`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"), ...)`

x

numeric matrix of the values to be plotted.

Rowv

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.Colv

determines if and how the *column* dendrogram should be
reordered. Has the same options as the *additionally* when

`Rowv`

argument above and
`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).distfun

function used to compute the distance (dissimilarity)
between both rows and columns. Defaults to

`dist`

.hclustfun

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.reorderfun

`function(d, w)`

of dendrogram and weights for
reordering the row and column dendrograms. The default uses
`reorder.dendrogram`

.add.expr

expression that will be evaluated after the call to

`image`

. Can be used to add components to the plot.symm

logical indicating if **symm**etrically; can only be true when

`x`

should be treated
`x`

is a square matrix.revC

logical indicating if the column order should be

`rev`

ersed for plotting, such that e.g., for the
symmetric case, the symmetry axis is as usual.scale

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.na.rm

logical indicating whether

`NA`

's should be removed.margins

numeric vector of length 2 containing the margins
(see

`par(mar = *)`

) for column and row names, respectively.ColSideColors

(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`

.RowSideColors

(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`

.cexRow, cexCol

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.labRow, labCol

character vectors with row and column labels to
use; these default to

`rownames(x)`

or `colnames(x)`

,
respectively.main, xlab, ylab

main, x- and y-axis titles; defaults to none.

keep.dendro

logical indicating if the dendrogram(s) should be
kept as part of the result (when

`Rowv`

and/or `Colv`

are
not NA).verbose

logical indicating if information should be printed.

...

additional arguments passed on to

`image`

,
e.g., `col`

specifying the colors.-
Invisibly, a list with components
- rowInd
**r**ow index permutation vector as returned by`order.dendrogram`

.- colInd
**c**olumn index permutation vector.- Rowv
- the row dendrogram; only if input
`Rowv`

was not NA and`keep.dendro`

is true. - Colv
- the column dendrogram; only if input
`Colv`

was not NA and`keep.dendro`

is true.

`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 \href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}RColorBrewerRColorBrewer package.

`image`

, `hclust`

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\")") ## "no nothing" heatmap(x, Rowv = NA, Colv = NA, scale = "column", main = "heatmap(*, NA, NA) ~= image(t(x))") 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)