setwd("~/Google Drive/Agrosavia/Colaboraciones/Lucero/data")
sensorial <- read.table("sensof.csv", header=T, sep=",")
senstime4 <- subset(sensorial, sensorial$time == "4")
senstime5 <- subset(sensorial, sensorial$time == "5")
senstime6 <- subset(sensorial, sensorial$time == "6")
senstime7 <- subset(sensorial, sensorial$time == "7")
attach(sensorial)
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.5-7
# Generando la matriz general de perfil sensorial - extrayendo columnas con la información de sabor
sens = sensorial[,9:ncol(sensorial)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Obteniendo las distancias Bray-Curtis de la matriz
set.seed(123)
nmds = metaMDS(m_sens, distance = "bray")
## Run 0 stress 0.1489369
## Run 1 stress 0.1530172
## Run 2 stress 0.1521733
## Run 3 stress 0.1568495
## Run 4 stress 0.1526783
## Run 5 stress 0.1496541
## Run 6 stress 0.1530753
## Run 7 stress 0.1482475
## ... New best solution
## ... Procrustes: rmse 0.03256426 max resid 0.1189394
## Run 8 stress 0.1547037
## Run 9 stress 0.153286
## Run 10 stress 0.1572527
## Run 11 stress 0.1533432
## Run 12 stress 0.1573279
## Run 13 stress 0.1537564
## Run 14 stress 0.1554653
## Run 15 stress 0.1553521
## Run 16 stress 0.1536686
## Run 17 stress 0.1598436
## Run 18 stress 0.1496834
## Run 19 stress 0.1519403
## Run 20 stress 0.1515435
## *** No convergence -- monoMDS stopping criteria:
## 1: no. of iterations >= maxit
## 19: stress ratio > sratmax
nmds
##
## Call:
## metaMDS(comm = m_sens, distance = "bray")
##
## global Multidimensional Scaling using monoMDS
##
## Data: m_sens
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1482475
## Stress type 1, weak ties
## No convergent solutions - best solution after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'm_sens'
# Generando el gráfico Non-Metric multidimensional scaling
plot(nmds)
# extrayendo los puntajes NMDS (coordenadas x and y)
data.scores = as.data.frame(scores(nmds))
# adicionando columnas al data frame
data.scores$gen = sensorial$gen
data.scores$trat = sensorial$trat
data.scores$gentrat = sensorial$gentrat
data.scores$gentime = sensorial$gentime
data.scores$tratime = sensorial$tratime
data.scores$gentratime = sensorial$gentratime
data.scores$time = sensorial$time
head(data.scores)
## NMDS1 NMDS2 gen trat gentrat gentime tratime gentratime time
## 1 -0.3061542 -0.02058306 ICS 95 Large ICS95L ICS954 L4 ICS95L4 4
## 2 -0.2425145 -0.18704525 ICS 95 Large ICS95L ICS954 L4 ICS95L4 4
## 3 -0.1687596 0.03355235 ICS 95 Large ICS95L ICS954 L4 ICS95L4 4
## 4 -0.2950584 -0.07227607 ICS 95 Large ICS95L ICS954 L4 ICS95L4 4
## 5 -0.3960118 -0.16981104 ICS 95 Large ICS95L ICS954 L4 ICS95L4 4
## 6 -0.2487604 -0.12027949 ICS 95 Large ICS95L ICS954 L4 ICS95L4 4
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(ggplot2)
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = trat)) +
stat_ellipse(aes(fill=gen), alpha=.2,type='t',size =1, geom="polygon") +
theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"),
axis.text.x = element_text(colour = "black", face = "bold", size = 12),
legend.text = element_text(size = 12, face ="bold", colour ="black"),
legend.position = "right", axis.title.y = element_text(face = "bold", size = 14),
axis.title.x = element_text(face = "bold", size = 14, colour = "black"),
legend.title = element_text(size = 14, colour = "black", face = "bold"),
panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.key=element_blank()) +
labs(x = "NMDS1", colour = "Batch", y = "NMDS2", shape ="Genotype", fill="Genotype") +
scale_colour_manual(values = cbp1)
xx
ggsave("NMDS.svg")
## Saving 7 x 5 in image
detach(sensorial)
attach(senstime4)
# Generando la matriz de perfil sensorial para cada tiempo - extrayendo columnas con la información de sabor
sens = senstime4[,9:ncol(senstime4)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Obteniendo las distancias Bray-Curtis de la matriz
set.seed(123)
nmds = metaMDS(m_sens, distance = "bray")
## Run 0 stress 0.1814664
## Run 1 stress 0.1814296
## ... New best solution
## ... Procrustes: rmse 0.008505469 max resid 0.03867465
## Run 2 stress 0.1801149
## ... New best solution
## ... Procrustes: rmse 0.02681279 max resid 0.09883305
## Run 3 stress 0.192803
## Run 4 stress 0.2133989
## Run 5 stress 0.1909052
## Run 6 stress 0.1909052
## Run 7 stress 0.2318464
## Run 8 stress 0.2111372
## Run 9 stress 0.192803
## Run 10 stress 0.180936
## Run 11 stress 0.192803
## Run 12 stress 0.1801149
## ... Procrustes: rmse 0.0001660318 max resid 0.0007746368
## ... Similar to previous best
## Run 13 stress 0.1809297
## Run 14 stress 0.211326
## Run 15 stress 0.1814296
## Run 16 stress 0.194121
## Run 17 stress 0.1801151
## ... Procrustes: rmse 0.0002630813 max resid 0.001246198
## ... Similar to previous best
## Run 18 stress 0.1809297
## Run 19 stress 0.1909052
## Run 20 stress 0.1801149
## ... New best solution
## ... Procrustes: rmse 8.314346e-05 max resid 0.000379899
## ... Similar to previous best
## *** Solution reached
nmds
