scholarly journals Techniques for Robust Imputation in Incomplete Two-Way Tables

2021 ◽  
Vol 4 (3) ◽  
pp. 62
Author(s):  
Sergio Arciniegas-Alarcón ◽  
Marisol García-Peña ◽  
Camilo Rengifo ◽  
Wojtek J. Krzanowski
Keyword(s):  
Real Data ◽  
Singular Values ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Prediction Errors ◽  
Genotype By Environment ◽  
Simple Imputation ◽  
Quality Coefficient ◽  
Value Decomposition ◽  
True Values

We describe imputation strategies resistant to outliers, through modifications of the simple imputation method proposed by Krzanowski and assess their performance. The strategies use a robust singular value decomposition, do not depend on distributional or structural assumptions and have no restrictions as to the pattern or missing data mechanisms. They are tested through the simulation of contamination and unbalance, both in artificially generated matrices and in a matrix of real data from an experiment with genotype-by-environment interaction. Their performance is assessed by means of prediction errors, the squared cosine between matrices, and a quality coefficient of fit between imputations and true values. For small matrices, the best results are obtained by applying robust decomposition directly, while for larger matrices the highest quality is obtained by eliminating the singular values of the imputation equation.

scholarly journals An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

2014 ◽  
Vol 51 (2) ◽  
pp. 75-88 ◽  
Author(s):  
Sergio Arciniegas-Alarcón ◽  
Marisol García-Peña ◽  
Wojtek Janusz Krzanowski ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Missing Values ◽  
Mean Squared Error ◽  
Real Data ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Simple Imputation ◽  
Alternative Methodology ◽  
Complete Set ◽  
Rank Approximation ◽  
Value Decomposition

Abstract A common problem in multi-environment trials arises when some genotypeby- environment combinations are missing. In Arciniegas-Alarcón et al. (2010) we outlined a method of data imputation to estimate the missing values, the computational algorithm for which was a mixture of regression and lower-rank approximation of a matrix based on its singular value decomposition (SVD). In the present paper we provide two extensions to this methodology, by including weights chosen by cross-validation and allowing multiple as well as simple imputation. The three methods are assessed and compared in a simulation study, using a complete set of real data in which values are deleted randomly at different rates. The quality of the imputations is evaluated using three measures: the Procrustes statistic, the squared correlation between matrices and the normalised root mean squared error between these estimates and the true observed values. None of the methods makes any distributional or structural assumptions, and all of them can be used for any pattern or mechanism of the missing values.

scholarly journals Deterministic Imputation in Multienvironment Trials

ISRN Agronomy ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Sergio Arciniegas-Alarcón ◽  
Marisol García-Peña ◽  
Wojtek Janusz Krzanowski ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Missing Values ◽  
Interaction Model ◽  
Environmental Parameters ◽  
Real Data ◽  
Original Data ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Data Set ◽  
Value Decomposition ◽  
Main Effects

This paper proposes five new imputation methods for unbalanced experiments with genotype by-environment interaction (G×E). The methods use cross-validation by eigenvector, based on an iterative scheme with the singular value decomposition (SVD) of a matrix. To test the methods, we performed a simulation study using three complete matrices of real data, obtained from G×E interaction trials of peas, cotton, and beans, and introducing lack of balance by randomly deleting in turn 10%, 20%, and 40% of the values in each matrix. The quality of the imputations was evaluated with the additive main effects and multiplicative interaction model (AMMI), using the root mean squared predictive difference (RMSPD) between the genotypes and environmental parameters of the original data set and the set completed by imputation. The proposed methodology does not make any distributional or structural assumptions and does not have any restrictions regarding the pattern or mechanism of missing values.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

Exploring genotype‐by‐environment interaction in sweet sorghum under tropical environments

2021 ◽  
Author(s):  
Vander Fillipe Souza ◽  
Pedro César de Oliveira Ribeiro ◽  
Indalécio Cunha Vieira Júnior ◽  
Isadora Cristina Martins Oliveira ◽  
Cynthia Maria Borges Damasceno ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Tropical Environments

Genotype by environment interaction on phenology and synchronous maturity of mungbean

2021 ◽  
Author(s):  
Siti Marwiyah ◽  
Willy Bayuardi Suwarno ◽  
Desta Wirnas ◽  
Trikoesoemaningtyas xxx ◽  
Surjono Hadi Sutjahjo
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Genotype and environment interaction of sweetpotato varieties

