summary.mpm: Summary Statistics for Spectral Map Analysis...
In mpm: Multivariate Projection Methods

Description Usage Arguments Details Value Author(s) References See Also Examples

Summary Statistics for Spectral Map Analysis Summary method for object of class mpm.

1 2	## S3 method for class 'mpm' summary(object, maxdim=4, ...)

`object`	an object of class `mpm` resulting from a call to `mpm`
`maxdim`	maximum number of principal factors to be reported. Defaults to `4`
`...`	further arguments; currently none are used

The function summary.mpm computes and returns a list of summary statistics of the spectral map analysis given in x.

An object of class summary.mpm with the following components:

`call`	the call to `mpm`
`Vxy`	sum of eigenvalues
`VPF`	a matrix with on the first line the eigenvalues and on the second line the cumulative eigenvalues of each of the principal factors (`PRF1` to `PRFmaxdim`) followed by the residual eigenvalues and the total eigenvalue.
`Rows`	a data frame with summary statistics for the row-items, as described below.
`Columns`	a data frame with with summary statistics for the column-items, as described below. The `Rows` and `Columns` data frames contain the following columns:
`Posit`	binary indication of whether the row or column was positioned (`1`) or not (`0`).
`Weight`	weight applied to the row or column in the function `mpm`.
`PRF1-PRFmaxdim`	factor scores or loadings for the first `maxdim` factors using eigenvalue scaling.
`Resid`	residual score or loading not accounted for by the first `maxdim` factors.
`Norm`	length of the vector representing the row or column in factor space.
`Contrib`	contribution of row or column to the sum of eigenvalues.
`Accuracy`	accuracy of the representation of the row or column by means of the first `maxdim` principal factors.

Luc Wouters

Wouters, L., Goehlmann, H., Bijnens, L., Kass, S.U., Molenberghs, G., Lewi, P.J. (2003). Graphical exploration of gene expression data: a comparative study of three multivariate methods. Biometrics 59, 1131-1140.

mpm, plot.mpm

# Example 1 weighted spectral map analysis Golub data
data(Golub)
r.sma <- mpm(Golub[,1:39], row.weight = "mean", col.weight = "mean")
# summary report
summary(r.sma)
# Example 2 using print function
data(Famin81A)
r.fam <- mpm(Famin81A, row.weight = "mean", col.weight = "mean")
r.sum <- summary(r.fam)
print(r.sum, what = "all")