Agrupamento e ordenamento multidimensional de comunidades
Bruno Vilela
18/05/2021
Pacotes necessários
Precisaremos dos seguintes pacotes antes das análises. Se não os tiver instalado, use o seguinte código antes. Note que é necessário que a internet esteja ligada.
install.packages("tidyverse")
install.packages("vegan")
install.packages("dendextend")
install.packages("ggrepel")
install.packages("factoextra")
install.packages("ggdendro")Depois de instalado, carregue os pacotes no seu computador.
library(tidyverse)
library(vegan)
library(dendextend)
library(ggrepel)
library(factoextra)
library(ggdendro)Carregar os dados
Carregue os seus dados de comunidades (locais nas linhas e espécies nas colunas) usando o comando read.csv (se estiver em usando windows provavelmente precisará trocar o read.csv por read.csv2). Aqui vamos usar os dados de exemplo do pacote vegan, para que todos possam acompanhar.
# Carregar os dados do pacote.
data(varespec)Veja como se a tabela está correta.
varespec| Callvulg | Empenigr | Rhodtome | Vaccmyrt | Vaccviti | Pinusylv | Descflex | Betupube | Vacculig | Diphcomp | Dicrsp | Dicrfusc | Dicrpoly | Hylosple | Pleuschr | Polypili | Polyjuni | Polycomm | Pohlnuta | Ptilcili | Barbhatc | Cladarbu | Cladrang | Cladstel | Cladunci | Cladcocc | Cladcorn | Cladgrac | Cladfimb | Cladcris | Cladchlo | Cladbotr | Cladamau | Cladsp | Cetreric | Cetrisla | Flavniva | Nepharct | Stersp | Peltapht | Icmaeric | Cladcerv | Claddefo | Cladphyl | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 0.55 | 11.13 | 0.00 | 0.00 | 17.80 | 0.07 | 0.00 | 0.00 | 1.60 | 2.07 | 0.00 | 1.62 | 0.00 | 0.00 | 4.67 | 0.02 | 0.13 | 0.00 | 0.13 | 0.12 | 0.00 | 21.73 | 21.47 | 3.50 | 0.30 | 0.18 | 0.23 | 0.25 | 0.25 | 0.23 | 0.00 | 0.00 | 0.08 | 0.02 | 0.02 | 0.00 | 0.12 | 0.02 | 0.62 | 0.02 | 0.00 | 0.00 | 0.25 | 0.00 |
| 15 | 0.67 | 0.17 | 0.00 | 0.35 | 12.13 | 0.12 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 10.92 | 0.02 | 0.00 | 37.75 | 0.02 | 0.23 | 0.00 | 0.03 | 0.02 | 0.00 | 12.05 | 8.13 | 0.18 | 2.65 | 0.13 | 0.18 | 0.23 | 0.25 | 1.23 | 0.00 | 0.00 | 0.00 | 0.00 | 0.15 | 0.03 | 0.00 | 0.00 | 0.85 | 0.00 | 0.00 | 0.00 | 1.00 | 0.00 |
| 24 | 0.10 | 1.55 | 0.00 | 0.00 | 13.47 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 23.43 | 0.00 | 1.68 | 0.00 | 32.92 | 0.00 | 0.23 | 0.00 | 0.32 | 0.03 | 0.00 | 3.58 | 5.52 | 0.07 | 8.93 | 0.00 | 0.20 | 0.48 | 0.00 | 0.07 | 0.10 | 0.02 | 0.00 | 0.00 | 0.78 | 0.12 | 0.00 | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.33 | 0.00 |
| 27 | 0.00 | 15.13 | 2.42 | 5.92 | 15.97 | 0.00 | 3.70 | 0.00 | 1.12 | 0.00 | 0.00 | 3.63 | 0.00 | 6.70 | 58.07 | 0.00 | 0.00 | 0.13 | 0.02 | 0.08 | 0.08 | 1.42 | 7.63 | 2.55 | 0.15 | 0.00 | 0.38 | 0.12 | 0.10 | 0.03 | 0.00 | 0.02 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.15 | 0.00 |
