dados hormonios - lia
Leonardo Reffatti
10 de março de 2019
Análises estatísticas dos dados:
Carregamento de pacotes e dados. Leitura e resumo dos dados.
setwd("C:/R/Lia")
dados<-read.table("dados_hormonios.txt", header = TRUE, row.names = 1 )
attach(dados)
summary(dados) ## ano estagio temp precipit UR
## Min. :2014 coty:6 Min. :14.85 Min. : 600.6 Min. :83.64
## 1st Qu.:2014 II :6 1st Qu.:15.41 1st Qu.: 630.6 1st Qu.:84.32
## Median :2014 III :6 Median :15.65 Median : 919.3 Median :84.75
## Mean :2014 IV :6 Mean :15.80 Mean :1075.0 Mean :84.76
## 3rd Qu.:2015 3rd Qu.:16.28 3rd Qu.:1456.5 3rd Qu.:85.41
## Max. :2015 Max. :16.80 Max. :1910.2 Max. :85.54
## Gaeixo ABAeixo S.Aeixo IAAeixo
## Min. : 0.00 Min. : 0 Min. :162.3 Min. : 0.000
## 1st Qu.: 10.46 1st Qu.: 1156 1st Qu.:388.1 1st Qu.: 5.145
## Median : 19.58 Median : 2812 Median :465.8 Median : 8.665
## Mean : 39.73 Mean : 4322 Mean :505.2 Mean : 8.209
## 3rd Qu.: 48.75 3rd Qu.: 6420 3rd Qu.:633.4 3rd Qu.:11.238
## Max. :128.46 Max. :15016 Max. :889.4 Max. :21.430
## Gacot ABAcot S.Acot IAAcot
## Min. : 5.09 Min. : 7.08 Min. :130.5 Min. : 0.000
## 1st Qu.: 11.34 1st Qu.:17.66 1st Qu.:165.4 1st Qu.: 3.090
## Median : 29.50 Median :26.48 Median :181.8 Median : 7.205
## Mean : 47.00 Mean :27.04 Mean :192.6 Mean : 8.394
## 3rd Qu.: 57.70 3rd Qu.:31.98 3rd Qu.:222.4 3rd Qu.:14.027
## Max. :151.34 Max. :58.89 Max. :373.9 Max. :24.330
## Germ TZ IVG CE
## Min. : 12.00 Min. : 84.00 Min. :0.0400 Min. : 34.64
## 1st Qu.: 72.50 1st Qu.: 91.92 1st Qu.:0.2412 1st Qu.: 58.40
## Median : 83.67 Median : 94.00 Median :0.4179 Median : 66.54
## Mean : 74.64 Mean : 93.88 Mean :0.4117 Mean : 77.45
## 3rd Qu.: 88.50 3rd Qu.:100.00 3rd Qu.:0.5563 3rd Qu.: 89.11
## Max. :100.00 Max. :100.00 Max. :0.7900 Max. :162.08
## MS
## Min. :2.246
## 1st Qu.:3.720
## Median :4.503
## Mean :4.276
## 3rd Qu.:4.926
## Max. :5.911str(dados) ## 'data.frame': 24 obs. of 18 variables:
## $ ano : int 2014 2014 2014 2014 2014 2014 2014 2014 2014 2014 ...
## $ estagio : Factor w/ 4 levels "coty","II","III",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ temp : num 15.6 15.6 15.6 15.5 15.5 ...
