xMLfeatureplotR Documentation

Function to visualise features used for machine learning

Description

xMLfeatureplot is supposed to visualise 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"), ...)

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)

...

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_dev"
## Not run: 
gp <- xMLfeatureplot(df_predictor, GSP, GSN, displayBy="ROC")

## End(Not run)