scholarly journals Principal component regression of carcass traits in meat line funaab alpha chicken genotype

2021 ◽  
Author(s):  
Mathew Wheto ◽  
Nkiruka Goodness Chima ◽  
Henry T Ojoawo ◽  
Matthew A Adeleke ◽  
Sunday O Peters ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Principal Component Regression ◽  
Principal Component ◽  
Liver Weight ◽  
Morphometric Traits ◽  
Mean Values ◽  
Weight Prediction ◽  
The Relationship ◽  
Single Predictor ◽  
Variance Explained

Abstract This study aimed to assess the relationship among carcass traits of meat line FUNAAB Alpha chicken genotype, to identify the components that defined bled weight in them using multivariate principal component regression. A total of 14 different carcass traits from sixty-eight birds were recorded and subjected to one-way analysis of variance to vet for sex effect. Phenotypic relationships among the carcass traits were also established to pave way for the principal component analysis. The results reveal significant effects between the traits measured. The male significantly (P<0.05) had greater mean values for the traits measured. Correlations among the considered carcass traits were found to be positive and significant ranging from r = 0.406 (LrWt) - 0.981 (EdWt) for the female chicken; r = 0.330 (Head Wt) - 0.978 (BdWt) for the male chicken. The extracted components PC1 to PC7 contributed 95.66% with PC1 accounting for 68.68% of the variability in the original parameters. Communality estimates varied from 0.466 (thigh weight) to 0.983 (liver weight). In the principal component regression models, Eviscerated weight accounted for 95% of the variation observed in bled weight. The use of PC1 as a single predictor, explained 96.4% of the variability, whilst combining PC1 and PC4 showed improvements in the variance explained (R2 = 96.7%) with a lower Mallow's cp (5.31). Using the principal components scores from the chicken morphometric traits was more appropriate than using the original traits in bled weight prediction.

Modeling relevance of mobile communication services by social setting dimensions

2016 ◽  
Vol 20 (1) ◽  
pp. 311-331
Author(s):  
Elena Menichelli ◽  
Richard Ling
Keyword(s):  
Mobile Communication ◽  
Communication Channel ◽  
Mixed Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Communication Services ◽  
Channel Choice ◽  
Social Settings ◽  
Negative Messages ◽  
The Relationship

There is little research examining the confluence of what communication channel is used for which purpose with which person. This study examines the “setting” for communication that includes what is communicated (e.g. positive or negative messages), the nature of the relationship (close versus distant), and the information channel. The respondents to a web-based questionnaire ( n = 627) were Norwegian smartphone users aged 16–35 years. Respondents evaluated mobile communication services that they used in specific social settings by “checking off” all that apply. Two methods of analysis are used to examine the material. First, a Principal Component Regression validated the main method, namely a mixed model for the Analysis of Variance. Results show the probability of using a mobile communication service is based on the effects of social group, communication purpose, communication channel, and their interaction. The relationship to the interlocutor was found to have the strongest effect on channel choice.

scholarly journals Southern Hemisphere Pressure Relationships during the 20th Century—Implications for Climate Reconstructions and Model Evaluation

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 413 ◽  
Author(s):  
Logan Clark ◽  
Ryan Fogt
Keyword(s):  
20Th Century ◽  
Southern Hemisphere ◽  
High Latitude ◽  
Principal Component Regression ◽  
Austral Summer ◽  
Principal Component ◽  
Direct Observations ◽  
Climate Reconstructions ◽  
Pcr Method ◽  
The Relationship

The relationship between Southern Hemisphere middle and high-latitude regions has made it possible to generate observationally-based Antarctic pressure reconstructions throughout the 20th century, even though routinely collected observations for this continent only began around 1957. While nearly all reconstructions inherently assume stability in these relationships through time and in the absence of direct observations, this stationarity constraint can be fully tested in a model setting. Seasonal pressure reconstructions based on the principal component regression (PCR) method spanning 1905–2013 are done entirely within the framework of the Community Atmospheric version 5 (CAM5) model in this study in order to evaluate this assumption, test the robustness of the PCR procedure for Antarctic pressure reconstructions and to evaluate the CAM5 model. Notably, the CAM5 reconstructions outperformed the observationally-based reconstruction in every season except the austral summer. Other tests indicate that relationships between Antarctic pressure and pressure across the Southern Hemisphere remain stable throughout the 20th century in CAM5. In contrast, 20th century reanalyses all display marked changes in mid-to-high latitude pressure relationships in the early 20th century. Overall, comparisons indicate both the CAM5 model and the pressure reconstructions evaluated here are reliable estimates of Antarctic pressure throughout the 20th century, with the largest differences between the two resulting from differences in the underlying reconstruction predictor networks and not from changes in the model experiments.

