PP_resutlat
QuentinFAYE
2024-11-20
library(factoextra)## Warning: le package 'factoextra' a été compilé avec la version R 4.3.3## Le chargement a nécessité le package : ggplot2## Warning: le package 'ggplot2' a été compilé avec la version R 4.3.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)## Warning: le package 'FactoMineR' a été compilé avec la version R 4.3.3#environment=read.table(choose.files() , sep=";", dec=",",header = T, )environment## function (fun = NULL)
## .Internal(environment(fun))
## <bytecode: 0x0000025a213744c0>
## <environment: namespace:base>existe t ’il des diférences entres les sites ? ANOVA-> avec 4 mesure difficke de savoir si Normale. en meme temps selon le TCL les moyennes tendent vers a des distribution normale. CPDT en medecin on demande 30 echnatillon pour consider que la moyenne a converger. On ne fait pas de Test de Normalite car on a beaucoup a faire, avec peu de puissance alors il y a forte probabilite que une soit normale par hasard on va juste regarder l’aspect globable -> on pourrait faire un qqplote . NN mais elles sont normale car pour toutes les prises ont ete echantille plein de fois et sont deja des moyennes- on peut aplique ANOVA
#for (i in seq(1,11,1)) {
# hist(environment[,i])
#}testons l’homocedatisicte des Données-> validé
#bartlett.test(environment)faison le anova
#environment[,2]
#for (i in seq(2,11,1)) {
# aov(environment$site~environment[,2])
#}changement de jeux de donné pour inclures les variances
T_lux=read.table(file = "Tandluxcsv.csv" , sep=";", dec=".",header = T,na.strings = "" )
T_lux$site=as.factor(T_lux$site)
#T_lux$couvert.ouvert=as.factor(T_lux$couvert.ouvert)
#T_luxNutriment=read.table(file = "nutriment.csv" , sep=";", dec=",",header = T,) ; Nutriment## site amonica N p T.max
## 1 amon_etang_du_haut 0.00 0.50 0.04 17.475
## 2 aval_etang_du_haut 0.00 0.15 0.40 19.662
## 3 amont_etang_paimpont 0.03 0.30 1.27 19.092
## 4 aval_etang_paimpont 0.15 0.30 0.35 17.665Turbidité=read.table(file="turbitité3.csv", sep=";", dec=",",header = T,) ; Turbidité## Dénomination cyano Total Turbidité_FTU
## 1 Station_1 0.0 9.3 156.0
## 2 Station_1 0.0 9.3 100.8
## 3 Station_1 0.0 0.3 439.8
## 4 Station_2 0.9 8.9 34.8
## 5 Station_2 3.6 16.0 37.1
## 6 Station_2 8.3 24.7 34.9
## 7 Station_2 0.5 22.9 97.5
## 8 Amont_lac_haut 0.0 49.9 316.6
## 9 Amont_lac_haut 1.5 9.1 34.7
## 10 Amont_lac_haut 0.0 7.1 52.9
## 11 Aval_pas_nous 64.1 120.0 56.1
## 12 Aval_pas_nous 16.0 97.4 179.2
## 13 Aval_pas_nous 1.9 44.2 71.9algo=read.table(file = "algotorch3.csv" , sep=";", dec=",",header = T,); #algo
algo$Dénomination=as.factor(algo$Dénomination)
algo$amont1_aval2=as.factor(algo$amont1_aval2)egalité des variances ? bonnnnnn falait pas multiplier les test T mais bon. mieux vaux observation
bartlett.test(algo$cyano_.microg.cm2.~algo$Dénomination)##
## Bartlett test of homogeneity of variances
##
## data: algo$cyano_.microg.cm2. by algo$Dénomination
## Bartlett's K-squared = 19.912, df = 3, p-value = 0.0001771bartlett.test(algo$algues_vertes_.microg.cm2.~algo$Dénomination)##
## Bartlett test of homogeneity of variances
##
## data: algo$algues_vertes_.microg.cm2. by algo$Dénomination
## Bartlett's K-squared = 20.464, df = 3, p-value = 0.000136bartlett.test(algo$diatomées_.microg.cm2.~algo$Dénomination)##
## Bartlett test of homogeneity of variances
##
## data: algo$diatomées_.microg.cm2. by algo$Dénomination
## Bartlett's K-squared = 12.631, df = 3, p-value = 0.005507bartlett.test(T_lux$Temp~T_lux$site) # nn homocedastique)##
## Bartlett test of homogeneity of variances
##
## data: T_lux$Temp by T_lux$site
## Bartlett's K-squared = 2.276, df = 3, p-value = 0.5171bartlett.test(T_lux$Intensité.Lux~T_lux$site) #nn homocedastisue car depend du temps##
## Bartlett test of homogeneity of variances
##
## data: T_lux$Intensité.Lux by T_lux$site
## Bartlett's K-squared = 29.71, df = 3, p-value = 1.588e-06bartlett.test(Turbidité$Total~Turbidité$Dénomination) #nn homocedastique##
## Bartlett test of homogeneity of variances
##
## data: Turbidité$Total by Turbidité$Dénomination
## Bartlett's K-squared = 8.3251, df = 3, p-value = 0.03975bartlett.test(Turbidité$Turbidité_FTU~Turbidité$Dénomination) #nn homocedastique##
## Bartlett test of homogeneity of variances
##
## data: Turbidité$Turbidité_FTU by Turbidité$Dénomination
## Bartlett's K-squared = 6.3587, df = 3, p-value = 0.0954bartlett.test(Turbidité$cyano~Turbidité$Dénomination)##
## Bartlett test of homogeneity of variances
##
## data: Turbidité$cyano by Turbidité$Dénomination
## Bartlett's K-squared = Inf, df = 3, p-value < 2.2e-16#bartlett.test(Nutriment$amonica~Nutriment$site)
#bartlett.test(Nutriment$N~Nutriment$site)
#bartlett.test(Nutriment$p~Nutriment$site)
#bartlett.test(Nutriment$T.max~Nutriment$site)anova ? meme pb pas censé faire mais bon car repeition des test
algo## Dénomination cyano_.microg.cm2. algues_vertes_.microg.cm2.
