Description Usage Arguments Details Value Note References See Also Examples
System of ordinary differential equations to simulate the effect of immigration of owned dogs, abandonment, sterilization of owned and unowned dogs and adoption, on population dynamics.
1 2 3 4 
pars 
a named 
init 
a named 
time 
time sequence for which output is wanted; the first value of times must be the initial time. 
alpha.owned 

immigration.reference 

s.range 
optional sequence (between 0 and 1) of the sterilization rates to be simulated. 
a.range 
optional 
alpha.range 
optional 
v.range 
optional 
s.fm 
logical. If 
... 
further arguments passed to ode function. 
The implemented model is described by Baquero, et. al., 2016 and the function is a wrapper around the defaults of ode function, whose help page must be consulted for details.
The pars
argument must contain named values, using the following conventions: 1
: owned animals; 2
: unowned animals; f
: females; m
: males. Then:
b1
and b2
: number of births.
df1
, dm1
, df2
and dm2
: death rate.
sf1
, sm1
, sf2
and sm2
: sterilization rate.
k1
and k2
: carrying capacity.
h1
and h2
: mean harem size.
a
: abandonment rate.
alpha
: adoption rate.
v
: immigration rate.
z
: proportion of sterilized immigrants.
The init
argument must contain named values for the inital number of animals, using the following conventions: 1
: owned animals; 2
: unowned animals; f
: females; m
: males; and s
: sterilized. Then, the names are:
f1
, fs1
, m1
, ms1
, f2
, fs2
, m2
and ms2
.
If any range is specified (e.g s.range
), the remaining ranges must be specified too (a.range
, alpha.range
and v.range
).
The function is a wrapper around the defaults of ode function, whose help page must be consulted for details. An exception is the method argument, which here has "rk4" as a default.
list
. The first element, name
, is a string with the name of the function, the second element, model
, is the model function. The third, fourth and fifth elements are vectors (pars
, init
, time
, respectively) containing the pars
, init
and time
arguments of the function. The sixth element results
is a data.frame
with up to as many rows as elements in time. The first column contain the time and subsequent columns contain the size of specific subpopulations, named according to conventions above. The group
column differentiate between owned and unowned. When *.range arguments are given, the last fourth columsn specify their instances.
Logistic growth models are not intended for scenarios in which population size is greater than carrying capacity and growth rate is negative.
Baquero, O. S., Marconcin, S., Rocha, A., & Garcia, R. D. C. M. (2018). Companion animal demography and population management in Pinhais, Brazil. Preventive Veterinary Medicine. Baquero, O. S., Akamine, L. A., Amaku, M., & Ferreira, F. (2016). Defining priorities for dog population management through mathematical modeling. Preventive veterinary medicine, 123, 121127.
http://oswaldosantos.github.io/capm
ode.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Parameters and intial conditions.
data(dogs)
dogs_iasa < GetDataIASA(dogs,
destination.label = "Pinhais",
total.estimate = 50444)
# Solve for point estimates.
solve_iasa_pt < SolveIASA(pars = dogs_iasa$pars,
init = dogs_iasa$init,
time = 0:15,
alpha.owned = TRUE,
method = 'rk4')
solve_iasa_rg < SolveIASA(pars = dogs_iasa$pars,
init = dogs_iasa$init,
time = 0:10,
alpha.owned = TRUE,
s.range = seq(0, .4, l = 15),
a.range = c(0, .2),
alpha.range = c(0, .05),
v.range = c(0, .1),
method = 'rk4')

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