Nothing
R/cim.R
In mixOmics: Omics Data Integration Project
Defines functions
cim
Documented in
cim
# -------------------------------------------- #
# CIM for objects "pca","spca","ipca","sipca","mlsplsda","splsda","plsda","rcc",
# "pls","spls","mlspls"
# -------------------------------------------- #
#' Clustered Image Maps (CIMs) ("heat maps")
#'
#' This function generates color-coded Clustered Image Maps (CIMs) ("heat
#' maps") to represent "high-dimensional" data sets.
#'
#' One matrix Clustered Image Map (default method) is a 2-dimensional
#' visualization of a real-valued matrix (basically
#' \code{\link{image}(t(mat))}) with rows and/or columns reordered according to
#' some hierarchical clustering method to identify interesting patterns.
#' Generated dendrograms from clustering are added to the left side and to the
#' top of the image. By default the used clustering method for rows and columns
#' is the \emph{complete linkage} method and the used distance measure is the
#' distance \emph{euclidean}.
#'
#' In \code{"pca"}, \code{"spca"}, \code{"ipca"}, \code{"sipca"},
#' \code{"plsda"}, \code{"splsda"} and multilevel variants methods the
#' \code{mat} matrix is \code{object$X}.
#'
#' For the remaining methods, if \code{mapping = "X"} or \code{mapping = "Y"}
#' the \code{mat} matrix is \code{object$X} or \code{object$Y} respectively. If
#' \code{mapping = "XY"}: \itemize{ \item in \code{rcc} method, the matrix
#' \code{mat} is created where element \eqn{(j,k)} is the scalar product value
#' between every pairs of vectors in dimension \code{length(comp)} representing
#' the variables \eqn{X_j} and \eqn{Y_k} on the axis defined by \eqn{Z_i} with
#' \eqn{i} in \code{comp}, where \eqn{Z_i} is the equiangular vector between
#' the \eqn{i}-th \eqn{X} and \eqn{Y} canonical variate.
#'
#' \item in \code{pls}, \code{spls} and multilevel spls methods, if
#' \code{object$mode} is \code{"regression"}, the element \eqn{(j,k)} of the
#' matrix \code{mat} is given by the scalar product value between every pairs
#' of vectors in dimension \code{length(comp)} representing the variables
#' \eqn{X_j} and \eqn{Y_k} on the axis defined by \eqn{U_i} with \eqn{i} in
#' \code{comp}, where \eqn{U_i} is the \eqn{i}-th \eqn{X} variate. If
#' \code{object$mode} is \code{"canonical"} then \eqn{X_j} and \eqn{Y_k} are
#' represented on the axis defined by \eqn{U_i} and \eqn{V_i} respectively.}
#'
#' By default four components will be displayed in the plot. At the top left is
#' the color key, top right is the column dendogram, bottom left is the row
#' dendogram, bottom right is the image plot. When \code{sideColors} are
#' provided, an additional row or column is inserted in the appropriate
#' location. This layout can be overriden by specifiying appropriate values for
#' \code{lwid} and \code{lhei}. \code{lwid} controls the column width, and
#' \code{lhei} controls the row height. See the help page for
#' \code{\link{layout}} for details on how to use these arguments.
#'
#' For visualization of "high-dimensional" data sets, a nice zooming tool was
#' created. \code{zoom = TRUE} open a new device, one for CIM, one for zoom-out
#' region and define an interactive 'zoom' process: click two points at imagen
#' map region by pressing the first mouse button. It then draws a rectangle
#' around the selected region and zoom-out this at new device. The process can
#' be repeated to zoom-out other regions of interest.
#'
#' The zoom process is terminated by clicking the second button and selecting
#' 'Stop' from the menu, or from the 'Stop' menu on the graphics window.
#'
#' @param mat numeric matrix of values to be plotted. Alternatively, an object
#' of class inheriting from \code{"pca"}, \code{"spca"}, \code{"ipca"},
#' \code{"sipca"}, \code{"rcc"}, \code{"pls"}, \code{"spls"}, \code{"plsda"},
#' \code{"splsda"}, \code{"mlspls"} or \code{"mlsplsda"} (where \code{"ml"}
#' stands for multilevel).
#' @param color a character vector of colors such as that generated by
#' \code{\link{terrain.colors}}, \code{\link{topo.colors}},
#' \code{\link{rainbow}}, \code{\link{color.jet}} or similar functions.
#' @param row.names,col.names logical, should the name of rows and/or columns
#' of \code{mat} be shown? If \code{TRUE} (defaults) \code{rownames(mat)}
#' and/or \code{colnames(mat)} are used. Possible character vectors with row
#' and/or column labels can be used.
#' @param row.sideColors (optional) character vector of length \code{nrow(mat)}
#' containing the color names for a vertical side bar that may be used to
#' annotate the rows of \code{mat}.
#' @param col.sideColors (optional) character vector of length \code{ncol(mat)}
#' containing the color names for a horizontal side bar that may be used to
#' annotate the columns of \code{mat}.
#' @param row.cex,col.cex positive numbers, used as \code{cex.axis} in for the
#' row or column axis labeling. The defaults currently only use number of rows
#' or columns, respectively.
#' @param mapping character string indicating whether to map \code{"X"},
#' \code{"Y"} or \code{"XY"}-association matrix. See Details.
#' @param cluster character string indicating whether to cluster \code{"none"},
#' \code{"row"}, \code{"column"} or \code{"both"}. Defaults to \code{"both"}.
#' @param dist.method character vector of length two. The distance measure used
#' in clustering rows and columns. Possible values are \code{"correlation"} for
#' Pearson correlation and all the distances supported by \code{\link{dist}},
#' such as \code{"euclidean"}, etc.
#' @param clust.method character vector of length two. The agglomeration method
#' to be used for rows and columns. Accepts the same values as in
#' \code{\link{hclust}} such as \code{"ward"}, \code{"complete"}, etc.
#' @param cut.tree numeric vector of length two with components in [0,1]. The
#' height proportions where the trees should be cut for rows and columns, if
#' these are clustered.
#' @param comp atomic or vector of positive integers. The components to
#' adequately account for the data association. For a non sparse method, the
#' similarity matrix is computed based on the variates and loading vectors of
#' those specified components. For a sparse approach, the similarity matric is
#' computed based on the variables selected on those specified components. See
#' example. Defaults to \code{comp = 1:object$ncomp}.
#' @param transpose logical indicating if the matrix should be transposed for
#' plotting. Defaults to \code{FALSE}.
#' @param center either a logical value or a numeric vector of length equal to
#' the number of columns of \code{mat}. See \code{\link{scale}} function.
#' @param scale either a logical value or a numeric vector of length equal to
#' the number of columns of \code{mat}. See \code{\link{scale}} function.
#' @param cutoff numeric between 0 and 1. Variables with correlations below
#' this threshold in absolute value are not plotted. To use only when mapping
#' is "XY".
#' @param symkey Logical indicating whether the color key should be made
#' symmetric about 0. Defaults to \code{TRUE}.
#' @param keysize vector of length two, indicating the size of the color key.
#' @param keysize.label vector of length 1, indicating the size of the labels
#' and title of the color key.
#' @param zoom logical. Whether to use zoom for interactive zoom. See Details.
#' @param title,xlab,ylab title, \eqn{x}- and \eqn{y}-axis titles; default to
#' none.
#' @param margins numeric vector of length two containing the margins (see
#' \code{\link{par}(mar)}) for column and row names respectively.
#' @param lhei,lwid arguments passed to \code{layout} to divide the device up
#' into two (or three if a side color is drawn) rows and two columns, with the
#' row-heights \code{lhei} and the column-widths \code{lwid}.
#' @param legend A list indicating the legend for each group, the color vector,
#' title of the legend and cex.
#' @param save should the plot be saved? If so, argument to be set to either
#' \code{'jpeg'}, \code{'tiff'}, \code{'png'} or \code{'pdf'}.
#' @param name.save character string for the name of the file to be saved.
#' @return A list containing the following components: \item{M}{the mapped
#' matrix used by \code{cim}.} \item{rowInd, colInd}{row and column index
#' permutation vectors as returned by \code{\link{order.dendrogram}}.}
#' \item{ddr, ddc}{object of class \code{"dendrogram"} which describes the row
#' and column trees produced by \code{cim}.} \item{mat.cor}{the correlation
#' matrix used for the heatmap. Available only when mapping = "XY".}
#' \item{row.names, col.names}{character vectors with row and column labels
#' used.} \item{row.sideColors, col.sideColors}{character vector containing the
#' color names for vertical and horizontal side bars used to annotate the rows
#' and columns.}
#' @author Ignacio González, Francois Bartolo, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{heatmap}}, \code{\link{hclust}}, \code{\link{plotVar}},
#' \code{\link{network}} and
#'
#' \url{http://mixomics.org/graphics/} for more details on all options
#' available.
#' @references Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D.
#' (1998). Cluster analysis and display of genome-wide expression patterns.
#' \emph{Proceeding of the National Academy of Sciences of the USA} \bold{95},
#' 14863-14868.
#'
#' Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace Jr.,
#' A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N.
#' L., Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A.,
#' Sausville, E. A., Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson,
#' G. S., Wittes, R. E. and Paull, K. D. (1997). An information-intensive
#' approach to the molecular pharmacology of cancer. \emph{Science} \bold{275},
#' 343-349.
