CalculateLocalSens {capm} R Documentation

## Local sensitivity analysis

### Description

Wraper for sensFun function, which estimates local effect of all model parameters on population size, applying the so-called sensitivity functions. The set of parameters used in any of the following functions can be assessed: SolveIASA, SolveSI or SolveTC.

### Usage

CalculateLocalSens(model.out = NULL, sensv = "n")


### Arguments

 model.out output from one of the previous functions or a list with equivalent structure. sensv string with the name of the output variables for which sensitivity are to be estimated.

### Details

For further arguments of sensFun, defaults are used. See the help page of this function for details. Methods for class "sensFun" can be used.

### Value

a data.frame of class sensFun containing the sensitivity functions. There is one row for each sensitivity variable at each independent time. The first column x, contains the time value; the second column var, the name of the observed variable; and remaining columns have the sensitivity parameters.

### References

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.

sensRange.

### Examples

## 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')

## Calculate local sensitivities to all parameters.
local_solve_iasa2 <- CalculateLocalSens(
model.out = solve_iasa_pt, sensv = "n2")
local_solve_iasa1 <- CalculateLocalSens(
model.out = solve_iasa_pt, sensv = "n1")



[Package capm version 0.14.0 Index]