scholarly journals Use of Body Linear Measurements to Estimate Live Weight in Communal Beef Cattle

2021 ◽  
Vol 2 (2) ◽  
pp. 11-20
Author(s):  
Soul Washaya ◽  
Wesley Bvirwa ◽  
Godfrey Nyamushamba
Keyword(s):  
Body Weight ◽  
Principal Component Regression ◽  
Live Weight ◽  
Principal Component ◽  
Body Measurements ◽  
Linear Regression Models ◽  
Linear Measurements ◽  
Smallholder Farms ◽  
Accuracy Of Prediction ◽  
The Relationship

Body measurements are important criteria in the selection of elite animals for breeding. The objective of this study was to determine the relationship, accuracy of prediction of body weight from body measurements, and identifying multicollinearity from three beef breeds.  Four classes of stock (bull, cows, steers, and heifers) were considered. Correlation, simple, and multiple linear regression models were fitted with body weight (BW) as the dependent variable and body length (BL), heart girth (HG), height at wither (HW), muzzle circumference (MC), and shank circumference (SC) as the independent variables. The BW of the animals ranged from 218 to 630 kg, the least being heifers and bulls were the heaviest. The pairwise phenotypic correlations showed a high and significant positive relationship between BW and body dimensions (r = 0.751- 0.96; P<0.01). However, negative correlations were observed between BW with BL and MC of r = -0.733 and -0.703 and -0.660, -0.650, for cows and heifers, respectively. Regressing BW on BL, HG, and HW measurements gave statistically significant (P<0.01) equations with R2 ranging from 0.60 to 0.79. Collinearity, as portrayed by high variance inflation factors (VIFs), tolerance values, and low eigenvalues, was evident in four of the variables. It was concluded that the regression model was useful in BW prediction for smallholder farms and the relationship between BW and other body measurements was influenced by breed and class of stock. It is recommended that ridge regression or principal component regression be used in cases where multicollinearity exisists.

Application of Multivariate Analyses to NIR Spectra of Gelatinized Starch

1992 ◽  
Vol 46 (9) ◽  
pp. 1420-1425 ◽  
Author(s):  
D. Bertrand ◽  
C. N. G. Scotter
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Multivariate Analyses ◽  
Principal Component Regression ◽  
Principal Component ◽  
Effect Of Temperature ◽  
Linear Discriminant ◽  
Starch Gelatinization ◽  
Gelatinized Starch ◽  
The Relationship

This paper describes an approach for studying collections of near-infrared spectra by using multivariate analyses. The method is illustrated with the use of two sets of spectra of gelatinized starch, recorded in the transmission mode between 650 and 1235 nm. The first set consisted of 99 spectra of partly gelatinized samples (from 24.5 to 100% gelatinization). Application of principal component analysis (PCA) made it possible to identify an outlying sample and to identify the importance of spectral variations due to the effect of scattering. Hence, it was possible to eliminate the scatter variations. From principal component regression (PCR), it was shown that the relationship between corrected spectra and gelatinization was not linear. Discriminant analysis was applied to seven classes of starch gelatinization. Only five samples out of 98 were incorrectly identified. The second set of samples was designed for studying the effect of temperature variation on the spectra of fully gelatinized starch samples. It was possible to show from PCR that the relationship between the spectra and temperature was linear. The “spectral patterns” assessed from discriminant analysis of starch gelatinization and from the PCR of temperature were compared.

scholarly journals Thermal Positioning Error Modeling of Servo Axis Based on Empirical Modeling Method

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 201
Author(s):  
Yang Li ◽  
Hexuan Shi ◽  
Shijun Ji ◽  
Fusheng Liang
Keyword(s):  
Machine Tool ◽  
Principal Component Regression ◽  
Thermal Error ◽  
Principal Component ◽  
Modeling Method ◽  
Error Modeling ◽  
Empirical Modeling ◽  
Positioning Error ◽  
The Relationship ◽  
Servo Axis

In order to investigate the thermal effect of a servo axis’ positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation.

scholarly journals Principal components for the in vivo and carcass conformations of Anglo-Nubian crossbred goats

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Marta Jeidjane Borges Ribeiro ◽  
Luís Fernando Batista Pinto ◽  
Ana Carla Borges Barbosa ◽  
Gladston Rafael de Arruda Santos ◽  
Ana Paula Gomes Pinto ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Body Condition ◽  
Principal Components ◽  
Carcass Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Morphometric Traits

