Description Usage Arguments Value Author(s) References See Also Examples

Estimates the covariate-specific ROC curve in the presence of multidimensional covariates by means of the ROC-GAM regression model presented in Rodriguez- Alvarez et al. (2011)

1 2 3 |

`marker` |
A character string with the name of the diagnostic test variable. |

`formula.h` |
Right-hand formula(s) giving the mean and variance model(s) to be fitted in healthy population. Atomic values are also valid, being recycled. |

`formula.ROC` |
Right-hand formula giving the ROC regression model to be fitted (ROC-GAM model). |

`group` |
A character string with the name of the variable that distinguishes healthy from diseased individuals. |

`tag.healthy` |
The value codifying the healthy individuals in the variable |

`data` |
Data frame representing the data and containing all needed variables. |

`ci.fit` |
A logical value. If TRUE, confidence intervals are computed. |

`test.partial` |
A numeric vector containing the position of the covariate components in the ROC-GAM formula to be tested for a possible effect. If NULL, no test is performed.. If NULL, no test is performed. |

`newdata` |
A data frame containing the values of the covariate at which predictions are required. |

`control` |
Output of the |

`weights` |
An optional vector of ‘prior weights’ to be used in the fitting process. |

As a result, the function `DNPROCreg()`

provides a list with the following components:

`call` |
The matched call. |

`model` |
Data frame containing all variables and observations used in the fitting process. |

`fpf` |
Set of false positive fractions (FPF) at which the covariate-specific ROC curve has been estimated. |

`newdata` |
Data frame containing the values of the covariates at which estimates has been obtained. |

`pfunctions` |
Matrices containing the estimates of each component of the additive predictor of the ROC-GAM. One matrix contains the effects of the covariates, the other the effect of the FPF. Confidence intervals are returned if required). |

`coefficients` |
Vector of parametric coefficient of the fitted ROC-GAM. |

`ROC` |
Estimated covariate-specific ROC curve. |

`AUC` |
Estimated covariate-specific AUC, and corresponding confidence intervals if required. |

`pvalue` |
If required, p-values are obtained - with two different bootstrap-based tests - for each model component indicated in argument |

Maria Xose Rodriguez-Alvarez and Javier Roca-Pardinas

Rodriguez- Alvarez, M.X., Roca-Pardinas, J. and Cadarso-Suarez, C. (2011). A new flexible direct ROC regression model - Application to the detection of cardiovascular risk factors by anthropometric measures. Computational Statistics and Data Analysis, 55(12), 3257–3270.

Rodriguez- Alvarez, M.X., Roca-Pardinas, J. and Cadarso-Suarez, C. (2016). Bootstrap-based procedures for inference in nonparametric ROC regression analysis. Technical report.

See Also as `INPROCreg`

, `summary.DNPROCreg`

, `plot.DNPROCreg`

, `controlDNPROCreg`

, `DNPROCregData`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
data(endosim)
# Fit a model including the interaction between age and gender.
m0 <- DNPROCreg(marker = "bmi", formula.h = "~ gender + s(age) + s(age, by = gender)",
formula.ROC = "~ gender + s(age) + s(age, by = gender)",
group = "idf_status",
tag.healthy = 0,
data = endosim,
control = list(card.P=50, kbin=30, step.p=0.02))
summary(m0)
plot(m0)
## Not run:
# For confidence intervals
set.seed(123)
m1 <- DNPROCreg(marker = "bmi", formula.h = "~ gender + s(age) + s(age, by = gender)",
formula.ROC = "~ gender + s(age) + s(age, by = gender)",
group = "idf_status",
tag.healthy = 0,
data = endosim,
control = list(card.P=50, kbin=30, step.p=0.02),
ci.fit = TRUE)
summary(m1)
plot(m1)
# For testing the presence of interaccion between age and gender
set.seed(123)
m2 <- DNPROCreg(marker = "bmi", formula.h = "~ gender + s(age) + s(age, by = gender)",
formula.ROC = "~ gender + s(age) + s(age, by = gender)",
group = "idf_status",
tag.healthy = 0,
data = endosim,
control = list(card.P=50, kbin=30, step.p=0.02),
test.partial = 3)
summary(m2)
plot(m2)
## End(Not run)
``` |

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