#'
#' González I., Lê Cao K.A., Davis M.J., Déjean S. (2012). Visualising
#' associations between paired 'omics' data sets. \emph{BioData Mining};
#' \bold{5}(1).
#'
#' mixOmics article:
#'
#' Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics
#' feature selection and multiple data integration. PLoS Comput Biol 13(11):
#' e1005752
#' @keywords multivariate iplot hplot graphs cluster
#' @example ./examples/cim-examples.R
#' @importFrom stats as.dendrogram as.dist
#' @export
cim <-
function(mat = NULL,
color = NULL,
row.names = TRUE,
col.names = TRUE,
row.sideColors = NULL,
col.sideColors = NULL,
row.cex = NULL,
col.cex = NULL,
cutoff = 0,
cluster = "both",
dist.method = c("euclidean", "euclidean"),
clust.method = c("complete", "complete"),
cut.tree = c(0, 0),
transpose = FALSE,
symkey = TRUE,
keysize = c(1, 1),
keysize.label = 1,
zoom = FALSE,
title = NULL,
xlab = NULL,
ylab = NULL,
margins = c(5, 5),
lhei = NULL,
lwid = NULL,
comp = NULL,
center = TRUE,
scale = FALSE,
mapping = "XY",
legend = NULL,
save = NULL,
name.save = NULL)
{
class.object <- class(mat)
#-- checking general input parameters -------------------------------------
#--------------------------------------------------------------------------#
#-- check that the user did not enter extra arguments
arg.call = match.call()
user.arg = names(arg.call)[-1]
err = tryCatch(
mget(names(formals()), sys.frame(sys.nframe())),
error = function(e)
e
)
if ("simpleError" %in% class(err))
stop(err[[1]], ".", call. = FALSE)
#-- color
if (is.null(color))
color = color.spectral(25)
#-- cluster
choices = c("both", "row", "column", "none")
cluster = choices[pmatch(cluster, choices)]
if (is.na(cluster))
stop("'cluster' should be one of 'both', 'row', 'column' or 'none'.",
call. = FALSE)
#-- cluster method
if (!is.character(clust.method) |
length(as.vector(clust.method)) != 2)
stop("'clust.method' must be a character vector of length 2.",
call. = FALSE)
choices = c("ward.D",
"single",
"complete",
"average",
"mcquitty",
"median",
"centroid")
clust.method = choices[c(pmatch(clust.method[1], choices),
pmatch(clust.method[2], choices))]
if (any(is.na(clust.method)))
stop("invalid clustering method.", call. = FALSE)
#-- distance method
if (!is.character(dist.method) |
length(as.vector(dist.method)) != 2)
stop("'dist.method' must be a character vector of length 2.", call. = FALSE)
choices = c(
"euclidean",
"correlation",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski"
)
dist.method = choices[c(pmatch(dist.method[1], choices),
pmatch(dist.method[2], choices))]
if (any(is.na(dist.method)))
stop("invalid distance method.", call. = FALSE)
#-- checking general input arguments -------------------------------------
#-------------------------------------------------------------------------#
#-- color
if (any(!sapply(color, function(color) {
tryCatch(
is.matrix(col2rgb(color)),
error = function(e)
FALSE
)
})))
stop("'color' must be a character vector of recognized colors.",
call. = FALSE)
#-- row.sideColors
if (any(!sapply(row.sideColors, function(row.sideColors) {
tryCatch(
is.matrix(col2rgb(row.sideColors)),
error = function(e)
FALSE
)
})))
stop(
"color names for vertical side bar must be a character vector
of recognized colors.",
call. = FALSE
)
#-- col.sideColors
if (any(!sapply(col.sideColors, function(col.sideColors) {
tryCatch(
is.matrix(col2rgb(col.sideColors)),
error = function(e)
FALSE
)
})))
stop(
"color names for horizontal side bar must be a character vector
of recognized colors.",
call. = FALSE
)
#-- row.cex
if (!is.null(row.cex)) {
if (!is.numeric(row.cex) || length(row.cex) != 1)
stop("'row.cex' must be a numerical value.", call. = FALSE)
}
#-- col.cex
if (!is.null(col.cex)) {
if (!is.numeric(col.cex) || length(col.cex) != 1)
stop("'col.cex' must be a numerical value.", call. = FALSE)
}
#-- transpose
if (!is.logical(transpose))
stop("'transpose' must be a logical constant (TRUE or FALSE).",
call. = FALSE)
#-- cut.tree
if (!is.numeric(cut.tree) || length(cut.tree) != 2)
stop("'cut.tree' must be a numeric vector of length 2.",
call. = FALSE)
else {
if (!(all(0 <= cut.tree & cut.tree <= 1)))
stop("Components of 'cut.tree' must be between 0 and 1.",
call. = FALSE)
}
#-- keysize
if (length(keysize) != 2 || any(!is.finite(keysize)))
stop("'keysize' must be a numeric vector of length 2.",
call. = FALSE)
#-- keysize.label
if (length(keysize.label) != 1 || any(!is.finite(keysize)))
stop("'keysize' must be a numeric vector of length 1.",
call. = FALSE)
#-- zoom
if (!is.logical(zoom))
stop("'zoom' must be a logical constant (TRUE or FALSE).",
call. = FALSE)
#-- margins
if (!is.numeric(margins) || length(margins) != 2)
stop("'margins' must be a numeric vector of length 2.",
call. = FALSE)
#-- symkey
if (!is.logical(symkey))
stop("'symkey' must be a logical constant (TRUE or FALSE).",
call. = FALSE)
#-- lhei
if (!is.null(lhei)) {
if (is.null(col.sideColors)) {
if (length(lhei) != 2 | !is.numeric(lhei) | any(is.na(lhei)))
stop("'lhei' must be a numeric vector of length 2.",
call. = FALSE)
}
else {
if (length(lhei) != 3 | !is.numeric(lhei) | any(is.na(lhei)))
stop("'lhei' must be a numeric vector of length 3.",
call. = FALSE)
}
}
#-- lwid
if (!is.null(lwid)) {
if (is.null(row.sideColors)) {
if (length(lwid) != 2 | !is.numeric(lwid) | any(is.na(lwid)))
stop("'lwid' must be a numeric vector of length 2.",
call. = FALSE)
}
else {
if (length(lwid) != 3 | !is.numeric(lwid) | any(is.na(lwid)))
stop("'lwid' must be a numeric vector of length 3.",
call. = FALSE)
}
}
#-- xlab
xlab = as.graphicsAnnot(xlab)
#-- ylab
ylab = as.graphicsAnnot(ylab)
#-- title
title = as.graphicsAnnot(title)
#-- threshold correlation
if (!is.numeric(cutoff) | (cutoff > 1) | (cutoff < 0))
stop("The value taken by 'cutoff' must be between 0 and 1",
call. = FALSE)
#-- save
if (!is.null(save)) {
if (!save %in% c("jpeg", "tiff", "png", "pdf"))
stop("'save' must be one of 'jpeg', 'png', 'tiff' or 'pdf'.",
call. = FALSE)
}
#-- name.save
if (!is.null(name.save)) {
if (!is.character(name.save) || length(name.save) > 1)
stop("'name.save' must be a character.", call. = FALSE)
} else {
if (!is.null(save))
name.save = paste0("cim_", gsub(".", "_", deparse(substitute(mat)),
fixed = TRUE))
}
#-- end checking --#
#------------------#
if (!is.null(save)) {
while (dev.cur() > 2)
dev.off()
if (save == "jpeg")
jpeg(
filename = paste0(name.save, ".jpeg"),
res = 600,
width = 4000,
height = 4000
)
if (save == "png")
jpeg(
filename = paste0(name.save, ".png"),
res = 600,
width = 4000,
height = 4000
)
if (save == "tiff")
tiff(
filename = paste0(name.save, ".tiff"),
res = 600,
width = 4000,
height = 4000
)
if (save == "pdf")
pdf(file = paste0(name.save, ".pdf"))
}
object.pca = c("pca",
"spca",
"ipca",
"sipca",
"mixo_mlsplsda",
"mixo_splsda",
"mixo_plsda")
object.rcc = c("rcc")
object.pls = c("mixo_pls", "mixo_spls", "mixo_mlspls")
object.list = c(
"pca",
"spca",
"ipca",
"sipca",
"mixo_mlsplsda",
"mixo_splsda",
"mixo_plsda",
"rcc",
"mixo_pls",
"mixo_spls",
"mixo_mlspls"
)
if (any(class.object == "block.splsda"))
stop("Please call the 'cimDiablo' function on your 'block.splsda' object",
call. = FALSE)
if (!any(class.object %in% c(object.list, "matrix")))
stop(
"'mat' has to be a matrix or one of the following object: ",
paste(object.list, collapse = ", "),
".",
call. = FALSE
)
#-- if mixOmics class
if (any(class.object %in% object.list))
{
#-- general checks -------------
p = ncol(mat$X)
q = ncol(mat$Y)
n = nrow(mat$X)
ncomp = mat$ncomp
#-- comp
if (is.null(comp)) {
comp = 1:mat$ncomp
}
if (length(comp) > 1) {
comp = unique(comp)
if (!is.numeric(comp) || any(comp < 1))
stop("invalid vector for 'comp'.", call. = FALSE)
if (any(comp > ncomp))
stop("the elements of 'comp' must be <= ",
ncomp,
".",
call. = FALSE)
}
if (length(comp) == 1) {
if (is.null(comp) || !is.numeric(comp) || comp <= 0 || comp > ncomp)
stop("invalid value for 'comp'.", call. = FALSE)
# comp <- c(comp, comp)
}
comp <- round(comp)
# if object is a pls or spls with a univariate Y, or multivariate but
# only 1 variable selected on all comp, we only plot a heatmap of X
if (any(class.object %in% object.pls))
{
temp = apply(mat$loadings$Y, 2,
function(x) {
which(x != 0, arr.ind = TRUE)
})
# gives which variables are selected
num.variable.selected.Y = table(unlist(temp))
if (length(num.variable.selected.Y) == 1)
#only one variable in Y to plot, will raise trouble so we
# switch from (s)pls to (s)plsda
class.object = "mixo_splsda"
}
#-- if c("mixo_pls","mixo_spls","mixo_mlspls") or pls with univarite Y ----
## or multivariate but only one Y kept in sparse model
if (!any(class.object %in% object.pca)) {
#-- mapping
choices = c("XY", "X", "Y")
mapping = choices[pmatch(mapping, choices)]
if (is.na(mapping))
stop("'mapping' should be one of 'XY', 'X' or 'Y'.", call. = FALSE)
if (mapping == "XY")
{
if (is.logical(row.names))
{
if (isTRUE(row.names))
row.names = mat$names$colnames$X
else
row.names = rep("", p)
} else {
if (length(row.names) != p)
stop("'row.names' must be a character vector of length ",
p,
".",
call. = FALSE)
}
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$Y
else
col.names = rep("", q)
} else {
if (length(col.names) != q)
stop("'col.names' must be a character vector of length ",
q,
".",
call. = FALSE)
}
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != p)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
