setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Lucero/data")
library(Plasticity)
library(agricolae)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
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## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(ggplot2)
library(psych)
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## Attaching package: 'psych'
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## %+%, alpha
library(FSA)
## ## FSA v0.9.1. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.
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## Attaching package: 'FSA'
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## headtail
## The following object is masked from 'package:plyr':
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## mapvalues
library(forcats)
library(Hmisc)
## Loading required package: survival
## Loading required package: Formula
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## Attaching package: 'Hmisc'
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## describe
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## src, summarize
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## is.discrete, summarize
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## format.pval, units
library("PerformanceAnalytics")
## Loading required package: xts
## Loading required package: zoo
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## Attaching package: 'zoo'
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## as.Date, as.Date.numeric
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## Attaching package: 'xts'
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## first, last
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## Attaching package: 'PerformanceAnalytics'
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## kurtosis, skewness
## The following object is masked from 'package:graphics':
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## legend
library(onewaytests)
## Registered S3 methods overwritten by 'car':
## method from
## hist.boot FSA
## confint.boot FSA
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## Attaching package: 'onewaytests'
## The following object is masked from 'package:Hmisc':
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## describe
## The following object is masked from 'package:psych':
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## describe
library(emmeans)
library(ggthemes)
library(multcompView)
library(RColorBrewer)
library(correlation)
## Warning: package 'correlation' was built under R version 4.1.2
##
## Attaching package: 'correlation'
## The following object is masked from 'package:agricolae':
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## correlation
library(tibble)
library(fmsb)
## Warning: package 'fmsb' was built under R version 4.1.2
##Reading data
clones <- read.table("sensof.csv", header=T, sep=",")
# Assigning some variables to factors
clones$gen<-as.factor(clones$gen)
clones$trat<-as.factor(clones$trat)
clones$time<-as.factor(clones$time)
clones$muestra<-as.factor(clones$muestra)
clones$gentrat<-as.factor(clones$gentrat)
## Generando bases de datos para los tiempos a evaluar
clones.4 <- filter(clones, clones$time == "4")
clones.5 <- filter(clones, clones$time == "5")
clones.6 <- filter(clones, clones$time == "6")
clones.7 <- filter(clones, clones$time == "7")
##Quitando los tiempos no utilizados
clones.4.fin <- droplevels(clones.4)
clones.5.fin <- droplevels(clones.5)
clones.6.fin <- droplevels(clones.6)
clones.7.fin <- droplevels(clones.7)
##Convirtiendo a factor los genotipos y ambientes
#genotipos
clones.4.fin$gen<-as.factor(clones.4.fin$gen)
clones.5.fin$gen<-as.factor(clones.5.fin$gen)
clones.6.fin$gen<-as.factor(clones.6.fin$gen)
clones.7.fin$gen<-as.factor(clones.7.fin$gen)
#ambientes
clones.4.fin$trat<-as.factor(clones.4.fin$trat)
clones.5.fin$trat<-as.factor(clones.5.fin$trat)
clones.6.fin$trat<-as.factor(clones.6.fin$trat)
clones.7.fin$trat<-as.factor(clones.7.fin$trat)
#interacción
clones.4.fin$gentrat<-as.factor(clones.4.fin$gentrat)
clones.5.fin$gentrat<-as.factor(clones.5.fin$gentrat)
clones.6.fin$gentrat<-as.factor(clones.6.fin$gentrat)
clones.7.fin$gentrat<-as.factor(clones.7.fin$gentrat)
##Diferencias entre genotipos, tratamientos y tratamientos por genotipo para los sabores
#144 hours
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(amargo, na.rm = TRUE),
sd = sd(amargo, na.rm = TRUE),
median = median(amargo, na.rm = TRUE),
IQR = IQR(amargo, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 4.92 0.669 5 0.25
## 2 ICS 95 12 6.25 0.866 6.5 1.25
## 3 TCS01 12 4.5 0.674 5 1
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(astringente, na.rm = TRUE),
sd = sd(astringente, na.rm = TRUE),
median = median(astringente, na.rm = TRUE),
IQR = IQR(astringente, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 4.58 1.31 4.5 1.5
## 2 ICS 95 12 5.75 0.622 6 1
## 3 TCS01 12 4.08 1.24 4 1.25
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(acido, na.rm = TRUE),
sd = sd(acido, na.rm = TRUE),
median = median(acido, na.rm = TRUE),
