simDat19: Simulate data for Chapter 19: Occupancy model

Description

Simulate detection/nondetection data of Chiltern gentians

Usage

simDat19(
  nSites = 150,
  nVisits = 3,
  alpha.occ = 0,
  beta.occ = 2,
  alpha.p = 0,
  beta.p = -3
)

Value

A list of simulated data and parameters.

nSites: Number of sites
nVisits: Number replicate visits per site
alpha.occ: Occupancy intercept
beta.occ: Occupancy slope
alpha.p: Detection probability intercept
beta.p: Detection probability slope
humidity: Humidity covariate
occ.prob: Probability of occupancy at each site
z: True occupancy state at each site
true_Nz: True number of occupied sites
lp: Linear predictor for detection
p: Probability of detection at each site
y: Simulated detection/non-detection data
obs_Nz: Observed number of occupied sites

Arguments

nSites: Number of sites
nVisits: Number of replicate visits per site
alpha.occ: Occupancy intercept
beta.occ: Occupancy slope
alpha.p: Detection probability intercept
beta.p: Detection probability slope

Author

Marc Kéry

Examples

Run this code

str(dat <- simDat19())             # Implicit default arguments
str(dat <- simDat19(nSites = 150, nVisits = 3, alpha.occ = 0, beta.occ = 2,
  alpha.p = 0, beta.p = -3))       # Explicit default arguments
str(dat <- simDat19(nSites = 500)) # More sites
str(dat <- simDat19(nVisits = 1))  # Single-visit data
str(dat <- simDat19(nVisits = 20)) # 20 visits, will yield cumulative detection prob of about 1
str(dat <- simDat19(alpha.occ = 2))# Much higher occupancy
str(dat <- simDat19(beta.occ = 0)) # No effect of humidity on occupancy
str(dat <- simDat19(beta.p = 3))   # Positive effect of humidity on detection
str(dat <- simDat19(beta.p = 0))   # No effect of humidity on detection

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