summary.mpm {mpm}R Documentation

Summary Statistics for Spectral Map Analysis

Description

Summary method for object of class mpm.

Usage

  ## S3 method for class 'mpm':
  summary(object, maxdim = 4, ...)
  ## S3 method for class 'summary.mpm':
  print(x, digits = 2, what = c("columns", "rows", "all"), ...)

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
x object of class summary.mpm
digits accuracy of printing
what optional character string specifying whether \dQuote{columns}, \dQuote{rows}, or both (\dQuote{all}) are to appear in the printed report. Defaults to \dQuote{columns}.
... further arguments to the (default) summary or print methods

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

  # 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")

[Package mpm version 1.0-12 Index]