## $ precipit: num 1202 1202 1202 1416 1416 ...
## $ UR : num 83.6 83.6 83.6 84.4 84.4 ...
## $ Gaeixo : num 9.14 10.9 13.04 7 7.66 ...
## $ ABAeixo : num 1567 2218 2011 3166 3221 ...
## $ S.Aeixo : num 402 371 507 266 413 ...
## $ IAAeixo : num 5.43 5.84 7.71 0 3.54 ...
## $ Gacot : num 26.7 19.7 25.2 32.3 32.7 ...
## $ ABAcot : num 25.6 54.9 28.6 40.3 29.2 ...
## $ S.Acot : num 191 374 180 144 169 ...
## $ IAAcot : num 6.89 15.17 7.52 4.12 5.91 ...
## $ Germ : num 12 12 30 87.5 87.5 ...
## $ TZ : num 100 100 100 100 92 ...
## $ IVG : num 0.04 0.05 0.225 0.2 0.35 0.35 0.67 0.79 0.745 0.5 ...
## $ CE : num 81.2 71.7 87 58.6 56.8 ...
## $ MS : num 2.48 2.25 2.75 3.88 4.55 ...library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-2library(permute)
library(lattice)Análise estatística multivariada:
Parte 1: Análise de variáveis fisiologicas+hormonais nos diferentes estagios
Etapas:
UA: Repetições das variáveis estágio e ano.
1 -> Montagem de matriz de distância dos dados Fisiológicos (Germ, TZ, IVG, CE, MS), Hormônios (Gaeixo, ABAeixo, S.Aeixo, IAAeixo, Gacot, ABAcot, S.Acot, IAAcot) através do método euclidiano.
2 -> Padronização dos dados referentes de hormônios e fisiológicos.
3 -> Montagem de matriz de distância dos dados Ambientais (temp, precipit, UR) através do método euclidiano.
4 -> Padronização dos dados referentes do ambiente.
5 -> Para comparar as duas matrizes (Fisiologicos+Hormonios x Ambiente) se fez o teste de Mantel, onde se aplica teste estatístico para comparar as matrizes através de 10000 permutações destas matrizes (statistic r: 0.3519, Significance: 0.00039996). Ou seja os agrupamentos formados através das variáveis fisiológicas e hormonais de estágio+ano+R apresentam significância estatística, esse agrupamentos formados no PCA não se formaram ao acaso.
6 -> Representação da matriz de distâncias das variáveis Fisiologicas+Hormonios através da PCA. Verificamos os agrupamentos por estagio.
7 -> Testamos os agrupamentos formados pelo estagio (coty, II, III, IV) na PCA através de uma Permanova. Verificamos se padrão da matriz de distâncias dentro dos estagios (coty, II, III, IV) é menor que entre os mesmo estagios. Essa verificação é feita através permutação de 10000 vezes a matriz da distãncias. De fato existiu uma associação dos estágios com a matriz de distâncias (Pr(>F): 0.019, R2: 0.37902).
#hormonios+fisiologicos com ambiente
total<-dados[,6:18]
total.pad<-decostand(total, method = "standardize")
dist.total<-dist(total, method="euclid")
ambiente<-dados[,3:5]
ambiente.pad<-decostand(ambiente, method = "standardize")
dist.amb<-vegdist(ambiente.pad, method="euclid")
mantel(dist.amb, dist.total, permutations = 10000) #mantel##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = dist.amb, ydis = dist.total, permutations = 10000)
##
## Mantel statistic r: 0.3519
## Significance: 0.00019998
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0908 0.1235 0.1538 0.1903
## Permutation: free
## Number of permutations: 10000resultado<-rda(total<-dados[,6:18], scale = TRUE)
summary(resultado)##
