| summary.mpm {mpm} | R Documentation |
Summary method for object of class mpm.
## S3 method for class 'mpm':
summary(object, maxdim = 4, ...)
## S3 method for class 'summary.mpm':
print(x, digits = 2, what = c("columns", "rows", "all"), ...)
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 "columns", "rows", or both ("all") are to appear in the printed report. Defaults to "columns". |
... |
further arguments to the (default) summary or print methods |
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.
# 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")