| 23 | 0.00 | 12.68 | 0.00 | 0.00 | 23.73 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.42 | 0.02 | 0.00 | 19.42 | 0.02 | 2.12 | 0.00 | 0.17 | 1.80 | 0.02 | 9.08 | 9.22 | 0.05 | 0.73 | 0.08 | 1.42 | 0.50 | 0.17 | 1.78 | 0.05 | 0.05 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | 0.00 | 1.58 | 0.33 | 0.00 | 0.00 | 1.97 | 0.00 |
| 19 | 0.00 | 8.92 | 0.00 | 2.42 | 10.28 | 0.12 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.32 | 0.02 | 0.00 | 21.03 | 0.02 | 1.58 | 0.18 | 0.07 | 0.27 | 0.02 | 7.23 | 4.95 | 22.08 | 0.25 | 0.10 | 0.25 | 0.18 | 0.10 | 0.12 | 0.05 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | 0.00 | 0.28 | 0.00 | 0.00 | 0.00 | 0.37 | 0.00 |
| 22 | 4.73 | 5.12 | 1.55 | 6.05 | 12.40 | 0.10 | 0.78 | 0.02 | 2.00 | 0.00 | 0.03 | 37.07 | 0.00 | 0.00 | 26.38 | 0.00 | 0.00 | 0.00 | 0.10 | 0.03 | 0.00 | 6.10 | 3.60 | 0.23 | 2.38 | 0.17 | 0.13 | 0.18 | 0.20 | 0.20 | 0.02 | 0.00 | 0.00 | 0.02 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.15 | 0.00 |
| 16 | 4.47 | 7.33 | 0.00 | 2.15 | 4.33 | 0.10 | 0.00 | 0.00 | 0.00 | 0.00 | 1.02 | 25.80 | 0.23 | 0.00 | 18.98 | 0.00 | 0.02 | 0.00 | 0.13 | 0.10 | 0.00 | 7.13 | 14.03 | 0.02 | 0.82 | 0.15 | 0.05 | 0.22 | 0.22 | 0.17 | 0.00 | 0.00 | 0.00 | 0.02 | 0.18 | 0.08 | 0.00 | 0.00 | 0.03 | 0.00 | 0.07 | 0.00 | 0.67 | 0.00 |
| 28 | 0.00 | 1.63 | 0.35 | 18.27 | 7.13 | 0.05 | 0.40 | 0.00 | 0.20 | 0.00 | 0.30 | 0.52 | 0.20 | 9.97 | 70.03 | 0.00 | 0.08 | 0.00 | 0.07 | 0.03 | 0.00 | 0.17 | 0.87 | 0.00 | 0.05 | 0.02 | 0.03 | 0.07 | 0.10 | 0.02 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.08 | 0.00 |
| 13 | 24.13 | 1.90 | 0.07 | 0.22 | 5.30 | 0.12 | 0.00 | 0.00 | 0.00 | 0.07 | 0.02 | 2.50 | 0.00 | 0.00 | 5.52 | 0.00 | 0.02 | 0.00 | 0.03 | 0.25 | 0.07 | 23.07 | 23.67 | 11.90 | 0.95 | 0.17 | 0.05 | 0.23 | 0.18 | 0.57 | 0.02 | 0.07 | 0.00 | 0.07 | 0.18 | 0.02 | 0.00 | 0.00 | 0.03 | 0.02 | 0.00 | 0.00 | 0.47 | 0.00 |
| 14 | 3.75 | 5.65 | 0.00 | 0.08 | 5.30 | 0.10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 11.32 | 0.00 | 0.00 | 7.75 | 0.00 | 0.30 | 0.02 | 0.07 | 0.00 | 0.00 | 17.45 | 1.32 | 0.12 | 23.68 | 0.22 | 0.50 | 0.15 | 0.23 | 0.97 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.02 | 0.00 | 0.00 | 0.33 | 0.00 | 0.02 | 0.00 | 1.57 | 0.05 |
| 20 | 0.02 | 6.45 | 0.00 | 0.00 | 14.13 | 0.07 | 0.00 | 0.00 | 0.47 | 0.00 | 0.85 | 1.87 | 0.08 | 1.35 | 13.73 | 0.07 | 0.05 | 0.00 | 0.12 | 0.00 | 0.00 | 6.80 | 11.22 | 0.05 | 4.75 | 0.03 | 0.12 | 0.22 | 0.18 | 0.07 | 0.00 | 0.02 | 0.00 | 0.02 | 0.15 | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 1.20 | 0.00 |
| 25 | 0.00 | 6.93 | 0.00 | 0.00 | 10.60 | 0.02 | 0.10 | 0.02 | 0.05 | 0.07 | 14.02 | 10.82 | 0.00 | 0.02 | 28.77 | 0.00 | 6.98 | 0.13 | 0.00 | 0.22 | 0.00 | 6.00 | 2.25 | 0.00 | 0.80 | 0.12 | 0.57 | 0.17 | 0.15 | 0.07 | 0.00 | 0.00 | 0.00 | 0.02 | 0.03 | 0.02 | 0.00 | 4.87 | 0.10 | 0.07 | 0.00 | 0.02 | 0.05 | 0.00 |