## Call:
## rda(X = total <- dados[, 6:18], scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 13 1
## Unconstrained 13 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 4.6824 2.8626 1.7994 1.1844 0.79789 0.57133 0.52691
## Proportion Explained 0.3602 0.2202 0.1384 0.0911 0.06138 0.04395 0.04053
## Cumulative Proportion 0.3602 0.5804 0.7188 0.8099 0.87128 0.91523 0.95576
## PC8 PC9 PC10 PC11 PC12 PC13
## Eigenvalue 0.22162 0.129128 0.091142 0.070134 0.047302 0.015745
## Proportion Explained 0.01705 0.009933 0.007011 0.005395 0.003639 0.001211
## Cumulative Proportion 0.97281 0.982744 0.989755 0.995150 0.998789 1.000000
##
## 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: 4.158319
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5 PC6
## Gaeixo 0.867709 -0.55239 0.04954 -0.265166 0.37262 -0.02950
## ABAeixo 0.827909 -0.63709 -0.26571 0.062008 -0.10927 -0.31327
## S.Aeixo -0.850574 -0.30018 -0.07211 -0.389642 0.44765 -0.07754
## IAAeixo -0.569030 -0.44996 -0.58634 0.330127 0.24924 0.17600
## Gacot 0.878551 -0.57383 -0.12072 -0.159370 0.11664 -0.36253
## ABAcot -0.604390 0.38060 -0.66384 -0.542772 -0.13621 -0.03272
## S.Acot 0.434616 0.48198 -0.61879 -0.146189 0.54633 0.10788
## IAAcot 0.002029 -0.05471 -0.83984 0.684305 -0.12490 -0.12087
## Germ -0.537389 -0.83843 0.30027 -0.145488 -0.05213 0.04359
## TZ -0.773731 0.52638 -0.08111 -0.226284 -0.05529 -0.58090
## IVG -0.894659 -0.63952 0.03382 0.082335 0.17723 0.10078
## CE -0.449527 0.33969 0.54520 0.592925 0.46246 -0.31453
## MS -0.725631 -0.76826 -0.18115 0.005196 -0.22518 -0.12948
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5 PC6
## A2014.coty-1 0.42239 1.7246189 0.10583 0.140787 -0.380223 -0.48731
## A2014.coty-2 0.52866 2.3847287 -1.91883 -0.489751 0.925734 -0.03659
## A2014.coty-3 0.05621 1.2504767 0.07311 0.120392 -0.037476 -0.49491
## A2014.II-1 0.02407 0.6828327 0.51857 -0.860075 -2.151052 -0.59816
## A2014.II-2 -0.02571 0.0745331 0.34589 -0.356232 -1.304879 0.58490
## A2014.II-3 -0.02263 -0.1850858 0.86442 -0.404983 -1.171975 0.38479
## A2014.III-1 -1.34018 -0.1632617 -0.81328 -1.577055 -0.067162 0.05567
## A2014.III-2 -1.18293 -0.6970607 -0.30448 -0.142922 -0.115030 0.84231
## A2014.III-3 -1.46906 -0.5153870 -0.59840 -1.144266 0.687094 1.08901
## A2014.IV-1 -0.49639 0.5124067 0.62325 -0.482294 -0.154064 -0.43329
## A2014.IV-2 -0.78314 0.1073292 0.95945 -0.164408 0.189890 -0.64443
## A2014.IV-3 -0.77381 -0.3287031 0.89322 -0.415949 1.168469 0.74714
## A2015.coty-1 1.65371 -0.0006629 0.82148 -0.348432 0.483306 0.45753
## A2015.coty-2 1.41340 -0.0194390 0.85506 -0.700923 1.077683 0.82083
## A2015.coty-3 1.24776 -0.3288681 0.48380 -0.005112 1.101347 1.11337
## A2015.II-1 1.01025 -1.5822055 -0.61711 -0.245248 -0.255723 -1.49550
## A2015.II-2 0.52122 -1.0452721 -1.36929 -0.126499 0.575712 -1.27437
## A2015.II-3 0.55650 -1.1013287 0.23377 -0.654576 -0.005967 -1.50474
## A2015.III-1 0.54049 -0.4025974 -0.58659 1.138200 -1.315773 0.91045
## A2015.III-2 -0.06864 -0.4031266 -1.68665 1.167881 -0.462274 0.63636
## A2015.III-3 0.32565 -0.3204738 -0.77420 1.057548 -0.378099 1.06710
## A2015.IV-1 -0.54278 0.0698345 0.92396 1.446362 0.084140 -0.34064
## A2015.IV-2 -1.07674 0.2126718 0.18489 1.293198 1.220131 -1.46942
## A2015.IV-3 -0.51827 0.0740403 0.78213 1.754355 0.286190 0.06989resultado.env<-envfit(resultado, dados[,3:5])
plot(resultado, type="t")
