Carga de paquetes
library(ade4)
library(adegraphics)
##
## Attaching package: 'adegraphics'
## The following objects are masked from 'package:ade4':
##
## kplotsepan.coa, s.arrow, s.class, s.corcircle, s.distri,
## s.image, s.label, s.logo, s.match, s.traject, s.value,
## table.value, triangle.class
library(adespatial)
##
## Attaching package: 'adespatial'
## The following object is masked from 'package:ade4':
##
## multispati
library(cocorresp)
## Loading required package: vegan
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.5-4
##
## Attaching package: 'cocorresp'
## The following object is masked from 'package:ade4':
##
## coinertia
library(vegan)
library(vegan3d)
library(ape)
##
## Attaching package: 'ape'
## The following object is masked from 'package:adegraphics':
##
## zoom
library(MASS)
library(ellipse)
##
## Attaching package: 'ellipse'
## The following object is masked from 'package:graphics':
##
## pairs
library(FactoMineR)
##
## Attaching package: 'FactoMineR'
## The following object is masked from 'package:ade4':
##
## reconst
library(rrcov)
## Loading required package: robustbase
## Scalable Robust Estimators with High Breakdown Point (version 1.4-7)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0 ✔ purrr 0.3.1
## ✔ tibble 2.0.1 ✔ dplyr 0.8.0.1
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks MASS::select()
AnĂ¡lisis de redundancia.
peces.hel <- decostand(peces, "hellinger")
peces.rda <- rda(peces.hel ~ ., ambient)
peces.rda
## Call: rda(formula = peces.hel ~ das + alt + slo + flo + pH + har +
## pho + nit + amm + oxy + bdo, data = ambient)
##
## Inertia Proportion Rank
## Total 0.5025 1.0000
## Constrained 0.3676 0.7314 11
## Unconstrained 0.1350 0.2686 17
## Inertia is variance
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9
## 0.23020 0.05347 0.03396 0.02762 0.00680 0.00627 0.00315 0.00302 0.00142
## RDA10 RDA11
## 0.00087 0.00078
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 0.04383 0.02270 0.01774 0.01413 0.00932 0.00851 0.00522 0.00458
## (Showing 8 of 17 unconstrained eigenvalues)
summary(peces.rda)
##
## Call:
## rda(formula = peces.hel ~ das + alt + slo + flo + pH + har + pho + nit + amm + oxy + bdo, data = ambient)
##
## Partitioning of variance:
## Inertia Proportion
## Total 0.5025 1.0000
## Constrained 0.3676 0.7314
## Unconstrained 0.1350 0.2686
##
## Eigenvalues, and their contribution to the variance
##
## Importance of components:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## Eigenvalue 0.2302 0.05347 0.03396 0.02762 0.006804 0.006271
## Proportion Explained 0.4581 0.10640 0.06758 0.05496 0.013540 0.012479
## Cumulative Proportion 0.4581 0.56451 0.63208 0.68704 0.700580 0.713058
## RDA7 RDA8 RDA9 RDA10 RDA11
## Eigenvalue 0.003154 0.003017 0.001417 0.0008703 0.0007768
## Proportion Explained 0.006277 0.006004 0.002820 0.0017319 0.0015459
## Cumulative Proportion 0.719335 0.725339 0.728159 0.7298905 0.7314363
## PC1 PC2 PC3 PC4 PC5 PC6
## Eigenvalue 0.04383 0.02270 0.01774 0.01413 0.009321 0.008515
## Proportion Explained 0.08722 0.04518 0.03529 0.02811 0.018550 0.016945
## Cumulative Proportion 0.81865 0.86384 0.89913 0.92724 0.945790 0.962735
## PC7 PC8 PC9 PC10 PC11
## Eigenvalue 0.005219 0.004581 0.002958 0.002013 0.001342
## Proportion Explained 0.010386 0.009117 0.005887 0.004006 0.002670
