Crcvrnd

Purpose

Cross-validation for continuum regression models using SDEP.

Synopsis

[press,fiterr,minlvp,b] = crcvrnd(x,y,splt,iter,lv,powers,ss,mc)

Description

crcvrnd is used to cross-validate continuum regression models.

Inputs

• x = a matrix of predictor variables (x-block)
• y = matrix or vector of predicted variables (y-block)
• splt = the number of divisions into which to split the data
• iter = the number of iterations of the cross-validation procedure using different re-orderings of the data set
• lv = maximum number of latent variables
• powers = the row vector of continuum regression parameters to consider

Optional Inputs

• ss = causes the routine to choose contiguous blocks of data during cross-validation when set to 1 (default is 0)
• mc = when set to 0 the subsets are not mean-centered during cross-validation (default is 1)

Outputs

• press = the predictive residual error sum of squares where each element of the matrix represents the PRESS for a given combination of LVs and continuum parameter
• fiterr = the corresponding fit error
• minlvp = the number of LVs and power at minimum PRESS
• b = the final regression vector or matrix

A good smooth PRESS surface can usually be obtained by calculating about 20 models spaced logarithmically between 4 and 1/4 and using 10 to 30 iterations of the cross-validation. A good rule of thumb for dividing the data is to use either the square root of the number of samples or 10, which ever is smaller.

See Also

cr, pcr, pls