imgCor(X, Y, type = c("combine", "separate"), col = jet.colors,
X.names = TRUE, Y.names = TRUE,
XsideColor = "blue", YsideColor = "red",
symkey = TRUE, keysize = 1, interactive.dev = TRUE,
cexRow = NULL, cexCol = NULL,
margins = c(5, 5), lhei = NULL, lwid = NULL)NAs are allowed.NAs are allowed."combine" or "separated",
determining the kind of plots to be produced. See Details.heat.colors, topo.colors, rainbow
or similar funX be shown ?
Possible character vector giving the names of the X-variables.Y be shown ?
Possible character vector giving the names of the Y-variables.X.Y.TRUE.cex.axis in for the row or column
axis labeling. The defaults currently only use number of rows or columns, respectively.par(mar))
for column and row names respectively.layout to divide the device up into two
rows and two columns, with the row-heights lhei and the column-widths lwid.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 pre-processed before calling the image
function in order to get, as in the numerical representation, the diagonal
from upper-left corner to bottom-right one.
Missing values are handled by casewise deletion in the imgCor function.
If X.names = FALSE, the name of each X-variable is hidden.
Default value is TRUE.
If Y.names = FALSE, the name of each Y-variable is hidden.
Default value is TRUE.cor, image, jet.colors.data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
## 'combine' type plot (default)
imgCor(X, Y)
## 'separate' type plot
imgCor(X, Y, type = "separate")
## 'separate' type plot without the name of datas
imgCor(X, Y, X.names = FALSE, Y.names = FALSE, type = "separate")Run the code above in your browser using DataLab