scramb: scrambling

Description Usage Arguments Value References Examples

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

1
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

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# 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.