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A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

IR@CDRI: CSIR-Central Drug Research Institute, Lucknow


Field	Value

Creator	Prabhakar, Yenamandra S

Date	2008-02-25T05:24:55Z 2008-02-25T05:24:55Z 2003

Identifier	QSAR & Combinatorial Chemistry Science (2003), 22, 538 http://hdl.handle.net/123456789/81

Description	A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time.

Format	360884 bytes application/pdf

Language	en

Relation	CDRI Communication Number 6225

Subject	Regression analysis variable selection combinatorial approach antimycin A1 analogues antifilarial

Title	A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

Type	Article