R: Stochastic contribution analysis of Monte Carlo...

stochContr {propagate} | R Documentation

## Stochastic contribution analysis of Monte Carlo simulation-derived propagated uncertainty

### Description

Conducts a "stochastic contribution analysis" by calculating the change in propagated uncertainty when each of the simulated variables is kept constant at its mean, i.e. the uncertainty is removed.

### Usage

    stochContr(prop, plot = TRUE)

### Arguments

`prop` | a `propagate` object.
`plot` | logical. If `TRUE`, a boxplot with the original and mean-value propagated distribution.

### Details

This function takes the Monte Carlo simulated data X_n from a `propagate` object (`...$datSIM`), sequentially substitutes each variable β_i by its mean \bar{β_i} and then re-evaluates the output distribution Y_n = f(β, X_n). Optional boxplots are displayed that compare the original Y_n\mathrm{(orig)} to those obtained from removing σ from each β_i. Finally, the relative contribution C_i for all β_i is calculated by C_i = σ(Y_n\mathrm{(orig)})-σ(Y_n), and divided by its sum so that ∑_{i=1}^n C_i = 1.

### Value

The relative contribution C_i for all variables.

### Author(s)

Andrej-Nikolai Spiess

### Examples

    a <- c(15, 1)
    b <- c(100, 5)
    c <- c(0.5, 0.02)
    DAT <- cbind(a, b, c)
    EXPR <- expression(a * b^sin(c))
    RES <- propagate(EXPR, DAT, nsim = 100000)
    stochContr(RES)

[Package propagate version 1.0-6 Index]