## Usage

```
ge_cluster(
.data,
env = NULL,
gen = NULL,
resp = NULL,
table = FALSE,
distmethod = "euclidean",
clustmethod = "ward.D",
scale = TRUE,
cluster = "env",
nclust = NULL
)
```

## Arguments

- .data
The dataset containing the columns related to Environments, Genotypes and the response variable. It is also possible to use a two-way table with genotypes in lines and environments in columns as input. In this case you must use

`table = TRUE`

.- env
The name of the column that contains the levels of the environments. Defaults to

`NULL`

, in case of the input data is a two-way table.- gen
The name of the column that contains the levels of the genotypes. Defaults to

`NULL`

, in case of the input data is a two-way table.- resp
The response variable(s). Defaults to

`NULL`

, in case of the input data is a two-way table.- table
Logical values indicating if the input data is a two-way table with genotypes in the rows and environments in the columns. Defaults to

`FALSE`

.- distmethod
The distance measure to be used. This must be one of

`'euclidean'`

,`'maximum'`

,`'manhattan'`

,`'canberra'`

,`'binary'`

, or`'minkowski'`

.- clustmethod
The agglomeration method to be used. This should be one of

`'ward.D'`

(Default),`'ward.D2'`

,`'single'`

,`'complete'`

,`'average'`

(= UPGMA),`'mcquitty'`

(= WPGMA),`'median'`

(= WPGMC) or`'centroid'`

(= UPGMC).- scale
Should the data be scaled befor computing the distances? Set to TRUE. Let \(Y_{ij}\) be the yield of Hybrid

*i*in Location*j*, \(\bar Y_{.j}\) be the mean yield, and \(S_j\) be the standard deviation of Location*j*. The standardized yield (Zij) is computed as (Ouyang et al. 1995): \(Z_{ij} = (Y_{ij} - Y_{.j}) / S_j\).- cluster
What should be clustered? Defaults to

`cluster = "env"`

(cluster environments). To cluster the genotypes use`cluster = "gen"`

.- nclust
The number of clust to be formed. Set to

`NULL`

.

## Value

**data**The data that was used to compute the distances.**cutpoint**The cutpoint of the dendrogram according to Mojena (1977).**distance**The matrix with the distances.**de**The distances in an object of class`dist`

.**hc**The hierarchical clustering.**cophenetic**The cophenetic correlation coefficient between distance matrix and cophenetic matrix**Sqt**The total sum of squares.**tab**A table with the clusters and similarity.**clusters**The sum of square and the mean of the clusters for each genotype (if`cluster = "env"`

or environment (if`cluster = "gen"`

).**labclust The labels**of genotypes/environments within each cluster.

## References

Mojena, R. 2015. Hierarchical grouping methods and stopping rules: an evaluation. Comput. J. 20:359-363. doi:10.1093/comjnl/20.4.359

Ouyang, Z., R.P. Mowers, A. Jensen, S. Wang, and S. Zheng. 1995. Cluster analysis for genotype x environment interaction with unbalanced data. Crop Sci. 35:1300-1305. doi:10.2135/cropsci1995.0011183X003500050008x

## Author

Tiago Olivoto tiagoolivoto@gmail.com