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

## Description

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

## Usage

 ```1 2``` ```## 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.

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.

`mpm`, `plot.mpm`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```# 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") ```