23.9 Sensitivity Analysis, Variance Decomposition

23.9.1 run.sensitivity.analysis()

This function processes the output of the previous module into sensitivity analysis plots, sensitivityanalysis.pdf, and a variance decomposition plot, variancedecomposition.pdf . In the sensitivity plots you will see the parameter values on the x-axis, the model output on the Y, with the dots being the model evaluations and the line being the spline fit.

The variance decomposition plot is discussed more below. For your reference, the R list object, sensitivity.results, stored in sensitivity.results.Rdata, contains all the components of the variance decomposition table, as well as the the input parameter space and splines from the sensitivity analysis (reminder: the output parameter space from the sensitivity analysis was in outputs.R).

The variance decomposition plot contains three columns, the coefficient of variation (normalized posterior variance), the elasticity (normalized sensitivity), and the partial standard deviation of each model parameter. This graph is sorted by the variable explaining the largest amount of variability in the model output (right hand column). From this graph identify the top-tier parameters that you would target for future constraint.