2019 ◽  
Vol 44 (3) ◽  
pp. 501-512
Author(s):  
S Sultana ◽  
HC Mohanta ◽  
Z Alam ◽  
S Naznin ◽  
S Begum
Keyword(s):  
Tuber Yield ◽  
Agricultural Research ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
Environment Interaction ◽  
Dry Matter Content ◽  
Genotype By Environment ◽  
Discriminating Power ◽  
Study Results ◽  
Main Effect

The article presents results of additive main effect and multiplicative interaction (AMMI) and genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data of 15 sweetpotato varieties released from Bangladesh Agricultural Research Institute conducted during 2015–2018. The objective of this study was to determine the effects of genotype, environment and their interaction on tuber yield and to identify stable sweetpotato genotypes over the years. The experimental layout was a randomized complete block design with three replications at Gazipur location. Combined analysis of variance (ANOVA) indicated that the main effects due to genotypes, environments and genotype by environment interaction were highly significant. The contribution of genotypes, environments and genotype by environment interaction to the total variation in tuber yield was about 60.16, 10.72 and 12.82%, respectively. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 100% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 71.5% and 28.5% of variability, respectively. The study results identified BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 as the closest to the “ideal” genotype in terms of yield potential and stability. Varieties ‘BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12’ were also selected as superior genotypes. BARI Mistialu- 3 and BARI Mistialu- 13 was comparatively low yielder but was stable over the environment. Among them BARI Mistialu-12, BARI Mistialu-14 and BARI Mistialu-15 are rich in nutrient content while BARI Mistialu-8 and BARI Mistialu-11 are the best with dry matter content and organoleptic taste. Environments representing in 1st and 3rd year with comparatively short vectors had a low discriminating power and environment in 2nd year was characterized by a high discriminating power. Bangladesh J. Agril. Res. 44(3): 501-512, September 2019

EFFECTS OF GROWTH INHIBITION BY HYDROCORTISONE ACETATE ON VARIANCE IN CHICKENS

1970 ◽  
Vol 12 (3) ◽  
pp. 627-634
Author(s):  
J. S. Gavora ◽  
G. C. Hodgson
Keyword(s):  
Body Weight ◽  
External Environment ◽  
Hormonal Treatment ◽  
Genotype By Environment Interaction ◽  
Future Research ◽  
Environment Interaction ◽  
Internal Environment ◽  
Hydrocortisone Acetate ◽  
Genotype By Environment ◽  
Treatment Interaction

Traditionally genotype by environment interaction studies have dealt with changes in external environment. In this experiment an attempt was made to alter internal environment and keep external environment constant. Cockerels from each of six different commercial stocks were injected with 0,1,2 and 4 mgs hydrocortisone acetate per 100 gms body weight at 14 days of age. This type of hormonal treatment was shown to release additional variability in growth without producing any stock-treatment interaction at the level of means. The results indicate a possible new avenue for future research.

Genotype by environment interaction for yearling weight in Nellore cattle applying reaction norms models

2018 ◽  
Vol 58 (11) ◽  
pp. 1996
Author(s):  
S. Ribeiro ◽  
J. P. Eler ◽  
V. B. Pedrosa ◽  
G. J. M. Rosa ◽  
J. B. S. Ferraz ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Environmental Gradient ◽  
Genotype By Environment Interaction ◽  
Reaction Norms ◽  
Breeding Value ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Nellore Cattle ◽  
Gradient Variation

In the present study, a possible existence of genotype × environment interaction was verified for yearling weight in Nellore cattle, utilising a reaction norms model. Therefore, possible changes in the breeding value were evaluated for 46 032 animals, from three distinct herds, according to the environmental gradient variation of the different contemporary groups. Under a Bayesian approach, analyses were carried out utilising INTERGEN software resulting in solutions of contemporary groups dispersed in the environmental gradient from –90 to +100 kg. The estimates of heritability coefficients ranged from 0.19 to 0.63 through the environmental gradient and the genetic correlation between intercept and slope of the reaction norms was 0.76. The genetic correlation considering all animals of the herds in the environmental gradient ranged from 0.83 to 1.0, and the correlation between breeding values of bulls in different environments ranged from 0.79 to 1.0. The results showed no effect of genotype × environment interaction on yearling weight in the herds of this study. However, it is important to verify a possible influence of the genotype × environment in the genetic evaluation of beef cattle, as different environments might cause interference in gene expression and consequently difference in phenotypic response.

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