setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Lucero/Tesis/data")
sensorial <- read.table("sensors.csv", header=T, sep=",")
sensorial$dia<-as.factor(sensorial$dia)
sensorial$gendia<-as.factor(sensorial$gendia)
attach(sensorial)
# Packages
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.5-7
library(ggplot2)
library(concaveman)
library(ggforce)
# Data subsets by temperature ramp
senscurva1 <- subset(sensorial, sensorial$curva == "1")
senscurva2 <- subset(sensorial, sensorial$curva == "2")
senscurva3 <- subset(sensorial, sensorial$curva == "3")
# Seleccionando Curva 1
detach(sensorial)
attach(senscurva1)
# Generando la matriz general de perfil sensorial - extrayendo columnas con la información de sabor
sens = senscurva1[,6:ncol(senscurva1)]
# Convirtiendo las columnas extraidas en una matriz
m_sens=as.matrix(sens)
# Obteniendo las distancias Bray-Curtis de la matriz
set.seed(123)
nmds = metaMDS(m_sens, distance = "bray")
## Run 0 stress 0.1344322
## Run 1 stress 0.1352544
## Run 2 stress 0.1352543
## Run 3 stress 0.1557937
## Run 4 stress 0.1344322
## ... New best solution
## ... Procrustes: rmse 0.0002495923 max resid 0.0006899008
## ... Similar to previous best
## Run 5 stress 0.1435799
## Run 6 stress 0.1435805
## Run 7 stress 0.1352543
## Run 8 stress 0.142616
## Run 9 stress 0.1435805
## Run 10 stress 0.1643082
## Run 11 stress 0.1344323
## ... Procrustes: rmse 7.093723e-05 max resid 0.0001891645
## ... Similar to previous best
## Run 12 stress 0.1352542
## Run 13 stress 0.142616
## Run 14 stress 0.1352544
## Run 15 stress 0.1643087
## Run 16 stress 0.1352543
## Run 17 stress 0.1352542
## Run 18 stress 0.1344322
## ... Procrustes: rmse 7.045732e-05 max resid 0.0001946775
## ... Similar to previous best
## Run 19 stress 0.1352542
## Run 20 stress 0.1517266
## *** Solution reached
nmds
##
## Call:
## metaMDS(comm = m_sens, distance = "bray")
##
## global Multidimensional Scaling using monoMDS
##
## Data: m_sens
## Distance: bray
##
## Dimensions: 2
## Stress: 0.1344322
## Stress type 1, weak ties
## Two convergent solutions found after 20 tries
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'm_sens'
# Generando el gráfico Non-Metric multidimensional scaling
plot(nmds)
# extrayendo los puntajes NMDS (coordenadas x and y)
data.scores = as.data.frame(scores(nmds))
# adicionando columnas al data frame
data.scores$gen = senscurva1$gen
data.scores$dia = senscurva1$dia
data.scores$gendia = senscurva1$gendia
head(data.scores)
## NMDS1 NMDS2 gen dia gendia
## 1 -0.20920959 0.09422714 CCN 51 5 CCN 51_5
## 2 0.05377915 -0.07932617 CCN 51 5 CCN 51_5
## 3 -0.11367390 0.02441217 CCN 51 5 CCN 51_5
## 4 -0.04272649 -0.09682374 ICS 95 5 ICS 95_5
## 5 0.02218775 -0.04708223 ICS 95 5 ICS 95_5
## 6 0.03726757 0.05730263 ICS 95 5 ICS 95_5
data.scores$dia<-as.factor(data.scores$dia)
# Creando escala de color para graficar
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# Gráfica del análisis
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = dia)) +
geom_mark_hull(concavity = 5,expand=0,radius=0,aes(fill=gen))+
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 = "Día", y = "NMDS2", shape ="Genotipo", fill="Genotipo") +
scale_colour_manual(values = cbp1)
xx
# Seleccionando Curva 2
detach(senscurva1)
attach(senscurva2)
# Generando la matriz general de perfil sensorial - extrayendo columnas con la información de sabor
sens = senscurva2[,6:ncol(senscurva2)]
