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
\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 |
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")