get_sensitivity {COINr} | R Documentation |
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.
get_sensitivity(
coin,
SA_specs,
N,
SA_type = "UA",
dset,
iCode,
Nboot = NULL,
quietly = FALSE,
check_addresses = TRUE
)
coin |
A coin |
SA_specs |
Specifications of the input uncertainties |
N |
The number of regenerations |
SA_type |
The type of analysis to run. |
dset |
The data set to extract the target variable from (passed to |
iCode |
The variable within |
Nboot |
Number of bootstrap samples to take when estimating confidence intervals on sensitivity indices. |
quietly |
Set to |
check_addresses |
Logical: if |
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.
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.
# 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)