Description Usage Arguments Details Author Examples
Title Plot a detection histogram of apparent density and the predicted detction curve for the model.
1 | plot.distanceFit(model)
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model |
A distanceFit object |
Make a histogram of the observations and the predicted detection curve in function of the distance class
Christian Roy
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(alcidae)
alcids <- mcds.filter(alcidae, transect.id = "WatchID", distance.field = "Distance", distance.labels = c("A", "B", "C", "D"),
distance.midpoints = c(25, 75, 150, 250), effort.field = "WatchLenKm", lat.field = "LatStart",
long.field = "LongStart", sp.field = "Alpha", date.field = "Date")
### Run analysis with the MCDS engine. Here, the WatchID is used as the sample.
dist.out1 <- mcds.wrap(alcids, SMP_EFFORT="WatchLenKm",DISTANCE="Distance",SIZE="Count",Type="Line",
units=list(Distance="Perp",Length_units="Kilometers",
Distance_units="Meters",Area_units="Square kilometers"),
breaks=c(0,50,100,200,300), estimator=list(c("HN","CO")),
STR_LABEL="STR_LABEL", STR_AREA="STR_AREA",SMP_LABEL="WatchID",
path="c:/temp/distance",
pathMCDS="C:/Distance 6",verbose=FALSE)
summary(dist.out1)
plot(dist.out1)
#END
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