A heat map is a false color image (basically
image(t(x))) with a dendrogram added to the left side
and to the top. Typically, reordering of the rows and columns
according to some set of values (row or column means) within the
restrictions imposed by the dendrogram is carried out.
heatmap(x, Rowv = NULL, Colv = if(symm)"Rowv" else NULL,
distfun = dist, hclustfun = hclust,
reorderfun = function(d, w) reorder(d, w),
add.expr, symm = FALSE, revC = identical(Colv, "Rowv"),
scale = c("row", "column", "none"), na.rm = TRUE,
margins = c(5, 5), ColSideColors, RowSideColors,
cexRow = 0.2 + 1/log10(nr), cexCol = 0.2 + 1/log10(nc),
labRow = NULL, labCol = NULL, main = NULL,
xlab = NULL, ylab = NULL,
keep.dendro = FALSE, verbose = getOption("verbose"), …)numeric matrix of the values to be plotted.
determines if and how the row dendrogram should be
computed and reordered. Either a dendrogram or a
vector of values used to reorder the row dendrogram or
NA to suppress any row dendrogram (and reordering) or
by default, NULL, see ‘Details’ below.
determines if and how the column dendrogram should be
reordered. Has the same options as the Rowv argument above and
additionally when x is a square matrix, Colv =
"Rowv" means that columns should be treated identically to the
rows (and so if there is to be no row dendrogram there will not be a
column one either).
function used to compute the distance (dissimilarity)
between both rows and columns. Defaults to dist.
function used to compute the hierarchical clustering
when Rowv or Colv are not dendrograms. Defaults to
hclust. Should take as argument a result of distfun
and return an object to which as.dendrogram can be applied.
function(d, w) of dendrogram and weights for
reordering the row and column dendrograms. The default uses
reorder.dendrogram.
expression that will be evaluated after the call to
image. Can be used to add components to the plot.
logical indicating if x should be treated
symmetrically; can only be true when x is a square matrix.
logical indicating if the column order should be
reversed for plotting, such that e.g., for the
symmetric case, the symmetry axis is as usual.
character indicating if the values should be centered and
scaled in either the row direction or the column direction, or
none. The default is "row" if symm false, and
"none" otherwise.
logical indicating whether NA's should be removed.
numeric vector of length 2 containing the margins
(see par(mar = *)) for column and row names, respectively.
(optional) character vector of length ncol(x)
containing the color names for a horizontal side bar that may be used to
annotate the columns of x.
(optional) character vector of length nrow(x)
containing the color names for a vertical side bar that may be used to
annotate the rows of x.
positive numbers, used as cex.axis in
for the row or column axis labeling. The defaults currently only
use number of rows or columns, respectively.
character vectors with row and column labels to
use; these default to rownames(x) or colnames(x),
respectively.
main, x- and y-axis titles; defaults to none.
logical indicating if the dendrogram(s) should be
kept as part of the result (when Rowv and/or Colv are
not NA).
logical indicating if information should be printed.
additional arguments passed on to image,
e.g., col specifying the colors.
Invisibly, a list with components
row index permutation vector as returned by
order.dendrogram.
column index permutation vector.
the row dendrogram; only if input Rowv was not NA
and keep.dendro is true.
the column dendrogram; only if input Colv was not NA
and keep.dendro is true.
If either Rowv or Colv are dendrograms they are honored
(and not reordered). Otherwise, dendrograms are computed as
dd <- as.dendrogram(hclustfun(distfun(X))) where X is
either x or t(x).
If either is a vector (of ‘weights’) then the appropriate
dendrogram is reordered according to the supplied values subject to
the constraints imposed by the dendrogram, by reorder(dd,
Rowv), in the row case.
If either is missing, as by default, then the ordering of the
corresponding dendrogram is by the mean value of the rows/columns,
i.e., in the case of rows, Rowv <- rowMeans(x, na.rm = na.rm).
If either is NA, no reordering will be done for
the corresponding side.
By default (scale = "row") the rows are scaled to have mean
zero and standard deviation one. There is some empirical evidence
from genomic plotting that this is useful.
The default colors are not pretty. Consider using enhancements such as the RColorBrewer package.
# NOT RUN {
require(graphics); require(grDevices)
x <- as.matrix(mtcars)
rc <- rainbow(nrow(x), start = 0, end = .3)
cc <- rainbow(ncol(x), start = 0, end = .3)
hv <- heatmap(x, col = cm.colors(256), scale = "column",
RowSideColors = rc, ColSideColors = cc, margins = c(5,10),
xlab = "specification variables", ylab = "Car Models",
main = "heatmap(<Mtcars data>, ..., scale = \"column\")")
utils::str(hv) # the two re-ordering index vectors
## no column dendrogram (nor reordering) at all:
heatmap(x, Colv = NA, col = cm.colors(256), scale = "column",
RowSideColors = rc, margins = c(5,10),
xlab = "specification variables", ylab = "Car Models",
main = "heatmap(<Mtcars data>, ..., scale = \"column\")")
# }
# NOT RUN {
## "no nothing"
heatmap(x, Rowv = NA, Colv = NA, scale = "column",
main = "heatmap(*, NA, NA) ~= image(t(x))")
# }
# NOT RUN {
<!-- %% also want example using the `add.exp' argument! -->
# }
# NOT RUN {
round(Ca <- cor(attitude), 2)
symnum(Ca) # simple graphic
heatmap(Ca, symm = TRUE, margins = c(6,6)) # with reorder()
heatmap(Ca, Rowv = FALSE, symm = TRUE, margins = c(6,6)) # _NO_ reorder()
## slightly artificial with color bar, without and with ordering:
cc <- rainbow(nrow(Ca))
heatmap(Ca, Rowv = FALSE, symm = TRUE, RowSideColors = cc, ColSideColors = cc,
margins = c(6,6))
heatmap(Ca, symm = TRUE, RowSideColors = cc, ColSideColors = cc,
margins = c(6,6))
## For variable clustering, rather use distance based on cor():
symnum( cU <- cor(USJudgeRatings) )
hU <- heatmap(cU, Rowv = FALSE, symm = TRUE, col = topo.colors(16),
distfun = function(c) as.dist(1 - c), keep.dendro = TRUE)
## The Correlation matrix with same reordering:
round(100 * cU[hU[[1]], hU[[2]]])
## The column dendrogram:
utils::str(hU$Colv)
# }