The model fitted in the analysis is \(y_\) a residual. The intercept expresses general performance across all environments, the slope represents adaptability, and the residuals may indicate a measure for stability. The environmental index is the average of all genotypes in an environment. With the Finlay-Wilkinson Analysis (Finlay and Wilkinson 1963) we describe genotype by environment interaction by the heterogeneity of the slopes of a regression of individual genotypic performance on an environmental index. This will print an incidence matrix of missing values for each of the terms in the random part of the fitted model. The diagnostics for the fitted model can be printed using the diagnostics function. When using asreml for modeling standard errors will be available. Note that because the model is fitted with lme4, in this and further tables no standard errors are outputted. dropsVarComp Fitted model formula #> grain.yield ~ scenarioFull + scenarioFull:trial + (1 | genotype) + (1 | genotype:scenarioFull) #> #> Sources of variation #> component % variance expl.
# Fit a model where trials are nested within scenarios.
Trait = region + region:location + year + region:year + region:location:year + genotype + genotype:region + genotype:region:location + genotype:year + genotype:region:year + genotype:region:location:year Trials correspond to locations within regions across years Trait = location + location:trial + genotype + genotype:location + genotype:location:trial Trait = year + year:trial + genotype + genotype:year + genotype:year:trial Trait = year + location + year:location + genotype + genotype:year + genotype:location + genotype:year:location Trials form a factorial structure of locations x years
#ASREML PLUS TRIAL#
Trait = trial + genotype + genotype:trial These models are described in the table below, together with the function parameters used in gxeVarComp to fit the model. Six different types of models can be fitted depending on the structure of the environments in the data. In the statgenG圎 package this can be done using the gxeVarComp function. To investigate the structure of the genotype by environment data various mixed models can be fitted. Mixed model analysis of G圎 table of means In practice precision of the output can always be specified by the user. Note that due to technical restrictions the number of significant digits printed in tables throughout this vignette is not always optimal.
#ASREML PLUS PDF#
#ASREML PLUS HOW TO#
This vignette describes how to perform the different types of analysis that are available in the package. The statgenG圎 package is developed as an easy-to-use package for Genotype by Environment (G圎) analysis for data of plant breeding experiments with many options for plotting and reporting the results of the analyses.