##
## Call:
## metaMDS(comm = m_sens, distance = "bray")
##
## global Multidimensional Scaling using monoMDS
##
## Data: m_sens
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1801149
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'm_sens'
# Generando el gráfico Non-Metric multidimensional scaling
plot(nmds)
# extrayendo los puntajes NMDS (coordenadas x and y)
data.scores = as.data.frame(scores(nmds))
# adicionando columnas al data frame
data.scores$gen = senstime4$gen
data.scores$trat = senstime4$trat
data.scores$gentrat = senstime4$gentrat
data.scores$gentime = senstime4$gentime
data.scores$tratime = senstime4$tratime
data.scores$gentratime = senstime4$gentratime
data.scores$time = senstime4$time
head(data.scores)
## NMDS1 NMDS2 gen trat gentrat gentime tratime gentratime
## 1 -0.102632741 -0.07645270 ICS 95 Large ICS95L ICS954 L4 ICS95L4
## 2 -0.081675107 -0.22175524 ICS 95 Large ICS95L ICS954 L4 ICS95L4
## 3 0.003558008 0.01225024 ICS 95 Large ICS95L ICS954 L4 ICS95L4
## 4 -0.112573416 -0.09979150 ICS 95 Large ICS95L ICS954 L4 ICS95L4
## 5 -0.212164398 -0.18110499 ICS 95 Large ICS95L ICS954 L4 ICS95L4
## 6 -0.054502983 -0.17087750 ICS 95 Large ICS95L ICS954 L4 ICS95L4
## time
## 1 4
## 2 4
## 3 4
## 4 4
## 5 4
## 6 4
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(ggplot2)
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = trat)) +
stat_ellipse(aes(fill=gen), alpha=.2,type='t',size =1, geom="polygon") +
theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"),
axis.text.x = element_text(colour = "black", face = "bold", size = 12),
legend.text = element_text(size = 12, face ="bold", colour ="black"),
legend.position = "right", axis.title.y = element_text(face = "bold", size = 14),
axis.title.x = element_text(face = "bold", size = 14, colour = "black"),
legend.title = element_text(size = 14, colour = "black", face = "bold"),
panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.key=element_blank()) +
labs(x = "NMDS1", colour = "Batch", y = "NMDS2", shape ="Genotype", fill="Genotype") +
scale_colour_manual(values = cbp1)
xx
ggsave("NMDS4.svg")
## Saving 7 x 5 in image
detach(senstime4)
attach(senstime5)
# Generando la matriz de perfil sensorial - extrayendo columnas con la información de sabor
sens = senstime5[,9:ncol(senstime5)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Obteniendo las distancias Bray-Curtis de la matriz
set.seed(123)
nmds = metaMDS(m_sens, distance = "bray")
## Run 0 stress 0.1668436
## Run 1 stress 0.1668436
## ... Procrustes: rmse 8.152131e-06 max resid 3.142023e-05
## ... Similar to previous best
## Run 2 stress 0.1668436
## ... Procrustes: rmse 1.158189e-05 max resid 5.376688e-05
## ... Similar to previous best
## Run 3 stress 0.2144496
## Run 4 stress 0.166092
## ... New best solution
## ... Procrustes: rmse 0.01648325 max resid 0.07378744
## Run 5 stress 0.1666191
## Run 6 stress 0.1668436
## Run 7 stress 0.1673176
## Run 8 stress 0.1673176
## Run 9 stress 0.166092
## ... New best solution
## ... Procrustes: rmse 4.272779e-05 max resid 0.0002245771
## ... Similar to previous best
## Run 10 stress 0.166619
## Run 11 stress 0.2151078
## Run 12 stress 0.166092
## ... Procrustes: rmse 2.638393e-05 max resid 0.0001398299
## ... Similar to previous best
## Run 13 stress 0.166092
## ... Procrustes: rmse 3.883905e-06 max resid 2.016202e-05
## ... Similar to previous best
## Run 14 stress 0.1673176
## Run 15 stress 0.166092
## ... Procrustes: rmse 1.554511e-05 max resid 8.324576e-05
## ... Similar to previous best
## Run 16 stress 0.1668436
## Run 17 stress 0.166346
## ... Procrustes: rmse 0.007596523 max resid 0.03229617
## Run 18 stress 0.166346
## ... Procrustes: rmse 0.007596686 max resid 0.03228999
## Run 19 stress 0.2293322
## Run 20 stress 0.1668436
## *** Solution reached
nmds
##
## Call:
## metaMDS(comm = m_sens, distance = "bray")
##
## global Multidimensional Scaling using monoMDS
##
## Data: m_sens
## Distance: bray
##
## Dimensions: 2
## Stress: 0.166092
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'm_sens'
# Generando el gráfico Non-Metric multidimensional scaling
plot(nmds)
# extrayendo los puntajes NMDS (coordenadas x and y)
data.scores = as.data.frame(scores(nmds))
# adicionando columnas al data frame
data.scores$gen = senstime5$gen
data.scores$trat = senstime5$trat
data.scores$gentrat = senstime5$gentrat
data.scores$gentime = senstime5$gentime
data.scores$tratime = senstime5$tratime
