The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO.

Suna Akkol

doi:10.5194/aab-61-451-2018

Abstract

The least absolute selection and shrinkage operator (LASSO) and adaptive LASSO methods have become a popular model in the last decade, especially for data with a multicollinearity problem. This study was conducted to estimate the live weight (LW) of Hair goats from biometric measurements and to select variables in order to reduce the model complexity by using penalized regression methods: LASSO and adaptive LASSO for and . The data were obtained from 132 adult goats in Honaz district of Denizli province. Age, gender, forehead width, ear length, head length, chest width, rump height, withers height, back height, chest depth, chest girth, and body length were used as explanatory variables. The adjusted coefficient of determination (), root mean square error (RMSE), Akaike's information criterion (AIC), Schwarz Bayesian criterion (SBC), and average square error (ASE) were used in order to compare the effectiveness of the methods. It was concluded that adaptive LASSO () estimated the LW with the highest accuracy for both male (; RMSE 3.6250; AIC 79.2974; SBC 65.2633; ASE 7.8843) and female (; RMSE 4.4069; AIC 392.5405; SBC 308.9888; ASE 18.2193) Hair goats when all the criteria were considered.

Highlights

Native goat breeds play important socio-economic roles in the livelihood strategies of poorer farmers, especially those in rural and hard-to-reach areas of the world
chest width (CW), rump height (RH), withers height (WH), back height (BH), chest depth (CD), and body length (BL) were measured with a measuring stick, and forehead width (FW), ear length (EL), head length (HL), and chest girth (CG) were measured with a measuring tape
Where Y = (y1, y2, . . . yn)T is a vector of observed dependent variables, 1n is a column vector of n variables (i = 1, 2, 3 . . . , n), μ is the intercept, X is an nxp matrix of explanatory variables, β is the vector of regression coefficients, and e is the vector of the residuals with a mean of zero and a variance I σe2

Summary

Introduction

Native goat breeds play important socio-economic roles in the livelihood strategies of poorer farmers, especially those in rural and hard-to-reach areas of the world. Studies to define adult live weights and body measurements are of great importance for the characterization of farm animal breeds. The prediction of body weight (BW) and the determination of its relationships with other biometric measurements generates considerable knowledge for breeding research relating to meat production per animal (Iqbal et al, 2013; Yılmaz et al, 2013; Khan et al, 2014). Multiple linear regression (MLR), based on ordinary least squares (OLS), is a traditional, simple method that has been used by researchers in order to predict the complex relationship between live weight and some body measurements in goat, sheep, cattle, fish, etc. When a multicollinearity problem exists among explanatory variables, the OLS method produces poor predictions (Montgomery et al, 2001; Yakubu, 2010; Dormann et al, 2013; Khan et al, 2014). The multicollinearity problem implies that the standard errors of regression coefficients are higher than expected, and it is difficult to find out the accuracy and robustness of the prediction models (Weisberg, 2005; Yakubu, 2009, 2010; Sangun et al, 2009)

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Conclusion