scramb {DEMOVA} | R Documentation |

Perform the y-scrambling method that consit to permute y values and try to develop new models. They have to be unperformants in order to validate the original one. The graph R2 vs r(y,yrandom) is created.

```
scramb(mydata, k, n, cercle = FALSE)
```

`mydata` |
Dataframe containing names and values of response and descriptors |

`k` |
Number of random run |

`n` |
Number of selected descriptors of the regression (determined using Select_MLR) |

`cercle` |
Value is TRUE or FALSE (by default) . If it TRUE it's draw a circle around the point representinf the original model |

Return a list of

`mean` |
Mean of R^2 new model |

`sd` |
RStandard deviation of R^2 new model |

And also

`Scramb.tiff ` |
Description of 'comp1' |

`Scramb.csv ` |
Description of 'comp2' |

Tropsha, A.; Gramatica, P.; Gombar, V. K. The Importance of Being Earnest: Validation Is the
Absolute Essential for Successful Application and Interpretation of QSPR Models. Qsar \&
Combinatorial Science 2003, 22, 69-77.

Rucker, C.; Rucker, G.; Meringer, M. y-Randomization and Its Variants in QSPR/QSAR. J.
Chem. Inf. Model. 2007, 47, 2345-2357.

Lindgren, F.; Hansen, B.; Karcher, W.; Sjostrom, M.; Eriksson, L. Model Validation by
Permutation Tests: Applications to Variable Selection. Journal of Chemometrics 1996, 10, 521-532.

```
# First run Select_MLR to define n
# scramb(mydata,1000,nom,dim(MLR)[2])
```

[Package *DEMOVA* version 1.0 Index]