data.scores$gentratime = senstime5$gentratime
data.scores$time = senstime5$time
head(data.scores)
## NMDS1 NMDS2 gen trat gentrat gentime tratime gentratime
## 7 -0.04149516 -0.14229134 ICS 95 Large ICS95L ICS955 L5 ICS95L5
## 8 -0.23714571 -0.13105445 ICS 95 Large ICS95L ICS955 L5 ICS95L5
## 9 0.05045478 -0.06054047 ICS 95 Large ICS95L ICS955 L5 ICS95L5
## 10 -0.12947458 -0.08978748 ICS 95 Large ICS95L ICS955 L5 ICS95L5
## 11 0.02378278 -0.19668392 ICS 95 Large ICS95L ICS955 L5 ICS95L5
## 12 0.07757157 -0.17309936 ICS 95 Large ICS95L ICS955 L5 ICS95L5
## time
## 7 5
## 8 5
## 9 5
## 10 5
## 11 5
## 12 5
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(ggplot2)
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = trat)) +
stat_ellipse(aes(fill=gen), alpha=.2,type='t',size =1, geom="polygon") +
theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"),
axis.text.x = element_text(colour = "black", face = "bold", size = 12),
legend.text = element_text(size = 12, face ="bold", colour ="black"),
legend.position = "right", axis.title.y = element_text(face = "bold", size = 14),
axis.title.x = element_text(face = "bold", size = 14, colour = "black"),
legend.title = element_text(size = 14, colour = "black", face = "bold"),
panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.key=element_blank()) +
labs(x = "NMDS1", colour = "Batch", y = "NMDS2", shape ="Genotype", fill="Genotype") +
scale_colour_manual(values = cbp1)
xx
ggsave("NMDS5.svg")
## Saving 7 x 5 in image
detach(senstime5)
attach(senstime6)
# Generando la matriz de perfil sensorial - extrayendo columnas con la información de sabor
sens = senstime6[,9:ncol(senstime6)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Obteniendo las distancias Bray-Curtis de la matriz
set.seed(123)
nmds = metaMDS(m_sens, distance = "bray")
## Run 0 stress 0.1450454
## Run 1 stress 0.1489288
## Run 2 stress 0.1570882
## Run 3 stress 0.1610604
## Run 4 stress 0.1443303
## ... New best solution
## ... Procrustes: rmse 0.01253153 max resid 0.05814487
## Run 5 stress 0.1548939
## Run 6 stress 0.1583799
## Run 7 stress 0.151111
## Run 8 stress 0.1449351
## Run 9 stress 0.1645562
## Run 10 stress 0.1512594
## Run 11 stress 0.167403
## Run 12 stress 0.1489976
## Run 13 stress 0.1449351
## Run 14 stress 0.1576266
## Run 15 stress 0.1610704
## Run 16 stress 0.1548938
## Run 17 stress 0.1551659
## Run 18 stress 0.1511105
## Run 19 stress 0.1442032
## ... New best solution
## ... Procrustes: rmse 0.003459466 max resid 0.01443468
## Run 20 stress 0.157441
## *** No convergence -- monoMDS stopping criteria:
## 17: stress ratio > sratmax
## 3: scale factor of the gradient < sfgrmin
nmds
##
## Call:
## metaMDS(comm = m_sens, distance = "bray")
##
## global Multidimensional Scaling using monoMDS
##
## Data: m_sens
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1442032
## Stress type 1, weak ties
## No convergent solutions - best solution after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'm_sens'
# Generando el gráfico Non-Metric multidimensional scaling
plot(nmds)
# extrayendo los puntajes NMDS (coordenadas x and y)
data.scores = as.data.frame(scores(nmds))
# adicionando columnas al data frame
data.scores$gen = senstime6$gen
data.scores$trat = senstime6$trat
data.scores$gentrat = senstime6$gentrat
data.scores$gentime = senstime6$gentime
data.scores$tratime = senstime6$tratime
data.scores$gentratime = senstime6$gentratime
data.scores$time = senstime6$time
head(data.scores)
## NMDS1 NMDS2 gen trat gentrat gentime tratime gentratime time
## 13 -0.4133056 0.02017611 ICS 95 Large ICS95L ICS956 L6 ICS95L6 6
## 14 -0.4288186 -0.10508298 ICS 95 Large ICS95L ICS956 L6 ICS95L6 6
## 15 -0.3645059 0.10118830 ICS 95 Large ICS95L ICS956 L6 ICS95L6 6
## 16 -0.2983109 -0.14585245 ICS 95 Large ICS95L ICS956 L6 ICS95L6 6
## 17 -0.3097928 0.04559518 ICS 95 Large ICS95L ICS956 L6 ICS95L6 6
## 18 -0.3189945 0.01154651 ICS 95 Large ICS95L ICS956 L6 ICS95L6 6
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(ggplot2)
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = trat)) +
stat_ellipse(aes(fill=gen), alpha=.2,type='t',size =1, geom="polygon") +
theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"),
axis.text.x = element_text(colour = "black", face = "bold", size = 12),
legend.text = element_text(size = 12, face ="bold", colour ="black"),
legend.position = "right", axis.title.y = element_text(face = "bold", size = 14),
axis.title.x = element_text(face = "bold", size = 14, colour = "black"),
legend.title = element_text(size = 14, colour = "black", face = "bold"),
panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.key=element_blank()) +
labs(x = "NMDS1", colour = "Batch", y = "NMDS2", shape ="Genotype", fill="Genotype") +
