library(vegan)
## Carregando pacotes exigidos: permute
## Carregando pacotes exigidos: lattice
## This is vegan 2.6-2
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
load("C:/Users/vahum/AppData/Local/R/win-library/4.2/rmarkdown/rmarkdown/templates/github_document/NEwR-2ed_code_data/NEwR2-Data/Doubs.RData")
spe
## Cogo Satr Phph Babl Thth Teso Chna Pato Lele Sqce Baba Albi Gogo Eslu Pefl
## 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 5 4 3 0 0 0 0 0 0 0 0 0 0 0
## 3 0 5 5 5 0 0 0 0 0 0 0 0 0 1 0
## 4 0 4 5 5 0 0 0 0 0 1 0 0 1 2 2
## 5 0 2 3 2 0 0 0 0 5 2 0 0 2 4 4
## 6 0 3 4 5 0 0 0 0 1 2 0 0 1 1 1
## 7 0 5 4 5 0 0 0 0 1 1 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 1 3 0 0 0 0 0 5 0 0 0 0 0
## 10 0 1 4 4 0 0 0 0 2 2 0 0 1 0 0
## 11 1 3 4 1 1 0 0 0 0 1 0 0 0 0 0
## 12 2 5 4 4 2 0 0 0 0 1 0 0 0 0 0
## 13 2 5 5 2 3 2 0 0 0 0 0 0 0 0 0
## 14 3 5 5 4 4 3 0 0 0 1 1 0 1 1 0
## 15 3 4 4 5 2 4 0 0 3 3 2 0 2 0 0
## 16 2 3 3 5 0 5 0 4 5 2 2 1 2 1 1
## 17 1 2 4 4 1 2 1 4 3 2 3 4 1 1 2
## 18 1 1 3 3 1 1 1 3 2 3 3 3 2 1 3
## 19 0 0 3 5 0 1 2 3 2 1 2 2 4 1 1
## 20 0 0 1 2 0 0 2 2 2 3 4 3 4 2 2
## 21 0 0 1 1 0 0 2 2 2 2 4 2 5 3 3
## 22 0 0 0 1 0 0 3 2 3 4 5 1 5 3 4
## 23 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 24 0 0 0 0 0 0 1 0 0 2 0 0 1 0 0
## 25 0 0 0 0 0 0 0 0 1 1 0 0 2 1 0
## 26 0 0 0 1 0 0 1 0 1 2 2 1 3 2 1
## 27 0 0 0 1 0 0 1 1 2 3 4 1 4 4 1
## 28 0 0 0 1 0 0 1 1 2 4 3 1 4 3 2
## 29 0 1 1 1 1 1 2 2 3 4 5 3 5 5 4
## 30 0 0 0 0 0 0 1 2 3 3 3 5 5 4 5
## Rham Legi Scer Cyca Titi Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0 0 0 0
## 5 0 0 2 0 3 0 0 0 5 0 0 0
## 6 0 0 0 0 2 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 4 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 1 0 0 0 0 0 0 0
## 16 0 1 0 1 1 0 0 0 1 0 0 0
## 17 1 1 0 1 1 0 0 0 2 0 2 1
## 18 2 1 0 1 1 0 0 1 2 0 2 1
## 19 2 1 1 1 2 1 0 1 5 1 3 1
## 20 3 2 2 1 4 1 0 2 5 2 5 2
## 21 3 2 2 2 4 3 1 3 5 3 5 2
## 22 3 3 2 3 4 4 2 4 5 4 5 2
## 23 0 0 0 0 0 0 0 0 1 0 2 0
## 24 0 1 0 0 0 0 0 2 2 1 5 0
## 25 0 0 1 0 0 0 0 1 1 0 3 0
## 26 2 2 1 1 3 2 1 4 4 2 5 2
## 27 3 3 1 2 5 3 2 5 5 4 5 3
## 28 4 4 2 4 4 3 3 5 5 5 5 4
## 29 5 5 2 3 3 4 4 5 5 4 5 4
## 30 5 3 5 5 5 5 5 5 5 5 5 5
env