## 1 station_1 0.00 0.03
## 2 station_1 0.00 0.03
## 3 station_1 0.00 0.05
## 4 station_1 1.14 0.00
## 5 station_1 0.00 0.05
## 6 station_1 0.00 0.04
## 7 station_1 0.00 0.04
## 8 station_1 0.00 0.06
## 9 station_1 0.00 0.02
## 10 station_1 0.00 0.04
## 11 station_1 0.01 0.04
## 12 station_1 0.00 0.05
## 13 station_1 0.00 0.02
## 14 station_1 0.00 0.04
## 15 station_1 0.01 0.02
## 16 station_1 0.08 0.00
## 17 station_1 0.00 0.04
## 18 station_1 0.00 0.03
## 19 station_1 0.00 0.19
## 20 station_1 0.01 0.00
## 21 station_1 0.00 0.04
## 22 station_4 0.88 0.00
## 23 station_4 0.05 0.06
## 24 station_4 0.29 0.00
## 25 station_4 0.20 0.00
## 26 station_4 0.21 0.00
## 27 station_4 0.06 0.08
## 28 station_4 0.04 0.18
## 29 station_4 0.55 0.00
## 30 station_4 0.01 0.00
## 31 station_4 0.01 0.06
## 32 station_4 0.01 0.06
## 33 station_4 0.01 0.06
## 34 station_4 0.04 0.01
## 35 station_4 0.14 0.00
## 36 station_4 0.06 0.02
## 37 Amont_lac_haut 0.00 0.02
## 38 Amont_lac_haut 0.00 0.02
## 39 Amont_lac_haut 0.47 0.04
## 40 Amont_lac_haut 1.57 0.00
## 41 Amont_lac_haut 0.26 0.13
## 42 Amont_lac_haut 0.00 0.01
## 43 Amont_lac_haut 0.00 0.01
## 44 Amont_lac_haut 0.00 0.02
## 45 Amont_lac_haut 0.18 0.00
## 46 Amont_lac_haut 0.00 0.02
## 47 Amont_lac_haut 0.13 0.00
## 48 Amont_lac_haut 0.00 0.03
## 49 Amont_lac_haut 0.02 0.04
## 50 Amont_lac_haut 0.01 0.00
## 51 Amont_lac_haut 0.03 0.05
## 52 Amont_lac_haut 0.02 0.01
## 53 Amont_lac_haut 0.03 0.00
## 54 Amont_lac_haut 0.01 0.03
## 55 Amont_lac_haut 0.00 0.03
## 56 Amont_lac_haut 0.54 0.38
## 57 Aval_lac_haut 0.65 0.12
## 58 Aval_lac_haut 1.45 0.00
## 59 Aval_lac_haut 0.47 0.00
## 60 Aval_lac_haut 0.59 0.00
## 61 Aval_lac_haut 0.76 0.00
## 62 Aval_lac_haut 0.11 0.07
## 63 Aval_lac_haut 0.06 0.00
## 64 Aval_lac_haut 0.00 0.00
## 65 Aval_lac_haut 0.01 0.04
## 66 Aval_lac_haut 0.00 0.05
## 67 Aval_lac_haut 1.04 0.00
## 68 Aval_lac_haut 0.03 0.00
## 69 Aval_lac_haut 0.30 0.00
## 70 Aval_lac_haut 1.66 0.00
## 71 Aval_lac_haut 0.70 0.00
## 72 Aval_lac_haut 0.35 0.00
## 73 Aval_lac_haut 0.72 0.00
## 74 Aval_lac_haut 2.05 0.00
## 75 Aval_lac_haut 0.11 0.00
## 76 Aval_lac_haut 0.00 0.06
## diatomées_.microg.cm2. amont1_aval2
## 1 0.00 1
## 2 0.00 1
## 3 0.03 1
## 4 1.23 1
## 5 0.00 1
## 6 0.00 1
## 7 0.08 1
## 8 0.02 1
## 9 0.00 1
## 10 0.00 1
## 11 0.12 1
## 12 0.00 1
## 13 0.00 1
## 14 0.02 1
## 15 0.09 1
## 16 0.37 1
## 17 0.01 1
## 18 0.08 1
## 19 0.07 1
## 20 0.11 1
## 21 0.02 1
## 22 1.16 2
## 23 0.22 2
## 24 0.29 2
## 25 0.22 2
## 26 0.25 2
## 27 0.06 2
## 28 0.12 2
## 29 0.61 2
## 30 0.09 2
## 31 0.04 2
## 32 0.04 2
## 33 0.07 2
## 34 0.03 2
## 35 0.22 2
## 36 0.26 2
## 37 0.00 1
## 38 0.00 1
## 39 0.99 1
## 40 0.91 1
## 41 0.66 1
## 42 0.00 1
## 43 0.00 1
## 44 0.00 1
## 45 0.12 1
## 46 0.00 1
## 47 0.41 1
## 48 0.02 1
## 49 0.05 1
## 50 0.08 1
## 51 0.03 1
## 52 0.09 1
## 53 0.11 1
## 54 0.04 1
## 55 0.05 1
## 56 0.24 1
## 57 0.66 2
## 58 0.71 2
## 59 0.27 2
## 60 1.57 2
## 61 0.74 2
## 62 0.04 2
## 63 0.14 2
## 64 0.02 2
## 65 0.02 2
## 66 0.00 2
## 67 1.77 2
## 68 0.08 2
## 69 0.35 2
## 70 NA 2
## 71 0.48 2
## 72 0.93 2
## 73 0.48 2
## 74 1.31 2
## 75 0.15 2
## 76 0.01 2a=aov(algo$cyano_.microg.cm2.~algo$Dénomination) #sig differences
summary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## algo$Dénomination 3 2.832 0.9441 5.921 0.00114 **
## Residuals 72 11.480 0.1594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1shapiro.test(a$residuals) #normale##
## Shapiro-Wilk normality test
##
## data: a$residuals
## W = 0.75379, p-value = 5.814e-10b=aov(algo$algues_vertes_.microg.cm2.~algo$Dénomination)# pas sig
summary(b)## Df Sum Sq Mean Sq F value Pr(>F)
## algo$Dénomination 3 0.0077 0.002567 0.833 0.48
## Residuals 72 0.2220 0.003083shapiro.test(b$residuals) # normale##
## Shapiro-Wilk normality test
##
## data: b$residuals
## W = 0.608, p-value = 6.176e-13c=aov(algo$diatomées_.microg.cm2.~algo$Dénomination) # sig differences
summary(c) # normale## Df Sum Sq Mean Sq F value Pr(>F)
## algo$Dénomination 3 1.799 0.5998 4.319 0.00744 **
## Residuals 71 9.860 0.1389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation effacée parce que manquanted=aov(Turbidité$cyano~Turbidité$Dénomination) #pas sig
summary(d)## Df Sum Sq Mean Sq F value Pr(>F)
