Description Usage Arguments Details Value See Also Examples
View source: R/coin_sensitivity.R
Performs global uncertainty and sensitivity analysis on a COIN.
1 2 3 4 5 6 7 8 9 10 11  sensitivity(
COIN,
v_targ,
SA_specs,
N,
SA_type = "UA",
NrepWeights = 1000,
store_results = "results+params",
Nboot = NULL,
quietly = FALSE
)

COIN 
A COIN (this function does not support data frame input) 
v_targ 
The target variable to perform SA or UA on. Currently just supports one variable, which
should be an indicator/aggregate code present in 
SA_specs 
A list which specifies which variables to perturb, and which alternatives/distributions to use 
N 
The number of Monte Carlo replications. 
SA_type 
The type of analysis to run. 
NrepWeights 
The number of weightreplications to generate. Default 1000. 
store_results 
Which results to store

Nboot 
Number of bootstrap draws for estimates of confidence intervals on sensitivity indices.
If this is not specified, bootstrapping is not applied. Ignored if 
quietly 
If 
To perform a sensitivity or uncertainty analysis, you must specify which parameters/assumptions to vary and what their alternative
values are. This is the SA_specs
argument below. To understand how this works, please see the COINr online documentation.
The output of this function can be visualised with the functions plotSARanks()
and plotSA()
.
Sensitivity analysis results as a list, containing:
.$Scores
a data frame with a row for each unit, and columns are the scores for each replication.
.$Parameters
a record of the parameters used for each iteration
.$Ranks
as .$Scores
but for unit ranks
.$RankStats
summary statistics for ranks of each unit
.$Nominal
the nominal scores and ranks of each unit (i.e. from the original COIN)
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.
plotSARanks()
Plot confidence intervals of ranks following UA or SA
plotSA()
Plot sensitivity indices following a sensitivity analysis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  # build ASEM COIN up to aggregation
ASEM < build_ASEM()
# define noise to be applied to weights
nspecs < data.frame(AgLevel = c(2,3), NoiseFactor = c(0.25,0.25))
# create list specifying assumptions to vary and alternatives
SAspecs < list(
impute = list(imtype = c("indgroup_mean", "ind_mean", "none")),
normalise = list(ntype = c("minmax", "rank", "dist2max")),
weights = list(NoiseSpecs = nspecs, Nominal = "Original")
)
# run uncertainty analysis
# here we set N deliberately much lower than normal to enable quick testing
# Would recommend in a practical case to increase to perhaps 500 (more is always better)
SAresults < sensitivity(ASEM, v_targ = "Index",
SA_specs = SAspecs,
N = 15,
SA_type = "UA")
# to run a sensitivity analysis set SA_type = "SA" (takes longer)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.