scale_colour_manual(values = cbp1)
xx
ggsave("NMDS6.svg")
## Saving 7 x 5 in image
detach(senstime6)
attach(senstime7)
# Generando la matriz de perfil sensorial - extrayendo columnas con la información de sabor
sens = senstime7[,9:ncol(senstime7)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Obteniendo las distancias Bray-Curtis de la matriz
set.seed(123)
nmds = metaMDS(m_sens, distance = "bray")
## Run 0 stress 0.1423392
## Run 1 stress 0.1535313
## Run 2 stress 0.1537953
## Run 3 stress 0.1514841
## Run 4 stress 0.1570495
## Run 5 stress 0.1556572
## Run 6 stress 0.1472975
## Run 7 stress 0.1411384
## ... New best solution
## ... Procrustes: rmse 0.03535789 max resid 0.1735487
## Run 8 stress 0.1563372
## Run 9 stress 0.1547771
## Run 10 stress 0.1543733
## Run 11 stress 0.1472973
## Run 12 stress 0.1511298
## Run 13 stress 0.1499119
## Run 14 stress 0.1415652
## ... Procrustes: rmse 0.05135007 max resid 0.215461
## Run 15 stress 0.156228
## Run 16 stress 0.1556344
## Run 17 stress 0.147714
## Run 18 stress 0.1423395
## Run 19 stress 0.1498844
## Run 20 stress 0.1423458
## *** No convergence -- monoMDS stopping criteria:
## 1: no. of iterations >= maxit
## 18: stress ratio > sratmax
## 1: scale factor of the gradient < sfgrmin
nmds
##
## Call:
## metaMDS(comm = m_sens, distance = "bray")
##
## global Multidimensional Scaling using monoMDS
##
## Data: m_sens
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1411384
## Stress type 1, weak ties
## No convergent solutions - best solution after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'm_sens'
# Generando el gráfico Non-Metric multidimensional scaling
plot(nmds)
# extrayendo los puntajes NMDS (coordenadas x and y)
data.scores = as.data.frame(scores(nmds))
# adicionando columnas al data frame
data.scores$gen = senstime7$gen
data.scores$trat = senstime7$trat
data.scores$gentrat = senstime7$gentrat
data.scores$gentime = senstime7$gentime
data.scores$tratime = senstime7$tratime
data.scores$gentratime = senstime7$gentratime
data.scores$time = senstime7$time
head(data.scores)
## NMDS1 NMDS2 gen trat gentrat gentime tratime gentratime
## 19 -0.1234501 0.0124455706 ICS 95 Large ICS95L ICS957 L7 ICS95L7
## 20 -0.2886038 -0.0005105661 ICS 95 Large ICS95L ICS957 L7 ICS95L7
## 21 -0.1390190 0.0130434330 ICS 95 Large ICS95L ICS957 L7 ICS95L7
## 22 -0.2964903 0.1148874134 ICS 95 Large ICS95L ICS957 L7 ICS95L7
## 23 -0.3502034 -0.1018313350 ICS 95 Large ICS95L ICS957 L7 ICS95L7
## 24 -0.4267919 -0.0142100363 ICS 95 Large ICS95L ICS957 L7 ICS95L7
## time
## 19 7
## 20 7
## 21 7
## 22 7
## 23 7
## 24 7
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
library(ggplot2)
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = trat)) +
stat_ellipse(aes(fill=gen), alpha=.2,type='t',size =1, geom="polygon") +
theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"),
axis.text.x = element_text(colour = "black", face = "bold", size = 12),
legend.text = element_text(size = 12, face ="bold", colour ="black"),
legend.position = "right", axis.title.y = element_text(face = "bold", size = 14),
axis.title.x = element_text(face = "bold", size = 14, colour = "black"),
legend.title = element_text(size = 14, colour = "black", face = "bold"),
panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
legend.key=element_blank()) +
labs(x = "NMDS1", colour = "Batch", y = "NMDS2", shape ="Genotype", fill="Genotype") +
scale_colour_manual(values = cbp1)
xx
ggsave("NMDS7.svg")
## Saving 7 x 5 in image
detach(senstime7)
## Probando si el perfil del sabor es estadísticamente distinto basado en los grupos (genotipos, tratamientos, genotipos*tratamientos) para cada tiempo evaluado
## Tiempo 4
attach(senstime4)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime4[,9:ncol(senstime4)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Análisis de similaridad identificando las diferencias en el perfil de sabor según los grupos evaluados
anogen <- anosim(m_sens, gen, distance = "bray", permutations = 9999)
anogen
##
## Call:
## anosim(x = m_sens, grouping = gen, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3528
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
anotrat <- anosim(m_sens, trat, distance = "bray", permutations = 9999)
anotrat
##
## Call:
## anosim(x = m_sens, grouping = trat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.0343
## Significance: 0.172
##
## Permutation: free
## Number of permutations: 9999
anogentrat <- anosim(m_sens, gentrat, distance = "bray", permutations = 9999)
anogentrat
##
## Call:
## anosim(x = m_sens, grouping = gentrat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.4128
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluado.
