roc.eda: Exploratory data analysis for a three-class ROC marker
In trinROC: Statistical Tests for Assessing Trinormal ROC Data

roc.eda

R Documentation

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")
)

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 α=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"`.

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.

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`).

Warning

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

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)

trinROC documentation built on Oct. 29, 2022, 1:12 a.m.