sloboda: Sloboda's Growth Function

In fritools: Utilities for the Forest Research Institute of the State Baden-Wuerttemberg

Sloboda's Growth Function

Description

Implement the growth function

y_t = k^{\beta_{1}} \times \left(\frac{y_0}{k^{\beta_{1}}}\right)^{\exp \left[ \frac{\beta_{2}}{(\beta_{3}-1) \times t ^{(\beta_{3}-1)}} - \frac{\beta_{2}}{(\beta_{3}-1) \times t_0 ^{(\beta_{3}-1)}} \right] }

published in Sloboda, B., 1971: Zur Darstellung von Wachstumsprozessen mit Hilfe von Differentialgleichungen erster Ordnung. Mitt. d. Baden-Württembergischen Forstlichen Versuchs- und Forschungsanstalt.

Usage

sloboda(a, b, c, y0, t0, t, type = c("classic", "kaendler"), k = 65)

Arguments

a	Sloboda's \beta_{3}.
b	Sloboda's \beta_{2}.
c	Sloboda's \beta_{1}.
y0	Sloboda's \beta_{1}.
t0	Sloboda's t_{0}.
t	Sloboda's t.
type	Gerald Kaendler reformulated the algorithm, but it doesn't get faster, see the examples.
k	Sloboda's k.

Value

The value y_t of Sloboda's growth function.

See Also

Other statistics: column_sums(), count_groups(), relative_difference(), round_half_away_from_zero(), weighted_variance()

Examples

microbenchmark::microbenchmark(cl = sloboda(0.2, 0.7, 3, 30, 30, 35),
                               g =  sloboda(0.2, 0.7, 3, 30, 30, 35,
                                            "kaendler"),
                               check = "equivalent")

fritools documentation built on Nov. 19, 2023, 1:06 a.m.