Description Usage Arguments Value Author(s) References See Also Examples
View source: R/performs_ammi.R
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 kth interaction principal component axis (IPCA); \mjseqna_ik is the ith element of the kth 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()
1 
.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 commaseparated list of unquoted variable names,
i.e., 
block 
Defaults to 
verbose 
Logical argument. If 
... 
Arguments passed to the function

ANOVA: The analysis of variance for the AMMI model.
PCA: The principal component analysis
MeansGxE: The means of genotypes in the environments
model: scores for genotypes and environments in all the possible axes.
augment: Information about each observation in the dataset. This
includes predicted values in the fitted
column, residuals in the
resid
column, standardized residuals in the stdres
column,
the diagonal of the 'hat' matrix in the hat
, and standard errors for
the fitted values in the se.fit
column.
Tiago Olivoto tiagoolivoto@gmail.com
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:8392.
impute_missing_val()
, waas()
, waas_means()
, waasb()
, get_model_data()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  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")))

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