bdm.qMap {bigMap}

ptSNE quantile-maps

Description

Shows the mapping of quantitative variables into the embedding space.

Usage

bdm.qMap(bdm, data = NULL, labels = NULL, subset = NULL,
  qMap.levels = 8, qMap.cex = 0.3, qMap.bg = "#FFFFFF",
  class.pltt = NULL, layer = 1)

Arguments

bdm

A bdm instance as generated by bdm.init().

data

A matrix/data.frame to be mapped. By default, the input data bdm$data is mapped.

labels

A vector of class labels of length equal to nrow(bdm$data). Label values are factorized as as.numeric(as.factor(labels)). If !is.null(bdm$lbls), these labels are used by default.

subset

A numeric vector with the indexes of a subset of data. Data-points in the subset are heat-mapped and the rest are shown in light grey. By default all data-points are heat-mapped.

qMap.levels

The number of levels of the quantile-map (8 by default).

qMap.cex

The size of the data-points (as in par()).

qMap.bg

The background colour of the qMap plot. Default value is ptsne.bg = #FFFFFF (white).

class.pltt

If !is.null(labels) or !is.null(bdm$lbls), a colour palette to show class labels with the qMap plots. By default (qMap.pltt = NULL) the default palette is used.

layer

The number of a layer (1 by default).

Details

This is not a heat-map but a quantile-map plot. This function splits the range of each variable into as many quantiles as specified by levels so that the color gradient will hardly ever correspond to a constant numeric gradient. Thus, the mapping will show more evenly distributed colors though at the expense of possibly exaggerating artifacts. For variables with very extrem distributions, it will be impossible to find as many quantiles as desired and the distribution of colors will not be so homogeneous.

Value

None.

Examples

bdm.example()
bdm.qMap(exMap)
# --- show only components (1, 2, 4, 8) of the GMM
bdm.qMap(exMap, subset = which(exMap$lbls %in% c(1, 4, 8, 16)))