p,
".",
call. = FALSE
)
}
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != q)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
q,
".",
call. = FALSE
)
}
}
if (mapping == "X")
{
if (is.logical(row.names))
{
if (isTRUE(row.names)) {
row.names = mat$names$sample
}
else
row.names = rep("", n)
} else {
if (length(row.names) != n)
stop("'row.names' must be a character vector of length ",
n,
".",
call. = FALSE)
}
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$X
else
col.names = rep("", p)
} else {
if (length(col.names) != p)
stop("'col.names' must be a character vector of length ",
p,
".",
call. = FALSE)
}
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != n)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
n,
".",
call. = FALSE
)
}
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != p)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
p,
".",
call. = FALSE
)
}
}
if (mapping == "Y")
{
if (is.logical(row.names))
{
if (isTRUE(row.names))
{
if (any(class.object %in% object.rcc))
row.names = mat$names$sample
else
row.names = mat$names$sample
}
else
row.names = rep("", n)
} else {
if (length(row.names) != n)
stop("'row.names' must be a character vector of length ",
n,
".",
call. = FALSE)
}
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$Y
else
col.names = rep("", q)
} else {
if (length(col.names) != q)
stop("'col.names' must be a character vector of length ",
q,
".",
call. = FALSE)
}
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != n)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
n,
".",
call. = FALSE
)
}
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != q)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
q,
".",
call. = FALSE
)
}
}
}
#-- if NOT c("mixo_pls","mixo_spls","mixo_mlspls") or pls with univarite Y ----
if (any(class.object %in% object.pca))
{
#-- row.sideColors
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != n)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
n,
".",
call. = FALSE
)
}
#-- col.sideColors
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != p)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
p,
".",
call. = FALSE
)
}
sample.sideColors = row.sideColors
## ----- DA object
if (any(class.object %in% c("mixo_splsda", "mixo_plsda",
'mixo_mlsplsda')))
{
#-- row.names
if (is.logical(row.names))
{
if (isTRUE(row.names))
row.names = mat$names$sample
}
#-- col.names
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$X
}
if (any(class.object %in% c("mixo_splsda", 'mixo_mlsplsda')))
keep.X = apply(abs(mat$loadings$X[, comp, drop = FALSE]), 1,
sum) > 0
else
keep.X = apply(abs(mat$loadings$X), 1, sum) > 0
cord.X = cor(mat$X[, keep.X], mat$variates$X[, comp],
use = "pairwise")
X.mat = as.matrix(mat$variates$X[, comp])
}
else{
#-- row.names
if (is.logical(row.names))
{
if (isTRUE(row.names))
row.names = mat$names$sample
}
#-- col.names
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$X
}
if (any(class.object %in% c("spca", "sipca")))
keep.X = apply(abs(mat$rotation[, comp]), 1, sum) > 0
else
keep.X = apply(abs(mat$rotation), 1, sum) > 0
cord.X = cor(mat$X[, keep.X], mat$x[, comp], use = "pairwise")
X.mat = as.matrix(mat$x[, comp])
}
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$X)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$X)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
## ---- clustering ----------------------------------------------
object = scale(mat$X[, keep.X], center = center, scale = scale)
col.names = col.names[keep.X]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.X,])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(X.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
X.mat
)),
method = "pearson"))
else
dist.mat = dist(X.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.X)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.X), method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
#-- calling the image.map function --------------------------------#
#------------------------------------------------------------------#
#-- output --------------------------------------------------------#
#------------------------------------------------------------------#
res = list(mat = object,
row.names = row.names,
col.names = col.names)
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = paste("cim", class.object[1], sep = "_")
}
else if (any(class.object %in% object.rcc))
{
bisect = mat$variates$X[, comp] + mat$variates$Y[, comp]
cord.X = cor(mat$X, bisect, use = "pairwise")
cord.Y = cor(mat$Y, bisect, use = "pairwise")
XY.mat = as.matrix(cord.X %*% t(cord.Y))
#-- if mapping = "XY"
if (mapping == "XY") {
object = XY.mat
cut = list()
if (cutoff != 0) {
cut[[1]] = unlist(lapply(1:nrow(object),
function(x) {
any(abs(object[x, ]) > cutoff)
}))
object = object[cut[[1]], ]
if (dist.method[1] != "correlation")
cord.X = cord.X[cut[[1]], ]
if (is.null(nrow(object)) || nrow(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
cut[[2]] = unlist(lapply(1:ncol(object),
function(x) {
any(abs(object[, x]) > cutoff)
}))
object = object[, cut[[2]]]
if (dist.method[2] != "correlation")
cord.Y = cord.Y[cut[[2]], ]
if (is.null(ncol(object)) || ncol(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
}
if ((cluster == "both") || (cluster == "row")) {
#Rowv = rowMeans(XY.mat)
Rowv = rowMeans(cord.X)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
object
)),
method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[1])
if (cutoff > 0) {
row.names = row.names[cut[[1]]]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[cut[[1]],])
}
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(as.matrix(object),
method = "pearson"))
else
dist.mat = dist(cord.Y, method = dist.method[2])
if (cutoff > 0) {
col.names = col.names[cut[[2]]]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[cut[[2]],])
}
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "X"
if (mapping == "X") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$X)))
stop(
"'center' should be either a logical value or a
numeric vector of length equal to the number of columns of
'X'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$X)))
stop(
"'scale' should be either a logical value or a
numeric vector of length equal to the number of columns of
'X'.",
call. = FALSE
)
}
object = scale(mat$X, center = center, scale = scale)
X.mat = as.matrix(mat$variates$X[, comp])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(X.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
X.mat
)),
method = "pearson"))
else
dist.mat = dist(X.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.X)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.X), method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "Y"
if (mapping == "Y") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$Y)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$Y)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
object = scale(mat$Y, center = center, scale = scale)
Y.mat = as.matrix(mat$variates$Y[, comp])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(Y.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
Y.mat
)),
method = "pearson"))
else
dist.mat = dist(Y.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.Y), method = "pearson"))
else
dist.mat = dist(cord.Y, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- calling the image.map function --------------------------------#
#------------------------------------------------------------------#
#-- output --------------------------------------------------------#
#------------------------------------------------------------------#
res = list(mat = object,
row.names = row.names,
col.names = col.names)
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = "cim_rcc"
}
else if (any(class.object %in% object.pls))
{
if (any(class.object %in% c("mixo_spls", "mixo_mlspls")))
{
keep.X = apply(abs(mat$loadings$X[, comp, drop = FALSE]), 1,
sum) > 0
keep.Y = apply(abs(mat$loadings$Y[, comp, drop = FALSE]), 1,
sum) > 0
}
else
{
keep.X = apply(abs(mat$loadings$X), 1, sum) > 0
keep.Y = apply(abs(mat$loadings$Y), 1, sum) > 0
}
if (mat$mode == "canonical") {
bisect = mat$variates$X[, comp] + mat$variates$Y[, comp]
cord.X = cor(mat$X[, keep.X, drop = FALSE],
bisect, use = "pairwise")
cord.Y = cor(mat$Y[, keep.Y, drop = FALSE],
bisect, use = "pairwise")
}
else {
cord.X = cor(mat$X[, keep.X, drop = FALSE],
mat$variates$X[, comp], use = "pairwise")
cord.Y = cor(mat$Y[, keep.Y, drop = FALSE],
mat$variates$X[, comp], use = "pairwise")
}
XY.mat = as.matrix(cord.X %*% t(cord.Y))
sample.sideColors = row.sideColors
#-- if mapping = "XY"
if (mapping == "XY") {
object = XY.mat
row.names = row.names[keep.X]
col.names = col.names[keep.Y]
cut = list()
if (cutoff != 0) {
cut[[1]] = unlist(lapply(1:nrow(object),
function(x) {
any(abs(object[x, ]) > cutoff)
}))
object = object[cut[[1]], ]
if (dist.method[1] != "correlation")
cord.X = cord.X[cut[[1]], ]
if (is.null(nrow(object)) || nrow(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
cut[[2]] = unlist(lapply(1:ncol(object),
function(x) {
any(abs(object[, x]) > cutoff)
}))
object = object[, cut[[2]]]
if (dist.method[2] != "correlation")
cord.Y = cord.Y[cut[[2]], ]
if (is.null(ncol(object)) || ncol(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
}
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[keep.X,])