library(pairwiseAdonis)
## Loading required package: cluster
pairwise.adonis2(m_sens ~ gen, data = senstime4)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`ICS 95_vs_EET8`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.09777 0.22669 6.4491 0.003 **
## Residual 22 0.33354 0.77331
## Total 23 0.43131 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`ICS 95_vs_TCS01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.06945 0.17524 4.6744 0.003 **
## Residual 22 0.32687 0.82476
## Total 23 0.39632 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8_vs_TCS01
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.13588 0.30343 9.5835 0.001 ***
## Residual 22 0.31193 0.69657
## Total 23 0.44782 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ trat, data = senstime4)
## $parent_call
## [1] "m_sens ~ trat , strata = Null , permutations 999"
##
## $`Large _vs_Small`
## Df SumOfSqs R2 F Pr(>F)
## trat 1 0.02420 0.03516 1.239 0.305
## Residual 34 0.66405 0.96484
## Total 35 0.68824 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gentrat, data = senstime4)
## $parent_call
## [1] "m_sens ~ gentrat , strata = Null , permutations 999"
##
## $ICS95L_vs_ICS95SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.056685 0.32533 4.822 0.005 **
## Residual 10 0.117553 0.67467
## Total 11 0.174238 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.038723 0.24222 3.1964 0.045 *
## Residual 10 0.121146 0.75778
## Total 11 0.159869 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.078534 0.52642 11.116 0.001 ***
## Residual 10 0.070651 0.47358
## Total 11 0.149185 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.079585 0.44574 8.0419 0.004 **
## Residual 10 0.098963 0.55426
## Total 11 0.178548 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.074107 0.38843 6.3513 0.01 **
## Residual 10 0.116681 0.61157
## Total 11 0.190788 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.05389 0.24798 3.2976 0.032 *
## Residual 10 0.16342 0.75202
## Total 11 0.21731 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.12386 0.52309 10.969 0.001 ***
## Residual 10 0.11293 0.47691
## Total 11 0.23679 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.032462 0.18689 2.2984 0.044 *
## Residual 10 0.141239 0.81311
## Total 11 0.173702 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.021702 0.12013 1.3653 0.302
## Residual 10 0.158957 0.87987
## Total 11 0.180659 1.00000
##
## $EET8L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.04278 0.26855 3.6715 0.026 *
## Residual 10 0.11652 0.73145
## Total 11 0.15930 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.038074 0.20816 2.6288 0.068 .
## Residual 10 0.144832 0.79184
## Total 11 0.182905 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.052497 0.24412 3.2296 0.039 *
## Residual 10 0.162549 0.75588
## Total 11 0.215046 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.112756 0.54447 11.952 0.001 ***
## Residual 10 0.094337 0.45553
## Total 11 0.207093 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.12349 0.52427 11.02 0.002 **
## Residual 10 0.11206 0.47573
## Total 11 0.23554 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $TCS01SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.012268 0.08037 0.874 0.514
## Residual 10 0.140367 0.91963
## Total 11 0.152635 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senstime4)
## Tiempo 5
attach(senstime5)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime5[,9:ncol(senstime5)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Análisis de similaridad identificando las diferencias en el perfil de sabor según los grupos evaluados
anogen <- anosim(m_sens, gen, distance = "bray", permutations = 9999)
anogen
##
## Call:
## anosim(x = m_sens, grouping = gen, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.293
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
anotrat <- anosim(m_sens, trat, distance = "bray", permutations = 9999)
anotrat
##
## Call:
## anosim(x = m_sens, grouping = trat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.04856
## Significance: 0.9622
##
## Permutation: free
## Number of permutations: 9999
anogentrat <- anosim(m_sens, gentrat, distance = "bray", permutations = 9999)
anogentrat
##
## Call:
## anosim(x = m_sens, grouping = gentrat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2255
## Significance: 6e-04
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluado.
library(pairwiseAdonis)
pairwise.adonis2(m_sens ~ gen, data = senstime5)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`ICS 95_vs_EET8`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.060948 0.19795 5.4296 0.008 **
## Residual 22 0.246955 0.80205
## Total 23 0.307903 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`ICS 95_vs_TCS01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.08456 0.25308 7.4543 0.001 ***
## Residual 22 0.24957 0.74692
## Total 23 0.33413 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8_vs_TCS01
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.055744 0.19787 5.4271 0.002 **
