "boot.acre()". If fit is provided, then all other parameters will be taken from the fitted object, so no other parameters need to be provided.
detfn: A character string specifying the detection function to be used. Either "hn" (halfnormal), "hhn" (hazard halfnormal), "hr" (hazard rate), "th" (threshold), "lth" (log-link threshold), or "ss" (signal strength). If "ss" is used, signal strength information must be included in data. See the section below on parameter names for further details.
param: A named list. Component names are parameter names, and each component is the value of the associated parameter. A value for the parameter D, animal density (or call density, if it an acoustic survey) must always be provided, along with values for parameters associated with the chosen detection function and additional information type(s).
model: A list with named components. Each component name must match a parameter name. The component itself must be a formula specifying the relationship between covariates and the parameter. See the section on model specifications below.
traps: A matrix or a data frame with two columns, or a list of such matrices or data frames for a multi-session model. Each row provides the x- and y-coordinates of a detector location, in metres. For multi-session models, each component of the list provides detector locations for one session.
control.mask: A list specifying arguments for create.mask(), other than traps, that are used to create a mask object. The buffer should be provided, and, optionally, the spacing.
session.cov: A data frame containing session covariates. It must contain a column session, and additional columns for the session-level covariates. If spatiotemporal covariates are included using time.loc.cov, then a column time must be included, indicating when the session took place.
trap.cov: A data frame containing trap covariates. It must contain a column trap and additional columns for the trap-level covariates. The column session must be included for multisession data.
loc.cov: A data frame, or a list of data frames, containing spatial covariates. Data frames must contain columns x and y for x- and y-coordinates, in metres, and additional columns for spatial covariates measured at these locations. Missing NA values are allowed. Spatial interpolation is performed to produce covariate values at the mask point locations; see the section on spatial covariates below.
dist.cov: A list containing locations of features from which distances are calculated, and can be used as spatial covariates. Each component must be named after a feature, with a data frame containing columns x and y, recording the the location of a feature.
time.loc.cov: A list, or list of data frames, containing spatial covariates that change over time. The data frames must contain columns x, y and time, and additional columns for the spatiotemporal covariates. When these spatiotemporal covariates are provided, the column time must also appear in session.cov.
convert.loc2mask: A list to control the spatial interpolation method used to compute covariate values for mask locations based on data provided in loc.cov and time.loc.cov. See the section on spatial covariates below.
survey.length: A numeric vector or a scalar, containing the length of each session. If it is a scalar and there are multiple sessions, the value will be assigned to all sessions.
ss.opts: A list with information required to fit models that include signal strengths as auxiliary detection data. One component must be named cutoff, a detection threshold. An optional component is ss.link, which specifies the relationship between distance and the expected received signal strength. See the section below on signal strength models for further details.
cue.rates: A numeric vector containing the recorded cue rates of individuals, collected separately to the SCR survey. See the section on cue rate data, below.
n.sessions: An integer for the number of sessions.
sound.speed: A scalar, the speed of sound in metres per second. This argument is only used when time-of-arrival data are included in the captures data frame. Defaults to 330, the approximate speed of sound in air.