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.Y,])
if ((cluster == "both") || (cluster == "row")) {
#Rowv = rowMeans(XY.mat)
Rowv = rowMeans(cord.X)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
object
)),
method = "pearson"))
else
#dist.mat = dist(mat, method = dist.method[1])
dist.mat = dist(cord.X, method = dist.method[1])
if (cutoff > 0) {
row.names = row.names[cut[[1]]]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[cut[[1]],])
}
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
#Colv = colMeans(mat)
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(as.matrix(object),
method = "pearson"))
else
#dist.mat = dist(t(mat), method = dist.method[2])
dist.mat = dist(cord.Y, method = dist.method[2])
if (cutoff > 0) {
col.names = col.names[cut[[2]]]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[cut[[2]],])
}
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "X"
if (mapping == "X") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$X)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$X)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
object = scale(mat$X[, keep.X], center = center, scale = scale)
X.mat = as.matrix(mat$variates$X[, comp])
col.names = col.names[keep.X]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.X,])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(X.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
X.mat
)),
method = "pearson"))
else
dist.mat = dist(X.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.X)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.X), method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "Y"
if (mapping == "Y") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$Y)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$Y)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
object = scale(mat$Y[, keep.Y], center = center, scale = scale)
Y.mat = as.matrix(mat$variates$Y[, comp])
col.names = col.names[keep.Y]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.Y,])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(Y.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
Y.mat
)),
method = "pearson"))
else
dist.mat = dist(Y.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.Y), method = "pearson"))
else
dist.mat = dist(cord.Y, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- calling the image.map function --------------------------------#
#------------------------------------------------------------------#
#-- output --------------------------------------------------------#
#------------------------------------------------------------------#
res = list(mat = object,
row.names = row.names,
col.names = col.names)
if (!is.null(sample.sideColors)) {
res$sample.sideColors = sample.sideColors
}
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = paste("cim", class.object[1], sep = "_")
}
} else {
#-- if matrix class -------------------------------------
#-- mat
isMat = tryCatch(
is.matrix(mat),
error = function(e)
e
)
if ("simpleError" %in% class(isMat))
stop(isMat[[1]], ".", call. = FALSE)
if (!is.matrix(mat) || !is.numeric(mat))
stop("'mat' must be a numeric matrix.", call. = FALSE)
p = nrow(mat)
q = ncol(mat)
#-- row.names
if (is.logical(row.names)) {
if (isTRUE(row.names))
row.names = rownames(mat)
else
row.names = rep("", p)
}
else {
row.names = as.vector(row.names)
if (length(row.names) != p)
stop("'row.names' must be a character vector of length ",
p,
".",
call. = FALSE)
}
#-- col.names
if (is.logical(col.names)) {
if (isTRUE(col.names))
col.names = colnames(mat)
else
col.names = rep("", q)
}
else {
col.names = as.vector(col.names)
if (length(col.names) != q)
stop("'col.names' must be a character vector of length ",
q,
".",
call. = FALSE)
}
#-- row.sideColors
if (!is.null(row.sideColors)) {
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != p)
stop(
"'row.sideColors' must be a colors character vector (matrix)
of length (nrow) ",
p,
".",
call. = FALSE
)
}
#-- col.sideColors
if (!is.null(col.sideColors)) {
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != q)
stop(
"'col.sideColors' must be a colors character vector (matrix)
of length (nrow) ",
q,
".",
call. = FALSE
)
}
#-- clustering -------------------------------------------------------#
#---------------------------------------------------------------------#
object = mat
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(mat)), method = "pearson"))
else
dist.mat = dist(mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = mat[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = colMeans(mat)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(as.matrix(mat), method = "pearson"))
else
dist.mat = dist(t(mat), method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
#-- calling the image.map function ------------------------------------#
#-- output ------------------------------------------------------------#
#----------------------------------------------------------------------#
res = list(
mat = object,
row.names = row.names,
col.names = col.names,
row.sideColors = row.sideColors,
col.sideColors = col.sideColors
)
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = "cim_default"
}
#-- call imageMap -------------------------------------
opar = par(no.readonly = TRUE)
try_plot <- tryCatch({
imageMap(
object,
color = color,
row.names = row.names,
col.names = col.names,
row.sideColors = row.sideColors,
col.sideColors = col.sideColors,
row.cex = row.cex,
col.cex = col.cex,
cluster = cluster,
ddr = ddr,
ddc = ddc,
cut.tree = cut.tree,
transpose = transpose,
symkey = symkey,
keysize = keysize,
keysize.label = keysize.label,
zoom = zoom,
title = title,
xlab = xlab,
ylab = ylab,
margins = margins,
lhei = lhei,
lwid = lwid
)
}, error = function(e)
e)
if (is(try_plot, "error")) {
message(sprintf(
"Error in cim plot: %s. See ?cim for help.",
try_plot$message
))
} else {
#-- add to plot -------------------------------------
if (!is.null(legend))
{
if (is.null(legend$x))
legend$x = "topright"
if (is.null(legend$bty))
legend$bty = "n"
if (is.null(legend$cex))
legend$cex = 0.8
if (any(class.object %in% c("mixo_splsda", "mixo_plsda")))
{
if (is.null(legend$legend))
legend$legend = mat$names$colnames$Y
#-- col
if (is.null(legend$col)) {
if (!is.null(sample.sideColors))
legend$col = unique(as.matrix(sample.sideColors[order(map(mat$ind.mat)), 1]))
}
}
else if (any(class.object %in% c("mixo_mlsplsda")))
{
if (is.null(legend$legend) && is.null(legend$col)) {
if (ncol(mat$multilevel) >= 2) {
df = data.frame(mat$multilevel[, 2], sample.sideColors[, 1])
df = unique(df)
legend$legend = as.character(df[, 1])
legend$col = as.character(df[, 2])
}
if (ncol(mat$multilevel) == 3) {
df = data.frame(mat$multilevel[, 3], sample.sideColors[, 2])
df = unique(df)
legend$legend = c(legend$legend, as.character(df[, 1]))
legend$col = c(legend$col, as.character(df[, 2]))
}
}
}
else if (any(class.object %in% c("mixo_mlspls")))
{
if (mapping != "XY") {
if (is.null(legend$legend) && is.null(legend$col)) {
if (ncol(mat$multilevel) >= 2) {
df = data.frame(mat$multilevel[, 2],
sample.sideColors[, 1])
df = unique(df)
legend$legend = as.character(df[, 1])
legend$col = as.character(df[, 2])
}
if (ncol(mat$multilevel) == 3) {
df = data.frame(mat$multilevel[, 3],
sample.sideColors[, 2])
df = unique(df)
legend$legend = c(legend$legend,
as.character(df[, 1]))
legend$col = c(legend$col, as.character(df[, 2]))
}
}
}
}
if (is.null(legend$legend))
stop("argument \"legend$legend\" is missing, with no default")
#-- fill
if (is.null(legend$fill))
legend$fill = legend$col
par(mar = c(0, 0, 0, 0), new = TRUE)
plot(
0,
0,
axes = FALSE,
type = "n",
xlab = "",
ylab = ""
)
if (!is.null(legend$title))
{
legend(
x = legend$x,
y = legend$y,
legend = legend$legend,
col = legend$col,
fill = legend$fill,
bty = legend$bty,
title = legend$title,
cex = legend$cex
)
} else{
legend(
x = legend$x,
y = legend$y,
legend = legend$legend,
col = legend$col,
fill = legend$fill,
bty = legend$bty,
cex = legend$cex
)
}
}
if (any(class.object %in% object.list) &
!any(class.object %in%
object.pca) &
mapping == "XY")
res$mat.cor = object
par(opar)
if (!is.null(save))
dev.off()
}
return(invisible(res))
}
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Defines functions cim
Documented in cim
# -------------------------------------------- #
# CIM for objects "pca","spca","ipca","sipca","mlsplsda","splsda","plsda","rcc",
# "pls","spls","mlspls"
# -------------------------------------------- #
#' Clustered Image Maps (CIMs) ("heat maps")
#'
#' This function generates color-coded Clustered Image Maps (CIMs) ("heat
#' maps") to represent "high-dimensional" data sets.
#'
#' One matrix Clustered Image Map (default method) is a 2-dimensional
#' visualization of a real-valued matrix (basically
#' \code{\link{image}(t(mat))}) with rows and/or columns reordered according to
#' some hierarchical clustering method to identify interesting patterns.
#' Generated dendrograms from clustering are added to the left side and to the
#' top of the image. By default the used clustering method for rows and columns
#' is the \emph{complete linkage} method and the used distance measure is the
#' distance \emph{euclidean}.
#'
#' In \code{"pca"}, \code{"spca"}, \code{"ipca"}, \code{"sipca"},
#' \code{"plsda"}, \code{"splsda"} and multilevel variants methods the
#' \code{mat} matrix is \code{object$X}.