# 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 0.1225971
## Run 1 stress 0.1316511
## Run 2 stress 0.1316514
## Run 3 stress 0.1225973
## ... Procrustes: rmse 0.0003376587 max resid 0.0007577101
## ... Similar to previous best
## Run 4 stress 0.1261682
## Run 5 stress 0.1300444
## Run 6 stress 0.1225974
## ... Procrustes: rmse 0.0003970981 max resid 0.0008917697
## ... Similar to previous best
## Run 7 stress 0.1352429
## Run 8 stress 0.1225972
## ... Procrustes: rmse 0.0001868526 max resid 0.0004134671
## ... Similar to previous best
## Run 9 stress 0.1225973
## ... Procrustes: rmse 0.0002846071 max resid 0.0006350884
## ... Similar to previous best
## Run 10 stress 0.130044
## Run 11 stress 0.1229467
## ... Procrustes: rmse 0.01056907 max resid 0.03286849
## Run 12 stress 0.1296877
## Run 13 stress 0.1335357
## Run 14 stress 0.1225972
## ... Procrustes: rmse 0.0002036979 max resid 0.0004589528
## ... Similar to previous best
## Run 15 stress 0.1296879
## Run 16 stress 0.1316512
## Run 17 stress 0.1225972
## ... Procrustes: rmse 0.0002146584 max resid 0.0004755761
## ... Similar to previous best
## Run 18 stress 0.1300441
## Run 19 stress 0.1435567
## Run 20 stress 0.1225973
## ... Procrustes: rmse 0.0003322521 max resid 0.0007433156
## ... 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: 0.1225971
## 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 = senscurva2$gen
data.scores$dia = senscurva2$dia
data.scores$gendia = senscurva2$gendia
head(data.scores)
## NMDS1 NMDS2 gen dia gendia
## 19 -0.08554926 0.032743069 CCN 51 5 CCN 51_5
## 20 -0.01779492 -0.072475612 CCN 51 5 CCN 51_5
## 21 0.01991807 0.003904825 CCN 51 5 CCN 51_5
## 22 0.03850237 -0.035068532 ICS 95 5 ICS 95_5
## 23 -0.03238702 -0.024122352 ICS 95 5 ICS 95_5
## 24 -0.01401242 -0.021692349 ICS 95 5 ICS 95_5
data.scores$dia<-as.factor(data.scores$dia)
# Creando escala de color para graficar
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# Gráfica del análisis
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = dia)) +
geom_mark_hull(concavity = 5,expand=0,radius=0,aes(fill=gen))+
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 = "Día", y = "NMDS2", shape ="Genotipo", fill="Genotipo") +
scale_colour_manual(values = cbp1)
xx
# Seleccionando Curva 3
detach(senscurva2)
attach(senscurva3)
# Generando la matriz general de perfil sensorial - extrayendo columnas con la información de sabor
sens = senscurva3[,6:ncol(senscurva3)]
# 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 0.1147942
## Run 1 stress 0.1147942
## ... Procrustes: rmse 6.940025e-07 max resid 1.838944e-06
## ... Similar to previous best
## Run 2 stress 0.1147942
## ... Procrustes: rmse 4.950019e-06 max resid 1.701912e-05
## ... Similar to previous best
## Run 3 stress 0.1147942
## ... New best solution
## ... Procrustes: rmse 1.693478e-06 max resid 5.564547e-06
## ... Similar to previous best
## Run 4 stress 0.1619049
## Run 5 stress 0.1619049
## Run 6 stress 0.1147942
## ... Procrustes: rmse 2.827065e-06 max resid 9.628942e-06
## ... Similar to previous best
## Run 7 stress 0.1147942
## ... Procrustes: rmse 1.151125e-06 max resid 2.030255e-06
## ... Similar to previous best
## Run 8 stress 0.1619049
## Run 9 stress 0.1147942
## ... Procrustes: rmse 1.902378e-06 max resid 6.382527e-06
## ... Similar to previous best
## Run 10 stress 0.1147942
## ... Procrustes: rmse 3.893125e-06 max resid 1.337285e-05
## ... Similar to previous best
## Run 11 stress 0.1147942
## ... Procrustes: rmse 6.171607e-06 max resid 2.119204e-05
## ... Similar to previous best
## Run 12 stress 0.1147942
## ... Procrustes: rmse 2.981592e-06 max resid 1.010976e-05
## ... Similar to previous best
## Run 13 stress 0.185411
## Run 14 stress 0.1147942
## ... Procrustes: rmse 9.687729e-07 max resid 2.249469e-06
## ... Similar to previous best
## Run 15 stress 0.1147942
## ... New best solution
## ... Procrustes: rmse 6.719214e-07 max resid 1.242106e-06
## ... Similar to previous best
## Run 16 stress 0.1147942
## ... Procrustes: rmse 4.832258e-06 max resid 1.615926e-05
## ... Similar to previous best
## Run 17 stress 0.1619049
## Run 18 stress 0.1147942
## ... Procrustes: rmse 1.880684e-06 max resid 3.000391e-06
## ... Similar to previous best
## Run 19 stress 0.1619049
## Run 20 stress 0.1147942
## ... Procrustes: rmse 1.899445e-05 max resid 6.450898e-05
## ... 