FR.sampling: Sampling tools for cross-validation
In gcostaneto/frGIS: Yield adaptability analysis based on factorial regression and environmental covariates
FR.sampling R Documentation
Sampling tools for cross-validation
Usage
FR.sampling(df.y, f = 0.2, part.env = 1, intercept = TRUE)
Arguments
df.y
is a data.frame containing the following colunms: environment (factor), genotype (factor), GxE or G+GxE effects (numeric) and covariates (numeric).
f
is a fraction of genotypes that must be used as validation set (default f = 0.2, i.e., 20
\itempart.envis the number of environments that be leave out of the training set (0 > part.env > number of environments). Default is 1 (leave-one-out scheme).
\iteminterceptTRUE (default) or FALSE if considering include fixed genotypic intercept
\itembootis the number of boots (default is equal to 1000)
Sampling tools for cross-validation
Germano M F Costa Neto
gcostaneto/frGIS documentation built on Jan. 4, 2024, 1:41 p.m.
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| FR.sampling | R Documentation |
Sampling tools for cross-validation
Usage
FR.sampling(df.y, f = 0.2, part.env = 1, intercept = TRUE)
Arguments
df.y |
is a data.frame containing the following colunms: environment (factor), genotype (factor), GxE or G+GxE effects (numeric) and covariates (numeric). |
f |
is a fraction of genotypes that must be used as validation set (default f = 0.2, i.e., 20 \itempart.envis the number of environments that be leave out of the training set (0 > part.env > number of environments). Default is 1 (leave-one-out scheme). \iteminterceptTRUE (default) or FALSE if considering include fixed genotypic intercept \itembootis the number of boots (default is equal to 1000) |
Sampling tools for cross-validation Germano M F Costa Neto
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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