QuadraticFit
Related Doc:
package metamodel
the vector-to-scalar function to fit.
the dimensionality of the domain of f
the number (odd number) of values for each dimension, e.g., 5, 7, 9
Given a design matrix and response vector, use multiple regression to fit the surface, i.e., determine the coefficients of the regression equation.
Given a design matrix and response vector, use multiple regression to fit the surface, i.e., determine the coefficients of the regression equation.
the data/design matrix
the response vector
Given a center point x, form a square grid around it.
Given a center point x, form a square grid around it. This can be used to create a design matrix for use in multiple regression.
Print all the vectors/points in the grid.
Given a vector 'x', compute the values for all of its quadratic, linear and constant forms/terms, returning them as a vector.
Given a vector 'x', compute the values for all of its quadratic, linear and constant forms/terms, returning them as a vector. for 1D: VectorD (1., x(0), x(0)~2.) for 2D: VectorD (1., x(0), x(0)~2., x(1), x(1)*x(0), x(1)~^2.)
the source vector for creating forms/terms
Given a point x, use the quadratic regression equation to estimate a value for the function at x.
Given a point x, use the quadratic regression equation to estimate a value for the function at x. for 1D: b(0) + b(1)*x(0) + b(2)*x(0)~2. for 2D: b(0) + b(1)*x(0) + b(2)*x(0)~2. + b(3)*x(1) + b(4)*x(1)*x(0) + b(5)*x(1)~^2.
the point whose functional value is to be predicted
Reduce from the full model to one with fewer variable.
Reduce from the full model to one with fewer variable. FIX: adjust qFormEval to skip left out variable
Given a grid of design points, create a design matrix 'xx' and response vector 'yy' returning them as a tuple.
The
QuadraticFitclass uses multiple regression to fit a quadratic surface to the function 'f'. This is useful when computing 'f' is costly, for example in simulation optimization. The fit is over a multi-dimensional grid and can be used for interpolation and limited extrapolation.