- .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.
- resp
The response variable(s). To analyze multiple variables in a
single procedure a vector of variables may be used. For example resp = c(var1, var2, var3)
. Select helpers are also allowed.
- mresp
The new maximum value after rescaling the response variable. By
default, all variables in resp
are rescaled so that de maximum value
is 100 and the minimum value is 0 (i.e., mresp = NULL
). It must be a
character vector of the same length of resp
if rescaling is assumed
to be different across variables, e.g., if for the first variable smaller
values are better and for the second one, higher values are better, then
mresp = c("l, h")
must be used. Character value of length 1 will be
recycled with a warning message.
- wresp
The weight for the response variable(s) for computing the WAASBY
index. Must be a numeric vector of the same length of resp
. Defaults
to 50, i.e., equal weights for stability and mean performance.
- min_expl_var
The minimum explained variance. Defaults to 85.
Interaction Principal Compoment Axis are iteractively retained up to the
explained variance (eigenvalues in the singular value decomposition of the
matrix with the interaction effects) be greather than or equal to
min_expl_var
. For example, if the explained variance (in percentage)
in seven possible IPCAs are 56, 21, 9, 6, 4, 3, 1
, resulting in a
cumulative proportion of 56, 77, 86, 92, 96, 99, 100
, then
p = 3
, i.e., three IPCAs will be used to compute the index WAAS.
- verbose
Logical argument. If verbose = FALSE
the code is run
silently.
- ...
Arguments passed to the function
impute_missing_val()
for imputation of missing values in case
of unbalanced data.