## Turbidité$Dénomination 3 1566 521.8 2.167 0.162
## Residuals 9 2167 240.8shapiro.test(c$residuals) ##
## Shapiro-Wilk normality test
##
## data: c$residuals
## W = 0.79507, p-value = 7.726e-09kruskal.test(T_lux$Temp~T_lux$site) #sig differneces##
## Kruskal-Wallis rank sum test
##
## data: T_lux$Temp by T_lux$site
## Kruskal-Wallis chi-squared = 45.102, df = 3, p-value = 8.801e-10kruskal.test(T_lux$Intensité.Lux~T_lux$site) #sig differneces##
## Kruskal-Wallis rank sum test
##
## data: T_lux$Intensité.Lux by T_lux$site
## Kruskal-Wallis chi-squared = 13.407, df = 3, p-value = 0.003834kruskal.test(Turbidité$Total~Turbidité$Dénomination) # pas sig à voir si regroupement en grouep##
## Kruskal-Wallis rank sum test
##
## data: Turbidité$Total by Turbidité$Dénomination
## Kruskal-Wallis chi-squared = 6.4738, df = 3, p-value = 0.0907kruskal.test(Turbidité$Turbidité_FTU~Turbidité$Dénomination) # pas sig a voir si regroupent en groupe##
## Kruskal-Wallis rank sum test
##
## data: Turbidité$Turbidité_FTU by Turbidité$Dénomination
## Kruskal-Wallis chi-squared = 5.0495, df = 3, p-value = 0.1682On recapitules on a observer des differences entres les sites pour les VA suivantes quis seront donc les VA les plus interessantes ACP lumière, température, cyano, diatomé,
les donnes de nutriments sont trop peux pour faire comparaison
#verifier la Coorrelation des VA selectione
#TT_lux=T_lux[,-1] ;TT_lux
#aalgo=algo[,-1] ; aalgo
#cor(TT_lux) ; cor(aalgo)
#cor(TT_lux~aalgo)
cor(T_lux$Temp,T_lux$Intensité.Lux, use = "complete.obs" )## [1] 0.1396885cor(T_lux$Temp,sample(algo$cyano_.microg.cm2.,208,replace=T), use = "complete.obs")## [1] -0.04015797cor(T_lux$Temp,sample(algo$diatomées_.microg.cm2.,208,replace=T),use = "complete.obs")## [1] 0.04569404cor(T_lux$Intensité.Lux,sample(algo$cyano_.microg.cm2.,208,replace=T), use = "complete.obs")## [1] 0.06156509cor(T_lux$Intensité.Lux,sample(algo$diatomées_.microg.cm2.,208,replace=T),use = "complete.obs")## [1] 0.1321546cor(algo$cyano_.microg.cm2.,algo$diatomées_.microg.cm2.,use = "complete.obs")## [1] 0.8185005cor(algo$cyano_.microg.cm2.,algo$algues_vertes_.microg.cm2.,use = "complete.obs")## [1] -0.1326688cor(algo$algues_vertes_.microg.cm2.,algo$diatomées_.microg.cm2.,use = "complete.obs")## [1] -0.1635825# aucune correlation qui n'est supérieur a 0.2realison l’ACP il nous faut des lignes de meme taille en gardan les categorie
T_lux$cyano=c(sample(algo[37:56,2],48,replace=T),sample(algo[57:76,2],50,replace=T),sample(algo[1:21,2],55,replace=T),sample(algo[22:36,2],55,replace=T))
T_lux$diatome=c(sample(algo[37:56,4],48,replace=T),sample(algo[57:76,4],50,replace=T),sample(algo[1:21,4],55,replace=T),sample(algo[22:36,4],55,replace=T))T_lux## site Temp Intensité.Lux cyano diatome
## 1 amont1 17.475 NA 0.00 0.08
## 2 amont1 NA NA 0.03 0.03
## 3 amont1 13.942 193.8 0.02 0.41
## 4 amont1 13.942 161.5 0.47 0.00
## 5 amont1 13.942 269.1 0.47 0.02
## 6 amont1 13.942 32.3 0.18 0.00
## 7 amont1 13.942 21.5 0.00 0.00
## 8 amont1 13.942 NA 0.03 0.12
## 9 amont1 13.846 NA 0.00 0.00
## 10 amont1 13.750 NA 0.00 0.11
## 11 amont1 13.750 NA 1.57 0.99
## 12 amont1 13.750 NA 0.00 0.02
## 13 amont1 13.654 NA 0.00 0.00
## 14 amont1 13.558 NA 0.54 0.00
## 15 amont1 13.558 NA 0.54 0.00
## 16 amont1 13.461 NA 0.02 0.99
## 17 amont1 13.461 NA 0.26 0.05
## 18 amont1 13.365 NA 0.00 0.00
## 19 amont1 13.365 NA 0.00 0.04
## 20 amont1 13.365 NA 0.03 0.00
## 21 amont1 13.269 NA 0.01 0.99
## 22 amont1 13.269 NA 0.03 0.41
## 23 amont1 13.269 NA 0.00 0.00
## 24 amont1 13.269 NA 0.00 0.66
## 25 amont1 13.269 NA 0.26 0.00
## 26 amont1 13.269 NA 0.00 0.00
## 27 amont1 13.173 NA 0.00 0.24
## 28 amont1 13.173 NA 0.00 0.00
## 29 amont1 13.173 NA 0.00 0.12
## 30 amont1 13.173 NA 0.54 0.00
## 31 amont1 13.173 NA 0.00 0.03
## 32 amont1 13.173 NA 0.26 0.11
## 33 amont1 13.173 NA 0.00 0.66
## 34 amont1 13.173 NA 0.00 0.00
## 35 amont1 13.173 43.1 0.00 0.41
## 36 amont1 13.173 118.4 0.02 0.04
## 37 amont1 13.173 161.5 0.00 0.03
## 38 amont1 13.269 462.9 0.18 0.08
## 39 amont1 13.269 258.3 0.00 0.05
## 40 amont1 13.365 376.7 0.13 0.00
## 41 amont1 13.365 183.0 0.01 0.41
## 42 amont1 13.461 473.6 0.13 0.05
## 43 amont1 13.558 387.5 0.47 0.00
## 44 amont1 13.654 344.4 1.57 0.41
## 45 amont1 13.654 279.9 0.03 0.00
## 46 amont1 13.750 656.6 0.00 0.41
## 47 amont1 17.379 NA 0.00 0.04
## 48 amont1 15.760 193.8 0.03 0.00
## 49 aval1 17.570 NA 0.70 1.31
## 50 aval1 15.091 150.7 0.01 0.01
## 51 aval1 15.091 53.8 2.05 0.02
## 52 aval1 14.996 215.3 1.45 0.14
## 53 aval1 14.996 NA 0.00 0.04
## 54 aval1 14.996 NA 1.45 0.71
## 55 aval1 14.900 NA 0.30 1.77
## 56 aval1 14.709 NA 2.05 0.14
## 57 aval1 14.709 NA 0.06 0.15
## 58 aval1 14.709 NA 0.72 0.27
## 59 aval1 14.709 NA 0.01 0.14
## 60 aval1 14.613 NA 0.30 0.00
## 61 aval1 14.421 NA 0.65 0.15
## 62 aval1 14.325 NA 0.59 0.02
## 63 aval1 14.230 NA 0.11 0.27
## 64 aval1 14.038 NA 2.05 0.71
## 65 aval1 13.942 NA 0.00 0.66
## 66 aval1 13.846 NA 1.04 0.74
## 67 aval1 13.846 NA 0.03 0.01
## 68 aval1 13.846 NA 0.59 0.71
## 69 aval1 13.750 NA 0.00 0.00
## 70 aval1 13.750 NA 0.11 0.02
## 71 aval1 13.654 NA 0.06 NA
## 72 aval1 13.654 NA 1.45 0.04
## 73 aval1 13.654 NA 1.45 0.48
## 74 aval1 13.558 NA 0.47 0.35
## 75 aval1 13.558 NA 0.11 0.48
## 76 aval1 13.558 NA 0.03 0.02
## 77 aval1 13.461 NA 0.00 0.02
## 78 aval1 13.461 NA 0.47 0.48
## 79 aval1 13.461 NA 0.72 1.31
## 80 aval1 13.365 NA 