## Cumulative Proportion 0.973121 0.982238 0.988125 0.992131 0.994801
## PC12 PC13 PC14 PC15 PC16
## Eigenvalue 0.0009585 0.0007297 0.0003818 0.0003340 0.0001299
## Proportion Explained 0.0019073 0.0014520 0.0007597 0.0006646 0.0002586
## Cumulative Proportion 0.9967081 0.9981601 0.9989199 0.9995845 0.9998431
## PC17
## Eigenvalue 7.886e-05
## Proportion Explained 1.569e-04
## Cumulative Proportion 1.000e+00
##
## Accumulated constrained eigenvalues
## Importance of components:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## Eigenvalue 0.2302 0.05347 0.03396 0.02762 0.006804 0.006271
## Proportion Explained 0.6263 0.14547 0.09239 0.07513 0.018512 0.017060
## Cumulative Proportion 0.6263 0.77178 0.86417 0.93930 0.957814 0.974874
## RDA7 RDA8 RDA9 RDA10 RDA11
## Eigenvalue 0.003154 0.003017 0.001417 0.0008703 0.0007768
## Proportion Explained 0.008582 0.008208 0.003855 0.0023678 0.0021135
## Cumulative Proportion 0.983456 0.991664 0.995519 0.9978865 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 1.93676
##
##
## Species scores
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## Cogo 0.14261 0.1180253 -0.22904 0.086458 -0.028042 -0.013691
## Satr 0.63485 0.0273682 0.20085 0.163764 0.019805 0.005858
## Phph 0.48361 0.1077673 -0.07980 -0.133313 0.034833 -0.004144
## Neba 0.36118 0.1090800 -0.01143 -0.219383 0.029874 0.037263
## Thth 0.14244 0.1122444 -0.22054 0.106069 -0.044501 -0.008622
## Teso 0.07292 0.1280078 -0.18815 0.059441 -0.008623 -0.001556
## Chna -0.17236 0.0784750 -0.01415 -0.012202 0.024412 0.062130
## Chto -0.12548 0.1660939 -0.03644 0.002070 0.087969 0.023223
## Lele -0.07912 0.0398662 -0.02835 -0.050439 0.011284 -0.093644
## Lece -0.09581 -0.1434198 -0.13995 -0.122810 -0.079051 -0.011121
## Baba -0.17975 0.2151163 -0.04819 0.043810 0.015552 0.014657
## Spbi -0.15589 0.1641509 -0.01369 0.003517 0.059083 0.001573
## Gogo -0.20301 0.0348690 -0.03783 -0.027001 0.044723 -0.081508
## Eslu -0.11339 0.0291095 0.06240 -0.048558 0.031607 -0.069690
## Pefl -0.09975 0.1120913 0.04452 -0.095320 0.011882 0.011139
## Rham -0.21017 0.1602633 0.04184 0.021526 0.010718 0.001693
## Legi -0.23232 0.1103727 0.01809 -0.005716 -0.011545 0.043028
## Scer -0.16516 -0.0006841 0.03199 0.006260 0.013625 -0.098070
## Cyca -0.18069 0.1411915 0.03496 0.016758 -0.006660 0.003324
## Titi -0.14230 0.1179099 0.05249 -0.141851 -0.032298 0.007223
## Abbr -0.19435 0.1092121 0.07605 0.033536 -0.055275 0.006789
## Icme -0.15512 0.0727019 0.07999 0.034135 -0.088141 -0.009924
## Acce -0.31286 0.0113937 0.03276 0.017709 -0.001507 -0.043683
## Ruru -0.31310 -0.1517049 -0.05604 -0.140122 0.004038 0.040470
## Blbj -0.24750 0.0837801 0.06250 0.012999 -0.059178 0.048588
## Alal -0.43297 -0.2232880 -0.09319 0.124580 0.089120 0.048742
## Anan -0.19684 0.1387592 0.04844 0.020291 -0.007706 -0.002579
##
##
## Site scores (weighted sums of species scores)
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## 1 0.39199 -0.31267 0.98240 1.29491 -0.1258591 0.32865