| 7 | 0.00 | 5.30 | 0.00 | 0.00 | 8.20 | 0.00 | 0.05 | 0.00 | 8.10 | 0.28 | 0.00 | 0.45 | 0.03 | 0.00 | 0.10 | 0.00 | 0.25 | 0.00 | 0.03 | 0.00 | 0.00 | 35.00 | 42.50 | 0.28 | 0.35 | 0.08 | 0.20 | 0.25 | 0.18 | 0.13 | 0.08 | 0.00 | 0.00 | 0.00 | 0.05 | 0.00 | 0.23 | 0.20 | 0.93 | 0.00 | 0.03 | 0.00 | 0.10 | 0.00 |
| 5 | 0.00 | 0.13 | 0.00 | 0.00 | 2.75 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.03 | 0.00 | 0.03 | 0.18 | 0.65 | 0.00 | 0.00 | 0.00 | 0.00 | 18.50 | 59.00 | 0.98 | 0.28 | 0.23 | 0.23 | 0.23 | 0.10 | 0.05 | 0.00 | 0.00 | 0.03 | 0.00 | 0.18 | 0.00 | 0.28 | 0.00 | 10.28 | 0.00 | 0.10 | 0.00 | 0.25 | 0.00 |
| 6 | 0.30 | 5.75 | 0.00 | 0.00 | 10.50 | 0.10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.85 | 0.00 | 0.00 | 0.05 | 0.03 | 0.08 | 0.00 | 0.00 | 0.08 | 0.00 | 39.00 | 37.50 | 11.30 | 3.45 | 0.18 | 0.20 | 0.25 | 0.25 | 0.23 | 0.03 | 0.00 | 0.00 | 0.03 | 0.35 | 0.00 | 0.08 | 0.00 | 0.03 | 0.00 | 0.00 | 0.00 | 0.28 | 0.00 |
| 3 | 0.03 | 3.65 | 0.00 | 0.00 | 4.43 | 0.00 | 0.00 | 0.00 | 1.65 | 0.50 | 0.00 | 0.55 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | 0.00 | 0.03 | 0.03 | 0.00 | 8.80 | 29.50 | 55.60 | 0.25 | 0.08 | 0.25 | 0.25 | 0.15 | 0.10 | 0.03 | 0.00 | 0.03 | 0.00 | 0.05 | 0.00 | 0.15 | 0.15 | 0.28 | 0.00 | 0.00 | 0.00 | 0.08 | 0.00 |
| 4 | 3.40 | 0.63 | 0.00 | 0.00 | 1.98 | 0.05 | 0.05 | 0.00 | 0.03 | 0.00 | 0.00 | 0.20 | 0.00 | 0.00 | 1.53 | 0.00 | 0.10 | 0.00 | 0.05 | 0.00 | 0.00 | 15.73 | 20.03 | 28.20 | 0.73 | 0.10 | 0.15 | 0.13 | 0.10 | 0.15 | 0.00 | 0.00 | 0.00 | 0.05 | 0.28 | 0.05 | 10.03 | 0.00 | 0.95 | 0.00 | 0.00 | 0.05 | 0.08 | 0.00 |
| 2 | 0.05 | 9.30 | 0.00 | 0.00 | 8.50 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.00 | 0.00 | 0.75 | 0.00 | 0.03 | 0.00 | 0.00 | 0.03 | 0.00 | 0.48 | 24.50 | 75.00 | 0.20 | 0.00 | 0.03 | 0.03 | 0.05 | 0.03 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.03 | 0.00 |
| 9 | 0.00 | 3.47 | 0.00 | 0.25 | 20.50 | 0.25 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 | 0.38 | 0.25 | 0.00 | 4.07 | 0.00 | 0.25 | 0.00 | 0.25 | 0.25 | 0.00 | 0.46 | 4.00 | 84.30 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.25 | 0.25 | 0.25 | 0.67 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.25 |
| 12 | 0.25 | 11.50 | 0.00 | 0.00 | 15.80 | 1.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.25 | 0.00 | 0.00 | 6.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 | 0.00 | 3.60 | 14.60 | 63.30 | 1.30 | 0.00 | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 |
| 10 | 0.25 | 11.00 | 0.00 | 0.00 | 11.90 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.25 | 0.00 | 0.67 | 0.00 | 0.25 | 0.00 | 0.25 | 0.00 | 0.00 | 1.30 | 8.70 | 84.30 | 0.25 | 0.25 | 0.25 | 0.00 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 | 0.25 | 0.25 | 0.25 | 0.00 | 0.25 | 0.00 | 0.00 | 0.00 | 0.25 | 0.25 |
