scramb {DEMOVA}

scrambling

Description

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.

Usage

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

Arguments

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

Value

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'

References

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.

Examples

# First run Select_MLR to define n

# scramb(mydata,1000,nom,dim(MLR)[2])