## 2 0.52860 -0.04444 0.49719 0.10778 0.4259551 0.63121
## 3 0.48740 -0.02096 0.49331 -0.07076 0.5699500 0.29678
## 4 0.32937 0.02809 0.38254 -0.51181 0.2540256 -0.18083
## 5 0.02656 -0.18286 0.21678 -0.72427 0.0467813 -1.40635
## 6 0.24215 -0.08873 0.21046 -0.77964 -0.0138240 -0.32595
## 7 0.46183 -0.11999 0.28437 -0.18382 0.0403432 0.11466
## 9 0.04221 -0.52153 -0.30776 -1.06830 -1.1177967 0.90976
## 10 0.31401 -0.14722 -0.08142 -0.55441 0.0004456 -0.69622
## 11 0.48177 -0.03460 -0.30084 0.30647 -0.6211610 0.24013
## 12 0.49116 0.01863 -0.28336 0.28353 -0.5526465 0.38811
## 13 0.49826 0.19287 -0.45932 0.66506 -0.3554733 0.30170
## 14 0.38432 0.23745 -0.61472 0.44511 -0.3586002 -0.25255
## 15 0.29122 0.24047 -0.65399 0.13806 -0.5744138 -0.61858
## 16 0.09316 0.42148 -0.35490 -0.15339 0.5215487 -0.37988
## 17 -0.04925 0.45811 -0.39677 -0.01491 0.9780228 0.37292
## 18 -0.13761 0.42321 -0.38275 -0.05496 0.8161603 0.16502
## 19 -0.27972 0.31288 -0.11236 -0.33397 0.8933487 0.22061
## 20 -0.39479 0.22514 0.04995 -0.18906 0.5524046 0.07848
## 21 -0.42845 0.27228 0.18669 -0.06116 0.1685934 -0.05714
## 22 -0.46606 0.23214 0.22711 0.01341 -0.2537680 -0.02033
## 23 -0.27461 -1.14655 -0.45652 0.29007 -0.0135080 1.25334
## 24 -0.40481 -0.76490 -0.22822 0.37504 -0.0609283 1.09137
## 25 -0.34882 -0.79890 -0.18120 0.26592 0.6572636 -1.77138
## 26 -0.46948 0.07575 0.22954 0.02782 -0.3290586 -0.01789
## 27 -0.47071 0.19146 0.25713 0.03821 -0.3683865 -0.05750
## 28 -0.47379 0.20692 0.28560 0.06099 -0.5260879 -0.05532
## 29 -0.37500 0.36927 0.15499 0.19260 -0.2675599 -0.13575
## 30 -0.49089 0.27719 0.35606 0.19549 -0.3857710 -0.41707
##
##
## Site constraints (linear combinations of constraining variables)
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## 1 0.56175 -0.1760841 0.88764 0.754729 0.05699 -0.024789
## 2 0.27817 0.0278236 0.64854 -0.186273 0.71868 0.103142
## 3 0.40029 -0.1410937 0.44478 0.093254 0.06243 0.159225
## 4 0.38057 0.0304488 0.27060 -0.451187 0.11348 -0.163639
## 5 0.28434 -0.4313776 -0.07763 -0.624922 -0.31890 -0.121072
## 6 0.32365 -0.1717502 0.33216 -0.239434 0.10094 0.430241
## 7 0.43342 -0.1878085 0.23023 -0.249967 -0.43848 -0.112702
## 9 0.03876 -0.2453624 -0.06488 -1.063159 -0.28405 -0.165997
## 10 0.20896 -0.1304587 0.09944 -0.029712 -0.15900 -0.003754
## 11 0.40310 0.2072872 -0.38689 0.263810 -0.34896 -0.675910
## 12 0.31060 0.1676808 -0.35285 0.131850 0.23982 0.374171
## 13 0.36401 0.1083071 -0.45977 0.254110 -0.16169 0.154682
## 14 0.37243 0.1568990 -0.54561 0.280403 -0.29513 0.137795
## 15 0.30217 0.2952073 -0.51539 0.302620 -0.26909 -0.210679
## 16 -0.03361 0.2527439 -0.16089 -0.109020 0.07654 -0.063949
## 17 -0.04798 0.2826767 -0.42392 -0.163557 0.50606 0.277953
## 18 -0.04201 0.3220218 -0.27114 -0.101241 0.36862 -0.205147
## 19 -0.04238 0.3815154 -0.25768 -0.005306 0.34826 -0.051361
## 20 -0.22510 0.3772267 -0.06452 0.029576 0.73223 0.129096
## 21 -0.36748 0.2531034 0.06637 -0.113143 0.22778 0.340391
## 22 -0.31101 0.0588354 0.04318 0.184265 -0.22087 0.507177
## 23 -0.23685 -1.0546821 -0.31876 0.653124 -0.04596 0.560361
## 24 -0.50645 -0.5459548 -0.31521 -0.311281 -0.17450 0.594229
## 25 -0.37598 -0.9060399 -0.21468 0.249405 0.74559 -1.161192
## 26 -0.50636 0.0003718 0.12134 -0.254528 -0.36086 -0.278472
## 27 -0.57930 0.0667865 0.37489 0.211246 -0.22598 0.098458
## 28 -0.60704 0.3723919 0.35636 -0.090151 -0.04124 -0.118013
## 29 -0.34377 0.3294603 0.26629 0.309673 -0.30573 -0.235032
## 30 -0.43691 0.2998244 0.28800 0.274817 -0.64698 -0.275212
##
##
## Biplot scores for constraining variables
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## das -0.9128 0.12530 -0.14564 0.28559 -0.14025 -0.08596