#type = "t", insere os nomes das UA
ordihull(resultado, groups=estagio, show="coty", col="green4")
ordihull(resultado, groups=estagio, show="II", col="red")
ordihull(resultado, groups=estagio, show="III", col="blue")
ordihull(resultado, groups=estagio, show="IV", col="purple")
plot(resultado.env)
adonis(dist.total~estagio) #permanova##
## Call:
## adonis(formula = dist.total ~ estagio)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## estagio 3 152366227 50788742 4.069 0.37902 0.019 *
## Residuals 20 249634407 12481720 0.62098
## Total 23 402000634 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Parte2:
#hormonios com ambiente
hormonios<-dados[,6:13]
hormonios## Gaeixo ABAeixo S.Aeixo IAAeixo Gacot ABAcot S.Acot IAAcot
## A2014.coty-1 9.14 1567.38 401.52 5.43 26.72 25.63 190.91 6.89
## A2014.coty-2 10.90 2218.46 370.79 5.84 19.69 54.88 373.90 15.17
## A2014.coty-3 13.04 2011.29 507.44 7.71 25.19 28.64 179.81 7.52
## A2014.II-1 7.00 3166.13 265.86 0.00 32.29 40.34 144.19 4.12
## A2014.II-2 7.66 3220.68 412.83 3.54 32.70 29.22 168.90 5.91
## A2014.II-3 13.66 4811.51 476.55 4.29 32.97 20.86 142.97 0.00
## A2014.III-1 17.82 42.42 889.38 9.27 5.51 58.89 194.31 8.01
## A2014.III-2 16.11 33.51 704.40 14.34 8.23 33.18 165.70 9.88
## A2014.III-3 13.47 0.00 848.90 21.43 5.09 49.49 183.75 0.00
## A2014.IV-1 21.34 382.54 489.81 0.00 7.46 32.43 210.88 5.80
## A2014.IV-2 31.09 599.95 708.12 5.96 11.43 27.33 130.46 5.14
## A2014.IV-3 34.74 594.07 873.04 11.04 11.06 21.33 164.67 0.00
## A2015.coty-1 116.53 6183.97 162.30 0.00 132.76 11.05 238.59 0.00
## A2015.coty-2 127.99 7746.92 425.57 0.00 81.49 13.08 234.91 0.00
## A2015.coty-3 117.97 6372.94 394.90 7.72 94.64 7.08 224.88 4.25
## A2015.II-1 128.46 15015.67 495.21 8.57 151.34 15.76 169.47 14.68
## A2015.II-2 85.88 10797.30 614.04 12.15 149.38 31.83 228.61 18.73
## A2015.II-3 78.06 12083.43 493.54 8.76 136.93 18.30 179.16 0.00
## A2015.III-1 38.98 8590.33 302.25 12.09 49.77 25.40 141.30 16.95
## A2015.III-2 28.88 6243.15 377.18 17.85 37.49 30.52 222.81 24.33
## A2015.III-3 30.79 6560.83 391.80 9.25 38.68 20.44 222.32 19.95
## A2015.IV-1 0.00 2458.40 455.05 9.89 13.71 11.59 145.56 8.56
## A2015.IV-2 0.00 1685.60 691.58 11.83 14.34 29.03 193.39 13.81
## A2015.IV-3 4.07 1341.73 373.31 10.06 9.08 12.68 171.82 11.75dist.hormonios<-dist(hormonios, method="euclid")
head(dados)## ano estagio temp precipit UR Gaeixo ABAeixo
