predict_roc.metaSDTdata: Observed ROC points

Description

The observed points of the ROC curve from a 'metaSDTdata' object.

Usage

# S3 method for metaSDTdata
predict_roc(object, type = c("1", "n", "s"), s0 = 0, s1 = 1, ...)

Value

A matrix two-column matrix of class 'predict_roc' with one row of c(FA, HR) per threshold (FA: False Alarm rate, HR: Hit Rate).

Arguments

object: A 'metaSDTdata' object from which to calculate observed ROC points.
type: The type of ROC curve to predict. A character string, where '1' requests the type 1 ROC curve, 'n' requests the type 2 noise-specific and 's' the type 2 signal-specific ROC curve.
s0: Numeric, the value of object$signal to regard as 'noise'. Defaults to 0.
s1: Numeric, the value of object$signal to regard as 'signal'. Defaults to 1.
...: For future methods

Details

Note that the type 1 ROC points arise by using each criterion in turn to decide between 'signal' and 'noise'. Since this involves also the type 2 thresholds, such a curve is also sometimes referred to as a 'pseudo' ROC curve.

References

Maniscalco, B., & Lau, H. (2014). Signal Detection Theory Analysis of Type 1 and Type 2 Data: Meta-d , Response-Specific Meta-d , and the Unequal Variance SDT Model. In S. M. Fleming, & C. D. Frith (Eds.), The Cognitive Neuroscience of Metacognition (pp. 25 66). : Springer Berlin Heidelberg.

Examples

Run this code

## Declare simulated data as metaSDTdata
metadata <- metaSDTdata(simMetaData, type1='resp', type2='conf', signal='S')

## Observed signal-specific ROC curve
signalROC <- predict_roc(metadata, type = 's')

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