sr_uq.RdDefine the parameters for quantifying the uncertainty in the estimation of the spread-rate.
sr_uq(nsim = 1L, space = 0, time = 0, neigh_tol = -4.5)
| nsim | Integer > 0. Number of Monte Carlo replicates. |
|---|---|
| space | Numeric, non-negative. Uncertainty of spatial coordinates. |
| time | Numeric, non-negative. Uncertainty of temporal values. |
| neigh_tol | Number or interval (numeric vector of length 2). Neighouring-tolerance parameter in the units of the dataset coordinates if positive, or relative to the dataset diameter if negative. |
An object of class sr_uq which is a list with the
given or default values, after some sanity checks.
The actual observations will be The number of Monte Carlo replicates
The space and time arguments are expressed in the
data spatial and temporal units.
For each one of the nsim replicates, the observations will
be randomly shifted in space within a circle of radious
space, while the dates will be shifted within the interval
+- time with a discrete approximation to a Gaussian
curve.
The neighbouring-tolerance parameter neigh_tol is used to
filter the earliest observed cases in a neighbourhood. If
two points are closer than this value, they are considered roughly
in the same neighbourhood. The date of invasion of a location is
the earliest observed case in the neighbourhood. It can be defined
as a numeric value in the units of the coordinates, or as a range
(i.e. a numeric vector of length 2). Negative values will be
interpreted as a percentage of the diameter of the dataset.
## Use the default values: work with current observations ## without quantifying uncertainty uq_def <- sr_uq() ## Set the neighbouring-tolerance parameter only uq1 <- sr_uq(neigh_tol = 800) ## 10 MC replicates, shift locations within a circle of radious ## 1 km, shift dates by zero/one day up or down, consider points ## closer than 5% of the diameter of the dataset as in the same ## neighbourhood uq <- sr_uq(nsim = 10, space = 1e3, time = 1, neigh_tol = -5)