plotMeanSd(x,
ranks = TRUE,
xlab = ifelse(ranks, "Rank of means (ascending order)", "mean"),
ylab = "Standard deviation",
pch = ".",
plot = TRUE,
...)
matrix
FALSE
) or on the rank scale (TRUE
). The latter
distributes the data more evenly along the x-axis and allows a
better visual assessment of the standard deviation as a function of
the mean.TRUE
(default), a plot is produced.
Calling the function with plot=FALSE
can be useful if only
its return value is of interest.px
and
py
are the x- and y-coordinates of the individual data points
in the plot; its first and second element are the x-coordinates and values of
the running median estimator (the red dots in the plot).
Depending on the value of plot
, the method can also have a side effect, which is to create a plot on the
active graphics device.x
. The scatterplot of these versus each other
allows to visually verify whether there is a dependence of the standard
deviation (or variance) on the mean.
The red dots depict the running median estimator (window-width 10%).
If there is no variance-mean dependence, then the line formed by the
red dots should be approximately horizontal.library(vsn)
data(kidney)
kidney.t = microVS(exprs(kidney))
plotMeanSd(kidney.t)
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