predict_roc.metaSDTreg | R Documentation |

Predict ROC curves from metaSDTreg object.

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

`object` |
An object of class |

`type` |
The type of ROC curve to predict. A character string, where '1' requests the type 1 ROC curve (the default), 'n' requests the type 2 noise-specific and 's' the type 2 signal-specific ROC curve. |

`s0` |
Numeric, the value of 'signal' to regard as 'noise'. Defaults to 0. |

`s1` |
Numeric, the value of 'signal' to regard as 'signal'. Defaults to 1. |

`...` |
For future methods |

The 'metaSDTreg' object given to the function must have named coefficients with names as they would be if `metaSDTreg`

is run without user-supplied starting values.

A ROC curve is a 2-D curve parametrised by some x given by c(FA(x), HR(x)) where FA is the false alarm rate and HR is the hit rate. For example, for type 1 ROC,

`FA(x) = 1 - pnorm(x - s0*d),`

`HR(x) = 1 - pnorm(x - s1*d),`

where `d`

is the signal sensitivity.

Note that the predicted ROC curve is for a reference individual in the regression, i.e. additional covariates are not entered into the ROC so that reparametrisation of the 'metaSDTreg' model is needed to change predictions.

A function of class 'predict_roc' containing the appropriate ROC curve. This is a function of x which returns c(FA,HR), where FA is the false alarm rate and HR is the hit rate.

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.

```
## Declare simulated data as metaSDTdata
metadata <- metaSDTdata(simMetaData, type1='resp', type2='conf', signal='S')
## Fit model to subset of data
fit <- metaSDTreg(A ~ signal,
data=metadata,
subset = m <= 20)
## Model-predicted signal-specific ROC curve
signalROC <- predict_roc(fit, type = 's')
```

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