| phi {UBL} | R Documentation |
Relevance function.
Description
This function allows to obtain the relevance function values on a set of target variable values given the interpolating points.
Usage
phi(y, control.parms)
Arguments
y |
The target variable values of the problem. |
control.parms |
A named list supplied by the phi.control function with the parameters needed for obtaining the relevance values. |
Details
The phi function specifies the regions of interest in the target variable. It does so by performing a Monotone Cubic Spline Interpolation over a set of maximum and minimum relevance points. The notion of relevance can be associated with rarity. Nonetheless, this notion may depend on the domain experts knowledge.
Value
The function returns the relevance values.
Author(s)
Rita Ribeiro rpribeiro@dcc.fc.up.pt, Paula Branco paobranco@gmail.com, and Luis Torgo ltorgo@dcc.fc.up.pt
References
Ribeiro, R., 2011. Utility-based regression (Doctoral dissertation, PhD thesis, Dep. Computer Science, Faculty of Sciences - University of Porto).
Fritsch, F.N. and Carlson, R.E., 1980. Monotone piecewise cubic interpolation. SIAM Journal on Numerical Analysis, 17(2), pp.238-246.
See Also
Examples
# example of a relevance function where the extremes are the important values.
data(morley)
# the target variable
y <- morley$Speed
phiF.args <- phi.control(y,method="extremes",extr.type="both")
y.phi <- phi(y, control.parms=phiF.args)
plot(y, y.phi)