siplab (version 1.6)

influence: Influence Functions

Description

Compute influence values depending on distance and plant marks, for use in assimilation().

Note: In previous versions of siplab the function names had .inf in place of _inf.

Usage

zoi_inf(dx, dy, marks, par = list(k = 0.2, smark = 1))
tass_inf(dx, dy, marks, par = list(b = 3.52 * 0.975, c = 6.1,
    smark = 1))
gates_inf(dx, dy, marks, par = list(a = 1, b = 4, smark = 1))
gnomon_inf(dx, dy, marks, par = list(a = 1, b = 4, smark = 1))

Arguments

dx

Vector of x-distances. Coordinates x for a number of points minus coordinate x of the subjectt plant.

dy

Vector of y-distances. Coordinates y for a number of points minus coordinate y of the subjectt plant.

marks

Mark data for the subject plant.

par

List of parameters.

Value

Vector of influence values, of length equal to the length of dx and dy.

Details

The user can program her/his own influence function. It must take the arguments dx, dy, marks, and optionally par.

Influence function values are normally non-negative. Otherwise, they are set to 0 in assimilation().

The values of par are taken from the argument infpar of assimilation(), if not NULL. Otherwise the default is used.

smark in par must be 1 or “mark” if there is only one mark. If the marks are a data frame, smark must be the number or name of the column with the plant size variable.

Let \(S\) be the plant size, and \(R\) be the Euclidean plant-to-point distance \(R = \sqrt{\mathrm{dx}^2 + \mathrm{dy}^2}\). Then the built-in influence functions are:

zoi_inf():

1 if \(R < k S\), 0 otherwise

tass_inf():

\(\max\{0, \, S - c[\exp(R/b) - 1]\}\)

gates_inf():

\(\max\{0, \, [S^a - (b R)^a]^{1/a}\}\)

gnomon_inf():

\(\max\{0, \, S - b R^a\}\)

Other influence functions can be written following these examples.

References

https://github.com/ogarciav/siplab

Garc<U+00ED>a, O. “A generic approach to spatial individual-based modelling and simulation of plant communities”. Mathematical and Computational Forestry and Nat.-Res. Sci. (MCFNS) 6(1), 36-47. 2014.

See Also

assimilation

Examples

# NOT RUN {
# Example multi-species influence function (spruce/hardwoods)
multi_inf <- function (dx, dy, marks, par) {
    out <- numeric(length(dx))
    s <- marks$SPECIES == "Spruce"
    out[s] <- gnomon_inf(dx[s], dy[s], marks[s, ], par=list(a=par$aS,
        b=par$bS, smark=par$smark))
    out[!s] <- gnomon_inf(dx[!s], dy[!s], marks[!s, ], par=list(a=par$aH,
        b=par$bH, smark=par$smark)) # Hardwoods
    return(out)
}
# }