Description Usage Arguments Details Value Author(s) See Also Examples
Visualize the relations between predictors and response variable ('tssOverlap').
1 2 3 4 5 6 | plotFeatures(object, plot.type = c("box", "density"), feature, ncol, xlab,
ylab, color = c("#E41A1C", "#377EB8"), alpha = 1)
## S4 method for signature 'ChipDataSet'
plotFeatures(object, plot.type = c("box", "density"),
feature, ncol, xlab, ylab, color = c("#E41A1C", "#377EB8"), alpha = 1)
|
object |
A |
plot.type |
One of ["box", "density"]. Default: "box" |
feature |
Feature to plot. By default, all the features are plotted. |
ncol |
|
xlab |
|
ylab |
|
color |
A character vector of length two. Default: ["#E41A1C","#377EB8"]. |
alpha |
Color transparency. In a range [0, 1]. Default: 1. |
In order to discriminate between functional or gene associated peaks and
non-functional or background peaks, each peak in the data set is
characterized by several features. Moreover, the user might supply her/his
own list of features with the addFeature
. Prior to fitting
the logistic model, the relations between predictors and response variable
(tssOverlap) can be explored with plotFeatures
. Based on the plots,
poor predictors can be excluded from the analysis to improve the model
fit.
ggplot2 object.
Armen R. Karapetyan
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ### Load ChipDataSet object
data(cds)
### The data can be plotted in two ways
### As a boxplot
plotFeatures(object = cds, plot.type = "box")
### Or as a density plot
plotFeatures(object = cds, plot.type = "density")
### Additionally, only the subset of features can be shown
plotFeatures(object = cds, plot.type = "box", feature = c("pileup", "length"))
### The position of the graphs on the plot, can be adjusted by 'ncol' argument
plotFeatures(object = cds, plot.type = "box", ncol = 2)
|
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