CalculateGlobalSens {capm} | R Documentation |

## Global sensitivity analysis

### Description

Wraper for `sensRange`

function, which calculates sensitivities of population sizes to parameters used in one of the following functions: `SolveIASA`

, `SolveSI`

or `SolveTC`

.

### Usage

```
CalculateGlobalSens(model.out = NULL, ranges = NULL, sensv = NULL,
all = FALSE)
```

### Arguments

`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 |

### Details

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.

### Value

A `data.frame`

(extended by `summary.sensRange`

when `all == TRUE`

) containing the parameter set and the corresponding values of the sensitivity output variables.

### 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.

http://oswaldosantos.github.io/capm

### See Also

### 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')
## 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)
```

*capm*version 0.14.0 Index]