analyticsPolyLeg: Simulate a Dataset and Calculate Legendre Polynomials

Description Usage Arguments Details Value Note References See Also Examples

View source: R/analyticsPolyLeg.R

Description

This function simulates a LHS and calculates the Legendre polynomials, optionally reducted to the most significant monomials.

The inputs are generated by the function randomLHS (from package lhs). Note that they are uniformly and independently sampled. The output is calculated by using the Ishigami [Saltelli, 2000, Chap. 2] or Sobol [Sobol', 2003] functions. Legendre polynomials are then computed after calibration within the bounds [-1, +1].

Usage

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analyticsPolyLeg(nlhs, degree, model.fun, forward=NULL)

Arguments

nlhs

integer equal to the number of rows of the dataset.

degree

integer equal to the degree of the polynomial. Should be greater than 1 and less than 11.

model.fun

string equal to the required model. Valid values are 'ishigami' and 'sobol'.

forward

NULL or an integer equal to the required number of monomials. A null value (the default), or a value less than the number of inputs or greater than the total number of monomials, means that all the monomials are kept. See details.

Details

Value

An objet of class PCEpoly.

Note

The returned values are dependent on the random seed.

References

See Also

Examples

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nlhs <- 200 # number of rows in the dataset
degree <- 6 # polynomial degree
set.seed(42) # fix the seed for reproductible results
### Data simulation and creation of the full polynomials
pce <- analyticsPolyLeg(nlhs, degree, 'ishigami') 
print(pce)
### Selection of the 50 most significant monomials
pcef <- analyticsPolyLeg(nlhs, degree, 'ishigami', forward=50)
print(pcef)

plspolychaos documentation built on May 29, 2017, 10:44 a.m.

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