performs_ammi: Additive Main effects and Multiplicative Interaction

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/performs_ammi.R

Description

[Stable]

Compute the Additive Main effects and Multiplicative interaction (AMMI) model. The estimate of the response variable for the ith genotype in the jth environment (\mjseqny_ij) using the AMMI model, is given as follows: \loadmathjax \mjsdeqny_ij = \mu + \alpha_i + \tau_j + \sum\limits_k = 1^p \lambda_ka_ik t_jk + \rho_ij + \varepsilon _ij

where \mjseqn\lambda_k is the singular value for the k-th interaction principal component axis (IPCA); \mjseqna_ik is the i-th element of the k-th eigenvector; \mjseqnt_jk is the jth element of the kth eigenvector. A residual \mjseqn\rho _ij remains, if not all p IPCA are used, where \mjseqnp \le min(g - 1; e - 1).

This function also serves as a helper function for other procedures performed in the metan package such as waas() and wsmp()

Usage

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performs_ammi(.data, env, gen, rep, resp, block = NULL, 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 comma-separated list of unquoted variable names, i.e., resp = c(var1, var2, var3), or any select helper like resp = contains("_PLA").

block

Defaults to NULL. In this case, a randomized complete block design is considered. If block is informed, then a resolvable alpha-lattice design (Patterson and Williams, 1976) is employed. All effects, except the error, are assumed to be fixed.

verbose

Logical argument. If verbose = FALSE the code will run silently.

...

Arguments passed to the function impute_missing_val() for imputation of missing values in case of unbalanced data.

Value

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com

References

Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.

See Also

impute_missing_val(), waas(), waas_means(), waasb(), get_model_data()

Examples

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library(metan)
model <- performs_ammi(data_ge, ENV, GEN, REP, resp = c(GY, HM))

# PC1 x PC2 (variable GY)
p1 <- plot_scores(model)
p1

# PC1 x PC2 (variable HM)
plot_scores(model,
            var = 2, # or "HM"
            type = 2)

# Nominal yield plot (variable GY)
# Draw a convex hull polygon
plot_scores(model, type = 4)

# Unbalanced data (GEN 2 in E1 missing)
mod <-
  data_ge %>%
   remove_rows(4:6) %>%
   droplevels() %>%
   performs_ammi(ENV, GEN, REP, GY)
p2 <- plot_scores(mod)
arrange_ggplot(p1, p2, tag_levels = list(c("Balanced data", "Unbalanced data")))

metan documentation built on Nov. 10, 2021, 9:11 a.m.