Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis

ABSTRACT The relationships between egg measurements [egg weight (EGWT), egg width (EGWD), egg shape index (EGSI), egg volume (EGV) and egg density (EGD)], and egg components [eggshell (SWT), yolk (YWT) and albumen (AWT)] were investigated in laying hens with 32, 45, and 59 weeks of age with an objec...

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Main Authors: TM Shafey, ES Hussein, AH Mahmoud, MA Abouheif, HA Al-Batshan
Format: Article
Language:English
Published: Fundação APINCO de Ciência e Tecnologia Avícolas 2015-12-01
Series:Brazilian Journal of Poultry Science
Subjects:
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2015000400473&lng=en&tlng=en
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spelling doaj-5cbc5f606569421b884c3e05f04a275b2020-11-24T22:49:51ZengFundação APINCO de Ciência e Tecnologia AvícolasBrazilian Journal of Poultry Science1806-90612015-12-0117447348210.1590/1516-635X1704473-482S1516-635X2015000400473Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression AnalysisTM ShafeyES HusseinAH MahmoudMA AbouheifHA Al-BatshanABSTRACT The relationships between egg measurements [egg weight (EGWT), egg width (EGWD), egg shape index (EGSI), egg volume (EGV) and egg density (EGD)], and egg components [eggshell (SWT), yolk (YWT) and albumen (AWT)] were investigated in laying hens with 32, 45, and 59 weeks of age with an objective of managing multicollinearity (MC), using stepwise regression (SR) and ridge regression (RR) analyses. There were significant correlations among egg traits that led to MC problems in all eggs. Hen age influenced egg characteristics and the magnitude of the correlations among egg characteristics. Eggs produced at older age had significantly (p<0.01) higher EGWT, EGWD, EGV, YWT and AWT than those produced at younger age. The SR model alleviated MC problem in eggs produced at 32 weeks, with condition index greater than 30, and one predictor, EGWT had a model fit predicted egg components with R2 ranged from 60 to 99%. The SR model of eggs produced at 45 and 59 weeks indicated MC problem with variance inflation factors (VIF) values greater than 10, and 4 predictors; EGWT, EGWD, EGV and EGD had a model fit that significantly predicted egg components with R2 % ranged from 76 to 99 %. The RR analysis provided lower VIF values than 10 and eliminated the MC problem for eggs produced at any age group. It is concluded that the RR analysis provided an ideal solution for managing the MC problem and successfully predicting egg components of laying hens from egg measurements.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2015000400473&lng=en&tlng=enAge of laying hensmorphological characters of eggspredicting egg componentsregression analysis
collection DOAJ
language English
format Article
sources DOAJ
author TM Shafey
ES Hussein
AH Mahmoud
MA Abouheif
HA Al-Batshan
spellingShingle TM Shafey
ES Hussein
AH Mahmoud
MA Abouheif
HA Al-Batshan
Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis
Brazilian Journal of Poultry Science
Age of laying hens
morphological characters of eggs
predicting egg components
regression analysis
author_facet TM Shafey
ES Hussein
AH Mahmoud
MA Abouheif
HA Al-Batshan
author_sort TM Shafey
title Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis
title_short Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis
title_full Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis
title_fullStr Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis
title_full_unstemmed Managing Colllinearity in Modeling the Effect of Age in the Prediction of Egg Components of Laying Hens Using Stepwise and Ridge Regression Analysis
title_sort managing colllinearity in modeling the effect of age in the prediction of egg components of laying hens using stepwise and ridge regression analysis
publisher Fundação APINCO de Ciência e Tecnologia Avícolas
series Brazilian Journal of Poultry Science
issn 1806-9061
publishDate 2015-12-01
description ABSTRACT The relationships between egg measurements [egg weight (EGWT), egg width (EGWD), egg shape index (EGSI), egg volume (EGV) and egg density (EGD)], and egg components [eggshell (SWT), yolk (YWT) and albumen (AWT)] were investigated in laying hens with 32, 45, and 59 weeks of age with an objective of managing multicollinearity (MC), using stepwise regression (SR) and ridge regression (RR) analyses. There were significant correlations among egg traits that led to MC problems in all eggs. Hen age influenced egg characteristics and the magnitude of the correlations among egg characteristics. Eggs produced at older age had significantly (p<0.01) higher EGWT, EGWD, EGV, YWT and AWT than those produced at younger age. The SR model alleviated MC problem in eggs produced at 32 weeks, with condition index greater than 30, and one predictor, EGWT had a model fit predicted egg components with R2 ranged from 60 to 99%. The SR model of eggs produced at 45 and 59 weeks indicated MC problem with variance inflation factors (VIF) values greater than 10, and 4 predictors; EGWT, EGWD, EGV and EGD had a model fit that significantly predicted egg components with R2 % ranged from 76 to 99 %. The RR analysis provided lower VIF values than 10 and eliminated the MC problem for eggs produced at any age group. It is concluded that the RR analysis provided an ideal solution for managing the MC problem and successfully predicting egg components of laying hens from egg measurements.
topic Age of laying hens
morphological characters of eggs
predicting egg components
regression analysis
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-635X2015000400473&lng=en&tlng=en
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