simJamil {coenocliner} | R Documentation |
Simulate species probability of occurrence data according to the method used by Tahira Jamil and Cajo ter Braak in their recent paper Generalized linear mixed models can detect unimodal species-environment relationships.
simJamil(
n,
m,
x,
gl = 4,
randx = TRUE,
tol = 0.5,
tau = gl/2,
randm = TRUE,
expectation = FALSE
)
n |
numeric; the number of samples/sites. |
m |
numeric, the number of species/variables. |
x |
numeric; values for the environmental gradient. Can be missing, in which case suitable values are generated. See Details. |
gl |
numeric; gradient length in arbitrary units. The default is 4 units with gradient values ranging from -2 to 2. |
randx |
logical; should locations along the gradient ( |
tol |
numeric; the species tolerances. Can be a vector of
length |
tau |
numeric; constant that ensures some of the optima are located beyond the observed gradient end points. |
randm |
logical; should species optima along the gradient be located randomly or equally-spaced? |
expectation |
logical; if |
a matrix of n
rows and m
columns containing the
simulated species abundance data.
Gavin L. Simpson
Jamil and ter Braak (2013) Generalized linear mixed models can detect unimodal species-environment relationships. PeerJ 1:e95; DOI doi: 10.7717/peerj.95.
set.seed(42)
N <- 100 # Number of locations on gradient (samples)
glen <- 4 # Gradient length
grad <- sort(runif(N, -glen/2, glen/2)) # sample locations
M <- 10 # Number of species
sim <- simJamil(n = N, m = M, x = grad, gl = glen, randx = FALSE,
randm = FALSE, expectation = TRUE)
## visualise the response curves
matplot(grad, sim, type = "l", lty = "solid")
## simulate binomial responses from those response curves
sim <- simJamil(n = N, m = M, x = grad, gl = glen, randx = FALSE,
randm = FALSE)