#'
#' For the remaining methods, if \code{mapping = "X"} or \code{mapping = "Y"}
#' the \code{mat} matrix is \code{object$X} or \code{object$Y} respectively. If
#' \code{mapping = "XY"}: \itemize{ \item in \code{rcc} method, the matrix
#' \code{mat} is created where element \eqn{(j,k)} is the scalar product value
#' between every pairs of vectors in dimension \code{length(comp)} representing
#' the variables \eqn{X_j} and \eqn{Y_k} on the axis defined by \eqn{Z_i} with
#' \eqn{i} in \code{comp}, where \eqn{Z_i} is the equiangular vector between
#' the \eqn{i}-th \eqn{X} and \eqn{Y} canonical variate.
#'
#' \item in \code{pls}, \code{spls} and multilevel spls methods, if
#' \code{object$mode} is \code{"regression"}, the element \eqn{(j,k)} of the
#' matrix \code{mat} is given by the scalar product value between every pairs
#' of vectors in dimension \code{length(comp)} representing the variables
#' \eqn{X_j} and \eqn{Y_k} on the axis defined by \eqn{U_i} with \eqn{i} in
#' \code{comp}, where \eqn{U_i} is the \eqn{i}-th \eqn{X} variate. If
#' \code{object$mode} is \code{"canonical"} then \eqn{X_j} and \eqn{Y_k} are
#' represented on the axis defined by \eqn{U_i} and \eqn{V_i} respectively.}
#'
#' By default four components will be displayed in the plot. At the top left is
#' the color key, top right is the column dendogram, bottom left is the row
#' dendogram, bottom right is the image plot. When \code{sideColors} are
#' provided, an additional row or column is inserted in the appropriate
#' location. This layout can be overriden by specifiying appropriate values for
#' \code{lwid} and \code{lhei}. \code{lwid} controls the column width, and
#' \code{lhei} controls the row height. See the help page for
#' \code{\link{layout}} for details on how to use these arguments.
#'
#' For visualization of "high-dimensional" data sets, a nice zooming tool was
#' created. \code{zoom = TRUE} open a new device, one for CIM, one for zoom-out
#' region and define an interactive 'zoom' process: click two points at imagen
#' map region by pressing the first mouse button. It then draws a rectangle
#' around the selected region and zoom-out this at new device. The process can
#' be repeated to zoom-out other regions of interest.
#'
#' The zoom process is terminated by clicking the second button and selecting
#' 'Stop' from the menu, or from the 'Stop' menu on the graphics window.
#'
#' @param mat numeric matrix of values to be plotted. Alternatively, an object
#' of class inheriting from \code{"pca"}, \code{"spca"}, \code{"ipca"},
#' \code{"sipca"}, \code{"rcc"}, \code{"pls"}, \code{"spls"}, \code{"plsda"},
#' \code{"splsda"}, \code{"mlspls"} or \code{"mlsplsda"} (where \code{"ml"}
#' stands for multilevel).
#' @param color a character vector of colors such as that generated by
#' \code{\link{terrain.colors}}, \code{\link{topo.colors}},
#' \code{\link{rainbow}}, \code{\link{color.jet}} or similar functions.
#' @param row.names,col.names logical, should the name of rows and/or columns
#' of \code{mat} be shown? If \code{TRUE} (defaults) \code{rownames(mat)}
#' and/or \code{colnames(mat)} are used. Possible character vectors with row
#' and/or column labels can be used.
#' @param row.sideColors (optional) character vector of length \code{nrow(mat)}
#' containing the color names for a vertical side bar that may be used to
#' annotate the rows of \code{mat}.
#' @param col.sideColors (optional) character vector of length \code{ncol(mat)}
#' containing the color names for a horizontal side bar that may be used to
#' annotate the columns of \code{mat}.
#' @param row.cex,col.cex positive numbers, used as \code{cex.axis} in for the
#' row or column axis labeling. The defaults currently only use number of rows
#' or columns, respectively.
#' @param mapping character string indicating whether to map \code{"X"},
#' \code{"Y"} or \code{"XY"}-association matrix. See Details.
#' @param cluster character string indicating whether to cluster \code{"none"},
#' \code{"row"}, \code{"column"} or \code{"both"}. Defaults to \code{"both"}.
#' @param dist.method character vector of length two. The distance measure used
#' in clustering rows and columns. Possible values are \code{"correlation"} for
#' Pearson correlation and all the distances supported by \code{\link{dist}},
#' such as \code{"euclidean"}, etc.
#' @param clust.method character vector of length two. The agglomeration method
#' to be used for rows and columns. Accepts the same values as in
#' \code{\link{hclust}} such as \code{"ward"}, \code{"complete"}, etc.
#' @param cut.tree numeric vector of length two with components in [0,1]. The
#' height proportions where the trees should be cut for rows and columns, if
#' these are clustered.
#' @param comp atomic or vector of positive integers. The components to
#' adequately account for the data association. For a non sparse method, the
#' similarity matrix is computed based on the variates and loading vectors of
#' those specified components. For a sparse approach, the similarity matric is
#' computed based on the variables selected on those specified components. See
#' example. Defaults to \code{comp = 1:object$ncomp}.
#' @param transpose logical indicating if the matrix should be transposed for
#' plotting. Defaults to \code{FALSE}.
#' @param center either a logical value or a numeric vector of length equal to
#' the number of columns of \code{mat}. See \code{\link{scale}} function.
#' @param scale either a logical value or a numeric vector of length equal to
#' the number of columns of \code{mat}. See \code{\link{scale}} function.
#' @param cutoff numeric between 0 and 1. Variables with correlations below
#' this threshold in absolute value are not plotted. To use only when mapping
#' is "XY".
#' @param symkey Logical indicating whether the color key should be made
#' symmetric about 0. Defaults to \code{TRUE}.
#' @param keysize vector of length two, indicating the size of the color key.
#' @param keysize.label vector of length 1, indicating the size of the labels
#' and title of the color key.
#' @param zoom logical. Whether to use zoom for interactive zoom. See Details.
#' @param title,xlab,ylab title, \eqn{x}- and \eqn{y}-axis titles; default to
#' none.
#' @param margins numeric vector of length two containing the margins (see
#' \code{\link{par}(mar)}) for column and row names respectively.
#' @param lhei,lwid arguments passed to \code{layout} to divide the device up
#' into two (or three if a side color is drawn) rows and two columns, with the
#' row-heights \code{lhei} and the column-widths \code{lwid}.
#' @param legend A list indicating the legend for each group, the color vector,
#' title of the legend and cex.
#' @param save should the plot be saved? If so, argument to be set to either
#' \code{'jpeg'}, \code{'tiff'}, \code{'png'} or \code{'pdf'}.
#' @param name.save character string for the name of the file to be saved.
#' @return A list containing the following components: \item{M}{the mapped
#' matrix used by \code{cim}.} \item{rowInd, colInd}{row and column index
#' permutation vectors as returned by \code{\link{order.dendrogram}}.}
#' \item{ddr, ddc}{object of class \code{"dendrogram"} which describes the row
#' and column trees produced by \code{cim}.} \item{mat.cor}{the correlation
#' matrix used for the heatmap. Available only when mapping = "XY".}
#' \item{row.names, col.names}{character vectors with row and column labels
#' used.} \item{row.sideColors, col.sideColors}{character vector containing the
#' color names for vertical and horizontal side bars used to annotate the rows
#' and columns.}
#' @author Ignacio González, Francois Bartolo, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{heatmap}}, \code{\link{hclust}}, \code{\link{plotVar}},
#' \code{\link{network}} and
#'
#' \url{http://mixomics.org/graphics/} for more details on all options
#' available.
#' @references Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D.
#' (1998). Cluster analysis and display of genome-wide expression patterns.
#' \emph{Proceeding of the National Academy of Sciences of the USA} \bold{95},
#' 14863-14868.
#'
#' Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace Jr.,
#' A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L. V., Anderson, N.
#' L., Buolamwini, J. K., van Osdol, W. W., Monks, A. P., Scudiero, D. A.,
#' Sausville, E. A., Zaharevitz, D. W., Bunow, B., Viswanadhan, V. N., Johnson,
#' G. S., Wittes, R. E. and Paull, K. D. (1997). An information-intensive
#' approach to the molecular pharmacology of cancer. \emph{Science} \bold{275},
#' 343-349.
#'
#' González I., Lê Cao K.A., Davis M.J., Déjean S. (2012). Visualising
#' associations between paired 'omics' data sets. \emph{BioData Mining};
#' \bold{5}(1).