## alt 0.8174 -0.19940 0.42653 -0.31363 0.04070 0.04191
## slo 0.7304 -0.18160 0.44131 0.12857 -0.03275 0.05724
## flo -0.7734 0.22945 -0.11776 0.35275 -0.20906 -0.20090
## pH 0.1032 0.17963 -0.23622 0.15717 -0.27998 -0.00646
## har -0.5651 0.06594 -0.58956 0.05411 -0.38278 -0.21829
## pho -0.4885 -0.66510 -0.22158 0.20580 0.19624 -0.37929
## nit -0.7699 -0.21306 -0.25074 0.19239 0.30094 -0.31772
## amm -0.4700 -0.70062 -0.19662 0.17917 0.34014 -0.29055
## oxy 0.7593 0.57745 -0.03543 0.21067 0.03237 0.15692
## bdo -0.5124 -0.80242 -0.19822 0.12204 0.05668 -0.05770
coef(peces.rda)
## RDA1 RDA2 RDA3 RDA4 RDA5
## das -2.010177e-03 -6.608331e-04 4.071666e-03 -0.0007737831 -4.879723e-04
## alt -1.362522e-04 -1.940140e-04 1.584080e-03 -0.0007113863 -1.912051e-04
## slo 2.628938e-02 -4.812851e-02 5.088824e-02 0.1600880853 -1.247432e-01
## flo 6.300348e-05 4.629605e-05 -4.921894e-05 0.0001327932 -1.037798e-05
## pH 9.287651e-02 -8.634439e-02 -1.104621e-01 0.4085611837 -4.823312e-01
## har 9.775838e-04 -2.188270e-03 -4.710546e-03 -0.0064392451 -8.045440e-03
## pho 7.025002e-04 2.019299e-04 7.318641e-05 -0.0004476953 -1.972434e-03
## nit -1.321711e-04 8.279428e-04 2.744723e-04 -0.0002662408 5.496718e-04
## amm -9.834747e-04 -3.545150e-03 -3.541437e-05 0.0025904176 8.311865e-03
## oxy 3.172353e-03 9.545637e-04 7.456746e-04 0.0064171073 4.725933e-03
## bdo 8.490136e-04 -3.065095e-03 -1.780181e-03 0.0031954416 -2.120654e-03
## RDA6 RDA7 RDA8 RDA9 RDA10
## das 0.0047839425 0.0046815522 -0.0015359027 -0.0040833159 -1.416482e-03
## alt 0.0005956930 0.0016706252 0.0016945643 0.0000687003 -1.567319e-03
## slo -0.1134485914 -0.2433342914 -0.1433130367 0.0634790412 8.982321e-02
## flo -0.0002228683 -0.0001670232 0.0002557783 0.0002140328 4.793737e-05
## pH -0.1669999046 0.0247676509 -0.3568088308 0.9516657070 6.939359e-01
## har -0.0020686320 -0.0036608079 0.0050621827 -0.0020359947 -1.158263e-03
## pho -0.0044370161 0.0051546453 -0.0057318270 -0.0051168495 -3.561727e-03
## nit -0.0011216301 -0.0004445305 0.0017221099 0.0033539197 -3.521483e-03
## amm 0.0057241952 -0.0081100429 0.0041614434 -0.0024554952 2.195211e-02
## oxy 0.0119479055 0.0133260985 0.0104091231 -0.0041067968 -1.493132e-02
## bdo 0.0085989892 0.0020924330 0.0101797519 0.0078128921 -1.229218e-02
## RDA11
## das 1.150295e-02
## alt 3.401140e-03
## slo -8.370854e-02
## flo -4.854237e-04
## pH -2.898171e-01
## har 1.758118e-02
## pho 4.068172e-04
## nit 6.668115e-07
## amm 6.505853e-03
## oxy 2.177482e-02
## bdo -2.482311e-03
(R2 <- RsquareAdj(peces.rda)$r.squared)
## [1] 0.7314363
(R2adj <- RsquareAdj(peces.rda)$adj.r.squared)
## [1] 0.5576598
plot(peces.rda,
scaling = 1,
display = c("sp", "lc", "cn"),
main = "Triplot RDA peces.hel ~ ambient - escalamiento 1 - puntajes ajustados"
)
plot(peces.rda,
display = c("sp", "lc", "cn"),
main = "Triplot RDA peces.hel ~ ambient - escalamiento 2 - puntajes ajustados"
)
peces.sc2 <-
scores(peces.rda,
choices = 1:2,
display = "sp"
)
arrows(0, 0,
peces.sc2[, 1] * 0.92,
peces.sc2[, 2] * 0.92,
length = 0,
lty = 1,
col = "red"
)
peces.good <- goodness(peces.rda)
sel.sp <- which(peces.good[, 2] >= 0.6)
par(mfrow = c(2, 1))
triplot.rda(peces.rda,
site.sc = "lc",
scaling = 1,
cex.char2 = 0.7,
pos.env = 3,
pos.centr = 1,
mult.arrow = 1.1,
mar.percent = 0.05,
select.spe = sel.sp
)