ABSTRACT: This study aimed to identify the principal components (PC) that explain the highest percentages of total variance and best characterize the in vivo and carcass morphologies of Anglo-Nubian crossbred goats. Nineteen carcass morphometric traits and six in vivo morphometric traits were measured in 28 kids at eight months of age. Principal component analysis indicated that five PC were able to explain 83.57% of the total variance in the 19 original carcass traits. Those components were termed PC1-Carcass Size, PC2 - Body Condition, PC3-Carcass Width, PC4-Chest Depth, and PC5 - Hindquarter. For in vivo morphometric traits, the first two principal components explained 78.86% of the total variance. These components were called PC1-In vivo Size and PC2-In vivo Conformation.

scholarly journals Presence or Absence of Earlobes Reveals Different Biometric Structure among the Peluca Hen from Guatemala

2021 ◽  
pp. 93-99
Author(s):  
Pere M. Parés-casanova ◽  
Raúl Jáuregui
Keyword(s):  
Body Length ◽  
Principal Component ◽  
Body Structure ◽  
Leg Length ◽  
Morphometric Traits ◽  
Local Breed ◽  
Face Length ◽  
Length Width ◽  
Thoracic And Abdominal ◽  
The Relationship

Background and Objective: Earlobe, a head furnishing trait, is a non-putative trait in Peluca hen (“naked-necked” hen), a local breed from Guatemala. The objective of this study is to determine if presence or absence of earlobe is linked to a body linear trait. Materials and Methods: Quantitative data collected on 311 mature hens belonging to Peluca breed were subjected to analyses for two different phenotypic subsets according to presence/absence of earlobes (212 with earlobes and 99 without earlobes). Measured morphometric traits were 18: Weight, Perimeter, Length, Width and Height of Body, Wing Length, Leg Length, Lengths of Head, Beak and Face, Length and Width of Shank, Metatarsal Perimeter, Dorso-sternal Height, Bicostal Length, Withers Height, and Thoracic and Abdominal Perimeters. A Principal Component Analysis was applied to the study of variable between both groups to explore the relationship between traits. Results: body length and height, and abdominal and thoracic perimeters were the most discriminative traits between groups. “Non-lobe” group presented significative higher values only for body length. Conclusion: Presence/absence of earlobes describe a different body structure within the Peluca hen. Moreover, as this represents no adaptative response, presence or absence of earlobe must be considered to be more related to the productive aptitude rather than different ecotypes. This association of earlobe with some body traits is important since it can ease the task of selecting productive characteristics of the “Peluca” hen.

scholarly journals Entrepreneurial competencies and financial performance of farmers in South Africa

2017 ◽  
Vol 20 (1) ◽  
Author(s):  
Simone Nieuwoudt ◽  
Johannes I.F. Henning ◽  
Henry Jordaan
Keyword(s):  
South Africa ◽  
Financial Performance ◽  
Performance Indicator ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Operating Efficiency ◽  
Least Squares Regression ◽  
Entrepreneurial Competencies ◽  
The Relationship

Aim:The main objective of this study was to explore the relationship between the entrepreneurial competencies of farmers and their financial performance. Setting: The study was conducted in South Africa among farmer clients of a commercial financial organisation.Methods: The financial performance of the farmers was calculated by means of financial ratios which were used to compile a single performance indicator: operating efficiency. The operating efficiency indicator was calculated using a financial-based data envelopment analysis. An entrepreneurial competencies instrument was used to measure the entrepreneurial competencies of the farmers. Ordinary least squares regression was used within the principal component regression framework to explore the relationship between entrepreneurial competencies and financial performance.Results: The results indicate there is a positive relationship between entrepreneurial competencies and financial performance of farmers. Each of the individual competencies also indicated positive correlation between the entrepreneurial competencies and financial performance.Conclusion: An increase in specific entrepreneurial competencies behaviour may increase the operating efficiency of the farm. Educational opportunities exist to educate farmers on the potential benefits of using entrepreneurial behaviour to their advantage (to benefit their operating efficiency). Sectors involved with agriculture, for example agricultural advisors, financial advisors and educational institutes, should emphasise the importance of utilising the competencies of farmers.

scholarly journals COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1237
Author(s):  
Christian Acal ◽  
Manuel Escabias ◽  
Ana M. Aguilera ◽  
Mariano J. Valderrama
Keyword(s):  
Intensive Care ◽  
Principal Components ◽  
Missing Values ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Equation ◽  
Functional Responses ◽  
Principal Components Regression ◽  
Functional Principal Components ◽  
The Relationship

The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables.

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