#'
#' mixOmics article:
#'
#' Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics
#' feature selection and multiple data integration. PLoS Comput Biol 13(11):
#' e1005752
#' @keywords multivariate iplot hplot graphs cluster
#' @example ./examples/cim-examples.R
#' @importFrom stats as.dendrogram as.dist
#' @export
cim <-
function(mat = NULL,
color = NULL,
row.names = TRUE,
col.names = TRUE,
row.sideColors = NULL,
col.sideColors = NULL,
row.cex = NULL,
col.cex = NULL,
cutoff = 0,
cluster = "both",
dist.method = c("euclidean", "euclidean"),
clust.method = c("complete", "complete"),
cut.tree = c(0, 0),
transpose = FALSE,
symkey = TRUE,
keysize = c(1, 1),
keysize.label = 1,
zoom = FALSE,
title = NULL,
xlab = NULL,
ylab = NULL,
margins = c(5, 5),
lhei = NULL,
lwid = NULL,
comp = NULL,
center = TRUE,
scale = FALSE,
mapping = "XY",
legend = NULL,
save = NULL,
name.save = NULL)
{
class.object <- class(mat)
#-- checking general input parameters -------------------------------------
#--------------------------------------------------------------------------#
#-- check that the user did not enter extra arguments
arg.call = match.call()
user.arg = names(arg.call)[-1]
err = tryCatch(
mget(names(formals()), sys.frame(sys.nframe())),
error = function(e)
e
)
if ("simpleError" %in% class(err))
stop(err[[1]], ".", call. = FALSE)
#-- color
if (is.null(color))
color = color.spectral(25)
#-- cluster
choices = c("both", "row", "column", "none")
cluster = choices[pmatch(cluster, choices)]
if (is.na(cluster))
stop("'cluster' should be one of 'both', 'row', 'column' or 'none'.",
call. = FALSE)
#-- cluster method
if (!is.character(clust.method) |
length(as.vector(clust.method)) != 2)
stop("'clust.method' must be a character vector of length 2.",
call. = FALSE)
choices = c("ward.D",
"single",
"complete",
"average",
"mcquitty",
"median",
"centroid")
clust.method = choices[c(pmatch(clust.method[1], choices),
pmatch(clust.method[2], choices))]
if (any(is.na(clust.method)))
stop("invalid clustering method.", call. = FALSE)
#-- distance method
if (!is.character(dist.method) |
length(as.vector(dist.method)) != 2)
stop("'dist.method' must be a character vector of length 2.", call. = FALSE)
choices = c(
"euclidean",
"correlation",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski"
)
dist.method = choices[c(pmatch(dist.method[1], choices),
pmatch(dist.method[2], choices))]
if (any(is.na(dist.method)))
stop("invalid distance method.", call. = FALSE)
#-- checking general input arguments -------------------------------------
#-------------------------------------------------------------------------#
#-- color
if (any(!sapply(color, function(color) {
tryCatch(
is.matrix(col2rgb(color)),
error = function(e)
FALSE
)
})))
stop("'color' must be a character vector of recognized colors.",
call. = FALSE)
#-- row.sideColors
if (any(!sapply(row.sideColors, function(row.sideColors) {
tryCatch(
is.matrix(col2rgb(row.sideColors)),
error = function(e)
FALSE
)
})))
stop(
"color names for vertical side bar must be a character vector
of recognized colors.",
call. = FALSE
)
#-- col.sideColors
if (any(!sapply(col.sideColors, function(col.sideColors) {
tryCatch(
is.matrix(col2rgb(col.sideColors)),
error = function(e)
FALSE
)
})))
stop(
"color names for horizontal side bar must be a character vector
of recognized colors.",
call. = FALSE
)
#-- row.cex
if (!is.null(row.cex)) {
if (!is.numeric(row.cex) || length(row.cex) != 1)
stop("'row.cex' must be a numerical value.", call. = FALSE)
}
#-- col.cex
if (!is.null(col.cex)) {
if (!is.numeric(col.cex) || length(col.cex) != 1)
stop("'col.cex' must be a numerical value.", call. = FALSE)
}
#-- transpose
if (!is.logical(transpose))
stop("'transpose' must be a logical constant (TRUE or FALSE).",
call. = FALSE)
#-- cut.tree
if (!is.numeric(cut.tree) || length(cut.tree) != 2)
stop("'cut.tree' must be a numeric vector of length 2.",
call. = FALSE)
else {
if (!(all(0 <= cut.tree & cut.tree <= 1)))
stop("Components of 'cut.tree' must be between 0 and 1.",
call. = FALSE)
}
#-- keysize
if (length(keysize) != 2 || any(!is.finite(keysize)))
stop("'keysize' must be a numeric vector of length 2.",
call. = FALSE)
#-- keysize.label
if (length(keysize.label) != 1 || any(!is.finite(keysize)))
stop("'keysize' must be a numeric vector of length 1.",
call. = FALSE)
#-- zoom
if (!is.logical(zoom))
stop("'zoom' must be a logical constant (TRUE or FALSE).",
call. = FALSE)
#-- margins
if (!is.numeric(margins) || length(margins) != 2)
stop("'margins' must be a numeric vector of length 2.",
call. = FALSE)
#-- symkey
if (!is.logical(symkey))
stop("'symkey' must be a logical constant (TRUE or FALSE).",
call. = FALSE)
#-- lhei
if (!is.null(lhei)) {
if (is.null(col.sideColors)) {
if (length(lhei) != 2 | !is.numeric(lhei) | any(is.na(lhei)))
stop("'lhei' must be a numeric vector of length 2.",
call. = FALSE)
}
else {
if (length(lhei) != 3 | !is.numeric(lhei) | any(is.na(lhei)))
stop("'lhei' must be a numeric vector of length 3.",
call. = FALSE)
}
}
#-- lwid
if (!is.null(lwid)) {
if (is.null(row.sideColors)) {
if (length(lwid) != 2 | !is.numeric(lwid) | any(is.na(lwid)))
stop("'lwid' must be a numeric vector of length 2.",
call. = FALSE)
}
else {
if (length(lwid) != 3 | !is.numeric(lwid) | any(is.na(lwid)))
stop("'lwid' must be a numeric vector of length 3.",
call. = FALSE)
}
}
#-- xlab
xlab = as.graphicsAnnot(xlab)
#-- ylab
ylab = as.graphicsAnnot(ylab)
#-- title
title = as.graphicsAnnot(title)
#-- threshold correlation
if (!is.numeric(cutoff) | (cutoff > 1) | (cutoff < 0))
stop("The value taken by 'cutoff' must be between 0 and 1",
call. = FALSE)
#-- save
if (!is.null(save)) {
if (!save %in% c("jpeg", "tiff", "png", "pdf"))
stop("'save' must be one of 'jpeg', 'png', 'tiff' or 'pdf'.",
call. = FALSE)
}
#-- name.save
if (!is.null(name.save)) {
if (!is.character(name.save) || length(name.save) > 1)
stop("'name.save' must be a character.", call. = FALSE)
} else {
if (!is.null(save))
name.save = paste0("cim_", gsub(".", "_", deparse(substitute(mat)),
fixed = TRUE))
}
#-- end checking --#
#------------------#
if (!is.null(save)) {
while (dev.cur() > 2)
dev.off()
if (save == "jpeg")
jpeg(
filename = paste0(name.save, ".jpeg"),
res = 600,
width = 4000,
height = 4000
)
if (save == "png")
jpeg(
filename = paste0(name.save, ".png"),
res = 600,
width = 4000,
height = 4000
)
if (save == "tiff")
tiff(
filename = paste0(name.save, ".tiff"),
res = 600,
width = 4000,
height = 4000
)
if (save == "pdf")
pdf(file = paste0(name.save, ".pdf"))
}
object.pca = c("pca",
"spca",
"ipca",
"sipca",
"mixo_mlsplsda",
"mixo_splsda",
"mixo_plsda")
object.rcc = c("rcc")
object.pls = c("mixo_pls", "mixo_spls", "mixo_mlspls")
object.list = c(
"pca",
"spca",
"ipca",
"sipca",
"mixo_mlsplsda",
"mixo_splsda",
"mixo_plsda",
"rcc",
"mixo_pls",
"mixo_spls",
"mixo_mlspls"
)
if (any(class.object == "block.splsda"))
stop("Please call the 'cimDiablo' function on your 'block.splsda' object",
call. = FALSE)
if (!any(class.object %in% c(object.list, "matrix")))
stop(
"'mat' has to be a matrix or one of the following object: ",
paste(object.list, collapse = ", "),
".",
call. = FALSE
)
#-- if mixOmics class
if (any(class.object %in% object.list))
{
#-- general checks -------------
p = ncol(mat$X)
q = ncol(mat$Y)
n = nrow(mat$X)
ncomp = mat$ncomp
#-- comp
if (is.null(comp)) {
comp = 1:mat$ncomp
}
if (length(comp) > 1) {
comp = unique(comp)
if (!is.numeric(comp) || any(comp < 1))
stop("invalid vector for 'comp'.", call. = FALSE)
if (any(comp > ncomp))
stop("the elements of 'comp' must be <= ",
ncomp,
".",
call. = FALSE)
}
if (length(comp) == 1) {
if (is.null(comp) || !is.numeric(comp) || comp <= 0 || comp > ncomp)
stop("invalid value for 'comp'.", call. = FALSE)
# comp <- c(comp, comp)
}
comp <- round(comp)
# if object is a pls or spls with a univariate Y, or multivariate but
# only 1 variable selected on all comp, we only plot a heatmap of X
if (any(class.object %in% object.pls))
{
temp = apply(mat$loadings$Y, 2,
function(x) {
which(x != 0, arr.ind = TRUE)
})
# gives which variables are selected
num.variable.selected.Y = table(unlist(temp))
if (length(num.variable.selected.Y) == 1)
#only one variable in Y to plot, will raise trouble so we
# switch from (s)pls to (s)plsda
class.object = "mixo_splsda"
}
#-- if c("mixo_pls","mixo_spls","mixo_mlspls") or pls with univarite Y ----
## or multivariate but only one Y kept in sparse model
if (!any(class.object %in% object.pca)) {
#-- mapping
choices = c("XY", "X", "Y")
mapping = choices[pmatch(mapping, choices)]
if (is.na(mapping))
stop("'mapping' should be one of 'XY', 'X' or 'Y'.", call. = FALSE)
if (mapping == "XY")
{
if (is.logical(row.names))
{
if (isTRUE(row.names))
row.names = mat$names$colnames$X
else
row.names = rep("", p)
} else {
if (length(row.names) != p)
stop("'row.names' must be a character vector of length ",
p,
".",
call. = FALSE)
}
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$Y
else
col.names = rep("", q)
} else {
if (length(col.names) != q)
stop("'col.names' must be a character vector of length ",
q,
".",
call. = FALSE)
}
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != p)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
p,
".",
call. = FALSE
)
}
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != q)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
q,
".",
call. = FALSE
)
}
}
if (mapping == "X")
{
