sloboda {fritools} | R Documentation |
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 |
b |
Sloboda's |
c |
Sloboda's |
y0 |
Sloboda's |
t0 |
Sloboda's |
t |
Sloboda's |
type |
Gerald Kaendler reformulated the algorithm, but it doesn't get faster, see the examples. |
k |
Sloboda's |
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")