0.06 0.15
## 81 aval1 13.365 NA 0.47 0.01
## 82 aval1 13.365 NA 0.00 0.01
## 83 aval1 13.365 10.8 0.11 0.14
## 84 aval1 13.365 21.5 0.30 0.02
## 85 aval1 13.365 53.8 0.30 1.77
## 86 aval1 13.461 53.8 0.00 0.48
## 87 aval1 13.461 344.4 0.03 NA
## 88 aval1 13.558 86.1 0.11 0.48
## 89 aval1 13.750 441.3 0.06 0.48
## 90 aval1 13.846 409.0 0.30 0.08
## 91 aval1 14.038 236.8 0.00 0.14
## 92 aval1 14.134 193.8 1.66 0.04
## 93 aval1 14.325 247.6 0.35 0.48
## 94 aval1 14.421 53.8 1.04 0.48
## 95 aval1 15.569 21.5 0.76 0.74
## 96 aval1 19.662 32.3 1.04 0.02
## 97 amont2 16.999 NA 0.59 0.35
## 98 amont2 15.760 495.1 1.45 1.57
## 99 amont2 15.282 279.9 0.00 0.11
## 100 amont2 15.282 495.1 0.00 0.00
## 101 amont2 19.092 1894.5 0.00 0.08
## 102 amont2 13.942 75.3 0.00 0.00
## 103 amont2 14.134 53.8 0.00 0.00
## 104 amont2 14.230 43.1 0.01 0.12
## 105 amont2 14.325 53.8 1.14 0.02
## 106 amont2 14.517 75.3 0.00 0.02
## 107 amont2 14.613 53.8 0.00 0.00
## 108 amont2 14.709 53.8 0.00 0.09
## 109 amont2 14.804 10.8 0.00 0.01
## 110 amont2 14.804 NA 0.00 0.00
## 111 amont2 14.804 NA 0.00 0.03
## 112 amont2 14.804 NA 0.00 0.00
## 113 amont2 14.709 NA 0.00 0.37
## 114 amont2 14.613 NA 0.00 0.02
## 115 amont2 14.517 NA 0.01 0.09
## 116 amont2 14.517 NA 0.00 0.00
## 117 amont2 14.421 NA 0.01 0.08
## 118 amont2 14.421 NA 0.00 0.00
## 119 amont2 14.325 NA 0.00 0.08
## 120 amont2 14.230 NA 0.00 0.08
## 121 amont2 14.134 NA 0.00 0.01
## 122 amont2 14.038 NA 0.00 0.00
## 123 amont2 13.942 NA 0.08 0.37
## 124 amont2 13.846 NA 0.00 0.00
## 125 amont2 13.846 NA 0.00 0.09
## 126 amont2 13.846 NA 0.01 0.00
## 127 amont2 13.846 NA 0.00 0.02
## 128 amont2 13.750 NA 0.00 0.00
## 129 amont2 13.750 NA 0.00 0.01
## 130 amont2 13.654 NA 0.00 1.23
## 131 amont2 13.654 NA 0.00 0.12
## 132 amont2 13.654 NA 0.00 0.00
## 133 amont2 13.654 NA 0.01 0.00
## 134 amont2 13.558 NA 0.08 0.09
## 135 amont2 13.558 NA 0.00 0.11
## 136 amont2 13.558 NA 0.00 0.00
## 137 amont2 13.558 NA 0.00 0.37
## 138 amont2 13.461 NA 0.00 0.00
## 139 amont2 13.461 NA 0.00 0.07
## 140 amont2 13.461 NA 0.01 0.00
## 141 amont2 13.461 NA 0.00 0.00
## 142 amont2 13.558 NA 0.00 0.00
## 143 amont2 13.558 21.5 0.00 1.23
## 144 amont2 13.558 10.8 0.00 0.02
## 145 amont2 13.654 32.3 0.08 0.37
## 146 amont2 13.654 43.1 0.01 0.00
## 147 amont2 13.750 32.3 0.00 0.02
## 148 amont2 13.846 53.8 0.00 0.02
## 149 amont2 13.942 NA 0.00 0.01
## 150 amont2 14.038 53.8 0.00 0.02
## 151 amont2 15.569 86.1 0.00 0.00
## 152 amont2 15.664 441.3 0.00 0.37
## 153 aval2 16.903 NA 0.01 0.02
## 154 aval2 15.760 527.4 0.01 0.06
## 155 aval2 15.282 193.8 0.55 0.06
## 156 aval2 15.282 344.4 0.21 0.12
## 157 aval2 17.665 NA 0.29 0.22
## 158 aval2 17.570 10.8 0.04 0.22
## 159 aval2 13.654 269.1 0.14 0.22
## 160 aval2 13.654 236.8 0.04 0.26
## 161 aval2 13.654 344.4 0.06 0.25
## 162 aval2 13.654 150.7 0.55 0.25
## 163 aval2 13.654 86.1 0.06 0.04
## 164 aval2 13.654 86.1 0.04 0.04
## 165 aval2 13.654 32.3 0.20 0.06
## 166 aval2 13.654 NA 0.04 0.26
## 167 aval2 13.654 NA 0.14 0.04
## 168 aval2 13.558 NA 0.06 0.22
## 169 aval2 13.558 NA 0.04 0.61
## 170 aval2 13.558 NA 0.01 0.26
## 171 aval2 13.558 NA 0.20 0.26
## 172 aval2 13.558 NA 0.01 0.25
## 173 aval2 13.461 NA 0.01 0.22
## 174 aval2 13.461 NA 0.06 0.22
## 175 aval2 13.461 NA 0.14 0.09
## 176 aval2 13.461 NA 0.01 0.26
## 177 aval2 13.461 NA 0.01 0.29
## 178 aval2 13.461 NA 0.20 0.25
## 179 aval2 13.461 NA 0.06 0.12
## 180 aval2 13.461 NA 0.06 0.22
## 181 aval2 13.461 NA 0.04 0.04
## 182 aval2 13.461 NA 0.06 0.03
## 183 aval2 13.461 NA 0.55 0.09
## 184 aval2 13.461 NA 0.29 0.61
## 185 aval2 13.461 NA 0.29 1.16
## 186 aval2 13.461 NA 0.01 0.12
## 187 aval2 13.461 NA 0.04 0.25
## 188 aval2 13.461 NA 0.20 0.03
## 189 aval2 13.461 NA 0.04 0.07
## 190 aval2 13.461 NA 0.29 0.04
## 191 aval2 13.461 NA 0.21 0.06
## 192 aval2 13.461 NA 0.06 0.07
## 193 aval2 13.461 NA 0.29 0.07
## 194 aval2 13.461 10.8 0.01 0.03
## 195 aval2 13.461 75.3 0.01 0.04
## 196 aval2 13.461 183.0 0.01 0.04
## 197 aval2 13.558 355.2 0.06 0.03
## 198 aval2 13.558 452.1 0.04 0.03
## 199 aval2 13.558 818.1 0.88 0.09
## 200 aval2 13.558 914.9 0.01 0.61
## 201 aval2 13.654 1377.8 0.88 1.16
## 202 aval2 13.654 581.3 0.88 0.22
## 203 aval2 13.654 1130.2 0.06 0.07
## 204 aval2 13.654 1087.2 0.05 1.16
## 205 aval2 13.654 301.4 0.01 1.16
## 206 aval2 13.750 1463.9 0.01 0.22
## 207 aval2 13.750 516.7 0.20 0.61
## 208 aval2 16.046 322.9 0.04 0.29acp=PCA(T_lux[,-1])## Warning in PCA(T_lux[, -1]): Missing values are imputed by the mean of the
## variable: you should use the imputePCA function of the missMDA package
acp$eig # obtention des valeurs propres## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.3573097 33.93274 33.93274
