\Sexpr[results=rd,stage=build]{tools:::Rd_package_title("plspolychaos")}

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Description

\Sexpr[results=rd,stage=build]{tools:::Rd_package_description("plspolychaos")}

Details

The Legendre chaos polynomials are calculated, either on a user provided dataset by function polyLeg, or on a simulated LHS by function analyticsPolyLeg. Then, function calcPLSPCE calculates PLS-regression coefficients, PLS-PCE sensitivity indexes and some other results.

Author(s)

\Sexpr[results=rd,stage=build]{tools:::Rd_package_author("plspolychaos")}

Maintainer: \Sexpr[results=rd,stage=build]{tools:::Rd_package_maintainer("plspolychaos")}

References

  • Metamodeling and global sensitivity analysis for computer models with correlated inputs: a practical approach tested with a 3D light interception computer model. J.-P. Gauchi et al. In Environmental Modelling & Software, 2016, submitted.

See Also

polychaosbasics package.

Examples

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### First example: the dataset is simulated
nlhs <- 200 # number of rows
degree <- 6 # polynomial degree
set.seed(42)# fix the seed for reproductible results
# Generate data and calculate Legendre polynomials
# Independent inputs; response calculated by the Ishigami function
pce <- analyticsPolyLeg(nlhs, degree, 'ishigami')
# Compute the PLS-PCE sensitivity indexes for ten components
ret <- calcPLSPCE(pce, nc=10) 
# Plot the result
## Not run: plot(ret, pce)

### Second example: the dataset is provided and the
### most significant monomials are selected
# Load the dataset
load(system.file("extdata",  "ishigami200.Rda", package="plspolychaos"))
X <- ishi200[, -ncol(ishi200)] # inputs
Y <- ishi200[,  ncol(ishi200)] # output
# Build Legendre polynomial with the 50 most significant monomials
pce <- polyLeg(X, Y, degree=6, forward=50) 
# Compute the PLS-PCE sensitivity indexes 
ret <-  calcPLSPCE(pce, nc=10) 
print(ret, all=TRUE)