## A2014.coty-1 2014 coty 15.55991 1202.4 83.64242 9.14 1567.38
## A2014.coty-2 2014 coty 15.55991 1202.4 83.64242 10.90 2218.46
## A2014.coty-3 2014 coty 15.55991 1202.4 83.64242 13.04 2011.29
## A2014.II-1 2014 II 15.49403 1416.4 84.38547 7.00 3166.13
## A2014.II-2 2014 II 15.49403 1416.4 84.38547 7.66 3220.68
## A2014.II-3 2014 II 15.49403 1416.4 84.38547 13.66 4811.51
## S.Aeixo IAAeixo Gacot ABAcot S.Acot IAAcot Germ TZ
## A2014.coty-1 401.52 5.43 26.72 25.63 190.91 6.89 12.000 100.00000
## A2014.coty-2 370.79 5.84 19.69 54.88 373.90 15.17 12.000 100.00000
## A2014.coty-3 507.44 7.71 25.19 28.64 179.81 7.52 30.000 100.00000
## A2014.II-1 265.86 0.00 32.29 40.34 144.19 4.12 87.500 100.00000
## A2014.II-2 412.83 3.54 32.70 29.22 168.90 5.91 87.500 92.00000
## A2014.II-3 476.55 4.29 32.97 20.86 142.97 0.00 83.335 91.66667
## IVG CE MS
## A2014.coty-1 0.040 81.21835 2.478667
## A2014.coty-2 0.050 71.70465 2.246000
## A2014.coty-3 0.225 87.01970 2.753167
## A2014.II-1 0.200 58.59177 3.883333
## A2014.II-2 0.350 56.84594 4.551667
## A2014.II-3 0.350 63.64590 4.912167ambiente<-dados[,3:5]
ambiente.pad<-decostand(ambiente, method = "standardize")
#criação de matriz de distância euclidiana para ambiente
dist.amb<-vegdist(ambiente.pad, method="euclid")
mantel(dist.amb, dist.hormonios, permutations = 10000)##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = dist.amb, ydis = dist.hormonios, permutations = 10000)
##
## Mantel statistic r: 0.3517
## Significance: 9.999e-05
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.086 0.118 0.145 0.188
## Permutation: free
## Number of permutations: 10000adonis(dist.hormonios~estagio)##
## Call:
## adonis(formula = dist.hormonios ~ estagio)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## estagio 3 152341799 50780600 4.0686 0.37899 0.024 *
## Residuals 20 249623598 12481180 0.62101
## Total 23 401965397 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#fisiologico com ambiente
fisiologico<-dados[,14:18]
fisiologico## Germ TZ IVG CE MS
## A2014.coty-1 12.000 100.00000 0.0400000 81.21835 2.478667
## A2014.coty-2 12.000 100.00000 0.0500000 71.70465 2.246000
## A2014.coty-3 30.000 100.00000 0.2250000 87.01970 2.753167
## A2014.II-1 87.500 100.00000 0.2000000 58.59177 3.883333
## A2014.II-2 87.500 92.00000 0.3500000 56.84594 4.551667
## A2014.II-3 83.335 91.66667 0.3500000 63.64590 4.912167
## A2014.III-1 92.000 100.00000 0.6700000 55.84640 5.911000
## A2014.III-2 100.000 96.00000 0.7900000 62.29745 5.837333
## A2014.III-3 96.000 98.00000 0.7450000 61.65296 5.462833
## A2014.IV-1 95.000 100.00000 0.5000000 95.37459 4.515667
## A2014.IV-2 90.000 100.00000 0.5500000 106.92730 4.154000
## A2014.IV-3 97.500 93.33333 0.5750000 95.85765 4.275667
## A2015.coty-1 60.000 84.00000 0.1900000 72.07415 3.230333
## A2015.coty-2 68.000 84.00000 0.2300000 70.24636 2.495000
## A2015.coty-3 74.000 84.00000 0.2450000 66.38112 2.752500
## A2015.II-1 85.000 92.00000 0.4387173 34.64286 4.767333
## A2015.II-2 85.000 92.00000 0.3931897 57.61576 4.878667
## A2015.II-3 82.500 94.00000 0.3969930 66.69559 5.402667
## A2015.III-1 56.000 84.00000 0.3062510 57.82364 4.439000
## A2015.III-2 76.000 92.00000 0.4650619 51.83301 4.967000
## A2015.III-3 74.000 86.00000 0.4502928 59.98399 4.541167
## A2015.IV-1 76.000 96.00000 0.5792822 127.70194 4.490000
## A2015.IV-2 84.000 100.00000 0.5453912 162.08019 5.239667
## A2015.IV-3 88.000 94.00000 0.5957545 134.67286 4.445667fisiologico.pad<-decostand(fisiologico, method = "standardize")
dist.fisiologico<-dist(fisiologico.pad, method="euclid")