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: 0.1147942
## 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 = senscurva3$gen
data.scores$dia = senscurva3$dia
data.scores$gendia = senscurva3$gendia
head(data.scores)
## NMDS1 NMDS2 gen dia gendia
## 37 -0.01779964 -0.03712848 CCN 51 5 CCN 51_5
## 38 -0.06610923 -0.06507581 CCN 51 5 CCN 51_5
## 39 -0.11467651 0.13259633 CCN 51 5 CCN 51_5
## 40 -0.02970430 0.04043599 ICS 95 5 ICS 95_5
## 41 -0.23302701 -0.01260452 ICS 95 5 ICS 95_5
## 42 -0.09363593 0.01507214 ICS 95 5 ICS 95_5
data.scores$dia<-as.factor(data.scores$dia)
# Creando escala de color para graficar
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# Gráfica del análisis
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) +
geom_point(size = 4, aes(shape = gen, colour = dia)) +
geom_mark_hull(concavity = 5,expand=0,radius=0,aes(fill=gen))+
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 = "Día", y = "NMDS2", shape ="Genotipo", fill="Genotipo") +
scale_colour_manual(values = cbp1)
xx
## Probando si el perfil del sabor es estadísticamente distinto basado en los grupos (genotipos, dia, genotipos*dia) para cada curva de temperatura
## Curva 1
detach(senscurva3)
attach(senscurva1)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senscurva1[,6:ncol(senscurva1)]
# 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: 0.5212
## Significance: 1e-04
##
## Permutation: free
## Number of permutations: 9999
anodia <- anosim(m_sens, dia, distance = "bray", permutations = 9999)
anodia
##
## Call:
## anosim(x = m_sens, grouping = dia, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.05041
## Significance: 0.7451
##
## Permutation: free
## Number of permutations: 9999
anogendia <- anosim(m_sens, gendia, distance = "bray", permutations = 9999)
anogendia
##
## Call:
## anosim(x = m_sens, grouping = gendia, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.421
## Significance: 7e-04
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluados
library(pairwiseAdonis)
## Loading required package: cluster
pairwise.adonis2(m_sens ~ gen, data = senscurva1)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`CCN 51_vs_ICS 95`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.019147 0.22038 2.8267 0.033 *
## Residual 10 0.067736 0.77962
## Total 11 0.086883 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`CCN 51_vs_TCS 01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.066725 0.5004 10.016 0.004 **
## Residual 10 0.066619 0.4996
## Total 11 0.133344 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`ICS 95_vs_TCS 01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.037838 0.47875 9.1848 0.005 **
## Residual 10 0.041197 0.52125
## Total 11 0.079035 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ dia, data = senscurva1)
## $parent_call
## [1] "m_sens ~ dia , strata = Null , permutations 999"
##
## $`5_vs_6`
## Df SumOfSqs R2 F Pr(>F)
## dia 1 0.002012 0.01182 0.1914 0.895
## Residual 16 0.168237 0.98818
## Total 17 0.170249 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gendia, data = senscurva1)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## $parent_call
## [1] "m_sens ~ gendia , strata = Null , permutations 999"
##
## $`CCN 51_5_vs_ICS 95_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.006190 0.17951 0.8751 0.5
## Residual 4 0.028295 0.82049
## Total 5 0.034485 1.00000
##
## $`CCN 51_5_vs_TCS 01_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.027729 0.53555 4.6123 0.1
## Residual 4 0.024048 0.46445
## Total 5 0.051777 1.00000
##
## $`CCN 51_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.006158 0.1322 0.6094 0.6
## Residual 4 0.040421 0.8678
## Total 5 0.046579 1.0000
##
## $`CCN 51_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.010943 0.27419 1.5111 0.3
## Residual 4 0.028968 0.72581
## Total 5 0.039912 1.00000
##
## $`CCN 51_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.031153 0.45936 3.3987 0.1
## Residual 4 0.036665 0.54064
## Total 5 0.067818 1.00000
##
## $`ICS 95_5_vs_TCS 01_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0117343 0.59818 5.9548 0.1
## Residual 4 0.0078823 0.40182
## Total 5 0.0196166 1.00000
##
## $`ICS 95_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.016605 0.40639 2.7384 0.1
## Residual 4 0.024256 0.59361
## Total 5 0.040861 1.00000
##
## $`ICS 95_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0083543 0.39488 2.6102 0.1
## Residual 4 0.0128025 0.60512
## Total 5 0.0211568 1.00000
##
## $`ICS 95_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.019862 0.4921 3.8756 0.1
## Residual 4 0.020499 0.5079
## Total 5 0.040361 1.0000
##
## $`TCS 01_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.037146 0.64992 7.426 0.1
## Residual 4 0.020009 0.35008
## Total 5 0.057155 1.00000
##
## $`TCS 01_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0201683 0.70214 9.429 0.1
## Residual 4 0.0085558 0.29786
## Total 5 0.0287241 1.00000
##
## $`TCS 01_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0037873 0.18899 0.9321 0.6
## Residual 4 0.0162526 0.81101
## Total 5 0.0200399 1.00000
##
## $`CCN 51_6_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.019067 0.43338 3.0594 0.1
## Residual 4 0.024929 0.56662
## Total 5 0.043996 1.00000
##
## $`CCN 51_6_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.047366 0.59214 5.8072 0.1
## Residual 4 0.032626 0.40786
## Total 5 0.079992 1.00000
##
## $`ICS 95_6_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.036054 0.63002 6.8114 0.1
## Residual 4 0.021173 0.36998
## Total 5 0.057227 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senscurva1)
## Curva 2
attach(senscurva2)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senscurva2[,6:ncol(senscurva2)]
# 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: 0.1327
## Significance: 0.0427
##
## Permutation: free
## Number of permutations: 9999
anodia <- anosim(m_sens, dia, distance = "bray", permutations = 9999)
anodia
##
## Call:
## anosim(x = m_sens, grouping = dia, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01818
## Significance: 0.3444
##
## Permutation: free
## Number of permutations: 9999
anogendia <- anosim(m_sens, gendia, distance = "bray", permutations = 9999)
anogendia
##
## Call:
## anosim(x = m_sens, grouping = gendia, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2642
## Significance: 0.0073
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluados
library(pairwiseAdonis)
pairwise.adonis2(m_sens ~ gen, data = senscurva2)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`CCN 51_vs_ICS 95`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.025351 0.19806 2.4697 0.04 *
## Residual 10 0.102646 0.80194
## Total 11 0.127997 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`CCN 51_vs_TCS 01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.003478 0.06125 0.6525 0.633