## dfs ele slo dis pH har pho nit amm oxy bod
## 1 0.3 934 48.0 0.84 7.9 45 0.01 0.20 0.00 12.2 2.7
## 2 2.2 932 3.0 1.00 8.0 40 0.02 0.20 0.10 10.3 1.9
## 3 10.2 914 3.7 1.80 8.3 52 0.05 0.22 0.05 10.5 3.5
## 4 18.5 854 3.2 2.53 8.0 72 0.10 0.21 0.00 11.0 1.3
## 5 21.5 849 2.3 2.64 8.1 84 0.38 0.52 0.20 8.0 6.2
## 6 32.4 846 3.2 2.86 7.9 60 0.20 0.15 0.00 10.2 5.3
## 7 36.8 841 6.6 4.00 8.1 88 0.07 0.15 0.00 11.1 2.2
## 8 49.1 792 2.5 1.30 8.1 94 0.20 0.41 0.12 7.0 8.1
## 9 70.5 752 1.2 4.80 8.0 90 0.30 0.82 0.12 7.2 5.2
## 10 99.0 617 9.9 10.00 7.7 82 0.06 0.75 0.01 10.0 4.3
## 11 123.4 483 4.1 19.90 8.1 96 0.30 1.60 0.00 11.5 2.7
## 12 132.4 477 1.6 20.00 7.9 86 0.04 0.50 0.00 12.2 3.0
## 13 143.6 450 2.1 21.10 8.1 98 0.06 0.52 0.00 12.4 2.4
## 14 152.2 434 1.2 21.20 8.3 98 0.27 1.23 0.00 12.3 3.8
## 15 164.5 415 0.5 23.00 8.6 86 0.40 1.00 0.00 11.7 2.1
## 16 185.9 375 2.0 16.10 8.0 88 0.20 2.00 0.05 10.3 2.7
## 17 198.5 349 0.5 24.30 8.0 92 0.20 2.50 0.20 10.2 4.6
## 18 211.0 333 0.8 25.00 8.0 90 0.50 2.20 0.20 10.3 2.8
## 19 224.6 310 0.5 25.90 8.1 84 0.60 2.20 0.15 10.6 3.3
## 20 247.7 286 0.8 26.80 8.0 86 0.30 3.00 0.30 10.3 2.8
## 21 282.1 262 1.0 27.20 7.9 85 0.20 2.20 0.10 9.0 4.1
## 22 294.0 254 1.4 27.90 8.1 88 0.20 1.62 0.07 9.1 4.8
## 23 304.3 246 1.2 28.80 8.1 97 2.60 3.50 1.15 6.3 16.4
## 24 314.7 241 0.3 29.76 8.0 99 1.40 2.50 0.60 5.2 12.3
## 25 327.8 231 0.5 38.70 7.9 100 4.22 6.20 1.80 4.1 16.7
## 26 356.9 214 0.5 39.10 7.9 94 1.43 3.00 0.30 6.2 8.9
## 27 373.2 206 1.2 39.60 8.1 90 0.58 3.00 0.26 7.2 6.3
## 28 394.7 195 0.3 43.20 8.3 100 0.74 4.00 0.30 8.1 4.5
## 29 422.0 183 0.6 67.70 7.8 110 0.45 1.62 0.10 9.0 4.2
## 30 453.0 172 0.2 69.00 8.2 109 0.65 1.60 0.10 8.2 4.4
1)
env_simp<-env[,-c(1,4)]
princomp(env_simp)-> pca_env
summary(pca_env)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4
## Standard deviation 267.1096679 11.533521173 6.9246972888 4.0343429098
## Proportion of Variance 0.9972202 0.001859241 0.0006702139 0.0002274875
## Cumulative Proportion 0.9972202 0.999079484 0.9997496976 0.9999771850
## Comp.5 Comp.6 Comp.7 Comp.8
## Standard deviation 1.018804e+00 7.091927e-01 2.482529e-01 1.600339e-01
## Proportion of Variance 1.450751e-05 7.029758e-06 8.613915e-07 3.579612e-07
## Cumulative Proportion 9.999917e-01 9.999987e-01 9.999996e-01 9.999999e-01
## Comp.9
## Standard deviation 6.461191e-02
## Proportion of Variance 5.834949e-08
## Cumulative Proportion 1.000000e+00
pca_env$scores %>%
as.tibble() %>%
ggplot(aes(Comp.1))+geom_histogram()
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pca_env$scores %>%
as.tibble() %>%
mutate(setor=ifelse(Comp.1<0,"medio-baixo", "alto"))-> env_set
fviz_pca_ind(pca_env,
geom.ind = "point", # show points only (nbut not "text")
col.ind = env_set$setor, # color by groups
palette = c("#00AFBB", "#E7B800"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Groups"
)
É possivel observar que o rio foi dividido em duas porções.
