CalculateGlobalSens {capm} | R Documentation |
Wraper for sensRange
function, which calculates sensitivities of population sizes to parameters used in one of the following functions: SolveIASA
, SolveSI
or SolveTC
.
CalculateGlobalSens(model.out = NULL, ranges = NULL, sensv = NULL, all = FALSE)
model.out |
output from one of the previous function or a |
ranges |
output from the |
sensv |
string with the name of the output variables for which the sensitivity are to be estimated. |
all |
logical. If |
When all
is equal to TRUE, dist
argument in sensRange
is defined as "latin" and when equal to FALSE
, as "grid". The num
argument in sensRange
is defined as 100.
A data.frame
(extended by summary.sensRange
when all == TRUE
) containing the parameter set and the corresponding values of the sensitivity output variables.
Soetaert K and Petzoldt T (2010). Inverse modelling, sensitivity and monte carlo analysis in R using package FME. Journal of Statistical Software, 33(3), pp. 1-28.
Reichert P and Kfinsch HR (2001). Practical identifiability analysis of large environmental simulation models. Water Resources Research, 37(4), pp.1015-1030.
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
## 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)