UtilORVarComponentsFactorial: Utility for estimating Obuchowski-Rockette variance...

In RJafroc: Artificial Intelligence Systems and Observer Performance

Utility for estimating Obuchowski-Rockette variance components for factorial datasets

Description

Utility for estimating Obuchowski-Rockette variance components for factorial datasets

Usage

UtilORVarComponentsFactorial(
  dataset,
  FOM,
  FPFValue = 0.2,
  covEstMethod = "jackknife",
  nBoots = 200,
  seed = NULL
)

Arguments

dataset	The factorial dataset object
FOM	The figure of merit
FPFValue	Only needed for LROC data and FOM = "PCL" or "ALROC"; where to evaluate a partial curve based figure of merit. The default is 0.2.
covEstMethod	The covariance estimation method, "jackknife" (the default) or "bootstrap" or "DeLong" (DeLongt is applicable only for FOM = Wilcoxon).
nBoots	Only needed for bootstrap covariance estimation method. The number of bootstraps, defaults to 200.
seed	Only needed for the bootstrap covariance estimation method. The initial seed for the random number generator, the default is NULL, as if no seed has been specified.

Details

The variance components are obtained using StSignificanceTesting with method = "OR".

Value

A list object containing the following data.frames:

• foms: the figures of merit for different treatment-reader combinations

• TRanova: the OR treatment-reader ANOVA table

• VarCom: the OR variance-components Cov1, Cov2, Cov3, Var and correlations rho1, rho2 and rho3

• IndividualTrt: the individual treatment mean-squares, Var and Cov2 values

• IndividualRdr: the individual reader mean-squares, Var and Cov1 values

Examples

## use the default jackknife for covEstMethod
vc <- UtilORVarComponentsFactorial(dataset02, FOM = "Wilcoxon")
str(vc) 

UtilORVarComponentsFactorial(dataset02, FOM = "Wilcoxon", 
   covEstMethod = "bootstrap", nBoots = 2000, seed = 100)$VarCom 

UtilORVarComponentsFactorial(dataset02, FOM = "Wilcoxon", covEstMethod = "DeLong")$VarCom 
  

RJafroc documentation built on Nov. 10, 2022, 5:45 p.m.