Description Usage Arguments Details Value Author(s)
View source: R/sensitivity_analysis.R
Perform a PEcAnlike parameter sensitivity analysis to analyze the contribution of parameters to predictive uncertainty.
1  sensitivity_analysis(dat, xcols, ycol, .type = "additive")

xcols 
Character vector of parameter column names 
ycol 
Name of column containing response variable 
.type 
Whether the multivariate fit is "additive" ( 
df 

This analysis produces three key metrics:
"Coefficient of variation (CV)" describes the relative uncertainty of the input parameter. It is calculated as the ratio between the input parameter variance and its median value.
"Elasticity" is the normalized sensitivity of the model to a change in one parameter.
"Partial variance" is the fraction of variance in the model output that is explained by the given parameter. In essence, it integrates the information provided by the CV and elasticity.
The theory and implementation are based on the sensitivity analysis described by LeBauer et al. (2013), but with several key differences:
LeBauer et al. use a cubic spline interpolation through each
point in the model output. This function uses a generalized
additive model regression (mgcv::gam()
).
LeBauer et al. fit individual splines to each parameteroutput combination where other parameters are held constant at their median. This function fits a multivariate generalized additive regression model, and then uses that fit to calculate the partial derivatives.
data.frame
of sensitivity analysis results. See Details.
Alexey Shiklomanov
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