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Cluster genotypes or environments

Source: R/ge_cluster.R
ge_cluster.Rd

[Stable]

Performs clustering for genotypes or tester environments based on a dissimilarity matrix.

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

Examples

# \donttest{
library(metan)

d1 <- ge_cluster(data_ge, ENV, GEN, GY, nclust = 3)
plot(d1, nclust = 3)

# }