compute.threshold.AROC.bnp {AROC} | R Documentation |

## AROC-based threshold values.

### Description

Estimates AROC-based threshold values using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018).

### Usage

```
compute.threshold.AROC.bnp(object, newdata, FPF = 0.5)
```

### Arguments

`object` |
An object of class |

`newdata` |
Data frame with the covariate values at which threshold values are required. |

`FPF` |
Numeric vector with the FPF at which to calculate the AROC-based threshold values. Atomic values are also valid. |

### Details

Estimation of the covariate-adjusted ROC curve (AROC) using the nonparametric Bayesian approach proposed by Inacio de Carvalho and Rodriguez-Alvarez (2018) involves the estimation of the conditional distribution function for the diagnostic test outcome in the healthy population

`F_{\bar{D}}(y|\mathbf{X}_{\bar{D}}) = Pr\{Y_{\bar{D}} \leq y | \mathbf{X}_{\bar{D}}\}.`

This function makes use of this estimate in order to calculate AROC-based threshold values. In particular, for a covariate value `\mathbf{x}`

and a FPF = t, the AROC-based threshold value at the `s`

-th posterior sample (`s = 1,\ldots,S`

) is calculated as follows

`c^{(s)}_{\mathbf{x}} = \hat{F}^{-1(s)}_{\bar{D}}(1-t|\mathbf{X}_{\bar{D}} = \mathbf{x}).`

from which the posterior mean can be computed

`\hat{c}_{\mathbf{x}} = \frac{1}{S}\sum_{s = 1}^{S}c^{(s)}_{\mathbf{x}}.`

### Value

As a result, the function provides a list with the following components:

`thresholds.est` |
A matrix with the posterior mean of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs. |

`thresholds.ql` |
A matrix with the posterior 2.5% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs. |

`thresholds.qh` |
A matrix with the posterior 97.5% quantile of the AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs. |

### References

Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. arXiv preprint arXiv:1806.00473.

### See Also

### Examples

```
library(AROC)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]
# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
m0 <- AROC.bnp(formula.healthy = l_marker1 ~ f(age, K = 0),
group = "status", tag.healthy = 0, data = newpsa, scale = TRUE,
p = seq(0,1,l=101), compute.lpml = TRUE, compute.WAIC = TRUE,
a = 2, b = 0.5, L = 10, nsim = 5000, nburn = 1000)
# Compute the threshold values
FPF = c(0.1, 0.3)
newdata <- data.frame(age = seq(52, 80, l = 50))
th_bnp <- compute.threshold.AROC.bnp(m0, newdata, FPF)
names(th_bnp)
```

*AROC*version 1.0-4 Index]