## comp 2 0.9984962 24.96240 58.89515
## comp 3 0.9802122 24.50530 83.40045
## comp 4 0.6639819 16.59955 100.00000acp$var # résultats liés aux variables## $coord
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.4701892 0.4306222 0.7063567 0.3074848
## Intensité.Lux 0.4104489 0.7368730 -0.4592880 -0.2785755
## cyano 0.7085099 -0.4261887 0.2410574 -0.5082009
## diatome 0.6824787 -0.2973920 -0.4606716 0.4832830
##
## $cor
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.4701892 0.4306222 0.7063567 0.3074848
## Intensité.Lux 0.4104489 0.7368730 -0.4592880 -0.2785755
## cyano 0.7085099 -0.4261887 0.2410574 -0.5082009
## diatome 0.6824787 -0.2973920 -0.4606716 0.4832830
##
## $cos2
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.2210779 0.1854355 0.49893972 0.09454693
## Intensité.Lux 0.1684683 0.5429819 0.21094547 0.07760433
## cyano 0.5019863 0.1816368 0.05810869 0.25826816
## diatome 0.4657772 0.0884420 0.21221832 0.23356248
##
## $contrib
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 16.28795 18.57148 50.901195 14.23938
## Intensité.Lux 12.41193 54.37996 21.520389 11.68772
## cyano 36.98392 18.19104 5.928174 38.89687
## diatome 34.31621 8.85752 21.650243 35.17603acp$var$coord # coordonnées des variables## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.4701892 0.4306222 0.7063567 0.3074848
## Intensité.Lux 0.4104489 0.7368730 -0.4592880 -0.2785755
## cyano 0.7085099 -0.4261887 0.2410574 -0.5082009
## diatome 0.6824787 -0.2973920 -0.4606716 0.4832830site=as.factor(T_lux$site) # Selection de la colonne Region
#site
fviz_pca_ind(acp, habillage=site, addEllipses=TRUE, palette = "Set1",
pointsize = 1,)
101 is out yer ?
fviz_pca_ind(acp, habillage=site, addEllipses=TRUE, palette = "Set1",
pointsize = 1, xlim=c(-1.5,0.5), ylim=c(-1.1,1))
on pourrait essayer de rajouter la turbitié, mais on ne peut pas exploité les donnes de nutriment qui sont trop similaire et peu assez nombreuse
classification
T_luxn=T_lux[-101,]
acp=PCA(T_luxn[,-1])## Warning in PCA(T_luxn[, -1]): Missing values are imputed by the mean of the
## variable: you should use the imputePCA function of the missMDA package
acp$eig # obtention des valeurs propres## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 1.3569086 33.92272 33.92272
## comp 2 1.1464309 28.66077 62.58349
## comp 3 0.8082510 20.20627 82.78976
## comp 4 0.6884095 17.21024 100.00000acp$var # résultats liés aux variables## $coord
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.3640021 -0.7084094 0.5558851 0.2380134
## Intensité.Lux 0.3532936 0.7057842 0.5869699 -0.1803292
## cyano 0.7471244 -0.2626869 -0.2466419 -0.5585414
## diatome 0.7357988 0.2783006 -0.3063934 0.5359776
##
## $cor
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.3640021 -0.7084094 0.5558851 0.2380134
## Intensité.Lux 0.3532936 0.7057842 0.5869699 -0.1803292
## cyano 0.7471244 -0.2626869 -0.2466419 -0.5585414
## diatome 0.7357988 0.2783006 -0.3063934 0.5359776
##
## $cos2
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.1324975 0.50184390 0.30900820 0.05665038
## Intensité.Lux 0.1248164 0.49813136 0.34453365 0.03251862
## cyano 0.5581949 0.06900443 0.06083221 0.31196847
## diatome 0.5413999 0.07745125 0.09387689 0.28727199
##
## $contrib
## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 9.764660 43.774456 38.231715 8.229169
## Intensité.Lux 9.198583 43.450621 42.627064 4.723732
## cyano 41.137249 6.019066 7.526402 45.317284
## diatome 39.899508 6.755858 11.614819 41.729815acp$var$coord # coordonnées des variables## Dim.1 Dim.2 Dim.3 Dim.4
## Temp 0.3640021 -0.7084094 0.5558851 0.2380134
## Intensité.Lux 0.3532936 0.7057842 0.5869699 -0.1803292
## cyano 0.7471244 -0.2626869 -0.2466419 -0.5585414
## diatome 0.7357988 0.2783006 -0.3063934 0.5359776site=as.factor(T_luxn$site) # Selection de la colonne Region
#site
fviz_pca_ind(acp, habillage=site, addEllipses=TRUE, palette = "Set1",
pointsize = 1,)
fviz_pca_ind(acp, habillage=site, addEllipses=TRUE, palette = "Set1",
pointsize = 1,axes=c(1,3))
fviz_pca_ind(acp, habillage=site, addEllipses=TRUE, palette = "Set1",
pointsize = 1,axes=c(1,4))
acp$ind$coord## Dim.1 Dim.2 Dim.3 Dim.4
## 1 0.52394316 -2.37315671 2.525658017 1.120720025
## 2 -0.65241869 -0.03563256 0.319220596 -0.059555439
## 3 -0.10605216 0.05138709 -0.347659457 0.736655442
## 4 -0.22256431 -0.63719181 -0.347405909 -0.725103632
## 5 -0.01254870 -0.24572648 0.005401706 -0.811456350
## 6 -0.87609892 -0.91855385 -0.604312336 -0.108389642
## 7 -1.17008345 -0.85051685 -0.523406800 0.194383499
## 8 -0.50857333 0.07949016 0.185911228 0.089867234
## 9 -0.80727208 0.07236725 0.263062719 -0.116795100
## 10 -0.63574060 0.22213381 0.091661097 0.061903952
## 11 3.39963928 -0.03298503 -1.815844867 -0.810774031
## 12 -0.80170417 0.15384190 0.181204834 -0.107823548
## 13 -0.86989784 0.20496460 0.139144166 -0.174286441
## 14 -0.07139065 -0.04615422 -0.277759243 -1.073992184
## 15 -0.07139065 -0.04615422 -0.277759243 -1.073992184
## 16 0.92348353 1.07770734 -0.983546983 1.602667507
## 17 -0.44110480 0.22336186 -0.206065343 -0.557135306
## 18 -0.96416265 0.40455122 -0.047379072 -0.260822887
## 19 -0.89040107 0.43490318 -0.087176289 -0.185388442
## 20 -0.91806154 0.38691691 -0.067098191 -0.309209558
## 21 0.84549073 1.21618278 -1.100892496 1.561305056
## 22 -0.19331817 0.76432317 -0.536978936 0.435247829
## 23 -0.99547553 0.47084989 -0.109338349 -0.289568558
## 24 0.22159062 0.97165722 -0.765992420 0.955099778
## 25 -0.59593254 0.31801925 -0.280237376 -0.708919703
## 26 -0.99547553 0.47084989 -0.109338349 -0.289568558
## 27 -0.58421890 0.71926032 -0.410080924 0.134292439
## 28 -1.02678841 0.53714856 -0.171297625 -0.318314228
## 29 -0.80550366 0.62820444 -0.290689274 -0.092010895
## 30 -0.19696834 0.21973108 -0.526241758 -1.189274300
## 31 -0.97146722 0.55991253 -0.201145537 -0.261738395
## 32 -0.42440106 0.46778581 -0.451638997 -0.530220651
## 33 0.19027774 1.03795589 -0.827951697 0.926354107
## 34 -1.02678841 0.53714856 -0.171297625 -0.318314228
## 35 -0.63010073 0.06720835 -1.352829540 0.712415524
## 36 -1.16049912 0.03802913 -0.737026287 -0.104436766
## 37 -1.14032313 0.19292291 -0.564640585 -0.140734757
## 38 -0.25523101 1.11278819 0.373250542 -0.655550255
## 39 -0.91637249 0.48032075 -0.187282869 -0.185888688
## 40 -0.58697743 0.71372498 0.249088813 -0.597634703
## 41 -0.32700049 0.41797901 -0.750896660 0.592463991
## 42 -0.30754470 1.02423650 0.596944999 -0.586328107
## 43 0.01583165 0.41835039 0.187578607 -1.100679311
## 44 2.42422451 -0.13415743 -1.030708249 -2.023211348
## 45 -0.80213997 0.23439884 0.166045398 -0.238192531
## 46 0.54526079 1.81420450 1.144620487 0.177782770
## 47 0.41886870 -2.33721000 2.503495957 1.016539910
## 48 -0.25375371 -1.52112967 1.227042770 0.491694632
## 49 3.89879128 -1.91690913 0.903094712 2.339753117
## 50 -0.55360949 -1.19049219 0.649170519 0.392186791
## 51 2.44378884 -2.72090716 -1.037322273 -2.767516072
## 52 1.97192772 -1.64677339 -0.264239704 -1.788145779
## 53 -0.35840829 -0.69148361 0.965486001 0.302988524
## 54 3.10531880 -1.03541302 -0.654208102 -0.772173611
## 55 3.26147852 0.51119425 -1.014894073 3.052915875
## 56 2.88262606 -1.62240914 -0.666712557 -2.900785437
## 57 -0.15697417 -0.44507893 0.631372999 0.327722327
## 58 1.07853511 -0.74197776 0.078160742 -0.510481093
## 59 -0.25224976 -0.42327642 0.674187501 0.389508167
## 60 -0.09608407 -0.63367455 0.560900336 -0.370995876
## 61 0.65574245 -0.59299091 0.057685839 -0.710119207
## 62 0.29250219 -0.59006750 0.164505753 -0.887253482
## 63 -0.01509244 -0.05261082 0.169965179 0.329952291
## 64 3.71486466 -0.72649363 -1.666892418 -2.026764862
## 65 0.44110695 0.50687591 -0.331632076 1.156618906
## 66 2.15548922 0.02255594 -1.156781894 -0.398662458
## 67 -0.74273057 0.06232094 0.233394296 -0.146323159
## 68 1.40865131 0.26430654 -0.831147204 0.270561769
## 69 -0.83858496 0.13866592 0.201103443 -0.145540771
## 70 -0.63266674 0.08918279 0.108901400 -0.285241341
## 71 -0.36319148 0.34025919 -0.123935014 0.152844999
## 72 1.43208430 -0.61700817 -0.853743778 -2.437541078
## 73 2.24346173 -0.28313662 -1.291513159 -1.607762188
## 74 0.46645395 0.26057214 -0.579973611 -0.301038562
## 75 0.15296572 0.57082766 -0.472685142 0.524763431
## 76 -0.81822881 0.26880494 0.037567163 -0.213701560
## 77 -0.89596898 0.35342853 -0.005318404 -0.194359994
## 78 0.67454005 0.42620529 -0.771919250 -0.084921722
## 79 2.58926889 0.90905590 -1.762037479 1.077120748
## 80 -0.59535448 0.48310246 -0.236056871 -0.074717061
## 81 -0.22347146 0.13586844 -0.366261233 -1.000022116
## 82 -0.94572226 0.41213921 -0.057328376 -0.241964276
## 83 -0.94830087 -0.44788058 -1.144464354 0.120550615
## 84 -0.86039496 -0.61320128 -1.112897661 -0.424539443
## 85 2.41864700 0.82765369 -2.742144739 2.838473464
## 86 -0.38986238 -0.04115262 -1.199534046 0.918325003
## 87 -0.34680344 0.76381378 0.041256398 0.053548695
## 88 -0.13721328 -0.05984427 -1.097351652 0.732708272
## 89 0.42011526 1.07912340 0.289778293 0.461274894
## 90 0.03064858 0.45527395 0.480075643 -0.614173203
## 91 -0.53417449 -0.05765525 0.145021046 0.238894538
## 92 1.79447322 -1.32597453 -0.933595130 -2.548759825
## 93 0.74163563 -0.16583373 -0.200670081 0.389060621
## 94 1.52143840 -1.31546189 -1.263537390 -0.471622873
## 95 1.89308885 -1.85936567 -0.709125654 0.851304297
## 96 2.34807289 -5.35919042 2.502247878 0.255005895
## 97 1.77322943 -2.18635807 1.562002729 0.535767386
## 98 5.30831759 -0.11082557 -0.224722885 0.814775277
## 99 -0.11438248 -0.78881393 1.127048234 0.505117526
## 100 0.02904278 -0.11970311 1.981903026 0.049532923
## 102 -1.08351604 -0.66237217 -0.337053689 0.132348529
## 103 -1.05548507 -0.87015744 -0.287607104 0.214630797
## 104 -0.80473733 -0.88869744 -0.388675310 0.465888721
## 105 0.79553767 -1.65699177 -0.933559072 -1.529153559
## 106 -0.85908415 -1.04429761 0.014157953 0.342240341
## 107 -0.89924685 -1.20096018 0.021543869 0.358059716
## 108 -0.70197040 -1.19896695 -0.006040592 0.556532887
## 109 -0.88769643 -1.47565478 -0.014076228 0.483692087
## 110 -0.49479564 -0.58923823 0.881364665 0.170062738
## 111 -0.43947445 -0.56647426 0.851516753 0.226638571
## 112 -0.49479564 -0.58923823 0.881364665 0.170062738
## 113 0.15651232 -0.24287455 0.451926545 0.839385114
## 114 -0.52021443 -0.44215552 0.738192913 0.150588053
## 115 -0.40707750 -0.32861902 0.600015468 0.237723770
## 116 -0.58840810 -0.39103283 0.696132245 0.084125160
## 117 -0.45683077 -0.26990834 0.548005496 0.190119489
## 118 -0.61972098 -0.32473416 0.634172968 0.055379490
## 119 -0.50351069 -0.19773157 0.492619259 0.177502708
## 120 -0.53449739 -0.13212351 0.431305392 0.149056472
## 121 -0.69489305 -0.11894077 0.438991244 -0.011699477
## 122 -0.74464632 -0.06023009 0.386981272 -0.059303759
## 123 0.02927177 0.23979939 -0.095686573 0.480688061
## 124 -0.80727208 0.07236725 0.263062719 -0.116795100
## 125 -0.64130851 0.14065916 0.173518982 0.052932400
## 126 -0.79190504 0.06648915 0.256489680 -0.132923990
## 127 -0.77039129 0.08754323 0.243164111 -0.079077878
## 128 -0.83858496 0.13866592 0.201103443 -0.145540771
## 129 -0.82014456 0.14625391 0.191154139 -0.126682159
## 130 1.39827090 1.13828735 -1.084620240 2.145322730
## 131 -0.64861308 0.29602047 0.019752517 0.052016893
## 132 -0.86989784 0.20496460 0.139144166 -0.174286441
## 133 -0.85453080 0.19908649 0.132571127 -0.190415331
## 134 -0.61231084 0.29253036 -0.064943163 -0.162335733
## 135 -0.69836636 0.35473115 -0.032257456 0.004412611
## 136 -0.90121072 0.27126327 0.077184890 -0.203032112
## 137 -0.21891606 0.55201889 -0.290939363 0.494736501
## 138 -0.93284977 0.33825255 0.014580204 -0.232077216
## 139 -0.80376700 0.39136848 -0.055064925 -0.100066938
## 140 -0.91748273 0.33237445 0.008007165 -0.248206107
## 141 -0.93284977 0.33825255 0.014580204 -0.232077216
## 142 -0.90121072 0.27126327 0.077184890 -0.203032112
## 143 0.97283377 0.34800059 -1.995008313 2.399009988
## 144 -1.27567111 -0.60756529 -0.828205308 0.129455849
## 145 -0.44141328 -0.38012120 -1.092584023 0.664430861
## 146 -1.21389936 -0.58196130 -0.641039319 0.067111364
## 147 -1.17845057 -0.66497471 -0.629814787 0.162156263
## 148 -1.11254291 -0.65608545 -0.493383542 0.166111007
## 149 -0.75751880 0.01365657 0.315072691 -0.069190818
## 150 -1.04991715 -0.78868279 -0.369464989 0.223602348
## 151 -0.53545013 -1.74822770 0.750436140 0.607074642
## 152 0.74936920 -0.29090563 1.673971949 0.923720320
## 153 0.24209525 -2.02953317 2.209606780 0.820163180
## 154 0.36293687 -0.29720866 2.336020869 0.252441141
## 155 0.50006261 -1.45115088 0.517043327 -0.376985478
## 156 0.33055006 -0.67910229 1.202481005 0.110896663
## 157 1.28972623 -2.56860592 2.318377348 0.973895238
## 158 0.46322238 -3.25004985 1.535897879 1.643441995
## 159 -0.24479168 0.29890668 -0.162551902 0.011732239
## 160 -0.37667310 0.27508291 -0.248499867 0.285699629
## 161 -0.19124461 0.63202807 0.121009582 0.110513297
## 162 0.25006542 -0.33338968 -0.872009832 -0.456453370
## 163 -0.99411302 -0.43062399 -0.564757797 0.012319504
## 164 -1.02484710 -0.41886779 -0.551611718 0.044577284
## 165 -0.82866110 -0.68588611 -0.863032069 -0.113732767
## 166 -0.32897938 0.37873993 -0.145829899 0.251521888
## 167 -0.58099772 0.15302313 0.007324397 -0.324656460
## 168 -0.40331977 0.40293043 -0.181138038 0.115083992
## 169 0.28512161 0.71061824 -0.556014819 0.882827607
## 170 -0.40639338 0.46267290 -0.188070057 0.271162888
## 171 -0.11441965 0.35098897 -0.312957807 -0.035286026
## 172 -0.42483377 0.45508491 -0.178120753 0.252304277
## 173 -0.51179402 0.49931022 -0.210877526 0.166683339
## 174 -0.43495883 0.46991972 -0.243742723 0.086038887
## 175 -0.55174767 0.32425104 -0.166986086 -0.288154179
## 176 -0.43803243 0.52966218 -0.250674742 0.242117783
## 177 -0.38271124 0.55242615 -0.280522655 0.298693617
## 178 -0.16449910 0.41039026 -0.365613189 -0.083189742
## 179 -0.61936279 0.39403982 -0.144249682 -0.102547224
## 180 -0.43495883 0.46991972 -0.243742723 0.086038887
## 181 -0.79762003 0.34509210 -0.051509170 -0.221158333
## 182 -0.78532635 0.32574791 -0.054705945 -0.272274724
## 183 0.07830090 0.08324887 -0.436480705 -0.949438678
## 184 0.63765851 0.63065498 -0.782945493 0.450560247
## 185 1.65188030 1.04799442 -1.330157219 1.487783860
## 186 -0.69619798 0.42343033 -0.111384485 -0.021902773
## 187 -0.41037171 0.50443989 -0.260444557 0.174872501
## 188 -0.57018782 0.24345449 -0.146728498 -0.498079187
## 189 -0.74229885 0.36785607 -0.081357083 -0.164582499
## 190 -0.41344408 0.19813956 -0.215835158 -0.624380588
## 191 -0.49949959 0.26034035 -0.183149450 -0.457632244
## 192 -0.71156477 0.35609987 -0.094503162 -0.196840280
## 193 -0.35812289 