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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