selRes {biospear} | R Documentation |

This function computes several criteria to assess the selection accuracy of a prediction model. Of note, this function is only available for simulated data sets for which true biomarkers are known.

selRes(res)

`res` |
an object of class ' |

Based on the 2x2 contingency table (active vs. inactive / selected vs. unselected),
four selection criteria are provided:

- the false discovery rate (`FDR`

) that is the proportion of inactive biomarkers
among the selected ones,

- the false non-discovery rate (`FNDR`

) that is the proportion of active biomarkers
among the unselected ones,

- the false negative rate (`FNR`

) that is the proportion of unselected
biomarkers among the active ones,

- the false positive rate (`FPR`

) that is the proportion of selected
biomarkers among the inactive ones.

These four criteria are between 0 and 1, and must be minimized.

We also provided two discrimination criteria, translating the ability to discard inactive
biomarkers more likely than active ones independently of the tuning parameters:

- the area under the ROC curve (`AUC`

) depending on the sensitivity [1 - FNR] and specificity [1 - FPR],

- the area under the precision-recall curve (`AUPRC`

) depending on the FNR and FDR (Davis and Goadrich, 2006).

Of note, the AUPRC is more meaningful than the AUC when there are many more inactive than active biomarkers.
These two criteria are between 0 and 1, and must be maximized.

A `matrix`

of dimension 6 x the number of methods used to fit `res`

.

Nils Ternes, Federico Rotolo, and Stefan Michiels

Maintainer: Nils Ternes nils.ternes@yahoo.com

Davis J and Goadrich M.
The relationship between Precision-Recall and ROC curves.
*Proceedings of the 23rd International Conference on Machine Learning*.
ACM, Pittsburgh PA, 233-240.

Ternes N, Rotolo F and Michiels S.
Empirical extensions of the lasso penalty to reduce
the false discovery rate in high-dimensional Cox regression models.
*Statistics in Medicine* 2016;35(15):2561-2573.
doi:10.1002/sim.6927

Ternes N, Rotolo F, Heinze G and Michiels S.
Identification of biomarker-by-treatment interactions in randomized
clinical trials with survival outcomes and high-dimensional spaces.
*Biometrical journal*. In press.
doi:10.1002/bimj.201500234

######################################## # Simulated data set ######################################## ## Low calculation time set.seed(654321) sdata <- simdata( n = 500, p = 20, q.main = 3, q.inter = 0, prob.tt = 0.5, alpha.tt = 0, beta.main = -0.8, b.corr = 0.6, b.corr.by = 4, m0 = 5, wei.shape = 1, recr = 4, fu = 2, timefactor = 1) resBM <- BMsel( data = sdata, method = c("lasso", "lasso-pcvl"), inter = FALSE, folds = 5) selAcc <- selRes(resBM) ## Not run: ## Moderate calculation time set.seed(123456) sdata <- simdata( n = 500, p = 100, q.main = 5, q.inter = 5, prob.tt = 0.5, alpha.tt = -0.5, beta.main = c(-0.5, -0.2), beta.inter = c(-0.7, -0.4), b.corr = 0.6, b.corr.by = 10, m0 = 5, wei.shape = 1, recr = 4, fu = 2, timefactor = 1, active.inter = c("bm003", "bm021", "bm044", "bm049", "bm097")) resBM <- BMsel( data = sdata, method = c("lasso", "lasso-pcvl"), inter = TRUE, folds = 5) selAcc <- selRes(resBM) ## End(Not run)

[Package *biospear* version 1.0.2 Index]