##
## -----------------------------------------------------------------------
## Site constraints (lc) selected. To obtain site scores that are weighted
## sums of species scores (default in vegan), argument site.sc must be set
## to wa.
## -----------------------------------------------------------------------
## No factor, hence levels cannot be plotted with symbols; 'plot.centr' is set to FALSE
text(-0.92, 0.62, "a", cex = 2)
triplot.rda(peces.rda,
site.sc = "lc",
scaling = 2,
cex.char2 = 0.7,
pos.env = 3,
pos.centr = 1,
mult.arrow = 1.1,
mar.percent = 0.05,
select.spe = sel.sp
)
##
## -----------------------------------------------------------------------
## Site constraints (lc) selected. To obtain site scores that are weighted
## sums of species scores (default in vegan), argument site.sc must be set
## to wa.
## -----------------------------------------------------------------------
## No factor, hence levels cannot be plotted with symbols; 'plot.centr' is set to FALSE
text(-2.82, 2, "b", cex = 2)
anova(peces.rda, permutations = how(nperm = 999))
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = peces.hel ~ das + alt + slo + flo + pH + har + pho + nit + amm + oxy + bdo, data = ambient)
## Df Variance F Pr(>F)
## Model 11 0.36755 4.2091 0.001 ***
## Residual 17 0.13496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(peces.rda, by = "axis", permutations = how(nperm = 999))
## Permutation test for rda under reduced model
## Forward tests for axes
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = peces.hel ~ das + alt + slo + flo + pH + har + pho + nit + amm + oxy + bdo, data = ambient)
## Df Variance F Pr(>F)
## RDA1 1 0.230202 28.9978 0.001 ***
## RDA2 1 0.053468 6.7353 0.001 ***
## RDA3 1 0.033958 4.2776 0.102
## RDA4 1 0.027616 3.4787 0.335
## RDA5 1 0.006804 0.8571 1.000
## RDA6 1 0.006271 0.7899 1.000
## RDA7 1 0.003154 0.3973 1.000
## RDA8 1 0.003017 0.3800 1.000
## RDA9 1 0.001417 0.1785 1.000
## RDA10 1 0.000870 0.1096 1.000
## RDA11 1 0.000777 0.0979 1.000
## Residual 17 0.134956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
peces.rda$CA$eig[peces.rda$CA$eig > mean(peces.rda$CA$eig)]
## PC1 PC2 PC3 PC4 PC5 PC6
## 0.043827430 0.022704284 0.017735831 0.014126067 0.009321390 0.008514941
vif.cca(peces.rda)
## das alt slo flo pH har
## 119.360074 47.145809 5.324581 41.000764 1.792699 4.026741
## pho nit amm oxy bdo
## 27.187637 16.461081 30.583561 16.606532 18.148646
ggplot(ambient, aes(das, alt)) +
geom_line()
ggplot(ambient, aes(das, flo)) +
geom_line()
(R2a.all <- RsquareAdj(peces.rda)$adj.r.squared)
## [1] 0.5576598
forward.sel(peces.hel, ambient, adjR2thresh = R2a.all)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Testing variable 4
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.575186 with 4 variables (> 0.557660)
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 das 1 0.38972513 0.3897251 0.3671224 17.242359 0.001
## 2 oxy 10 0.11822033 0.5079455 0.4700951 6.246724 0.001
## 3 bdo 11 0.07794818 0.5858936 0.5362009 4.705807 0.001