BinaryMatrix-class | R Documentation |
"BinaryMatrix"
The BinaryMatrix
object class underlies the threshLGF
and Mercator
methods and visualizations of the Mercator
package. The BinaryMatrix
function
returns a new object of BinaryMatrix
class.
BinaryMatrix(binmat, columnInfo, rowInfo)
binmat |
A binary |
columnInfo |
A |
rowInfo |
A |
The BinaryMatrix
function returns a new object of binaryMatrix
class.
Objects should be defined using the BinaryMatrix
constructor. In
the simplest case, you simply pass in the binary data matrix that you
want to visualize, and the BinaryMatrix
is constructed using the matrix's
existing column and row names.
binmat
:Object of class matrix
; the binary data
used for visualization.
columnInfo
:Object of class data.frame
; names and
definitions of columns.
rowInfo
:Object of class data.frame
; names and
definitions of rows.
info
:Object of class list
; identifies $notUsed
and $redundant
features.
history
:Object of class "character"
; returns a
history of manipulations by Mercator
functions to the BinaryMatrix
object, including "Newly created," "Subsetted," "Transposed," "Duplicate
features removed," and "Threshed."
[]
:Subsetting by []
returns a subsetted binary matrix,
including subsetted row and column names. Calling @history
will
return the history "Subsetted."
dim
:returns the dimensions of the @binmat
component
of the binaryMatrix
object.
print
:Shows the first ten rows and columns
of the @binmat
component.
show
:Shows the first ten rows and columns
of the @binmat
component.
summary
:For a given BinaryMatrix
, returns object class,
dimensions of the @binmat
component, and @history
.
t
:Transposes the @binmat
and its associated rowInfo
and columnInfo
. Calling @history
will return the history
"Subsetted."
Attempting to construct or manipulate a BinaryMatrix
containing NAs, missing values,
or columns containing exclusively 0 values may introduce error.
Kevin R. Coombes <krc@silicovore.com>, Caitlin E. Coombes
The removeDuplicateFeatures
function can be used to remove
duplicate columns from the binaryMatrix class before threshing or visualization.
The threshLGF
can be used to identify and remove uninformative features
before visualization or further analysis.
my.matrix <- matrix(rbinom(50*100, 1, 0.15), ncol=50)
my.rows <- as.data.frame(paste("R", 1:100, sep=""))
my.cols <- as.data.frame(paste("C", 1:50, sep=""))
my.binmat <- BinaryMatrix(my.matrix, my.cols, my.rows)
summary(my.binmat)
my.binmat <- my.binmat[1:50, 1:30]
my.binmat <- t(my.binmat)
dim(my.binmat)
my.binmat@history
my.binmat
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