| 11 | 2.37 | 0.67 | 0.00 | 0.00 | 12.90 | 0.80 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.25 | 0.00 | 17.70 | 0.25 | 0.25 | 0.00 | 0.25 | 0.67 | 0.00 | 9.67 | 29.80 | 31.80 | 2.53 | 0.25 | 0.25 | 0.25 | 0.00 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.25 | 0.00 | 0.00 | 0.93 | 0.25 | 0.00 | 0.00 | 0.00 | 0.25 |
| 21 | 0.00 | 16.00 | 4.00 | 15.00 | 25.00 | 0.25 | 0.50 | 0.25 | 0.00 | 0.00 | 0.25 | 0.25 | 3.00 | 0.00 | 2.00 | 0.00 | 0.25 | 0.25 | 0.25 | 10.00 | 3.00 | 0.70 | 4.70 | 10.90 | 0.25 | 0.00 | 0.05 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.00 | 0.00 | 0.00 | 0.67 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.40 | 0.00 |
Dissimilaridade
Para calcular as métricas de dissimilaridade entre as localidades, podemos usar a função vegdist do pacote vegan.
vare.dist.jac <- vegdist(varespec,
method = "jaccard")O resultado é uma matriz de distância triangular. podemos ver como uma matriz da seguinte maneira.
as.matrix(vare.dist.jac)| 18 | 15 | 24 | 27 | 23 | 19 | 22 | 16 | 28 | 13 | 14 | 20 | 25 | 7 | 5 | 6 | 3 | 4 | 2 | 9 | 12 | 10 | 11 | 21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 0.0000000 | 0.6936661 | 0.8010067 | 0.7196925 | 0.5451454 | 0.6750350 | 0.7680495 | 0.6960158 | 0.9141181 | 0.5133810 | 0.7054857 | 0.5589811 | 0.7744265 | 0.5298114 | 0.6627172 | 0.5054896 | 0.6908836 | 0.6389019 | 0.7280664 | 0.7939870 | 0.6383478 | 0.7691547 | 0.6167328 | 0.7147243 |
| 15 | 0.6936661 | 0.0000000 | 0.5291720 | 0.5770806 | 0.5350236 | 0.6264451 | 0.5271936 | 0.5690444 | 0.6864068 | 0.7549682 | 0.6489915 | 0.5490621 | 0.5047975 | 0.8036411 | 0.8357616 | 0.7771415 | 0.8622467 | 0.8126317 | 0.9014773 | 0.8739398 | 0.8091126 | 0.8665064 | 0.6409603 | 0.8641063 |
| 24 | 0.8010067 | 0.5291720 | 0.0000000 | 0.6608590 | 0.6684692 | 0.6748225 | 0.6675594 | 0.7427412 | 0.7290827 | 0.8621330 | 0.7903426 | 0.6059699 | 0.5040127 | 0.8846176 | 0.9225155 | 0.8533013 | 0.9119861 | 0.9076932 | 0.9114195 | 0.8630238 | 0.8161854 | 0.8791015 | 0.7242760 | 0.8421053 |
| 27 | 0.7196925 | 0.5770806 | 0.6608590 | 0.0000000 | 0.6000389 | 0.6557505 | 0.6352989 | 0.7248094 | 0.4648217 | 0.8599920 | 0.8550436 | 0.6629079 | 0.6668083 | 0.8759572 | 0.9323192 | 0.8569426 | 0.8944312 | 0.9138581 | 0.8624680 | 0.8516239 | 0.7695046 | 0.8301727 | 0.7747289 | 0.7060043 |
| 23 | 0.5451454 | 0.5350236 | 0.6684692 | 0.6000389 | 0.0000000 | 0.5300865 | 0.6498078 | 0.5809879 | 0.8221103 | 0.7670662 | 0.7217023 | 0.4468449 | 0.5973394 | 0.7798061 | 0.8436134 | 0.7694188 | 0.8323758 | 0.8316337 | 0.8405709 | 0.8019568 | 0.7000124 | 0.7970269 | 0.6404066 | 0.6623166 |