if (is.logical(row.names))
{
if (isTRUE(row.names)) {
row.names = mat$names$sample
}
else
row.names = rep("", n)
} else {
if (length(row.names) != n)
stop("'row.names' must be a character vector of length ",
n,
".",
call. = FALSE)
}
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$X
else
col.names = rep("", p)
} else {
if (length(col.names) != p)
stop("'col.names' must be a character vector of length ",
p,
".",
call. = FALSE)
}
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != n)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
n,
".",
call. = FALSE
)
}
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != p)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
p,
".",
call. = FALSE
)
}
}
if (mapping == "Y")
{
if (is.logical(row.names))
{
if (isTRUE(row.names))
{
if (any(class.object %in% object.rcc))
row.names = mat$names$sample
else
row.names = mat$names$sample
}
else
row.names = rep("", n)
} else {
if (length(row.names) != n)
stop("'row.names' must be a character vector of length ",
n,
".",
call. = FALSE)
}
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$Y
else
col.names = rep("", q)
} else {
if (length(col.names) != q)
stop("'col.names' must be a character vector of length ",
q,
".",
call. = FALSE)
}
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != n)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
n,
".",
call. = FALSE
)
}
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != q)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
q,
".",
call. = FALSE
)
}
}
}
#-- if NOT c("mixo_pls","mixo_spls","mixo_mlspls") or pls with univarite Y ----
if (any(class.object %in% object.pca))
{
#-- row.sideColors
if (!is.null(row.sideColors))
{
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != n)
stop(
"'row.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
n,
".",
call. = FALSE
)
}
#-- col.sideColors
if (!is.null(col.sideColors))
{
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != p)
stop(
"'col.sideColors' must be a colors character vector
(matrix) of length (nrow) ",
p,
".",
call. = FALSE
)
}
sample.sideColors = row.sideColors
## ----- DA object
if (any(class.object %in% c("mixo_splsda", "mixo_plsda",
'mixo_mlsplsda')))
{
#-- row.names
if (is.logical(row.names))
{
if (isTRUE(row.names))
row.names = mat$names$sample
}
#-- col.names
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$colnames$X
}
if (any(class.object %in% c("mixo_splsda", 'mixo_mlsplsda')))
keep.X = apply(abs(mat$loadings$X[, comp, drop = FALSE]), 1,
sum) > 0
else
keep.X = apply(abs(mat$loadings$X), 1, sum) > 0
cord.X = cor(mat$X[, keep.X], mat$variates$X[, comp],
use = "pairwise")
X.mat = as.matrix(mat$variates$X[, comp])
}
else{
#-- row.names
if (is.logical(row.names))
{
if (isTRUE(row.names))
row.names = mat$names$sample
}
#-- col.names
if (is.logical(col.names))
{
if (isTRUE(col.names))
col.names = mat$names$X
}
if (any(class.object %in% c("spca", "sipca")))
keep.X = apply(abs(mat$rotation[, comp]), 1, sum) > 0
else
keep.X = apply(abs(mat$rotation), 1, sum) > 0
cord.X = cor(mat$X[, keep.X], mat$x[, comp], use = "pairwise")
X.mat = as.matrix(mat$x[, comp])
}
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$X)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$X)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
## ---- clustering ----------------------------------------------
object = scale(mat$X[, keep.X], center = center, scale = scale)
col.names = col.names[keep.X]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.X,])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(X.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
X.mat
)),
method = "pearson"))
else
dist.mat = dist(X.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.X)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.X), method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
#-- calling the image.map function --------------------------------#
#------------------------------------------------------------------#
#-- output --------------------------------------------------------#
#------------------------------------------------------------------#
res = list(mat = object,
row.names = row.names,
col.names = col.names)
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = paste("cim", class.object[1], sep = "_")
}
else if (any(class.object %in% object.rcc))
{
bisect = mat$variates$X[, comp] + mat$variates$Y[, comp]
cord.X = cor(mat$X, bisect, use = "pairwise")
cord.Y = cor(mat$Y, bisect, use = "pairwise")
XY.mat = as.matrix(cord.X %*% t(cord.Y))
#-- if mapping = "XY"
if (mapping == "XY") {
object = XY.mat
cut = list()
if (cutoff != 0) {
cut[[1]] = unlist(lapply(1:nrow(object),
function(x) {
any(abs(object[x, ]) > cutoff)
}))
object = object[cut[[1]], ]
if (dist.method[1] != "correlation")
cord.X = cord.X[cut[[1]], ]
if (is.null(nrow(object)) || nrow(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
cut[[2]] = unlist(lapply(1:ncol(object),
function(x) {
any(abs(object[, x]) > cutoff)
}))
object = object[, cut[[2]]]
if (dist.method[2] != "correlation")
cord.Y = cord.Y[cut[[2]], ]
if (is.null(ncol(object)) || ncol(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
}
if ((cluster == "both") || (cluster == "row")) {
#Rowv = rowMeans(XY.mat)
Rowv = rowMeans(cord.X)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
object
)),
method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[1])
if (cutoff > 0) {
row.names = row.names[cut[[1]]]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[cut[[1]],])
}
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(as.matrix(object),
method = "pearson"))
else
dist.mat = dist(cord.Y, method = dist.method[2])
if (cutoff > 0) {
col.names = col.names[cut[[2]]]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[cut[[2]],])
}
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "X"
if (mapping == "X") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$X)))
stop(
"'center' should be either a logical value or a
numeric vector of length equal to the number of columns of
'X'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$X)))
stop(
"'scale' should be either a logical value or a
numeric vector of length equal to the number of columns of
'X'.",
call. = FALSE
)
}
object = scale(mat$X, center = center, scale = scale)
X.mat = as.matrix(mat$variates$X[, comp])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(X.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
X.mat
)),
method = "pearson"))
else
dist.mat = dist(X.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.X)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.X), method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "Y"
if (mapping == "Y") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$Y)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$Y)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
object = scale(mat$Y, center = center, scale = scale)
Y.mat = as.matrix(mat$variates$Y[, comp])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(Y.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
Y.mat
)),
method = "pearson"))
else
dist.mat = dist(Y.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.Y), method = "pearson"))
else
dist.mat = dist(cord.Y, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- calling the image.map function --------------------------------#
#------------------------------------------------------------------#
#-- output --------------------------------------------------------#
#------------------------------------------------------------------#
res = list(mat = object,
row.names = row.names,
col.names = col.names)
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = "cim_rcc"
}
else if (any(class.object %in% object.pls))
{
if (any(class.object %in% c("mixo_spls", "mixo_mlspls")))
{
keep.X = apply(abs(mat$loadings$X[, comp, drop = FALSE]), 1,
sum) > 0
keep.Y = apply(abs(mat$loadings$Y[, comp, drop = FALSE]), 1,
sum) > 0
}
else
{
keep.X = apply(abs(mat$loadings$X), 1, sum) > 0
keep.Y = apply(abs(mat$loadings$Y), 1, sum) > 0
}
if (mat$mode == "canonical") {
bisect = mat$variates$X[, comp] + mat$variates$Y[, comp]
cord.X = cor(mat$X[, keep.X, drop = FALSE],
bisect, use = "pairwise")
cord.Y = cor(mat$Y[, keep.Y, drop = FALSE],
bisect, use = "pairwise")
}
else {
cord.X = cor(mat$X[, keep.X, drop = FALSE],
mat$variates$X[, comp], use = "pairwise")
cord.Y = cor(mat$Y[, keep.Y, drop = FALSE],
mat$variates$X[, comp], use = "pairwise")
}
XY.mat = as.matrix(cord.X %*% t(cord.Y))
sample.sideColors = row.sideColors
#-- if mapping = "XY"
if (mapping == "XY") {
object = XY.mat
row.names = row.names[keep.X]
col.names = col.names[keep.Y]
cut = list()
if (cutoff != 0) {
cut[[1]] = unlist(lapply(1:nrow(object),
function(x) {
any(abs(object[x, ]) > cutoff)
}))
object = object[cut[[1]], ]
if (dist.method[1] != "correlation")
cord.X = cord.X[cut[[1]], ]
if (is.null(nrow(object)) || nrow(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
cut[[2]] = unlist(lapply(1:ncol(object),
function(x) {
any(abs(object[, x]) > cutoff)
}))
object = object[, cut[[2]]]
if (dist.method[2] != "correlation")
cord.Y = cord.Y[cut[[2]], ]
if (is.null(ncol(object)) || ncol(object) == 0)
stop("cutoff value very high. No variable was selected.",
call. = FALSE)
}
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[keep.X,])
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.Y,])
