get_sensitivity {COINr}

Sensitivity and uncertainty analysis of a coin

Description

This function performs global sensitivity and uncertainty analysis of a coin. You must specify which parameters of the coin to vary, and the alternatives/distributions for those parameters.

Usage

get_sensitivity(
  coin,
  SA_specs,
  N,
  SA_type = "UA",
  dset,
  iCode,
  Nboot = NULL,
  quietly = FALSE,
  check_addresses = TRUE
)

Arguments

coin

A coin

SA_specs

Specifications of the input uncertainties

N

The number of regenerations

SA_type

The type of analysis to run. "UA" runs an uncertainty analysis. "SA" runs a sensitivity analysis (which anyway includes an uncertainty analysis).

dset

The data set to extract the target variable from (passed to get_data()).

iCode

The variable within dset to use as the target variable (passed to get_data()).

Nboot

Number of bootstrap samples to take when estimating confidence intervals on sensitivity indices.

quietly

Set to TRUE to suppress progress messages.

check_addresses

Logical: if FALSE skips the check of the validity of the parameter addresses. Default TRUE, but useful to set to FALSE if running this e.g. in a Rmd document (because may require user input).

Details

COINr implements a flexible variance-based global sensitivity analysis approach, which allows almost any assumption to be varied, as long as the distribution of alternative values can be described. Variance-based "sensitivity indices" are estimated using a Monte Carlo design (running the composite indicator many times with a particular combination of input values). This follows the methodology described in doi:10.1111/j.1467-985X.2005.00350.x.

To understand how this function works, please see vignette("sensitivity"). Here, we briefly recap the main input arguments.

First, you can select whether to run an uncertainty analysis SA_type = "UA" or sensitivity analysis SA_type = "SA". The number of replications (regenerations of the coin) is specified by N. Keep in mind that the total number of replications is N for an uncertainty analysis but is N*(d + 2) for a sensitivity analysis due to the experimental design used.

To run either types of analysis, you must specify which parts of the coin to vary and what the distributions/alternatives are This is done using SA_specs, a structured list. See vignette("sensitivity") for details and examples.

You also need to specify the target of the sensitivity analysis. This should be an indicator or aggregate that can be found in one of the data sets of the coin, and is specified using the dset and iCode arguments.

Finally, if SA_type = "SA", it is advisable to set Nboot to e.g. 100 or more, which is the number of bootstrap samples to take when estimating confidence intervals on sensitivity indices. This does not perform extra regenerations of the coin, so setting this to a higher number shouldn't have much impact on computational time.

This function replaces the now-defunct sensitivity() from COINr < v1.0.

Value

Sensitivity analysis results as a list, containing:

• .$Scores a data frame with a row for each unit, and columns are the scores for each replication.

• .$Ranks as .$Scores but for unit ranks

• .$RankStats summary statistics for ranks of each unit

• .$Para a list containing parameter values for each run

• .$Nominal the nominal scores and ranks of each unit (i.e. from the original COIN)

• .$Sensitivity (only if SA_type = "SA") sensitivity indices for each parameter. Also confidence intervals if Nboot was specified.

• Some information on the time elapsed, average time, and the parameters perturbed.

• Depending on the setting of store_results, may also contain a list of Methods or a list of COINs for each replication.

Examples

# for examples, see `vignette("sensitivity")`
# (this is because package examples are run automatically and this function can
# take a few minutes to run at realistic settings)