PlotGlobalSens: Plot results of GlobalSens function

Description

Plot results of of CalculateGlobalSens function.

Usage

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")

Arguments

global.out

output from CalculateGlobalSens function.

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.

Details

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.

References

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

Examples

Run this code

# NOT RUN {
## 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)

# }