# ATmet-package: Advanced Tools for Metrology In ATmet: Advanced Tools for Metrology

## Description

This package provides functions for smart sampling and sensitivity analysis for metrology applications, including computationally expensive problems.

## Details

 Package: ATmet Type: Package Version: 1.2 Date: 2014-01-06 License: GPL-3

The function for smart sampling implements the Latin Hypercube Sampling (LHS) method using the lhs package. The functions for sensitivity analysis implement the Standardized Rank Regression Coefficient (SRRC) and the Sobol' sensitivity indices using the sensitivity package. These methods can be used for computationally expensive problems.

## Note

This work is part of a joint research project within the European Metrology Research Programme (EMRP) called "Novel Mathematical and Statistical Approaches to Uncertainty Evaluation". The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union.

## Author(s)

Severine Demeyer and Alexandre Allard

Maintainer: [email protected] [email protected]

`lhs`
`sensitivity`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55``` ```# ********************** # Smart sampling method # ********************** N<- 100 k<- 4 x<- list("X1","X2","X3","X4") distrib<- list("norm","norm","unif","t.scaled") distrib.pars<- list(list(0,2),list(0,1),list(20,150),list(2,0,1)) LHSdesign(N,k,distrib,distrib.pars,x) # ********************** # Sensitivity analysis # ********************** ##Simulate the input sample M=10000 Xmass <- data.frame(X1 = rnorm(M, 100, 5e-5), X2 = rnorm(M, 0.001234, 2e-5), X3 = runif(M, 1100, 1300), X4 = runif(M,7000000,9000000), X5 = runif(M,7950000,8050000))#Data-frame #Define the measurement model (GUM-S1, 9.3) calibMass <-function(x){ return(((x[,1]+x[,2])*(1+(x[,3]-1200)*(1/x[,4]-1/x[,5]))-100)*1e3) } ##### Use SRRC with a model function ##### #Apply sensitivityMet function to evaluate the associated SRRC indices S_SRRC=sensitivityMet(model=calibMass,x=Xmass, nboot=100, method="SRRC", conf=0.95) ##Print the results #First order indices S_SRRC\$S1 ##### Use Sobol with a computational code ##### #Creation of the design for the computation of Sobol sensitivity indices S_Sobol=sensitivityMet(model=NULL,x=Xmass,y=NULL, nboot=100, method="Sobol", conf=0.95) #Obtain the design of experiment to submit to the code XDesign=S_Sobol\$SI\$X #Run the computational code with XDesign as a sample of the input quantities #We use calibMass function (see GUM-S1) as an example YDesign=calibMass(XDesign) #Run the Sobol indices calculations with the outputs of the code S_Sobol\$SI=tell(x=S_Sobol\$SI,y=YDesign) ##Print the results #First order indices S_Sobol\$SI\$S #Total order indices S_Sobol\$SI\$T ```