## Residual 22 0.225969 0.80213
## Total 23 0.281713 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ trat, data = senstime5)
## $parent_call
## [1] "m_sens ~ trat , strata = Null , permutations 999"
##
## $`Large _vs_Small`
## Df SumOfSqs R2 F Pr(>F)
## trat 1 0.00648 0.01307 0.4504 0.779
## Residual 34 0.48894 0.98693
## Total 35 0.49542 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gentrat, data = senstime5)
## $parent_call
## [1] "m_sens ~ gentrat , strata = Null , permutations 999"
##
## $ICS95L_vs_ICS95SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.029802 0.2203 2.8254 0.054 .
## Residual 10 0.105476 0.7797
## Total 11 0.135278 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.033413 0.24778 3.2939 0.038 *
## Residual 10 0.101438 0.75222
## Total 11 0.134850 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.023223 0.18101 2.2102 0.112
## Residual 10 0.105076 0.81899
## Total 11 0.128299 1.00000
##
## $ICS95L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.051349 0.34661 5.3047 0.002 **
## Residual 10 0.096799 0.65339
## Total 11 0.148148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.081101 0.4435 7.9695 0.005 **
## Residual 10 0.101764 0.5565
## Total 11 0.182865 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.048097 0.30492 4.3868 0.032 *
## Residual 10 0.109638 0.69508
## Total 11 0.157735 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.049404 0.30369 4.3613 0.013 *
## Residual 10 0.113277 0.69631
## Total 11 0.162680 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.034846 0.24918 3.3187 0.007 **
## Residual 10 0.105000 0.75082
## Total 11 0.139846 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.044637 0.28872 4.0592 0.012 *
## Residual 10 0.109965 0.71128
## Total 11 0.154601 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.002439 0.02184 0.2233 0.883
## Residual 10 0.109238 0.97816
## Total 11 0.111677 1.00000
##
## $EET8L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.027022 0.21114 2.6765 0.067 .
## Residual 10 0.100961 0.78886
## Total 11 0.127982 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.039228 0.27025 3.7034 0.038 *
## Residual 10 0.105926 0.72975
## Total 11 0.145154 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.021416 0.16995 2.0474 0.063 .
## Residual 10 0.104599 0.83005
## Total 11 0.126015 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.039266 0.26383 3.5839 0.016 *
## Residual 10 0.109564 0.73617
## Total 11 0.148831 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $TCS01SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.013005 0.11379 1.284 0.286
## Residual 10 0.101287 0.88621
## Total 11 0.114292 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senstime5)
## Tiempo 6
attach(senstime6)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime6[,9:ncol(senstime6)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Análisis de similaridad identificando las diferencias en el perfil de sabor según los grupos evaluados
anogen <- anosim(m_sens, gen, distance = "bray", permutations = 9999)
anogen
##
## Call:
## anosim(x = m_sens, grouping = gen, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.6674
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
anotrat <- anosim(m_sens, trat, distance = "bray", permutations = 9999)
anotrat
##
## Call:
## anosim(x = m_sens, grouping = trat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.04205
## Significance: 0.1323
##
## Permutation: free
## Number of permutations: 9999
anogentrat <- anosim(m_sens, gentrat, distance = "bray", permutations = 9999)
anogentrat
##
## Call:
## anosim(x = m_sens, grouping = gentrat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.6635
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluado.
library(pairwiseAdonis)
pairwise.adonis2(m_sens ~ gen, data = senstime6)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`ICS 95_vs_EET8`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.20590 0.36664 12.736 0.001 ***
## Residual 22 0.35568 0.63336
## Total 23 0.56158 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`ICS 95_vs_TCS01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.56635 0.60827 34.161 0.001 ***
## Residual 22 0.36473 0.39173
## Total 23 0.93109 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8_vs_TCS01
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.13602 0.23016 6.5774 0.001 ***
## Residual 22 0.45494 0.76984
## Total 23 0.59096 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ trat, data = senstime6)
## $parent_call
## [1] "m_sens ~ trat , strata = Null , permutations 999"
##
## $`Large _vs_Small`
## Df SumOfSqs R2 F Pr(>F)
## trat 1 0.03006 0.02519 0.8786 0.413
## Residual 34 1.16313 0.97481
## Total 35 1.19319 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gentrat, data = senstime6)
## $parent_call
## [1] "m_sens ~ gentrat , strata = Null , permutations 999"
##
## $ICS95L_vs_ICS95SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.046480 0.35017 5.3887 0.001 ***
## Residual 10 0.086255 0.64983
## Total 11 0.132735 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.19618 0.64817 18.423 0.001 ***
## Residual 10 0.10649 0.35183
## Total 11 0.30267 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.13868 0.48039 9.2453 0.003 **
## Residual 10 0.15000 0.51961
## Total 11 0.28867 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.31249 0.65701 19.155 0.001 ***
## Residual 10 0.16314 0.34299
## Total 11 0.47563 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.45319 0.77418 34.283 0.002 **
## Residual 10 0.13219 0.22582
## Total 11 0.58538 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.11555 0.5345 11.482 0.002 **
## Residual 10 0.10063 0.4655
## Total 11 0.21618 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.066444 0.31553 4.6098 0.001 ***
## Residual 10 0.144138 0.68447
## Total 11 0.210583 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.16965 0.51892 10.787 0.007 **
## Residual 10 0.15728 0.48108
## Total 11 0.32693 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.27264 0.68335 21.581 0.003 **
## Residual 10 0.12633 0.31665
## Total 11 0.39897 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.058574 0.26272 3.5635 0.006 **
## Residual 10 0.164373 0.73728
## Total 11 0.222946 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.053275 0.23084 3.0012 0.031 *
## Residual 10 0.177512 0.76916
## Total 11 0.230787 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.059995 0.29044 4.0933 0.005 **
## Residual 10 0.146567 0.70956
## Total 11 0.206562 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.11848 0.34898 5.3606 0.013 *
## Residual 10 0.22102 0.65102
## Total 11 0.33950 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.12764 0.40174 6.7153 0.005 **
## Residual 10 0.19007 0.59826
## Total 11 0.31771 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $TCS01SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.028784 0.12407 1.4164 0.22
## Residual 10 0.203213 0.87593
## Total 11 0.231997 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senstime6)
## Tiempo 7
attach(senstime7)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime7[,9:ncol(senstime7)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Análisis de similaridad identificando las diferencias en el perfil de sabor según los grupos evaluados
anogen <- anosim(m_sens, gen, distance = "bray", permutations = 9999)
anogen
##
## Call:
## anosim(x = m_sens, grouping = gen, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.4992
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
anotrat <- anosim(m_sens, trat, distance = "bray", permutations = 9999)
anotrat
##
## Call:
## anosim(x = m_sens, grouping = trat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.164
## Significance: 0.0067
##
## Permutation: free
## Number of permutations: 9999
anogentrat <- anosim(m_sens, gentrat, distance = "bray", permutations = 9999)
anogentrat
##
## Call:
## anosim(x = m_sens, grouping = gentrat, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.6896
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluado.