2)
spe_simp<-spe
princomp(spe_simp)-> pca_spe
summary(pca_spe)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 6.5380634 2.8562806 1.92951554 1.62519017 1.36425689
## Proportion of Variance 0.6469085 0.1234657 0.05634315 0.03997173 0.02816676
## Cumulative Proportion 0.6469085 0.7703742 0.82671731 0.86668904 0.89485580
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 1.30481115 1.08392781 0.98318157 0.79659944 0.699842885
## Proportion of Variance 0.02576558 0.01778055 0.01462891 0.00960339 0.007412175
## Cumulative Proportion 0.92062137 0.93840193 0.95303084 0.96263423 0.970046405
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 0.664210686 0.59361733 0.573125360 0.492457727
## Proportion of Variance 0.006676614 0.00533283 0.004971001 0.003670139
## Cumulative Proportion 0.976723019 0.98205585 0.987026850 0.990696988
## Comp.15 Comp.16 Comp.17 Comp.18
## Standard deviation 0.440035770 0.320905598 0.314801834 0.270003099
## Proportion of Variance 0.002930357 0.001558473 0.001499751 0.001103271
## Cumulative Proportion 0.993627345 0.995185818 0.996685569 0.997788839
## Comp.19 Comp.20 Comp.21 Comp.22
## Standard deviation 0.244788845 0.1702064108 0.1359103790 0.1270213711
## Proportion of Variance 0.000906834 0.0004384261 0.0002795438 0.0002441733
## Cumulative Proportion 0.998695673 0.9991340994 0.9994136431 0.9996578164
## Comp.23 Comp.24 Comp.25 Comp.26
## Standard deviation 0.1013829076 7.470389e-02 7.100332e-02 3.248053e-02
## Proportion of Variance 0.0001555514 8.445611e-05 7.629602e-05 1.596581e-05
## Cumulative Proportion 0.9998133679 9.998978e-01 9.999741e-01 9.999901e-01
## Comp.27
## Standard deviation 2.559509e-02
## Proportion of Variance 9.914208e-06
## Cumulative Proportion 1.000000e+00
pca_spe$scores %>%
as.tibble() %>%
ggplot(aes(Comp.1))+geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pca_spe$scores %>%
as.tibble() %>%
mutate(setor=ifelse(Comp.1<0,"especies 1", "especies 2"))-> spev_set
fviz_pca_ind(pca_spe,
geom.ind = "point", # show points only (nbut not "text")
col.ind = env_set$setor, # color by groups
palette = c("#00AFBB", "#E7B800"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Groups"
)
nmds<-metaMDS(spe[-8,-c(20:30)])
## Run 0 stress 0.08903472
## Run 1 stress 0.1117096
## Run 2 stress 0.09349518
## Run 3 stress 0.1274673
## Run 4 stress 0.09178727
## Run 5 stress 0.09178723
## Run 6 stress 0.08903508
## ... Procrustes: rmse 0.0005374486 max resid 0.00257864
## ... Similar to previous best
## Run 7 stress 0.1336582
## Run 8 stress 0.09178715
## Run 9 stress 0.1158582
## Run 10 stress 0.1348621
## Run 11 stress 0.08903492
## ... Procrustes: rmse 0.0001412584 max resid 0.0006756296
## ... Similar to previous best
## Run 12 stress 0.08903471
## ... New best solution
## ... Procrustes: rmse 1.098389e-05 max resid 4.037948e-05
## ... Similar to previous best
## Run 13 stress 0.1336898
## Run 14 stress 0.1161219
## Run 15 stress 0.08903476
## ... Procrustes: rmse 4.890967e-05 max resid 0.0002347463
## ... Similar to previous best
## Run 16 stress 0.1269522
## Run 17 stress 0.0917871
## Run 18 stress 0.1298099
## Run 19 stress 0.08903477
## ... Procrustes: rmse 0.0003603548 max resid 0.001730208
## ... Similar to previous best
## Run 20 stress 0.08903488
## ... Procrustes: rmse 0.0001317221 max resid 0.000631469
## ... Similar to previous best
## *** Solution reached
nmds$points
## MDS1 MDS2
## 1 -1.75069944 0.71575978
## 2 -1.01676226 -0.15977122
## 3 -0.89873700 -0.02095081
## 4 -0.51932523 -0.06226539
## 5 0.14981488 -0.07717252
## 6 -0.38320407 -0.13209054
## 7 -0.74384974 -0.17434051
## 9 -0.18248545 -0.93250200
## 10 -0.31245033 -0.33487696
## 11 -0.87614268 -0.36119814
## 12 -0.86070620 -0.10251152
## 13 -1.08401822 0.14607515
## 14 -0.69387564 0.18634022
## 15 -0.37090227 0.23093415
## 16 -0.09187837 0.39003000
## 17 0.06190630 0.37598062
## 18 0.25994537 0.32058347
## 19 0.33683137 0.36628662
## 20 0.68834507 0.36773187
## 21 0.77853960 0.40922180
## 22 0.90321476 0.46841072
## 23 0.40167399 -1.71549517
## 24 0.81918144 -1.06520851
## 25 0.98469241 -0.72931031
## 26 0.85381846 0.05922881
## 27 0.91773440 0.28149288
## 28 0.92768118 0.36392196
## 29 0.68679290 0.59265143
## 30 1.01486477 0.59304413
## attr(,"centre")
## [1] TRUE
## attr(,"pc")
## [1] TRUE
## attr(,"halfchange")
## [1] TRUE
## attr(,"internalscaling")
## [1] 1.065066
nmds_dat<-data.frame(nmds$points, env_set$setor[-8])
colnames(nmds_dat) <- c("MDS1","MDS2","setor")
nmds_dat %>%
ggplot(aes(MDS1, MDS2, color=setor))+geom_point()
Nessa análise é possivel encontrar uma coincidência com a primeira.
Entretando, ela fica menos evidente que a anterior, sendo preferível
usar os dados env para dividir naturalmente.
3)
fviz_nbclust(spe, kmeans, method = "silhouette")
Os métodos coincidiram demonstrando ser melhor dividir esse rio em 2
áreas.