ge_reg: Eberhart and Russell's regression model

In TiagoOlivoto/WAASB: Multi Environment Trials Analysis

Eberhart and Russell's regression model

Description

Regression-based stability analysis using the Eberhart and Russell (1966) model.

Usage

ge_reg(.data, env, gen, rep, resp, verbose = TRUE)

Arguments

.data	The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s)
env	The name of the column that contains the levels of the environments.
gen	The name of the column that contains the levels of the genotypes.
rep	The name of the column that contains the levels of the replications/blocks
resp	The response variable(s). To analyze multiple variables in a single procedure use, for example, resp = c(var1, var2, var3).
verbose	Logical argument. If verbose = FALSE the code will run silently.

Value

An object of class ge_reg with the folloing items for each variable:

• data: The data with means for genotype and environment combinations and the environment index

• anova: The analysis of variance for the regression model.

• regression: A data frame with the following columns: GEN, the genotypes; b0 and b1 the intercept and slope of the regression, respectively; t(b1=1) the calculated t-value; pval_t the p-value for the t test; s2di the deviations from the regression (stability parameter); F(s2di=0) the F-test for the deviations; pval_f the p-value for the F test; RMSE the root-mean-square error; R2 the determination coefficient of the regression.

• b0_variance: The variance of b0.

• b1_variance: The variance of b1.

Author(s)

Tiago Olivoto, [e-mail]

References

Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing Varieties. Crop Sci. 6:36-40. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2135/cropsci1966.0011183X000600010011x")}

See Also

superiority(), ecovalence(), ge_stats()

Examples

library(metan)
reg <- ge_reg(data_ge2,
             env = ENV,
             gen = GEN,
             rep = REP,
             resp = PH)
plot(reg)

TiagoOlivoto/WAASB documentation built on March 20, 2024, 4:18 p.m.