## Residual 10 0.053294 0.93875
## Total 11 0.056771 1.00000
##
## $`ICS 95_vs_TCS 01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.018759 0.17074 2.059 0.073 .
## Residual 10 0.091108 0.82926
## Total 11 0.109867 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ dia, data = senscurva2)
## $parent_call
## [1] "m_sens ~ dia , strata = Null , permutations 999"
##
## $`5_vs_6`
## Df SumOfSqs R2 F Pr(>F)
## dia 1 0.011864 0.07642 1.3239 0.306
## Residual 16 0.143384 0.92358
## Total 17 0.155249 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gendia, data = senscurva2)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## $parent_call
## [1] "m_sens ~ gendia , strata = Null , permutations 999"
##
## $`CCN 51_5_vs_ICS 95_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0059964 0.22563 1.1655 0.4
## Residual 4 0.0205797 0.77437
## Total 5 0.0265760 1.00000
##
## $`CCN 51_5_vs_TCS 01_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0056713 0.21765 1.1128 0.5
## Residual 4 0.0203861 0.78235
## Total 5 0.0260574 1.00000
##
## $`CCN 51_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.007472 0.23051 1.1982 0.3
## Residual 4 0.024944 0.76949
## Total 5 0.032416 1.00000
##
## $`CCN 51_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.021208 0.29555 1.6782 0.3
## Residual 4 0.050548 0.70445
## Total 5 0.071756 1.00000
##
## $`CCN 51_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0036779 0.12626 0.578 0.7
## Residual 4 0.0254509 0.87374
## Total 5 0.0291288 1.00000
##
## $`ICS 95_5_vs_TCS 01_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0086295 0.47428 3.6086 0.1
## Residual 4 0.0095656 0.52572
## Total 5 0.0181951 1.00000
##
## $`ICS 95_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.020489 0.59196 5.8029 0.1
## Residual 4 0.014123 0.40804
## Total 5 0.034613 1.00000
##
## $`ICS 95_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.030502 0.43432 3.0712 0.1
## Residual 4 0.039728 0.56568
## Total 5 0.070230 1.00000
##
## $`ICS 95_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0060212 0.29156 1.6462 0.3
## Residual 4 0.0146304 0.70844
## Total 5 0.0206516 1.00000
##
## $`TCS 01_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0014235 0.09272 0.4088 0.9
## Residual 4 0.0139299 0.90728
## Total 5 0.0153534 1.00000
##
## $`TCS 01_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.034772 0.46796 3.5182 0.1
## Residual 4 0.039534 0.53204
## Total 5 0.074306 1.00000
##
## $`TCS 01_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0064408 0.3085 1.7845 0.3
## Residual 4 0.0144368 0.6915
## Total 5 0.0208776 1.0000
##
## $`CCN 51_6_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.040983 0.48173 3.718 0.1
## Residual 4 0.044092 0.51827
## Total 5 0.085075 1.00000
##
## $`CCN 51_6_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.010095 0.34704 2.1259 0.1
## Residual 4 0.018995 0.65296
## Total 5 0.029090 1.00000
##
## $`ICS 95_6_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.025038 0.35955 2.2456 0.1
## Residual 4 0.044599 0.64045
## Total 5 0.069637 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senscurva2)
## Curva 3
attach(senscurva3)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senscurva3[,6:ncol(senscurva3)]
# 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: 0.3025
## Significance: 0.0024
##
## Permutation: free
## Number of permutations: 9999
anodia <- anosim(m_sens, dia, distance = "bray", permutations = 9999)
anodia
##
## Call:
## anosim(x = m_sens, grouping = dia, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01355
## Significance: 0.3456
##
## Permutation: free
## Number of permutations: 9999
anogendia <- anosim(m_sens, gendia, distance = "bray", permutations = 9999)
anogendia
