sim_data_p: Simulate biased (via over-dispersion in p) point count and...
In philipshirk/nmmsims: Simulate & Analyze N-mixture Model Data

Description Usage Arguments Value Examples

Generate point counts and/or distance sampling data that is biased by over-dispersion in the detection probability, p. Distance sampling data is created with each dataset, but it can just be ignored if you only want the point count data. This is a rudementary function. Currently it assumes constant density, detection probability, transects length, etc.

sim_data_p(
  n_sites = 50,
  n_samps = 6,
  lambda = 10,
  mean_det_prob = 0.42,
  sigma_beta_dist_p = 0.01,
  alpha = NA,
  beta = NA,
  W = 20
)

`n_sites`	number of sites (transects)
`n_samps`	number of samples (replicates) per site
`lambda`	mean abundance at every site
`mean_det_prob`	mean detection probability across all sites and replicates (different value drawn for each site and replicate)
`sigma_beta_dist_p`	SD of a beta distribution from which the realized detection probability is drawn (keep this below ~ 0.16)
`alpha`	parameter of the beta distribution. This is deterministically calculated from mean_det_prob and sigma_beta_dist_p if they are provided.
`beta`	parameter of the beta distribution. This is deterministically calculated from mean_det_prob and sigma_beta_dist_p if they are provided.
`W`	transect half-width (meters)

Returns a list of 4 items: 1. "true_N" is a matrix of true abundance values for each site (row) and sample (column). 2. "n_obs" is a matrix of the number of observed organisms at each site (row) and sample (column). 3. "y_list" is a list of vectors. Each vector holds the distance data for a single survey. 4. "inputs" is a list of input values.