| 19 | 0.6750350 | 0.6264451 | 0.6748225 | 0.6557505 | 0.5300865 | 0.0000000 | 0.6419025 | 0.6203786 | 0.7828430 | 0.7292910 | 0.7754059 | 0.5666962 | 0.6025028 | 0.8206553 | 0.8825695 | 0.7248175 | 0.6930459 | 0.6822689 | 0.7003891 | 0.7007202 | 0.6002785 | 0.6719708 | 0.4955046 | 0.6941670 |
| 22 | 0.7680495 | 0.5271936 | 0.6675594 | 0.6352989 | 0.6498078 | 0.6419025 | 0.0000000 | 0.4224680 | 0.7488792 | 0.8199710 | 0.6977140 | 0.6326675 | 0.5531341 | 0.8545171 | 0.9253927 | 0.8405234 | 0.8905008 | 0.8898334 | 0.9083842 | 0.8716554 | 0.8270999 | 0.8811394 | 0.7351554 | 0.8074772 |
| 16 | 0.6960158 | 0.5690444 | 0.7427412 | 0.7248094 | 0.5809879 | 0.6203786 | 0.4224680 | 0.0000000 | 0.8245914 | 0.7109200 | 0.6510503 | 0.5441625 | 0.6019908 | 0.7949008 | 0.8361801 | 0.7886041 | 0.8122071 | 0.7851286 | 0.8475413 | 0.9002448 | 0.7761720 | 0.8672790 | 0.6818539 | 0.8554790 |
| 28 | 0.9141181 | 0.6864068 | 0.7290827 | 0.4648217 | 0.8221103 | 0.7828430 | 0.7488792 | 0.8245914 | 0.0000000 | 0.9247383 | 0.9034888 | 0.8210022 | 0.7566944 | 0.9451589 | 0.9764372 | 0.9481678 | 0.9602009 | 0.9680705 | 0.9503083 | 0.9297183 | 0.9214356 | 0.9482875 | 0.8600133 | 0.8404972 |
| 13 | 0.5133810 | 0.7549682 | 0.8621330 | 0.8599920 | 0.7670662 | 0.7292910 | 0.8199710 | 0.7109200 | 0.9247383 | 0.0000000 | 0.7136249 | 0.7330179 | 0.8514068 | 0.6238268 | 0.6797798 | 0.5202364 | 0.6635909 | 0.5592593 | 0.7475492 | 0.8434312 | 0.7423271 | 0.8345286 | 0.5987340 | 0.8380721 |
| 14 | 0.7054857 | 0.6489915 | 0.7903426 | 0.8550436 | 0.7217023 | 0.7754059 | 0.6977140 | 0.6510503 | 0.9034888 | 0.7136249 | 0.0000000 | 0.6768337 | 0.7131244 | 0.7916014 | 0.8410957 | 0.7675222 | 0.8787139 | 0.8048816 | 0.9242968 | 0.9060575 | 0.8576046 | 0.9073479 | 0.8054907 | 0.8948108 |
| 20 | 0.5589811 | 0.5490621 | 0.6059699 | 0.6629079 | 0.4468449 | 0.5666962 | 0.6326675 | 0.5441625 | 0.8210022 | 0.7330179 | 0.6768337 | 0.0000000 | 0.6224525 | 0.7468013 | 0.8340794 | 0.7045892 | 0.8017120 | 0.8075260 | 0.8201661 | 0.8223161 | 0.6826875 | 0.8028504 | 0.6170379 | 0.7745422 |
| 25 | 0.7744265 | 0.5047975 | 0.5040127 | 0.6668083 | 0.5973394 | 0.6025028 | 0.5531341 | 0.6019908 | 0.7566944 | 0.8514068 | 0.7131244 | 0.6224525 | 0.0000000 | 0.8608354 | 0.9247808 | 0.8468550 | 0.8992048 | 0.9130514 | 0.8998349 | 0.8816971 | 0.8278432 | 0.8792978 | 0.7635951 | 0.8549333 |
| 7 | 0.5298114 | 0.8036411 | 0.8846176 | 0.8759572 | 0.7798061 | 0.8206553 | 0.8545171 | 0.7949008 | 0.9451589 | 0.6238268 | 0.7916014 | 0.7468013 | 0.8608354 | 0.0000000 | 0.4891346 | 0.2985549 | 0.6802589 | 0.7180902 | 0.7853595 | 0.9082037 | 0.8229524 | 0.8698939 | 0.6895758 | 0.8847594 |
| 5 | 0.6627172 | 0.8357616 | 0.9225155 | 0.9323192 | 0.8436134 | 0.8825695 | 0.9253927 | 0.8361801 | 0.9764372 | 0.6797798 | 0.8410957 | 0.8340794 | 0.9247808 | 0.4891346 | 0.0000000 | 0.5698491 | 0.7207722 | 0.6993989 | 0.8410901 | 0.9481757 | 0.8769496 | 0.9191638 | 0.7149666 | 0.9411444 |