##
## Call:
## anosim(x = m_sens, grouping = gendia, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3391
## Significance: 0.0041
##
## Permutation: free
## Number of permutations: 9999
# PostHoc análisis de las diferencias cuantitativas del perfil de sabor entre las categorias de los grupos evaluados
library(pairwiseAdonis)
pairwise.adonis2(m_sens ~ gen, data = senscurva3)
## $parent_call
## [1] "m_sens ~ gen , strata = Null , permutations 999"
##
## $`CCN 51_vs_ICS 95`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.003102 0.04985 0.5246 0.723
## Residual 10 0.059124 0.95015
## Total 11 0.062226 1.00000
##
## $`CCN 51_vs_TCS 01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.044901 0.36321 5.7037 0.004 **
## Residual 10 0.078724 0.63679
## Total 11 0.123625 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`ICS 95_vs_TCS 01`
## Df SumOfSqs R2 F Pr(>F)
## gen 1 0.046158 0.33625 5.0659 0.015 *
## Residual 10 0.091114 0.66375
## Total 11 0.137272 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ dia, data = senscurva3)
## $parent_call
## [1] "m_sens ~ dia , strata = Null , permutations 999"
##
## $`5_vs_6`
## Df SumOfSqs R2 F Pr(>F)
## dia 1 0.008405 0.04742 0.7965 0.504
## Residual 16 0.168850 0.95258
## Total 17 0.177255 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
pairwise.adonis2(m_sens ~ gendia, data = senscurva3)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## $parent_call
## [1] "m_sens ~ gendia , strata = Null , permutations 999"
##
## $`CCN 51_5_vs_ICS 95_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0035916 0.14586 0.6831 0.8
## Residual 4 0.0210327 0.85414
## Total 5 0.0246243 1.00000
##
## $`CCN 51_5_vs_TCS 01_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.046679 0.69067 8.9313 0.1
## Residual 4 0.020906 0.30933
## Total 5 0.067584 1.00000
##
## $`CCN 51_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0089622 0.38354 2.4887 0.1
## Residual 4 0.0144047 0.61646
## Total 5 0.0233669 1.00000
##
## $`CCN 51_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0055624 0.17707 0.8607 0.6
## Residual 4 0.0258521 0.82293
## Total 5 0.0314145 1.00000
##
## $`CCN 51_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.016031 0.35584 2.2096 0.2
## Residual 4 0.029020 0.64416
## Total 5 0.045051 1.00000
##
## $`ICS 95_5_vs_TCS 01_5`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.052950 0.67376 8.2609 0.1
## Residual 4 0.025639 0.32624
## Total 5 0.078588 1.00000
##
## $`ICS 95_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0060601 0.2405 1.2666 0.4
## Residual 4 0.0191376 0.7595
## Total 5 0.0251977 1.0000
##
## $`ICS 95_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.005173 0.14466 0.6765 0.7
## Residual 4 0.030585 0.85534
## Total 5 0.035758 1.00000
##
## $`ICS 95_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.024180 0.41737 2.8655 0.1
## Residual 4 0.033753 0.58263
## Total 5 0.057933 1.00000
##
## $`TCS 01_5_vs_CCN 51_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.040013 0.67792 8.4192 0.1
## Residual 4 0.019011 0.32208
## Total 5 0.059024 1.00000
##
## $`TCS 01_5_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.032855 0.51893 4.3148 0.1
## Residual 4 0.030458 0.48107
## Total 5 0.063313 1.00000
##
## $`TCS 01_5_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.021731 0.39256 2.585 0.2
## Residual 4 0.033626 0.60744
## Total 5 0.055357 1.00000
##
## $`CCN 51_6_vs_ICS 95_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.0051244 0.17621 0.8556 0.7
## Residual 4 0.0239570 0.82379
## Total 5 0.0290814 1.00000
##
## $`CCN 51_6_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.017773 0.39585 2.6208 0.2
## Residual 4 0.027125 0.60415
## Total 5 0.044898 1.00000
##
## $`ICS 95_6_vs_TCS 01_6`
## Df SumOfSqs R2 F Pr(>F)
## gendia 1 0.009234 0.19316 0.9576 0.6
## Residual 4 0.038573 0.80684
## Total 5 0.047807 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"
detach(senscurva3)
