trinROC (version 0.6)

roc.eda: Exploratory data analysis for a three-class ROC marker

Description

A function that investigates data that arose from a single marker and containes the reference standard of the three classes "healthy", "intermediate" and "diseased".

Usage

roc.eda(
  x,
  y,
  z,
  dat = NULL,
  type = c("empirical", "trinormal"),
  plotVUS = FALSE,
  saveVUS = FALSE,
  sep.dens = FALSE,
  scatter = FALSE,
  conf.level = 0.95,
  n.boot = 1000,
  verbose = TRUE,
  alternative = c("two.sided", "less", "greater")
)

Value

A list with class "htest" containing the following components:

statistic

The value of the test(s).

p.value

The p-value for the test(s).

VUS

the VUS computed with the specific method defined in type.

dat.summary

A data frame displaying size, mean and standard deviation of the three classes.

alternative

The alternative hypothesis.

type

a character containing the the method used for the exploratory data analysis.

data.name

a character containing the name of the data.

xVUS, yVUS, zVUS

(if plotVUS = TRUE) numeric vectors and matrices computed by rocsurf.emp or rocsurf.trin, used for displaying the surface with package rgl.

histROC

a ggplot2 object, displaying the historgrams and densities of the three classes.

meas.overview

A ggplot2 object, displaying the boxplots (if scatter = FALSE) or scatter plots of the three classes (if scatter = TRUE).

Arguments

x, y, z

numeric vectors contaning the measurements from the healthy, intermediate and diseased class.

dat

a data frame of the following structure: The first column represents a factor with three levels, containing the true class membership of each measurement. The levels are ordered according to the convention of higher values for more severe disease status.

type

a character, specifying if the empirical VUS and tests or the trinormal VUS and tests are computed.

plotVUS

a logical whether to evaluate and plot the VUS (default is FALSE). Note: To save a png plotVUS needs to be TRUE too.

saveVUS

a logical whether to save a PNG of the VUS in your current working directory (default is FALSE).

sep.dens

a logical indicating if the densitie plots should be plotted on separate x-axes (TRUE) or on a common axe (FALSE, is default).

scatter

a logical indicating if the measurements per class plot should be plotted as a boxplot (default) or as a scatterplot (scatter = TRUE).

conf.level

A numeric value between 0 and 1 yielding the significance level \(\alpha=1-\code{conf.level}\).

n.boot

an integer incicating the number of bootstrap replicates sampled to obtain the variance of the VUS. Default is 1000.

verbose

a logical, indicating whether output should be displayed or not. Default is TRUE.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

Warning

If type = "empirical", computation may take a while, as roc.eda calls the function boot.test().

Details

For the preliminary assessment of a classifier, exporatory data analysis (EDA) on the markers is necessary. This function assesses measurements from a single marker and computes the VUS, statistical tests and returns a summary table as well as some plots of the data.

See Also

trinROC.test, trinVUS.test for trinormal data investigation, boot.test for empirical data analysis. rocsurf.emp, rocsurf.trin for the surface plot.

Examples

data(krebs)

# empirical EDA:
roc.eda(dat = krebs[,c(1,5)], type = "e", plotVUS = FALSE)

# equal data input via:
x <- with(krebs, krebs[trueClass=="healthy", 5])
y <- with(krebs, krebs[trueClass=="intermediate", 5])
z <- with(krebs, krebs[trueClass=="diseased", 5])
roc.eda(x, y, z, type = "e", sep.dens = TRUE)

data(cancer)
# trinormal EDA:
roc.eda(dat = cancer[,c(1,10)], type = "trin", plotVUS = FALSE)
# trinormal EDA with different plots:
roc.eda(dat = cancer[,c(1,5)], type = "t", sep.dens = TRUE, scatter = TRUE)