ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model

2019 ◽  
Vol 79 (02) ◽  
Author(s):  
B. C. Ajay ◽  
J. Aravind ◽  
R. Abdul Fiyaz
Keyword(s):  
R Package ◽  
Stability Parameters ◽  
Genotype By Environment ◽  
Stability Measure ◽  
Ammi Model ◽  
R Packages ◽  
Simultaneous Selection ◽  
Study Association ◽  
Target Environment ◽  
Selection Of

Selection of genotype for target environment is affected by genotype-by-environment interactions (GEI) and AMMI model is widely used tool to analyse GEI. AMMI does not quantify stability measure making it difficult to rank genotypes. To overcome this lacuna AMMI model output is used to quantify stability measures and rank genotypes. Of several stability measures available in literature, only AMMI stability value (ASV) is implemented in package ‘agricole’ and others have not been implemented in any other R packages. ‘ammistability’ uses output from ‘AMMI’ function in ‘agricolae’ package and computes various stability parameters for AMMI model. Further, genotypes are ranked on the basis of simultaneous selection of yield and stability (SSI). Package also helps to study association among several stability measures.

scholarly journals A comprehensive comparison and overview of R packages for calculating sample entropy

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Chang Chen ◽  
Shixue Sun ◽  
Zhixin Cao ◽  
Yan Shi ◽  
Baoqing Sun ◽  
...  
Keyword(s):  
Computing Time ◽  
R Package ◽  
Sample Entropy ◽  
Good Choice ◽  
R Language ◽  
Physiological Signal ◽  
Comprehensive Comparison ◽  
R Packages ◽  
Similarity Thresholds ◽  
Selection Of

Abstract Sample entropy is a powerful tool for analyzing the complexity and irregularity of physiology signals which may be associated with human health. Nevertheless, the sophistication of its calculation hinders its universal application. As of today, the R language provides multiple open-source packages for calculating sample entropy. All of which, however, are designed for different scenarios. Therefore, when searching for a proper package, the investigators would be confused on the parameter setting and selection of algorithms. To ease their selection, we have explored the functions of five existing R packages for calculating sample entropy and have compared their computing capability in several dimensions. We used four published datasets on respiratory and heart rate to study their input parameters, types of entropy, and program running time. In summary, NonlinearTseries and CGManalyzer can provide the analysis of sample entropy with different embedding dimensions and similarity thresholds. CGManalyzer is a good choice for calculating multiscale sample entropy of physiological signal because it not only shows sample entropy of all scales simultaneously but also provides various visualization plots. MSMVSampEn is the only package that can calculate multivariate multiscale entropies. In terms of computing time, NonlinearTseries, CGManalyzer, and MSMVSampEn run significantly faster than the other two packages. Moreover, we identify the issues in MVMSampEn package. This article provides guidelines for researchers to find a suitable R package for their analysis and applications using sample entropy.

scholarly journals Genotype × environment analysis of cowpea grain production in the forest and derived savannah cultivation ecologies

Agro-Science ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 20-24
Author(s):  
A.L. Nassir ◽  
M.O. Olayiwola ◽  
S.O. Olagunju ◽  
K.M. Adewusi ◽  
S.S. Jinadu
Keyword(s):  
Sum Of Squares ◽  
Grain Production ◽  
Environment Analysis ◽  
Late Season ◽  
Genotype By Environment ◽  
Differential Performance ◽  
Main Effect ◽  
Ammi Model ◽  
Main Effects ◽  
Selection Of

Differential performance of genotypes in different cultivation environments has remained a challenge to farmers and plant breeders, the emphasis being the selection of high yielding and stable genotypes, across similar ecologies. A set of nine cowpea genotypes were  cultivated in Ago-Iwoye and Ayetoro, two locations representing high and moderate moisture zones. Plantings were done with the early and late season rains in Ago-Iwoye and mid-late season rains of Ayetoro. Statistical analysis was done to understand genotype reaction to the different environments and the plant and environment factors mediating the performance. The Additive Main Effect and Multiplicative Interaction (AMMI) model captured 61.30% of the total sum of squares (TSS). The main effects: genotype (G) environment (E) and their interaction (GxE) were significant with the largest contribution of 28.70% by the environment while the interaction and genotype fractionscaptured 20.20% and 12.40%, respectively. The percentage contribution of the main effects and GxE to total sum of squares (TSS) for traits was not consistent. The Genotype plus Genotype-by-Environment (GGE) analysis summarized 91.30% of the variation in genotype performance across environment. The cultivation environments were separated into two, with IT 95M 118 as the vertex genotype in the Ayetoro while TVU 8905 was the topmost genotype in Ago-Iwoye. The two genotypes recorded the highest grain weight per plant (GWPP) but were also the most unstable The stable genotypes IT 95M 120 and IT 86 D 716 flowered relatively late compared to others, are taller, had higher vegetative score and are low grain producers. Key words: AMMI, drought, GGE, stability, Vigna unguiculata

scholarly journals SIMULTANEOUS SELECTION OF MOST STABLE AND HIGH YIELDING GENOTYPES IN BREEDING PROGRAMS BY NONPARAMETRIC METHODS

AGROFOR ◽  
2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Naser SABAGHNIA ◽  
Hamid HATAMI-MALEKI ◽  
Mohsen JANMOHAMMADI
Keyword(s):  
Statistical Methods ◽  
Environmental Effects ◽  
High Stability ◽  
Nonparametric Methods ◽  
High Yield ◽  
Ge Interaction ◽  
Breeding Programs ◽  
Genotype By Environment ◽  
Simultaneous Selection ◽  
Selection Of

