monti {yaConsensus} | R Documentation |
Compute and display the Monti's statistics for class discovery.
Description
The function computes Monti's statistics, and/or displays the correponding plots.
Usage
monti(obj, gMax = nclass.Sturges(obj$labels), just_compute = FALSE)
Arguments
obj |
An object of 'yaConsensus' class |
gMax |
an integer value indicating the maximum number cluster to be explored |
just_compute |
A logical value indicating if Monti's statistics have to be just computed (TRUE) or, in addition, displayed (FALSE, default) |
Details
If the 'fname' slot of the input object is instantiated, the input object is updated with the Monti's statistics and saved.
Value
The same input object of 'yaConsensus' class with a named list in the new 'monti' slot
monti |
A named list with the following slots: |
x |
a sequence of 500 knots from 0 to 1 |
y |
a real matrix of 500 rows and gMax - 1 colums. Each columns stores the values of the empirical distribution function corresponing to the number of clusters from 2 to gMax. |
area |
a list of real values, each of them corresponing to the area under the empirical distribution function (as stored in y) |
Note
In case the 'monti' slot is instantiated, the function provides the graphical result.
Author(s)
Stefano M. Pagnotta
References
Monti et al. (2003) - Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data - Machine Learning 52(1-2):91-118 <DOI: 10.1023/A:1023949509487>
See Also
Examples
## Generate data and annotation
n <- 50; m <- 3000
ddata <- matrix(rnorm(n * m), ncol = m)
ddata[1:20, ] <- ddata[1:20, ] + 0.2
ddata[21:35, ] <- ddata[21:35, ] + 0.4
row.names(ddata) <- c(paste0("A", 1:20), paste0("B", 1:15), paste0("C", 1:15))
ddist <- dist(ddata)
annotation <- data.frame(row.names = rownames(ddata), clust = substr(rownames(ddata), 1, 1))
annotation.colorCode <- c("red", "blue", "green")
names(annotation.colorCode) <- c("A", "B", "C")
####### run in sequential mode
####### sampling the samples ....
aConsensus <- yaConsensus(ddist)
ans <- plot(aConsensus, G = 3,
annotation = annotation,
annotation.colorCode = annotation.colorCode)
monti(aConsensus)