heatmap {stats} | R Documentation

## Draw a Heat Map

### Description

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.

### Usage

    heatmap(x, Rowv=NULL, Colv=if(symm)"Rowv" else NULL,
            distfun = dist, hclustfun = hclust, 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,
            verbose = getOption("verbose"), ...)

### Arguments

`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 `Rowv` argument above and additionally when `x` is a square matrix, `Colv = "Rowv"` means that columns should be treated identically to the rows.
`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`.
`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 `x` should be treated symmetrically; can only be true when `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.
`verbose` | logical indicating if information should be printed.
`...` | additional arguments passed on to `image`, e.g., `col` specifying the colors.

### Details

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 `NULL`, 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, http://cran.r-project.org/src/contrib/PACKAGES.html#RColorBrewer.

### Value

Invisibly, a list with components

`rowInd` | row index permutation vector as returned by `order.dendrogram`.
`colInd` | column index permutation vector.

### Note

Unless `Rowv = NA` (or `Colw = NA`), the original rows and columns are reordered in any case to match the dendrogram, e.g., the rows by `order.dendrogram(Rowv)` where `Rowv` is the (possibly `reorder()`ed) row dendrogram.

`heatmap()` uses `layout` and draws the `image` in the lower right corner of a 2x2 layout. Consequentially, it can not be used in a multi column/row layout, i.e., when `par(mfrow= *)` or `(mfcol= *)` has been called.

### Author(s)

Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber, revisions.

### See Also

`image`, `hclust`

### Examples

    require(graphics)
    data(mtcars)
    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, margin=c(5,10),
                  xlab = "specification variables", ylab= "Car Models",
                  main = "heatmap(<Mtcars data>, ..., scale = \"column\")")
    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, margin=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))")

    data(attitude)
    round(Ca <- cor(attitude), 2)
    symnum(Ca) # simple graphic
    heatmap(Ca,              symm = TRUE, margin=c(6,6))# with reorder()
    heatmap(Ca, Rowv=FALSE, symm = TRUE, margin=c(6,6))# _NO_ reorder()

    ## For variable clustering, rather use distance based on cor():
    data(USJudgeRatings)
    symnum( cU <- cor(USJudgeRatings) )

    hU <- heatmap(cU, Rowv = FALSE, symm = TRUE, col = topo.colors(16),
                 distfun = function(c) as.dist(1 - c))
    ## The Correlation matrix with same reordering:
    round(100 * cU[hU[[1]], hU[[2]]])

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