Multivariate, Multilinear and Mixed Linear Models

2021 ◽  
Keyword(s):  
Linear Models ◽  
Mixed Linear Models

Inheritance and Correlation Analysis of Main Agronomic Traits in Mungbean (Vigna radiata L.) by Mixed Linear Models

2013 ◽  
Vol 38 (4) ◽  
pp. 624-631
Author(s):  
Chang-You LIU ◽  
Bao-Jie FAN ◽  
Zhi-Min CAO ◽  
Yan WANG ◽  
Zhi-Xiao ZHANG ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Vigna Radiata ◽  
Linear Models ◽  
Agronomic Traits ◽  
Mixed Linear Models

scholarly journals A Method for Assessing Extent and Sources of Heterogeneity of Residual Variances in Mixed Linear Models

1990 ◽  
Vol 73 (6) ◽  
pp. 1612-1624 ◽  
Author(s):  
J.L. Foulley ◽  
D. Gianola ◽  
M. San Cristobal ◽  
S. Im
Keyword(s):  
Linear Models ◽  
Mixed Linear Models ◽  
Residual Variances

scholarly journals A Generalized Computing Procedure for Setting Up and Solving Mixed Linear Models

1990 ◽  
Vol 73 (2) ◽  
pp. 513-531 ◽  
Author(s):  
Eildert Groeneveld ◽  
Milena Kovac
Keyword(s):  
Linear Models ◽  
Mixed Linear Models ◽  
Computing Procedure

scholarly journals Application of Mixed Linear Models in the Analysis of Road Surface Features

2015 ◽  
Vol 54 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Jurgita Židanavičiūtė ◽  
Audrius Vaitkus
Keyword(s):  
Linear Models ◽  
Road Surface ◽  
Linear Regression Models ◽  
Mixed Linear Models ◽  
Time Intervals ◽  
Surface Strength ◽  
The Road ◽  
Unequally Spaced ◽  
Units Of Analysis ◽  
The Impact

The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength. In this statistical analysis, we apply linear mixed models (LMMs) to clustered longitudinal data, inwhich the units of analysis (points in the road) are nested within clusters (sample of four different road segments), and repeatedmeasures of road strength in these different points are collected over time with unequally spaced time intervals. The data arebalanced – each cluster has the same number of units, which are measured at the same number of time points. Because of correlateddata and different clusters in which data could be correlated, linear regression models are not appropriate here, and therefore linearmixed models are applied.

scholarly journals Stool and ruminal microbiome components associated with methane emission and feed efficiency in Nelore beef cattle.

2021 ◽  
Author(s):  
Bruno G.N. Andrade ◽  
Haithem Afli ◽  
Flavia A. Bressani ◽  
Rafael R. C. Cuadrat ◽  
Priscila S. N. de Oliveira ◽  
...  
Keyword(s):  
Methane Emission ◽  
Feed Efficiency ◽  
Linear Models ◽  
Anthropogenic Activities ◽  
Mixed Linear Models ◽  
Human Welfare ◽  
Feed Composition ◽  
Fecal Microbiome ◽  
Treatment Groups ◽  
The Impact

Abstract Background: The impact of extreme changes in weather patterns in the economy and human welfare are some of the biggest challenges that our civilization is facing. From the anthropogenic activities that contribute to climate change, reducing the impact of farming activities is a priority, since it is responsible for up to 18% of greenhouse gases linked to such activities. To this end, we tested if the ruminal and fecal microbiome components of 52 Brazilian Nelore bulls, belonging to two treatment groups based on the feed intervention, conventional and by-products based diet, could be used in the future as biomarkers for methane emission and feed efficiency in bovine.Results: We identified a total of 5,693 Amplicon Sequence Variants (ASVs) in the Nelore bulls microbiomes. Differential abundance (DA) analysis with the ANCOM approach identified 30 bacterial and 15 archaea ASVs as DA among treatment groups. Association analysis using Maaslin2 and Mixed Linear Models indicated that bacterial ASVs are linked to the residual methane emission (RCH4) and Residual Feed Intake (RFI) phenotypes, contributing to the host’s phenotypic variation, suggesting their potential as targets for interventions and/or biomarkers.Conclusion: Feed composition induced significant differences in abundance and richness of ruminal and fecal microbial populations. The diet based on industrial byproducts applied to our treatment groups influenced the microbiome diversity of bacteria and archaea, but not of protozoa. Different ASVs were associated with RCH4 emission and RFI in both ruminal and fecal microbiomes. While ruminal ASVs are expected to directly influence RCH4 emission and RFI, the relation of fecal taxa, such as Alistipes and Rikenellaceae (gut group RC9), with these traits might also be associated with host health due to their link to anti-inflammatory compounds, and these have the potential to be used as accessible biomarkers for these complex phenotypes.

scholarly journals Implementation of the mixed linear models in experimental infections with WSSV in the withe shrimp Litopenaeus vannamei

2017 ◽  
Vol 43 (3) ◽  
pp. 557-565
Author(s):  
María Alejandra Ramírez-Ruíz ◽  
Raúl Simá-Álvarez ◽  
Edgar Tórres-Irineo ◽  
Rossanna Rodríguez-Canul
Keyword(s):  
Litopenaeus Vannamei ◽  
Host Response ◽  
White Spot Syndrome Virus ◽  
Linear Models ◽  
Severe Infection ◽  
The Other ◽  
Mixed Linear Models ◽  
Dna Injection ◽  
Shrimp Industry ◽  
Experimental Strategies

The White Spot Syndrome Virus has been very detrimental for the shrimp industry. Up to date there is no cure for the disease, thus, it is necessary to implement reliable experimental strategies to evaluate the effect of drugs and the host response during the prognosis of the disease. In this study, we evaluated two ways of infection with WSSV (200 copies WSSV-DNA) (injection and immersion), at constant temperature (26 ± 0.5ºC), in juveniles of Litopenaeus vannamei (4.8 ± 0.38 g) in intermolt stage. In the infection by injection, the organisms were lethargic with reddish appearance 2 days after infection and mortality (100%) was observed within 2-5 days: 63% organisms with light degree of infection [20 copies of DNA & 1-5 Cowdry A-type inclusions in hypertrophied nuclei (CAI)/200 fields], 21% had moderate infection (200 copies of DNA & 1-2 CAI/20 fields) and 16% severely infected (2000 copies of DNA & more than 10 CAI/field). No mortality was observed in the controls. In the infection by immersion, the signs of WSSV were observed 3 days after infection: 38% of mortality was observed during 3-9 days: 25% of the organism with light degree of infection (20 copies of DNA & 1-2 CAI/20 fields), 5% moderately infected (200 copies of DNA & 1-2 CAI/20 fields) and 8% with severe infection (2000 copies of DNA & 1-5 CAI/2 fields. The other 62% organisms were necropsied at day 12 and tested positive to the WSSV-PCR (light infection = 20 copies of DNA), but did not show CAI by histology. No mortality was observed in the controls. For the statistical analysis, the data did not fit the criteria of independence and linearity needed for the analyses of variance, thus we used instead the mixed linear models and were able to observe a better prediction: in the injected organisms, the mortality reached the highest peak at day 5 after infection. In the organisms infected by immersion, the highest peak or mortality was observed at day 9 after infection. The analysis of variance of Kenward-Roger indicated significant differences between the days of mortality (F = 20.1, P = 0.001), as well as among the ways of infection (Random analysis) (P = 0.007).

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