data.fn {AHMbook} R Documentation

Simulate count data under a binomial N-mixture model

Description

Function to simulate point counts replicated at M sites during J occasions. Population closure is assumed for each site. Expected abundance may be affected by elevation (elev), forest cover (forest) and their interaction. Expected detection probability may be affected by elevation, wind speed (wind) and their interaction. Used in AHM1 to illustrate how a data set under some specific model of interest can be simulated.

Usage

data.fn(M = 267, J = 3, mean.lambda = 2, beta1 = -2, beta2 = 2, beta3 = 1,
mean.detection = 0.3, alpha1 = 1, alpha2 = -3, alpha3 = 0, show.plot = TRUE)

Arguments

 M Number of spatial replicates (sites) J Number of temporal replicates (occasions) mean.lambda Mean abundance at value 0 of abundance covariates beta1 Main effect of elevation on abundance beta2 Main effect of forest cover on abundance beta3 Interaction effect on abundance of elevation and forest cover mean.detection Mean detection prob. at value 0 of detection covariates alpha1 Main effect of elevation on detection probability alpha2 Main effect of wind speed on detection probability alpha3 Interaction effect on detection of elevation and wind speed show.plot if TRUE, plots of the data will be displayed; set to FALSE if you are running simulations

Value

A list with the input arguments and the following additional elements:

 elev Scaled elevation (a vector of length M) forest Scaled forest cover (a vector of length M) wind Scaled wind speed (an M x J matrix) lambda Expected number of individuals at each site (a vector of length M) N Realized number of individuals at each site (a vector of length M) p Probability of detection for each survey (an M x J matrix) C The number of detections for each survey (an M x J matrix) Ntotal Total abundance, sum(N) psi.true True occupancy in sample summaxC Sum of max counts (all sites) psi.obs Observed occupancy in sample

Note

The colors used for points in some of the plots indicate different temporal replicates.

Author(s)

Marc Kéry & Andy Royle

References

Kéry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology AHM1 - 4.3.

Examples

# Generate a simulated data set with default arguments and look at the structure:
tmp <- data.fn()
str(tmp)

str(data.fn(J = 2))              # Only 2 surveys
str(data.fn(J = 1))              # No temporal replicate
str(data.fn(M = 1, J = 100))     # No spatial replicates, but 100 counts
str(data.fn(beta3 = 1))          # With interaction elev-wind on p
str(data.fn(M = 267, J = 3, mean.lambda = 2, beta1 = -2, beta2 = 2, beta3 = 1,
mean.detection = 1))           # No obs. process (i.e., p = 1, perfect detection)
str(data.fn(mean.lambda = 50))   # Really common species
str(data.fn(mean.lambda = 0.05)) # Really rare species

[Package AHMbook version 0.2.3 Index]