plot(eigenvalues(dm)[start:end], ...)
plot.DiffusionMap(x, dims, new_dcs = NULL, col = NULL, col_by = NULL,
col_limits = NULL, col_new = "red", pal = NULL, ..., mar = NULL,
ticks = FALSE, axes = TRUE, box = FALSE, legend_main = col_by,
legend_opts = list(), interactive = FALSE,
draw_legend = !is.null(col_by) || (length(col) > 1 && !is.character(col)),
consec_col = TRUE, col_na = "grey", plot_more = function(p, ..., rescale
= NULL) NULL)# S4 method for DiffusionMap,numeric
plot(x, y, ...)
# S4 method for DiffusionMap,missing
plot(x, y, ...)
y
and plotted. (default: no more points)('fg')
)dataset(x)
or phenoData(dataset(x))
column to use as colorcol
is a continuous (=double) vector, this can be overridden to map the color range differently than from min to max (e.g. specify c(0, 1)
)new_dcs
is given, it will take on this color. (default: red)col
vector to colors. (default: use cube_helix
for continuous and palette()
for discrete data)interactive == TRUE
)par(mar)
)ticks
is TRUE)axes
if specified)col_by
is given)col_by
is given or col
is given and a vector to be mapped)col
or col_by
refers to an integer column, with gaps (e.g. c(5,0,0,3)
) use the palette color consecutively (e.g. c(3,1,1,2)
)NA
in the data. specify NA
to hide.p
argument is the rgl or scatterplot3d instance or NULL,
its rescale
argument is NULL
or of the shape list(from = c(a, b), to = c(c,d))
)plot(dm, c(-1,2))
), then the corresponding components will be flipped.data(guo)
plot(DiffusionMap(guo))
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