if ((cluster == "both") || (cluster == "row")) {
#Rowv = rowMeans(XY.mat)
Rowv = rowMeans(cord.X)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
object
)),
method = "pearson"))
else
#dist.mat = dist(mat, method = dist.method[1])
dist.mat = dist(cord.X, method = dist.method[1])
if (cutoff > 0) {
row.names = row.names[cut[[1]]]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[cut[[1]],])
}
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
#Colv = colMeans(mat)
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(as.matrix(object),
method = "pearson"))
else
#dist.mat = dist(t(mat), method = dist.method[2])
dist.mat = dist(cord.Y, method = dist.method[2])
if (cutoff > 0) {
col.names = col.names[cut[[2]]]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[cut[[2]],])
}
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "X"
if (mapping == "X") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$X)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$X)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'X'.",
call. = FALSE
)
}
object = scale(mat$X[, keep.X], center = center, scale = scale)
X.mat = as.matrix(mat$variates$X[, comp])
col.names = col.names[keep.X]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.X,])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(X.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
X.mat
)),
method = "pearson"))
else
dist.mat = dist(X.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.X)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.X), method = "pearson"))
else
dist.mat = dist(cord.X, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- if mapping = "Y"
if (mapping == "Y") {
#-- cheking center and scale
if (!is.logical(center)) {
if (!is.numeric(center) || (length(center) != ncol(mat$Y)))
stop(
"'center' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
if (!is.logical(scale)) {
if (!is.numeric(scale) || (length(scale) != ncol(mat$Y)))
stop(
"'scale' should be either a logical value or a numeric
vector of length equal to the number of columns of 'Y'.",
call. = FALSE
)
}
object = scale(mat$Y[, keep.Y], center = center, scale = scale)
Y.mat = as.matrix(mat$variates$Y[, comp])
col.names = col.names[keep.Y]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[keep.Y,])
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(Y.mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(
Y.mat
)),
method = "pearson"))
else
dist.mat = dist(Y.mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = object[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = rowMeans(cord.Y)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(t(cord.Y), method = "pearson"))
else
dist.mat = dist(cord.Y, method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
}
#-- calling the image.map function --------------------------------#
#------------------------------------------------------------------#
#-- output --------------------------------------------------------#
#------------------------------------------------------------------#
res = list(mat = object,
row.names = row.names,
col.names = col.names)
if (!is.null(sample.sideColors)) {
res$sample.sideColors = sample.sideColors
}
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = paste("cim", class.object[1], sep = "_")
}
} else {
#-- if matrix class -------------------------------------
#-- mat
isMat = tryCatch(
is.matrix(mat),
error = function(e)
e
)
if ("simpleError" %in% class(isMat))
stop(isMat[[1]], ".", call. = FALSE)
if (!is.matrix(mat) || !is.numeric(mat))
stop("'mat' must be a numeric matrix.", call. = FALSE)
p = nrow(mat)
q = ncol(mat)
#-- row.names
if (is.logical(row.names)) {
if (isTRUE(row.names))
row.names = rownames(mat)
else
row.names = rep("", p)
}
else {
row.names = as.vector(row.names)
if (length(row.names) != p)
stop("'row.names' must be a character vector of length ",
p,
".",
call. = FALSE)
}
#-- col.names
if (is.logical(col.names)) {
if (isTRUE(col.names))
col.names = colnames(mat)
else
col.names = rep("", q)
}
else {
col.names = as.vector(col.names)
if (length(col.names) != q)
stop("'col.names' must be a character vector of length ",
q,
".",
call. = FALSE)
}
#-- row.sideColors
if (!is.null(row.sideColors)) {
row.sideColors = as.matrix(row.sideColors)
if (nrow(row.sideColors) != p)
stop(
"'row.sideColors' must be a colors character vector (matrix)
of length (nrow) ",
p,
".",
call. = FALSE
)
}
#-- col.sideColors
if (!is.null(col.sideColors)) {
col.sideColors = as.matrix(col.sideColors)
if (nrow(col.sideColors) != q)
stop(
"'col.sideColors' must be a colors character vector (matrix)
of length (nrow) ",
q,
".",
call. = FALSE
)
}
#-- clustering -------------------------------------------------------#
#---------------------------------------------------------------------#
object = mat
if ((cluster == "both") || (cluster == "row")) {
Rowv = rowMeans(mat)
if (dist.method[1] == "correlation")
dist.mat = as.dist(1 - cor(t(as.matrix(mat)), method = "pearson"))
else
dist.mat = dist(mat, method = dist.method[1])
hcr = hclust(dist.mat, method = clust.method[1])
ddr = as.dendrogram(hcr)
ddr = reorder(ddr, Rowv)
rowInd = order.dendrogram(ddr)
object = mat[rowInd,]
row.names = row.names[rowInd]
if (!is.null(row.sideColors))
row.sideColors = as.matrix(row.sideColors[rowInd,])
}
if ((cluster == "both") || (cluster == "column")) {
Colv = colMeans(mat)
if (dist.method[2] == "correlation")
dist.mat = as.dist(1 - cor(as.matrix(mat), method = "pearson"))
else
dist.mat = dist(t(mat), method = dist.method[2])
hcc = hclust(dist.mat, method = clust.method[2])
ddc = as.dendrogram(hcc)
ddc = reorder(ddc, Colv)
colInd = order.dendrogram(ddc)
object = object[, colInd]
col.names = col.names[colInd]
if (!is.null(col.sideColors))
col.sideColors = as.matrix(col.sideColors[colInd,])
}
#-- calling the image.map function ------------------------------------#
#-- output ------------------------------------------------------------#
#----------------------------------------------------------------------#
res = list(
mat = object,
row.names = row.names,
col.names = col.names,
row.sideColors = row.sideColors,
col.sideColors = col.sideColors
)
if ((cluster == "both") || (cluster == "row")) {
res$rowInd = rowInd
res$ddr = ddr
}
if ((cluster == "both") || (cluster == "column")) {
res$colInd = colInd
res$ddc = ddc
}
class(res) = "cim_default"
}
#-- call imageMap -------------------------------------
opar = par(no.readonly = TRUE)
try_plot <- tryCatch({
imageMap(
object,
color = color,
row.names = row.names,
col.names = col.names,
row.sideColors = row.sideColors,
col.sideColors = col.sideColors,
row.cex = row.cex,
col.cex = col.cex,
cluster = cluster,
ddr = ddr,
ddc = ddc,
cut.tree = cut.tree,
transpose = transpose,
symkey = symkey,
keysize = keysize,
keysize.label = keysize.label,
zoom = zoom,
title = title,
xlab = xlab,
ylab = ylab,
margins = margins,
lhei = lhei,
lwid = lwid
)
}, error = function(e)
e)
if (is(try_plot, "error")) {
message(sprintf(
"Error in cim plot: %s. See ?cim for help.",
try_plot$message
))
} else {
#-- add to plot -------------------------------------
if (!is.null(legend))
{
if (is.null(legend$x))
legend$x = "topright"
if (is.null(legend$bty))
legend$bty = "n"
if (is.null(legend$cex))
legend$cex = 0.8
if (any(class.object %in% c("mixo_splsda", "mixo_plsda")))
{
if (is.null(legend$legend))
legend$legend = mat$names$colnames$Y
#-- col
if (is.null(legend$col)) {
if (!is.null(sample.sideColors))
legend$col = unique(as.matrix(sample.sideColors[order(map(mat$ind.mat)), 1]))
}
}
else if (any(class.object %in% c("mixo_mlsplsda")))
{
if (is.null(legend$legend) && is.null(legend$col)) {
if (ncol(mat$multilevel) >= 2) {
df = data.frame(mat$multilevel[, 2], sample.sideColors[, 1])
df = unique(df)
legend$legend = as.character(df[, 1])
legend$col = as.character(df[, 2])
}
if (ncol(mat$multilevel) == 3) {
df = data.frame(mat$multilevel[, 3], sample.sideColors[, 2])
df = unique(df)
legend$legend = c(legend$legend, as.character(df[, 1]))
legend$col = c(legend$col, as.character(df[, 2]))
}
}
}
else if (any(class.object %in% c("mixo_mlspls")))
{
if (mapping != "XY") {
if (is.null(legend$legend) && is.null(legend$col)) {
if (ncol(mat$multilevel) >= 2) {
df = data.frame(mat$multilevel[, 2],
sample.sideColors[, 1])
df = unique(df)
legend$legend = as.character(df[, 1])
legend$col = as.character(df[, 2])
}
if (ncol(mat$multilevel) == 3) {
df = data.frame(mat$multilevel[, 3],
sample.sideColors[, 2])
df = unique(df)
legend$legend = c(legend$legend,
as.character(df[, 1]))
legend$col = c(legend$col, as.character(df[, 2]))
}
}
}
}
if (is.null(legend$legend))
stop("argument \"legend$legend\" is missing, with no default")
#-- fill
if (is.null(legend$fill))
legend$fill = legend$col
par(mar = c(0, 0, 0, 0), new = TRUE)
plot(
0,
0,
axes = FALSE,
type = "n",
xlab = "",
ylab = ""
)
if (!is.null(legend$title))
{
legend(
x = legend$x,
y = legend$y,
legend = legend$legend,
col = legend$col,
fill = legend$fill,
bty = legend$bty,
title = legend$title,
cex = legend$cex
)
} else{
legend(
x = legend$x,
y = legend$y,
legend = legend$legend,
col = legend$col,
fill = legend$fill,
bty = legend$bty,
cex = legend$cex
)
}
}
if (any(class.object %in% object.list) &
!any(class.object %in%
object.pca) &
mapping == "XY")
res$mat.cor = object
par(opar)
if (!is.null(save))
dev.off()
}
return(invisible(res))
}
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