FR: Factorial Regression (FR) using additional information

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

FR(df.y, df.cov, scale = FALSE)

Arguments

df.y	dataframe contaning response values (e.g., GE matrix)
df.cov	dataframe contaning predictor values (e.g., covariate matrix)
scale	FALSE (default) or TRUE if scale df.y is required

Author(s)

Germano Costa Neto

References

Van Eeuwijk FA. Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models. Euphytica. 1995;84(1):1–7.

Brancourt-Hulmel M, Denis JB, Lecomte C. Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression. Theor Appl Genet. 2000;100(2):285–98.

Balfourier F, Oliveira JA, Charmet G, Arbones E. Factorial regression analysis of genotype by environment interaction in ryegrass populations, using both isozyme and climatic data as covariates. Euphytica. 1997;98(1):37–46.

Baril CP, Denis J-B, Wustman R, Van Eeuwijk FA. Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression. Euphytica. 1995;84(1):23–9.

Vargas M, Crossa J, Van Eeuwijk FA, Ramírez ME, Sayre K. Using partial least squares regression, factorial regression, and AMMI models for interpreting genotype x environment interaction. Crop Sci. 1999;39(4):955–67.

Costa-Neto GMF. Integrating environmental covariates and thematic maps into genotype by environment interaction analysis in upland rice. Master degree Thesis in Genetics and Plant Breeding, Agronomy School, Federal University of Goiás. Brazil, 2017. 122f.

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.