Description Usage Arguments Details References See Also Examples
View source: R/PlotGlobalSens.R
Plot results of of CalculateGlobalSens
function.
1 2 3 4 | PlotGlobalSens(global.out = NULL, x.label = "Time",
y.label = "Population", legend.label = "Sensitivity range",
qt.label = "Qt 0.05 - 0.95", sd.label = "mean +- sd ",
inner.color = "DarkRed", outer.color = "LightBlue")
|
global.out |
output from |
x.label |
string with the name for the x axis. |
y.label |
string with the name for the y axis. |
legend.label |
string with the name for the legend. |
qt.label |
string with the name for the envelope calculated using the quantiles 0.05 and 0.95. |
sd.label |
string with the name for the envelope calculated using the mean +- standard deviation ranges. |
inner.color |
any valid specification of a color for the inner envelope. |
outer.color |
any valid specification of a color for the outer envelope. |
Font size of saved plots is usually different to the font size seen in graphic browsers. Before changing font sizes, see the final result in saved (or preview) plots.
Other details of the plot can be modifyed using appropriate functions from ggplot2
package.
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.
http://oswaldosantos.github.io/capm
plot.deSolve.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ## IASA model
## 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')
## Set ranges 10 % greater and lesser than the
## point estimates.
rg_solve_iasa <- SetRanges(pars = dogs_iasa$pars)
## Calculate golobal sensitivity of combined parameters.
## To calculate global sensitivity to each parameter, set
## all as FALSE.
glob_all_solve_iasa <- CalculateGlobalSens(
model.out = solve_iasa_pt,
ranges = rg_solve_iasa,
sensv = "n2", all = TRUE)
PlotGlobalSens(glob_all_solve_iasa)
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