0.22090353 -0.245683070 -0.567804755
## 194 -1.27350273 -0.53886612 -0.907332337 0.103140465
## 195 -1.15127799 -0.30571435 -0.693865736 0.047626296
## 196 -0.97798227 0.07092471 -0.320813131 -0.076558951
## 197 -0.61086836 0.56915978 0.315344171 -0.345574936
## 198 -0.48568456 0.91978622 0.664133680 -0.425049285
## 199 1.50470485 1.75149696 1.320057906 -2.088746685
## 200 1.28243009 2.99598780 1.709845577 0.183497720
## 201 4.40973078 4.45444730 2.256137490 -0.687500863
## 202 1.39471756 0.95572582 0.432445433 -1.541792955
## 203 0.74122730 3.24347558 3.021960906 -1.135021249
## 204 2.66667388 3.92606872 1.795115860 0.986278109
## 205 1.34080669 1.20154979 -0.900455832 1.956873216
## 206 1.50925412 4.48737385 4.123420045 -1.127530205
## 207 0.99630204 1.35915616 0.329586248 0.393691172
## 208 0.59740069 -1.05299358 1.563705113 0.959242741acp2_CAH=as.data.frame(acp$ind$coord[,1:2])
#class=HCPC(acp2_CAH) #réalisation de la CAH sur les 2 axes retenus
#ATTENTION : pour finir la fonction R demande de choisir un nombre de groupe surle graphique. Le faire au hasard !
#plot(class$call$t$inert.gain,type="s") #realisation de la distance d'aggregation cumuleefaire 4 cluster
class2=HCPC(acp2_CAH,nb.clust=4) 
fviz_pca_ind(acp, addEllipse=T, habillage = class2$data.clust$clust)
fviz_dend(class2) ## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
refaire sans valeur 101 voir si il y a correspondances des sites avec les custer
faire des regression lienaire entres VA environnementale et Nb de indidivue par guilde par site pour cela il faut s’assurer que ’il y a de la variabilité et peut etre meme qu’il y a des correlation interesates. Il a t’il des tendances de regroupment ?
plot(algo$cyano_.microg.cm2.~algo$Dénomination) 
# legère tendance a l'augmenation apres la retenue coliniare
plot(algo$diatomées_.microg.cm2.~algo$Dénomination)
plot(algo$diatomées_.microg.cm2.~algo$Dénomination) # meme tendance encore plus legères
# tester differences algues entres les site avant apres
plot(algo$cyano_.microg.cm2.~algo$amont1_aval2)
shapiro.test(algo$cyano_.microg.cm2.) #normale##
## Shapiro-Wilk normality test
##
## data: algo$cyano_.microg.cm2.
## W = 0.61767, p-value = 9.154e-13qqnorm(sort(algo$cyano_.microg.cm2.)) # pas tres normale
t.test(algo$cyano_.microg.cm2.~algo$amont1_aval2) # sigficative differentes##
## Welch Two Sample t-test
##
## data: algo$cyano_.microg.cm2. by algo$amont1_aval2
## t = -2.8041, df = 54.332, p-value = 0.006985
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.47828167 -0.07951624
## sample estimates:
## mean in group 1 mean in group 2
## 0.1102439 0.3891429qqnorm(algo$diatomées_.microg.cm2.) # une valeur chelou
boxplot(algo$diatomées_.microg.cm2.) # outiler, mieux sans 
boxplot(algo$cyano_.microg.cm2.)
t.test(algo$diatomées_.microg.cm2.~algo$amont1_aval2) # signifiactiv differences##
## Welch Two Sample t-test
##
## data: algo$diatomées_.microg.cm2. by algo$amont1_aval2
## t = -2.6863, df = 52.833, p-value = 0.009636
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -0.43117926 -0.06252232
## sample estimates:
## mean in group 1 mean in group 2
## 0.1475610 0.3944118plot(T_lux$Temp~T_lux$site) # pas de tendance entre amont et aval mais plus les echantillon pris au centre sont dans des entdoirs moins couvert
plot(T_lux$Intensité.Lux~T_lux$site) # meme tendances , les volumes d' eau les moins couvert (aval 1 et amon1) sont les plus frais, les volumes d'eau les plus courvert âr la vegetation, la vegetation faitun tempon de temperature )
il y ’ t’il des valeurs signfiactif differences entres les regroupment couvert et ouvert que l’on a fait Température et Lux ne sont pas des VA normae car elle sont cyclique donc on utilise directement un teste non paramètrique
#wilcox.test(T_lux$Temp~T_lux$couvert.ouvert)
#wilcox.test(T_lux$Intensité.Lux~T_lux$couvert.ouvert, use = "complete.obs")
# les deux sont signfiactif donc le regroupemment est justifier. on pourrait aussi passer par classification vis a vis des VA non utilisé onbserve t’on des tendances ?
Nutriment$site=as.factor(Nutriment$site)
Turbidité$Dénomination=as.factor(Turbidité$Dénomination)
# Atention tendances car pas vraiment trop peu de valeur
plot(Nutriment$amonica~Nutriment$site) # plus présent apres retenue
plot(Nutriment$N~Nutriment$site) # effet stabilisateur des retenue sur cours
plot(Nutriment$p~Nutriment$site) #pas d'effet
plot(Turbidité$cyano~Turbidité$Dénomination) # semble plus importante ap retenue sur cous
plot(Turbidité$Total~Turbidité$Dénomination) #augmentation ap retenue sur cours
plot(Turbidité$Turbidité_FTU~Turbidité$Dénomination) # effet stabilisateur, moins de variabilité ap retenur sur cour d'eau.
# pas vraiment d'ineteret de regouper pour ensuite rajouter dans ACP, interet de rerouper pour faires des test de differences de moyennes ou de variances oui