opa {alsace} | R Documentation

## Finding the most dissimilar variables in a data matrix: the Orthogonal Projection Approach

### Description

This function finds the set of most dissimilar rows in a data matrix. If no initial selection is presented, the first object is selected by comparison with the vector of column means. As a distance function the determinant of the crossproduct matrix is used.

### Usage

    opa(x, ncomp, initXref = NULL)

### Arguments

`x` | Data matrix (numerical). May not contain missing values.
`ncomp` | Number of rows to be selected.
`initXref` | Optional matrix to be expanded - should be a subset of the rows to select.

### Value

The function returns a submatrix of X, where the columns contain the (unit-length scaled) spectra from the input data that are most dissimilar.

### Author(s)

Ron Wehrens

### References

F. Questa Sanchez et al.: Algorithm for the assessment of peak purity in liquid chromatography with photodiode-array detection. Analytica Chimica Acta 285:181-192 (1994)

R. Wehrens: Chemometrics with R. Springer Verlag, Heidelberg (2011)

### Examples

    data(tea)

    tea <- lapply(tea.raw, preprocess, maxI = 100)

    ncomp <- 7
    spectra <- opa(tea, ncomp)

    myPalette <- colorRampPalette(c("black", "red", "blue", "green"))
    mycols <- myPalette(ncomp)
    matplot(as.numeric(rownames(spectra)), spectra, type = "l", lty = 1,
            xlab = expression(lambda), ylab = "", col = mycols)
    legend("topright", legend = paste("Comp.", 1:ncomp), col = mycols,
           lty = 1, ncol = 2, bty = "n")

[Package alsace version 1.23.1 Index]