plot.shiftfit function

Plot results of an range-shift fit

Plotting functions for illustrating the results of a range-shift fit.

## S3 method for class 'shiftfit'
plot(x, ns = c(n.sims = 1000, n.times = 100, n.bins = 10),
  plot.ts = TRUE, stretch = 0, pt.cex = 0.8, pt.col = "antiquewhite",
  CI.cols = NULL, layout = NULL, par = NULL, ...)

Arguments

• x: a fitted range shift object, i.e. output of the estimate_shift
• ns: a vector of 3 simulation values, useful for smoothing the bars in the dumbbell plot. For smoothing, it might be recommended to increase the first value, n.sims - the number of draws from the fitted migation process.
• plot.ts: whether or not to plot the time series as well
• stretch: an extra parameter to extend the bars on the dumbbells (in real distance units).
• pt.cex: point character expansion.
• pt.col: points color.
• CI.cols: three shading colors, from lightest to darkest. The default is a sequence of blues.
• layout: the default layout places the x-y plot on the left and - if plot.ts==TRUE - the respective 1-d time series on the right.
• par: graphics window parameters that, by default, look nice with the default layout.
• ...: additional parameters to pass to plot function (e.g. labels, title, etc.)

Examples

# load simulated tracks
data(SimulatedTracks)

# white noise fit
MWN.fit <- with(MWN.sim, estimate_shift(T=T, X=X, Y=Y))
summary(MWN.fit)
plot(MWN.fit)

if(interactive()){
# OUF fit
MOUF.fit <- with(MOUF.sim.random, 
                estimate_shift(T=T, X=X, Y=Y, 
                               model = "ouf", 
                               method = "like"))
summary(MOUF.fit)
plot(MOUF.fit)

# Three range fit:
# it is helpful to have some initital values for these parameters 
# because the automated quickfit() method is unreliable for three ranges
# in the example, we set a seed that seems to work
# set.seed(1976)

 MOU.3range.fit <- with(MOU.3range, 
                       estimate_shift(T=T, X=X, Y=Y, 
                                      model = "ou", 
                                      method = "ar", 
                                      n.clust = 3))
 summary(MOU.3range.fit)
 plot(MOU.3range.fit)
}