Exploratory data analysis for a three-class ROC marker

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

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 \(\alpha=1-\code{conf.level}\).
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".

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).

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.

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)

#> 
#>  Data overview of empirical ROC Classifier 
#> --------------------------------------------------------------------- 
#> 
#>  Applied test: Bootstrap test 
#>  Significance level: 0.05
#>  Alternative hypothesis: two.sided
#> --------------------------------------------------------------------- 
#>  data: healthy, intermediate and diseased
#> 
#> Boot statistic: 1.501, Boot p.value: 0.13326
#> 
#>  empirical VUS:  0.283 
#> --------------------------------------------------------------------- 

# 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 overview of empirical ROC Classifier 
#> --------------------------------------------------------------------- 
#> 
#>  Applied test: Bootstrap test 
#>  Significance level: 0.05
#>  Alternative hypothesis: two.sided
#> --------------------------------------------------------------------- 
#>  data: x, y and z
#> 
#> Boot statistic: 1.501, Boot p.value: 0.13326
#> 
#>  empirical VUS:  0.283 
#> --------------------------------------------------------------------- 

data(cancer)
# trinormal EDA:
roc.eda(dat = cancer[,c(1,10)], type = "trin", plotVUS = FALSE)

#> 
#>  Data overview of trinormal ROC Classifier 
#> --------------------------------------------------------------------- 
#> 
#>  Applied tests: Trinormal based ROC and VUS test 
#>  Significance level: 0.05
#>  Alternative hypothesis: two.sided
#> --------------------------------------------------------------------- 
#>  data: healthy, intermediate and diseased
#> 
#> ROC test statistic: 40.56, ROC p.value: 0
#>  VUS test statistic:  4.373 ,  VUS p.value:  1e-05 
#> 
#>  trinormal VUS:  0.406 
#> 
#> Parameters: 
#>  	a	b	c	d	
#>  	1.2756	-0.4844	0.9887	0.9889	
#> --------------------------------------------------------------------- 
# trinormal EDA with different plots:
roc.eda(dat = cancer[,c(1,5)], type = "t", sep.dens = TRUE, scatter = TRUE)

#> 
#>  Data overview of trinormal ROC Classifier 
#> --------------------------------------------------------------------- 
#> 
#>  Applied tests: Trinormal based ROC and VUS test 
#>  Significance level: 0.05
#>  Alternative hypothesis: two.sided
#> --------------------------------------------------------------------- 
#>  data: healthy, intermediate and diseased
#> 
#> ROC test statistic: 27.129, ROC p.value: 2e-05
#>  VUS test statistic:  3.6 ,  VUS p.value:  0.00032 
#> 
#>  trinormal VUS:  0.356 
#> 
#> Parameters: 
#>  	a	b	c	d	
#>  	1.3477	-1.229	1.0791	0.2033	
#> ---------------------------------------------------------------------