## 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, dia, genotipos*dia) en cada curva de temperatura
library(indicspecies)
## Curva 1
attach(senscurva1)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senscurva1[,6:ncol(senscurva1)]
# 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: 13
## Selected number of species: 12
## Number of species associated to 1 group: 12
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group CCN 51 #sps. 4
## stat p.value
## violeta 0.403 0.354
## acido 0.333 0.531
## verde 0.277 0.685
## astringente 0.204 0.884
##
## Group ICS 95 #sps. 2
## stat p.value
## amargo 0.588 0.035 *
## humedad 0.316 0.739
##
## Group TCS 01 #sps. 6
## stat p.value
## frutal 0.750 0.0138 *
## frutos.secos 0.689 0.0013 **
## floral 0.686 0.0099 **
## dulce 0.670 0.0074 **
## cacao 0.617 0.0249 *
## herbal 0.369 0.4339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invdia <- multipatt(m_sens, dia, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invdia, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 13
## Selected number of species: 12
## Number of species associated to 1 group: 12
##
## List of species associated to each combination:
##
## Group 5 #sps. 6
## stat p.value
## frutal 0.236 0.615
## astringente 0.192 0.686
## frutos.secos 0.103 0.838
## amargo 0.092 1.000
## cacao 0.073 1.000
## dulce 0.000 1.000
##
## Group 6 #sps. 6
## stat p.value
## humedad 0.447 0.206
## violeta 0.342 0.332
## acido 0.282 0.442
## verde 0.196 0.690
## floral 0.081 1.000
## herbal 0.000 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgendia <- multipatt(m_sens, gendia, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgendia, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 13
## Selected number of species: 12
## Number of species associated to 1 group: 12
## 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 CCN 51_5 #sps. 1
## stat p.value
## astringente 0.258 0.987
##
## Group CCN 51_6 #sps. 3
## stat p.value
## violeta 0.561 0.253
## verde 0.439 0.528
## acido 0.210 1.000
##
## Group ICS 95_5 #sps. 1
## stat p.value
## amargo 0.372 0.708
##
## Group ICS 95_6 #sps. 1
## stat p.value
## humedad 0.6 0.341
##
## Group TCS 01_5 #sps. 2
## stat p.value
## frutal 0.632 0.146
## cacao 0.488 0.252
##
## Group TCS 01_6 #sps. 4
## stat p.value
## floral 0.542 0.1424
## dulce 0.522 0.2021
## frutos.secos 0.505 0.0728 .
## herbal 0.373 0.7824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senscurva1)
## Curva 2
attach(senscurva2)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senscurva2[,6:ncol(senscurva2)]
# 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: 13
## Selected number of species: 13
## Number of species associated to 1 group: 13
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group CCN 51 #sps. 5
## stat p.value
## acido 0.612 0.0362 *
## floral 0.500 0.1197
## dulce 0.369 0.4271
## astringente 0.322 0.5732
## frutal 0.316 0.7298
##
## Group ICS 95 #sps. 4
## stat p.value
## rancio 0.343 1.000
## humedad 0.290 0.594
## verde 0.236 0.835
## herbal 0.158 0.930
##
## Group TCS 01 #sps. 4
## stat p.value
## violeta 0.316 0.735
## frutos.secos 0.302 0.561
## cacao 0.283 0.561
## amargo 0.236 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invdia <- multipatt(m_sens, dia, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invdia, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 13
## Selected number of species: 13
## Number of species associated to 1 group: 13
##
## List of species associated to each combination:
##
## Group 5 #sps. 9
## stat