Summary Statistics for Spectral Map Analysis...

Description

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

Usage

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

Arguments

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

Details

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

Value

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.

Author(s)

Luc Wouters

References

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.

See Also

mpm, plot.mpm

Examples

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# 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")