Simulate Animals on Lines
This function is a simple substitute for the secr function
sim.popn() for the case of a linear habitat.
sim.linearpopn(mask, D, N, Ndist = c('poisson', 'fixed'), ...)mask |
linearmask object |
D |
numeric density animals / km |
N |
number of individuals |
Ndist |
character string for distribution of total number of individuals |
... |
other arguments passed to |
The linearmask input represents a discretized line - essentially a chain
of line segments. By default, each segment is populated with a Poisson number of
individuals. The user may specify D or N.
D may be a vector with one density per mask pixel, or a single
number that will be applied across all pixels.
If Ndist = 'fixed' then a constant number of individuals N are
simulated in each trial; otherwise N has a Poisson distribution across
trials. N = sum(D) x mask length if D is
specified.
This is a simplified wrapper for sim.popn called
with model2D = "linear".
Object of class c(‘linearpopn’, ‘popn’, ‘data.frame’).
The population output from sim.linearpopn may be used unchanged
with secr functions such as
sim.capthist. However, to be faithful to the
linear network you should set the ‘userdist’ argument of
sim.capthist to networkdistance.
x <- seq(0, 4*pi, length = 200) xy <- data.frame(x = x*100, y = sin(x)*300) mask <- read.linearmask(data = xy, spacing = 10) trps <- make.line(mask, n = 15, startbuffer = 1000, by = 30) newmask <- clipmask(mask, trps, buffer = 200) linpop <- sim.linearpopn(newmask, 200) CH <- sim.capthist(trps, linpop, userdist = networkdistance) plot(newmask) plot(CH, add = TRUE) secr.fit(CH, mask = mask, details = list(userdist = networkdistance))
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