p.value
## herbal 0.447 0.124
## frutal 0.447 0.215
## dulce 0.209 0.680
## cacao 0.200 0.590
## acido 0.192 0.697
## floral 0.177 0.722
## amargo 0.111 1.000
## verde 0.111 1.000
## frutos.secos 0.085 1.000
##
## Group 6 #sps. 4
## stat p.value
## astringente 0.342 0.333
## rancio 0.243 1.000
## violeta 0.149 1.000
## humedad 0.082 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgendia <- multipatt(m_sens, gendia, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgendia, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 13
## Selected number of species: 13
## Number of species associated to 1 group: 13
## 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 CCN 51_5 #sps. 1
## stat p.value
## verde 0.149 1
##
## Group CCN 51_6 #sps. 5
## stat p.value
## floral 0.553 0.146
## acido 0.516 0.267
## dulce 0.373 0.779
## frutos.secos 0.191 1.000
## amargo 0.149 1.000
##
## Group ICS 95_5 #sps. 2
## stat p.value
## herbal 0.6 0.109
## frutal 0.2 1.000
##
## Group ICS 95_6 #sps. 2
## stat p.value
## rancio 0.542 1
## humedad 0.183 1
##
## Group TCS 01_5 #sps. 1
## stat p.value
## cacao 0.268 0.928
##
## Group TCS 01_6 #sps. 2
## stat p.value
## astringente 0.357 0.86
## violeta 0.200 1.00
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senscurva2)
## Curva 3
attach(senscurva3)
# Generando la matriz de perfil sensorial para el tiempo a evaluar - extrayendo columnas con la información de sabor
sens = senscurva3[,6:ncol(senscurva3)]
# 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: 13
## Selected number of species: 13
## Number of species associated to 1 group: 13
## Number of species associated to 2 groups: 0
##
## List of species associated to each combination:
##
## Group ICS 95 #sps. 1
## stat p.value
## violeta 0.147 1
##
## Group TCS 01 #sps. 12
## stat p.value
## frutal 0.803 0.0028 **
## floral 0.732 0.0094 **
## frutos.secos 0.613 0.0355 *
## astringente 0.588 0.0633 .
## amargo 0.525 0.1112
## herbal 0.472 0.2481
## verde 0.472 0.2537
## dulce 0.471 0.2008
## cacao 0.408 0.2965
## rancio 0.316 0.7378
## humedad 0.232 0.6825
## acido 0.154 0.9696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invdia <- multipatt(m_sens, dia, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invdia, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 13
## Selected number of species: 13
## Number of species associated to 1 group: 13
##
## List of species associated to each combination:
##
## Group 5 #sps. 7
## stat p.value
## dulce 0.333 0.366
## herbal 0.267 0.577
## frutal 0.162 0.742
## rancio 0.149 1.000
## floral 0.094 1.000
## frutos.secos 0.079 1.000
## amargo 0.000 1.000
##
## Group 6 #sps. 6
## stat p.value
## verde 0.267 0.573
## acido 0.218 0.629
## violeta 0.209 0.731
## cacao 0.192 0.687
## astringente 0.092 1.000
## humedad 0.055 0.932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
invgendia <- multipatt(m_sens, gendia, func = "r.g", control = how(nperm=9999), duleg = T)
summary(invgendia, alpha = 1)
##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: r.g
## Significance level (alpha): 1
##
## Total number of species: 13
## Selected number of species: 13
## Number of species associated to 1 group: 13
## 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 ICS 95_6 #sps. 1
## stat p.value
## violeta 0.373 1
##
## Group TCS 01_5 #sps. 9
## stat p.value
## herbal 0.837 0.0285 *
## frutos.secos 0.811 0.0084 **
## dulce 0.745 0.0326 *
## frutal 0.725 0.0479 *
## floral 0.715 0.0754 .
## astringente 0.620 0.1570
## amargo 0.332 0.8870
## cacao 0.258 0.9858
## rancio 0.200 1.0000
##
## Group TCS 01_6 #sps. 3
## stat p.value
## verde 0.478 0.606
## humedad 0.368 0.616
## acido 0.293 1.000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
detach(senscurva3)