library(pairwiseAdonis)
pairwise.adonis2(m_sens ~ gen, data = senstime7)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`ICS 95_vs_EET8`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.08008 0.15063 3.9014 0.013 *
## Residual 22 0.45156 0.84937
## Total 23 0.53164 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`ICS 95_vs_TCS01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.40086 0.49752 21.782 0.001 ***
## Residual 22 0.40486 0.50248
## Total 23 0.80573 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8_vs_TCS01
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.26419 0.33386 11.026 0.001 ***
## Residual 22 0.52713 0.66614
## Total 23 0.79133 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ trat, data = senstime7)
## $parent_call
## [1] "m_sens ~ trat , strata = Null , permutations 999"
##
## $`Large _vs_Small`
## Df SumOfSqs R2 F Pr(>F)
## trat 1 0.05592 0.04705 1.6788 0.163
## Residual 34 1.13261 0.95295
## Total 35 1.18853 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gentrat, data = senstime7)
## $parent_call
## [1] "m_sens ~ gentrat , strata = Null , permutations 999"
##
## $ICS95L_vs_ICS95SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.062292 0.37834 6.086 0.004 **
## Residual 10 0.102352 0.62166
## Total 11 0.164644 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.024291 0.16479 1.973 0.128
## Residual 10 0.123117 0.83521
## Total 11 0.147408 1.00000
##
## $ICS95L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.16242 0.55374 12.408 0.002 **
## Residual 10 0.13090 0.44626
## Total 11 0.29331 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.21189 0.61333 15.862 0.001 ***
## Residual 10 0.13358 0.38667
## Total 11 0.34547 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.39792 0.75438 30.713 0.004 **
## Residual 10 0.12956 0.24562
## Total 11 0.52748 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.079302 0.39057 6.4088 0.003 **
## Residual 10 0.123741 0.60943
## Total 11 0.203043 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.091067 0.40913 6.9242 0.006 **
## Residual 10 0.131519 0.59087
## Total 11 0.222586 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.11259 0.4562 8.3893 0.002 **
## Residual 10 0.13421 0.5438
## Total 11 0.24680 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $ICS95SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.22043 0.62869 16.932 0.003 **
## Residual 10 0.13018 0.37131
## Total 11 0.35061 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_EET8SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.13463 0.46923 8.8407 0.005 **
## Residual 10 0.15228 0.53077
## Total 11 0.28691 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.21925 0.58588 14.148 0.002 **
## Residual 10 0.15497 0.41412
## Total 11 0.37422 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8L_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.35602 0.70226 23.586 0.003 **
## Residual 10 0.15095 0.29774
## Total 11 0.50697 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01SM
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.092948 0.36351 5.7111 0.005 **
## Residual 10 0.162750 0.63649
## Total 11 0.255697 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $EET8SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.073602 0.3168 4.6371 0.003 **
## Residual 10 0.158727 0.6832
## Total 11 0.232329 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $TCS01SM_vs_TCS01L
## Df SumOfSqs R2 F Pr(>F)
## gentrat 1 0.078807 0.32806 4.8823 0.014 *
## Residual 10 0.161414 0.67194
## Total 11 0.240221 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senstime7)
## Análisis de sabor indicador: este análisis permite identificar el sabor que es encontrado más a menudo
## en una categoría (nivel) de un grupo comparado con otro para todos los grupos analizados (genotipos, tratamientos, genotipos*tratamientos) en cada tiempo evaluado
library(indicspecies)
## Tiempo 4
attach(senstime4)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime4[,9:ncol(senstime4)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
## Análisis de sabor indicador
invgen <- multipatt(m_sens, gen, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgen, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group EET8 #sps. 3
## stat p.value
## acido 0.386 0.0302 *
## floral 0.205 0.6037
## astringente 0.191 0.5142
##
## Group ICS 95 #sps. 3
## stat p.value
## madera 0.494 0.0107 *
## amargo 0.469 0.0091 **
## herbal 0.378 0.0651 .
##
## Group TCS01 #sps. 4
## stat p.value
## cacao 0.444 0.0163 *
## nuez 0.442 0.0259 *
## dulce 0.274 0.2275
## frutal 0.233 0.3360
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invtrat <- multipatt(m_sens, trat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invtrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
##
## List of species associated to each combination:
##
## Group Large #sps. 8
## stat p.value
## astringente 0.511 0.0033 **
## dulce 0.232 0.2553
## madera 0.204 0.3039
## cacao 0.188 0.3634
## acido 0.136 0.5490
## herbal 0.134 0.5538
## floral 0.083 0.8730
## amargo 0.060 0.8637
##
## Group Small #sps. 2
## stat p.value
## frutal 0.082 0.748
## nuez 0.052 0.887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgentrat <- multipatt(m_sens, gentrat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgentrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
## Number of species associated to 3 groups: 0
## Number of species associated to 4 groups: 0
## Number of species associated to 5 groups: 0
##
## List of species associated to each combination:
##
## Group EET8L #sps. 1
## stat p.value
## floral 0.407 0.291
##
## Group EET8SM #sps. 1
## stat p.value
## acido 0.427 0.0777 .
##
## Group ICS95L #sps. 3
## stat p.value
## astringente 0.524 0.0021 **
## madera 0.508 0.0214 *
## amargo 0.458 0.0270 *
##
## Group ICS95SM #sps. 2
## stat p.value
## herbal 0.418 0.110
## frutal 0.332 0.229
##
## Group TCS01L #sps. 2
## stat p.value
## dulce 0.381 0.214
## nuez 0.349 0.235
##
## Group TCS01SM #sps. 1
## stat p.value
## cacao 0.365 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senstime4)
## Tiempo 5
attach(senstime5)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime5[,9:ncol(senstime5)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
## Análisis de sabor indicador
invgen <- multipatt(m_sens, gen, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgen, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group EET8 #sps. 1
## stat p.value
## astringente 0.476 0.0099 **
##
## Group ICS 95 #sps. 5
## stat p.value
## amargo 0.553 0.0028 **
## nuez 0.530 0.0051 **
## dulce 0.304 0.2476
## frutal 0.238 0.3283
## floral 0.224 0.4787
##
## Group TCS01 #sps. 4
## stat p.value
## cacao 0.602 0.0004 ***
## herbal 0.254 0.3020
## acido 0.205 0.4599
## madera 0.171 0.6402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invtrat <- multipatt(m_sens, trat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invtrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
##
## List of species associated to each combination:
##
## Group Large #sps. 7
## stat p.value
## astringente 0.192 0.352
## cacao 0.131 0.566
## acido 0.097 0.708
## madera 0.081 0.816
## amargo 0.075 0.831
## nuez 0.060 0.862
## dulce 0.054 0.603
##
## Group Small #sps. 3
## stat p.value
## frutal 0.067 0.674
## floral 0.000 1.000
## herbal 0.000 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgentrat <- multipatt(m_sens, gentrat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgentrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
