GGAW: Fixed effects of G, GE, G+GE and Ecovalence

In gcostaneto/YieldTrial: Phenotypic analysis and diagnosis over multienvironment trials in plant breeding

Description

Returns a dataframe containing outputs results from two-by-two analysis using mixed model REML/BLUP (assuming random genotypic effects and fixed block). Perform a analysis on the variety connectivity (number of the same genotypes among trials), calculate the Indicates what type of genoytpe x environment interaction are predominant.

Usage

GGAW(df, digits, plot = TRUE)

Arguments

df	dataframe object
digits	numeric, digits for round ouputs

Author(s)

Germano Martins F. Costa Neto <[e-mail]>

References

1. Colombari Filho JM, de Resende MDV, de Morais OP, de Castro AP, Guimarães ÉP, Pereira JA, et al. Upland rice breeding in Brazil: A simultaneous genotypic evaluation of stability, adaptability and grain yield. Euphytica. 2013;192(1):117–29.

2. Smith AB, Ganesalingam A, Kuchel H, Cullis BR. Factor analytic mixed models for the provision of grower information from national crop variety testing programs. Theor Appl Genet. 2014;128(1):55–72.

Examples

data(corsten.interaction)
m1 <- melt(corsten.interaction, measure.var='yield')
dmat <- acast(m1, loc~gen)
dmat<-ecovalence(as.matrix(dmat),digits=3)
dmat$Ecovalence
dmat$GA.matrix
dmat$Y.j

gcostaneto/YieldTrial documentation built on June 10, 2019, 5:45 a.m.