head(dados)## ano estagio temp precipit UR Gaeixo ABAeixo
## A2014.coty-1 2014 coty 15.55991 1202.4 83.64242 9.14 1567.38
## A2014.coty-2 2014 coty 15.55991 1202.4 83.64242 10.90 2218.46
## A2014.coty-3 2014 coty 15.55991 1202.4 83.64242 13.04 2011.29
## A2014.II-1 2014 II 15.49403 1416.4 84.38547 7.00 3166.13
## A2014.II-2 2014 II 15.49403 1416.4 84.38547 7.66 3220.68
## A2014.II-3 2014 II 15.49403 1416.4 84.38547 13.66 4811.51
## S.Aeixo IAAeixo Gacot ABAcot S.Acot IAAcot Germ TZ
## A2014.coty-1 401.52 5.43 26.72 25.63 190.91 6.89 12.000 100.00000
## A2014.coty-2 370.79 5.84 19.69 54.88 373.90 15.17 12.000 100.00000
## A2014.coty-3 507.44 7.71 25.19 28.64 179.81 7.52 30.000 100.00000
## A2014.II-1 265.86 0.00 32.29 40.34 144.19 4.12 87.500 100.00000
## A2014.II-2 412.83 3.54 32.70 29.22 168.90 5.91 87.500 92.00000
## A2014.II-3 476.55 4.29 32.97 20.86 142.97 0.00 83.335 91.66667
## IVG CE MS
## A2014.coty-1 0.040 81.21835 2.478667
## A2014.coty-2 0.050 71.70465 2.246000
## A2014.coty-3 0.225 87.01970 2.753167
## A2014.II-1 0.200 58.59177 3.883333
## A2014.II-2 0.350 56.84594 4.551667
## A2014.II-3 0.350 63.64590 4.912167ambiente<-dados[,3:5]
ambiente.pad<-decostand(ambiente, method = "standardize")
#criação de matriz de distância euclidiana para ambiente
dist.amb<-vegdist(ambiente.pad, method="euclid")
mantel(dist.amb, dist.fisiologico, permutations = 10000)##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = dist.amb, ydis = dist.fisiologico, permutations = 10000)
##
## Mantel statistic r: 0.3754
## Significance: 9.999e-05
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.0872 0.1179 0.1478 0.1800
## Permutation: free
## Number of permutations: 10000adonis(dist.fisiologico~estagio)##
## Call:
## adonis(formula = dist.fisiologico ~ estagio)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## estagio 3 69.987 23.3291 10.366 0.60859 0.001 ***
## Residuals 20 45.013 2.2506 0.39141
## Total 23 115.000 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#fisiologicos com hormonios
mantel(dist.hormonios, dist.fisiologico, permutations = 10000)##
## Mantel statistic based on Pearson's product-moment correlation
##
## Call:
## mantel(xdis = dist.hormonios, ydis = dist.fisiologico, permutations = 10000)
##
## Mantel statistic r: 0.05081
## Significance: 0.28127
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.143 0.191 0.242 0.298
## Permutation: free
## Number of permutations: 10000adonis(dist.fisiologico~estagio)##
## Call:
## adonis(formula = dist.fisiologico ~ estagio)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## estagio 3 69.987 23.3291 10.366 0.60859 0.001 ***
## Residuals 20 45.013 2.2506 0.39141
## Total 23 115.000 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1