| 6 | 0.5054896 | 0.7771415 | 0.8533013 | 0.8569426 | 0.7694188 | 0.7248175 | 0.8405234 | 0.7886041 | 0.9481678 | 0.5202364 | 0.7675222 | 0.7045892 | 0.8468550 | 0.2985549 | 0.5698491 | 0.0000000 | 0.6223643 | 0.6362179 | 0.7140541 | 0.8387706 | 0.7311627 | 0.7928483 | 0.5793384 | 0.8038378 |
| 3 | 0.6908836 | 0.8622467 | 0.9119861 | 0.8944312 | 0.8323758 | 0.6930459 | 0.8905008 | 0.8122071 | 0.9602009 | 0.6635909 | 0.8787139 | 0.8017120 | 0.8992048 | 0.6802589 | 0.7207722 | 0.6223643 | 0.0000000 | 0.5286208 | 0.3469985 | 0.5596809 | 0.4179532 | 0.5089512 | 0.4618457 | 0.8576503 |
| 4 | 0.6389019 | 0.8126317 | 0.9076932 | 0.9138581 | 0.8316337 | 0.6822689 | 0.8898334 | 0.7851286 | 0.9680705 | 0.5592593 | 0.8048816 | 0.8075260 | 0.9130514 | 0.7180902 | 0.6993989 | 0.6362179 | 0.5286208 | 0.0000000 | 0.6523951 | 0.7671322 | 0.6550772 | 0.7322565 | 0.4866789 | 0.8662970 |
| 2 | 0.7280664 | 0.9014773 | 0.9114195 | 0.8624680 | 0.8405709 | 0.7003891 | 0.9083842 | 0.8475413 | 0.9503083 | 0.7475492 | 0.9242968 | 0.8201661 | 0.8998349 | 0.7853595 | 0.8410901 | 0.7140541 | 0.3469985 | 0.6523951 | 0.0000000 | 0.3779776 | 0.3116874 | 0.2543010 | 0.5925005 | 0.8080808 |
| 9 | 0.7939870 | 0.8739398 | 0.8630238 | 0.8516239 | 0.8019568 | 0.7007202 | 0.8716554 | 0.9002448 | 0.9297183 | 0.8434312 | 0.9060575 | 0.8223161 | 0.8816971 | 0.9082037 | 0.9481757 | 0.8387706 | 0.5596809 | 0.7671322 | 0.3779776 | 0.0000000 | 0.3709677 | 0.2009988 | 0.6794548 | 0.7462830 |
| 12 | 0.6383478 | 0.8091126 | 0.8161854 | 0.7695046 | 0.7000124 | 0.6002785 | 0.8270999 | 0.7761720 | 0.9214356 | 0.7423271 | 0.8576046 | 0.6826875 | 0.8278432 | 0.8229524 | 0.8769496 | 0.7311627 | 0.4179532 | 0.6550772 | 0.3116874 | 0.3709677 | 0.0000000 | 0.3041317 | 0.5388770 | 0.7181243 |
| 10 | 0.7691547 | 0.8665064 | 0.8791015 | 0.8301727 | 0.7970269 | 0.6719708 | 0.8811394 | 0.8672790 | 0.9482875 | 0.8345286 | 0.9073479 | 0.8028504 | 0.8792978 | 0.8698939 | 0.9191638 | 0.7928483 | 0.5089512 | 0.7322565 | 0.2543010 | 0.2009988 | 0.3041317 | 0.0000000 | 0.6705291 | 0.7614469 |
| 11 | 0.6167328 | 0.6409603 | 0.7242760 | 0.7747289 | 0.6404066 | 0.4955046 | 0.7351554 | 0.6818539 | 0.8600133 | 0.5987340 | 0.8054907 | 0.6170379 | 0.7635951 | 0.6895758 | 0.7149666 | 0.5793384 | 0.4618457 | 0.4866789 | 0.5925005 | 0.6794548 | 0.5388770 | 0.6705291 | 0.0000000 | 0.8033527 |
| 21 | 0.7147243 | 0.8641063 | 0.8421053 | 0.7060043 | 0.6623166 | 0.6941670 | 0.8074772 | 0.8554790 | 0.8404972 | 0.8380721 | 0.8948108 | 0.7745422 | 0.8549333 | 0.8847594 | 0.9411444 | 0.8038378 | 0.8576503 | 0.8662970 | 0.8080808 | 0.7462830 | 0.7181243 | 0.7614469 | 0.8033527 | 0.0000000 |
Podemos calular a distância de bray-curtis mudando o argumento method para “bray”.
