scramb: scrambling

In DEMOVA: DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using Test Set

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])

Example output

Loading required package: leaps

DEMOVA documentation built on May 2, 2019, 2:09 a.m.