Explaining genotype by environment (GE) interaction is important in breedingprograms because environmental effects are very often greater than genotypiceffects in multi-environment trials. Statistical methods that select for high yield andstability have been proposed, but have not been compared for their usefulnessespecially for nonparametric methods. We compared fourteen nonparametricmethods used for analyzing GE interaction at a set of experimental lentil data (11genotypes at 20 environments). Nonparametric methods consist of six Huehn’sstatistics (S1, S2, S3, S4, S5 and S6), four Thennarasu’s statistics (NP1, NP2, NP3and NP4), tow Sabaghnia’s statistics (NS1 and NS2), Kang’s RS andnonparametric method of Fox et al. (1990). Considering mean yield versusnonparametric stability values via their plotting in a plot, indicated four differentsections as A, B, C and D. The genotype fall in the section D were the mostfavorable genotypes due to high mean yield as well as high stability performance.Plot of the most nonparametric methods showed that genotypes G1 (1.21 t ha-1), G2(1.34 t ha-1) and G5 (1.38 t ha-1) were the most favorable genotypes and so thesegenotypes considered both yield and stability simultaneously. Although, most ofthe nonparametric methods have static (biological) concept of stability and measurethe real concept of stability but plotting them versus mean yield and selecting thegenotypes of section D, could identify relatively the high mean yield genotypes asthe most stable ones.

scholarly journals Capturing GEI Patterns for Quality Traits in Biparental Wheat Populations

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1022
Author(s):  
Ivana Plavšin ◽  
Jerko Gunjača ◽  
Ruđer Šimek ◽  
Dario Novoselović
Keyword(s):  
Genotype By Environment Interaction ◽  
Grain Protein Content ◽  
Environment Interaction ◽  
Quality Traits ◽  
Dominant Source ◽  
Genotype By Environment ◽  
Ammi Model ◽  
Superior Genotypes ◽  
Main Effects ◽  
Selection Of

Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult. In order to reduce drawbacks caused by GEI and make the selection for wheat quality more effective, it is important to properly assess the effects of genotype, environment, and GEI on the trait of interest. In the present study, GEI patterns for the selected quality and mixograph traits were studied using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Two biparental wheat populations consisting of 145 and 175 RILs were evaluated in six environments. The environment was the dominant source of variation for grain protein content (GPC), wet gluten content (WGC), and test weight (TW), accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation has been shown to be trait and population-dependent. Overall, GEI has been shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which, MG124 is the most interesting, being the prevalent “winner” for GPC and WGC, but also the “winner” for non-correlated trait TW in environment SB10.

PROsetta: An R Package for Linking Patient-Reported Outcome Measures

2021 ◽  
pp. 014662162110131
Author(s):  
S. W. Choi ◽  
S. Lim ◽  
B. D. Schalet ◽  
A. J. Kaat ◽  
D. Cella
Keyword(s):  
Patient Reported Outcomes ◽  
R Package ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Measurement Information ◽  
Short History ◽  
Rosetta Stone ◽  
Patient Reported ◽  
R Packages ◽  
Measurement Information System

A common problem when using a variety of patient-reported outcomes (PROs) for diverse populations and subgroups is establishing a harmonized scale for the incommensurate outcomes. The lack of comparability in metrics (e.g., raw summed scores vs. scaled scores) among different PROs poses practical challenges in studies comparing effects across studies and samples. Linking has long been used for practical benefit in educational testing. Applying various linking techniques to PRO data has a relatively short history; however, in recent years, there has been a surge of published studies on linking PROs and other health outcomes, owing in part to concerted efforts such as the Patient-Reported Outcomes Measurement Information System (PROMIS®) project and the PRO Rosetta Stone (PROsetta Stone®) project ( www.prosettastone.org ). Many R packages have been developed for linking in educational settings; however, they are not tailored for linking PROs where harmonization of data across clinical studies or settings serves as the main objective. We created the PROsetta package to fill this gap and disseminate a protocol that has been established as a standard practice for linking PROs.

Stability Analysis of Agronomic Traits for Maize (Zea Mays L.) Genotypes Based on Ammi Model

2021 ◽  
Vol 50 (2) ◽  
pp. 343-350
Author(s):  
Meijin Ye ◽  
Zhaoyang Chen ◽  
Bingbing Liu ◽  
Haiwang Yue
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Kernel Weight ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Ammi Model ◽  
The Ideal

Stability and adaptability of promising maize hybrids in terms of three agronomic traits (grain yield, ear weight and 100-kernel weight) in multi-environments trials were evaluated. The analysis of AMMI model indicated that the all three agronomic traits showed highly significant differences (p < 0.01) on genotype, environment and genotype by environment interaction. Results showed that genotypes Hengyu321 (G9), Yufeng303 (G10) and Huanong138 (G3) were of higher stability on grain yield, ear weight and 100-kernel weight, respectively. Genotypes Hengyu1587 (G8) and Hengyu321 (G9) showed good performance in terms of grain yield, whereas Longping208 (G2) and Weike966 (G12) showed broad adaptability for ear weight. It was also found that the genotypes with better adaptability in terms of 100-kernel weight were Zhengdan958 (G5) and Weike966 (G12). The genotype and environment interaction model based on AMMI analysis indicated that Hengyu1587 and Hengyu321 were the ideal genotypes, due to extensive adaptability and high grain yield under both testing sites. Bangladesh J. Bot. 50(2): 343-350, 2021 (June)

scholarly journals Simultaneous Selection of Features and Metric for Optimal Nearest Neighbor Classification

2004 ◽  
Vol 33 (9) ◽  
pp. 2137-2157 ◽  
Author(s):  
David A. Johannsen ◽  
Edward J. Wegman ◽  
Jeffrey L. Solka ◽  
Carey E. Priebe
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Simultaneous Selection ◽  
Neighbor Classification ◽  
Selection Of
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