xMLfeatureplotR Documentation

Function to visualise/assess features used for machine learning

Description

xMLfeatureplot is supposed to visualise/assess features used for machine learning. Visualisation can be made using either boxplot or dot plot for AUC and F-max. It returns an object of class "ggplot" for AUC and F-max, and an object of class "trellis" for boxplot.

Usage

xMLfeatureplot(df_predictor, GSP, GSN, displayBy = c("boxplot", "ROC",
"Fmax"), font.family = "sans", ...)

Arguments

df_predictor

a data frame containing genes (in rows) and predictors (in columns), with their predictive scores inside it. This data frame must has gene symbols as row names

GSP

a vector containing Gold Standard Positive (GSP)

GSN

a vector containing Gold Standard Negative (GSN)

displayBy

which statistics will be used for displaying. It can be either "boxplot" for features themselves, "ROC" for AUC in ROC, "Fmax" for F-max in Precision-Recall curve)

font.family

the font family for texts

...

additional parameters. Please refer to 'lattice::bwplot' for the complete list.

Value

an object of class "ggplot" for AUC and F-max, and an object of class "trellis" for boxplot

Note

none

Examples

## Not run: 
# Load the library
library(Pi)

## End(Not run)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
## Not run: 
gp <- xMLfeatureplot(df_predictor, GSP, GSN, displayBy="ROC")

## End(Not run)