Description Usage Arguments Details Value Author(s) See Also Examples
Display twodimensional visualizations (image maps) of the correlation matrices within and between two data sets.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 
X 
numeric matrix or data frame (n x p), the observations
on the X variables. 
Y 
numeric matrix or data frame (n x q), the observations
on the Y variables. 
type 
character string, (partially) maching one of 
X.var.names, Y.var.names 
logical, should the name of X and/or
Yvariables be shown? If 
sideColors 
character vector of length two. The color name for horizontal and vertical side bars that may be used to annotate the X and Y correlation matrices. 
interactive.dev 
boolean. The current graphics device that will be opened is interactive? 
title 
logical, should the main titles be shown? 
color, xlab, ylab 
arguments passed to 
row.cex, col.cex 
positive numbers, used as 
symkey 
boolean indicating whether the color key should be made
symmetric about 0. Defaults to 
keysize 
positive numeric value indicating the size of the color key. 
margins 
numeric vector of length two containing the margins (see

lhei, lwid 
arguments passed to 
If type="combine"
, the correlation matrix is computed of the combined
matrices cbind(X, Y)
and then plotted. If type="separate"
,
three correlation matrices are computed, cor(X)
, cor(Y)
and
cor(X,Y)
and plotted separately on a device. In both cases, a color
correlation scales strip is plotted.
The correlation matrices are preprocessed before calling the image
function in order to get, as in the numerical representation, the diagonal
from upperleft corner to bottomright one.
Missing values are handled by casewise deletion in the imgCor
function.
If X.names = FALSE
, the name of each Xvariable is hidden. Default
value is TRUE
.
If Y.names = FALSE
, the name of each Yvariable is hidden. Default
value is TRUE
.
NULL (invisibly)
Ignacio González, KimAnh Lê Cao, Florian Rohart, Al J Abadi
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  data(nutrimouse)
X < nutrimouse$lipid
Y < nutrimouse$gene
## 'combine' type plot (default)
imgCor(X, Y)
## Not run:
## 'separate' type plot
imgCor(X, Y, type = "separate")
## 'separate' type plot without the name of datas
imgCor(X, Y, X.var.names = FALSE, Y.var.names = FALSE, type = "separate")
## End(Not run)

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