vare.dist.bray <- vegdist(varespec,
method = "bray")Agrupamento
Para fazer um upgma, podemos aplicar a função hclust do pacote stats (que já vem no R) à matriz de distância escolhida.
# Jaccard
h_jac <- hclust(vare.dist.jac, method = "average")
# Bray-Curtis
h_bray <- hclust(vare.dist.bray, method = "average")Podemos avaliar o nosso dendograma usando uma medida de correlação cofenética.
cor(cophenetic(h_jac), vare.dist.jac)## [1] 0.8132574cor(cophenetic(h_bray), vare.dist.bray)## [1] 0.760622Podemos plotar isso usando a função plot.
par(mfrow = c(1, 2))
plot(h_jac, ann = F)
mtext("Jaccard", cex = 2)
plot(h_bray, ann = F)
mtext("Bray-Curtis", cex = 2)
Para comparar as duas classificações podemos usar o seguinte código.
# Dendrograms
dend1 <- as.dendrogram(h_jac)
dend2 <- as.dendrogram(h_bray)
# Align and plot two dendrograms side by side
dendlist(dend1, dend2) %>%
untangle(method = "step1side") %>%
tanglegram(
main_left = "Jaccard",
main_right = "Bray-Curtis",
lwd = 2,
highlight_branches_lwd = F,
)
Para um plot mais organizado de cada um individualmente use o seguitne código.
ddata <- dendro_data(h_jac, type = "rectangle")
p <- ggplot(segment(ddata)) +
geom_segment(aes(x = x, y = y,
xend = xend,
yend = yend)) +
geom_text(data = ddata$labels,
aes(x, y, label = label,
hjust = -0.2, vjust = 0.5), size = 6) +
scale_color_brewer(palette = "Dark2") +
coord_flip() +
scale_y_reverse(expand = c(0.2, 0)) +
theme_bw() +
ylab("Distância de Jaccard") +
xlab("") +
ggtitle("Dendograma das áreas",
subtitle = "Distância de Jaccard") +
theme(axis.line = element_line(colour = "black"),
axis.text.y = element_blank(),
axis.text.x = element_text(size = 12),
axis.ticks.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
p
PCoA
Para fazer a PCoA vamos usar a função cmdscale
pcoa_bray <- cmdscale(vare.dist.bray, k = 2, eig = TRUE)Para calcular a explicação de cada eixo podemos usar o seguinte código.
porcs <- round(100 * (pcoa_bray$eig / sum(pcoa_bray$eig)),
2)
porcs## [1] 38.62 24.94 9.75 8.14 5.40 4.31 3.85 2.83 2.14 1.67 1.40 1.28
## [13] 0.87 0.38 0.11 0.00 -0.01 -0.14 -0.29 -0.56 -0.83 -1.06 -1.18 -1.63O plot pode ser feito usando o código abaixo.
mydata <- data.frame(pcoa_bray$points[, 1:2])
g <- ggplot(mydata, aes(x = X1,
y = X2)) +
geom_point(size = 1) +
geom_label_repel(aes(label = rownames(mydata))) +
theme_bw() +
theme(text = element_text(size = 17)) +
xlab(paste0("Eixo 1 (", porcs[1], "%)")) +
ylab(paste0("Eixo 2 (", porcs[2], "%)"))
g