Ggebiplotgui Package Download |BEST|

The GGEBiplotGUI package provides a graphical user interface for the construction of, interaction with, and manipulationof GGE biplots in R. Some of the functions of the package are: (i) ranking the cultivars based on their performance in any given environment, (ii) ranking the environments basedon the relative performance of any given cultivar, (iii) comparing the performance of anypair of cultivars in diferent environments, (iv) identifying the best cultivar in each environment,(v) grouping the environments based on the best cultivars, (vi) evaluating thecultivars based on both average yield and stability and (vii) evaluating the environmentsbased on both discriminating ability and representativeness. Three-dimensional biplotsare incorporated via the rgl package.

BiocManager::install("biocLite") would be the command used to install a package biocLite, but there is no biocLite package. biocLite is the name of the installer function a long time ago. All the other lines you ran are correct.

Ggebiplotgui Package Download

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To obtain the matrix of variances and covariances among the traits assessed, multivariate analysis of variance (MANOVA) was performed using the data from all the hybrids, traits, and locations. For that purpose, the model according to Ferreira [16] was used, expressed in the vectorial form in the following expression:in which the vector refers to the multivariate observations associated with the th hybrid , at the jth location in the kth replication of this combination of the and levels of the two factors; is the vector of constants of the multivariate linear model given by , is the vector of effects of the th hybrid given by , is the vector of effects of the jth location given by , is the vector of effects of the interaction between the th hybrid and the jth location given by , and is the vector of effects of the nonobservable experimental error corresponding to observation . The manova package of the software R v 3.0 [17] was used for MANOVA.

After obtaining the GIDI for each plot, joint analysis of variance was performed to verify the presence of the interaction for the GIDI and for grain yield. Once the presence of the interaction (F test significant) was observed, analysis of adaptability and stability was performed for the two variables, which allowed the adaptation and stability of each hybrid under testing to be measured, for the GIDI and for yield. This evaluation was made using the GGE biplot method. As the interpretation of the GIDI is the opposite of yield, that is, the lower the value of the index, the better the performance, the value of each plot was subtracted from 2000. This value was determined using the same principle used in the determination of ideotype of yield, that is, using the next thousand value above the greatest value of the index considering all the plots of all the trails. Graph interpretation was thereby able to be performed in the same manner as yield. GGE biplot analysis was performed by means of the package GGEBiplotGUI of the software R v 3.0 [17].

The graph accuracy of the identification methods of megaenvironments and winning genotypes was tested by the cross validation procedure proposed by Gabriel [19]. For that purpose, the PRESSm and PRESScorr statistics were used to measure the discrepancy between the observed and predicted values and the predictive correlation [20]. This cross validation analysis was performed by means of PROC IML of the statistical package of the software SAS v 9.0 [21].

GGE biplot analysis was employed using the R package GGEBiplotGUI [29]. GGE biplots were based on singular value decomposition with symmetrical scaling and focused on the environment [30]. Cluster analysis of yields for the ten sites was performed using the CLASSIFICATION procedure of the SPSS software. ff782bc1db