point_sample_cluster {biosurvey} | R Documentation |

Sample one or more points from a two-dimensional environmental space according to a selection rule and with the possibility of having distinct sets of points to be sampled independently. Points to be sampled can be disjoint in geographic space and when that happens two points are selected considering the most numerous clusters.

```
point_sample_cluster(data, variable_1, variable_2, distance_list,
n = 1, cluster_method = "hierarchical",
select_point = "E_centroid", id_column = NULL)
```

`data` |
matrix or data.frame that contains at least four columns: "Longitude" and "Latitude" to represent geographic position, and two other columns to represent the variables of the 2D environmental space. |

`variable_1` |
(character or numeric) name or position of the first variable (x-axis). |

`variable_2` |
(character or numeric) name or position of the second variable (y-axis). Must be different from the first one. |

`distance_list` |
list of vectors of geographic distances among all
points. If |

`n` |
(numeric) number of points that are close to the centroid to be detected. Default = 1. |

`cluster_method` |
(character) there are two options available: "hierarchical" and "k-means". Default = "hierarchical". |

`select_point` |
(character) how or which point will be selected. Three options are available: "random", "E_centroid", and "G_centroid". E_ or G_ centroid indicate that the point(s) closest to the respective centroid will be selected. Default = "E_centroid". |

`id_column` |
(character or numeric) name or numeric index of the column
in |

A data.frame containing `n`

rows corresponding to the point or points
that were sampled.

```
# Data
data("m_matrix", package = "biosurvey")
data("dist_list", package = "biosurvey")
# Making blocks for analysis
m_blocks <- make_blocks(m_matrix, variable_1 = "PC1", variable_2 = "PC2",
n_cols = 10, n_rows = 10, block_type = "equal_area")
datam <- m_blocks$data_matrix
datam <- datam[datam$Block %in% names(dist_list), ]
# Sampling points
point_clus <- point_sample_cluster(datam, variable_1 = "PC1",
variable_2 = "PC2",
distance_list = dist_list, n = 1,
cluster_method = "hierarchical",
select_point = "E_centroid",
id_column = "Block")
```

[Package *biosurvey* version 0.1.1 Index]