# SA_estimate: Estimate sensitivity indices In COINr: Composite Indicator Construction and Analysis

## Description

Post process a sample to obtain sensitivity indices. This function takes a univariate output which is generated as a result of running a Monte Carlo sample from `SA_sample()` through a system. Then it estimates sensitivity indices using this sample.

## Usage

 `1` ```SA_estimate(yy, N, d, Nboot = NULL) ```

## Arguments

 `yy` A vector of model output values, as a result of a N(d+2) Monte Carlo design. `N` The number of sample points per dimension. `d` The dimensionality of the sample `Nboot` Number of bootstrap draws for estimates of confidence intervals on sensitivity indices. If this is not specified, bootstrapping is not applied.

## Details

This function is built to be used inside `sensitivity()`. See COINr online documentation for more details.

## Value

A list with the output variance, plus a data frame of first order and total order sensitivity indices for each variable, as well as bootstrapped confidence intervals if `!is.null(Nboot)`.

• `sensitivity()` Perform global sensitivity or uncertainty analysis on a COIN
• `SA_sample()` Input design for estimating sensitivity indices
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# This is a generic example rather than applied to a COIN (for reasons of speed) # A simple test function testfunc <- function(x){ x[1] + 2*x[2] + 3*x[3] } # First, generate a sample X <- SA_sample(500, 3) # Run sample through test function to get corresponding output for each row y <- apply(X, 1, testfunc) # Estimate sensitivity indices using sample SAinds <- SA_estimate(y, N = 500, d = 3, Nboot = 1000) SAinds\$SensInd # Notice that total order indices have narrower confidence intervals than first order. ```