R: Summary Statistics for Spectral Map Analysis...
summary.mpm
R Documentation
Summary Statistics for Spectral Map Analysis...
Description
Summary Statistics for Spectral Map Analysis
Summary method for object of class mpm.
Usage
## 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.
Biometrics59, 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")