ENVCOR: Genotypic Correlation across Environments

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

ENVCOR(df, trials, gen, rep, y, plot = TRUE)

Arguments

df	dataframe object
trials	factor type column associated to environment levels within df object
gen	factor type column associated to genotype levels within df object
rep	factor type column associated to replication or blocks levels within df object
y	numeric type column associated to evaluated trait within df object
GE	TRUE or FALSE, return a plot of the predominant type of GxE interaction, genotypic correaltion across trials and variety connectivity

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(MET_maize)
env.corr<-envcorrelation(y = "YIELD", trials = "ENV",
                         gen = "GEN", rep = "REP", df = MET.maize)
head(env.corr)

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