## Number of species associated to 3 groups: 0
## Number of species associated to 4 groups: 0
## Number of species associated to 5 groups: 0
##
## List of species associated to each combination:
##
## Group EET8L #sps. 1
## stat p.value
## acido 0.216 0.737
##
## Group EET8SM #sps. 1
## stat p.value
## astringente 0.344 0.237
##
## Group ICS95L #sps. 2
## stat p.value
## amargo 0.500 0.0115 *
## nuez 0.456 0.0692 .
##
## Group ICS95SM #sps. 3
## stat p.value
## frutal 0.331 0.215
## floral 0.283 0.596
## dulce 0.264 0.723
##
## Group TCS01L #sps. 2
## stat p.value
## cacao 0.645 0.0015 **
## madera 0.434 0.0464 *
##
## Group TCS01SM #sps. 1
## stat p.value
## herbal 0.161 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senstime5)
## Tiempo 6
attach(senstime6)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime6[,9:ncol(senstime6)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
## Análisis de sabor indicador
invgen <- multipatt(m_sens, gen, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgen, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group EET8 #sps. 1
## stat p.value
## acido 0.387 0.0492 *
##
## Group ICS 95 #sps. 2
## stat p.value
## amargo 0.705 1e-04 ***
## astringente 0.528 5e-04 ***
##
## Group TCS01 #sps. 7
## stat p.value
## dulce 0.821 0.0001 ***
## frutal 0.641 0.0004 ***
## floral 0.568 0.0015 **
## nuez 0.545 0.0010 ***
## cacao 0.507 0.0043 **
## herbal 0.343 0.0529 .
## madera 0.076 0.9457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invtrat <- multipatt(m_sens, trat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invtrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
##
## List of species associated to each combination:
##
## Group Large #sps. 5
## stat p.value
## cacao 0.293 0.102
## amargo 0.270 0.152
## floral 0.098 0.539
## nuez 0.081 0.603
## astringente 0.022 1.000
##
## Group Small #sps. 5
## stat p.value
## madera 0.267 0.156
## herbal 0.255 0.170
## dulce 0.081 0.675
## frutal 0.074 0.752
## acido 0.061 0.608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgentrat <- multipatt(m_sens, gentrat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgentrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
## Number of species associated to 3 groups: 0
## Number of species associated to 4 groups: 0
## Number of species associated to 5 groups: 0
##
## List of species associated to each combination:
##
## Group EET8SM #sps. 1
## stat p.value
## acido 0.245 0.612
##
## Group ICS95L #sps. 2
## stat p.value
## amargo 0.699 0.0001 ***
## astringente 0.481 0.0074 **
##
## Group TCS01L #sps. 3
## stat p.value
## floral 0.465 0.0235 *
## cacao 0.452 0.0199 *
## nuez 0.345 0.1630
##
## Group TCS01SM #sps. 4
## stat p.value
## dulce 0.628 0.0018 **
## frutal 0.480 0.0176 *
## herbal 0.457 0.0531 .
## madera 0.406 0.1076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senstime6)
## Tiempo 7
attach(senstime7)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senstime7[,9:ncol(senstime7)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
## Análisis de sabor indicador
invgen <- multipatt(m_sens, gen, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgen, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group EET8 #sps. 4
## stat p.value
## amargo 0.512 0.0053 **
## cacao 0.376 0.0564 .
## madera 0.231 0.3866
## acido 0.225 0.3917
##
## Group ICS 95 #sps. 1
## stat p.value
## astringente 0.388 0.0436 *
##
## Group TCS01 #sps. 5
## stat p.value
## floral 0.705 0.0002 ***
## nuez 0.702 0.0001 ***
## herbal 0.456 0.0164 *
## dulce 0.415 0.0306 *
## frutal 0.237 0.3103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invtrat <- multipatt(m_sens, trat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invtrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
##
## List of species associated to each combination:
##
## Group Large #sps. 4
## stat p.value
## astringente 0.372 0.0343 *
## amargo 0.272 0.1045
## cacao 0.169 0.3855
## floral 0.073 0.7734
##
## Group Small #sps. 6
## stat p.value
## dulce 0.410 0.0139 *
## madera 0.145 0.3207
## frutal 0.126 0.4764
## acido 0.064 0.8524
## nuez 0.033 0.9233
## herbal 0.024 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgentrat <- multipatt(m_sens, gentrat, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgentrat, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 10
## Selected number of species: 10
## Number of species associated to 1 group: 10
## Number of species associated to 2 groups: 0
## Number of species associated to 3 groups: 0
## Number of species associated to 4 groups: 0
## Number of species associated to 5 groups: 0
##
## List of species associated to each combination:
##
## Group EET8L #sps. 2
## stat p.value
## amargo 0.628 0.0004 ***
## astringente 0.535 0.0047 **
##
## Group EET8SM #sps. 4
## stat p.value
## dulce 0.551 0.0074 **
## cacao 0.464 0.0225 *
## madera 0.195 0.8418
## acido 0.142 0.9667
##
## Group TCS01L #sps. 3
## stat p.value
## nuez 0.684 0.0004 ***
## floral 0.642 0.0011 **
## frutal 0.544 0.0088 **
##
## Group TCS01SM #sps. 1
## stat p.value
## herbal 0.48 0.0415 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senstime7)