plotFeatures {transcriptR} | R Documentation |
Visualize the relations between predictors and response variable ('tssOverlap').
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
### 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)