IQR = IQR(acido, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 4.5 1 4.5 1
## 2 ICS 95 12 4.25 0.622 4 1
## 3 TCS01 12 3.25 0.622 3 1
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(cacao, na.rm = TRUE),
sd = sd(cacao, na.rm = TRUE),
median = median(cacao, na.rm = TRUE),
IQR = IQR(cacao, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 5.25 1.42 6 2
## 2 ICS 95 12 2.75 0.622 3 1
## 3 TCS01 12 5.83 1.11 6 0.5
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(frutal, na.rm = TRUE),
sd = sd(frutal, na.rm = TRUE),
median = median(frutal, na.rm = TRUE),
IQR = IQR(frutal, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 2.58 1.16 3 1.5
## 2 ICS 95 12 1.5 0.798 1.5 1
## 3 TCS01 12 4.08 1.31 5 1.5
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(floral, na.rm = TRUE),
sd = sd(floral, na.rm = TRUE),
median = median(floral, na.rm = TRUE),
IQR = IQR(floral, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 2.08 0.793 2 1.25
## 2 ICS 95 12 0.5 0.798 0 1
## 3 TCS01 12 3 1.35 3 2
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(dulce, na.rm = TRUE),
sd = sd(dulce, na.rm = TRUE),
median = median(dulce, na.rm = TRUE),
IQR = IQR(dulce, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 1.08 0.900 1 2
## 2 ICS 95 12 0.417 0.515 0 1
## 3 TCS01 12 3.75 1.36 4 2
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(nuez, na.rm = TRUE),
sd = sd(nuez, na.rm = TRUE),
median = median(nuez, na.rm = TRUE),
IQR = IQR(nuez, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 2.17 1.27 2 2
## 2 ICS 95 12 1 0.953 1 2
## 3 TCS01 12 3.17 1.03 3.5 1.25
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(madera, na.rm = TRUE),
sd = sd(madera, na.rm = TRUE),
median = median(madera, na.rm = TRUE),
IQR = IQR(madera, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 1.42 0.669 1.5 1
## 2 ICS 95 12 1.58 0.669 1.5 1
## 3 TCS01 12 1.67 1.61 1.5 2.5
group_by(clones.6.fin, gen) %>%
summarise(
count = n(),
mean = mean(herbal, na.rm = TRUE),
sd = sd(herbal, na.rm = TRUE),
median = median(herbal, na.rm = TRUE),
IQR = IQR(herbal, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 0.833 0.835 1 1.25
## 2 ICS 95 12 1.42 0.669 1.5 1
## 3 TCS01 12 1.92 1.44 2 1.5
#Anova
fit.ama<-aov(clones.6.fin$amargo~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.ama)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 20.056 10.028 25.786 3.05e-07 ***
## clones.6.fin$trat 1 2.778 2.778 7.143 0.0121 *
## clones.6.fin$gen:clones.6.fin$trat 2 3.722 1.861 4.786 0.0157 *
## Residuals 30 11.667 0.389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.ast<-aov(clones.6.fin$astringente~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.ast)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 17.556 8.778 11.049 0.000254 ***
## clones.6.fin$trat 1 0.028 0.028 0.035 0.852928
## clones.6.fin$gen:clones.6.fin$trat 2 16.222 8.111 10.210 0.000415 ***
## Residuals 30 23.833 0.794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.acd<-aov(clones.6.fin$acido~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.acd)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 10.500 5.250 8.438 0.00124 **
## clones.6.fin$trat 1 0.111 0.111 0.179 0.67562
## clones.6.fin$gen:clones.6.fin$trat 2 0.722 0.361 0.580 0.56586
## Residuals 30 18.667 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.cac<-aov(clones.6.fin$cacao~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.cac)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 64.39 32.19 41.993 2.01e-09 ***
## clones.6.fin$trat 1 9.00 9.00 11.739 0.0018 **
## clones.6.fin$gen:clones.6.fin$trat 2 8.17 4.08 5.326 0.0105 *
## Residuals 30 23.00 0.77
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.frut<-aov(clones.6.fin$frutal~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.frut)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 40.39 20.194 15.943 1.92e-05 ***
## clones.6.fin$trat 1 0.44 0.444 0.351 0.558
## clones.6.fin$gen:clones.6.fin$trat 2 2.39 1.194 0.943 0.401
## Residuals 30 38.00 1.267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.flor<-aov(clones.6.fin$floral~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.flor)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 38.39 19.194 17.718 8.31e-06 ***
## clones.6.fin$trat 1 0.69 0.694 0.641 0.430
## clones.6.fin$gen:clones.6.fin$trat 2 0.72 0.361 0.333 0.719
## Residuals 30 32.50 1.083
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.dul<-aov(clones.6.fin$dulce~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.dul)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 74.67 37.33 43.355 1.41e-09 ***
## clones.6.fin$trat 1 0.69 0.69 0.806 0.3763
## clones.6.fin$gen:clones.6.fin$trat 2 5.56 2.78 3.226 0.0538 .
## Residuals 30 25.83 0.86
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.nuez<-aov(clones.6.fin$nuez~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.nuez)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 28.22 14.111 12.959 8.78e-05 ***
## clones.6.fin$trat 1 0.44 0.444 0.408 0.5278
## clones.6.fin$gen:clones.6.fin$trat 2 6.22 3.111 2.857 0.0731 .
## Residuals 30 32.67 1.089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.mad<-aov(clones.6.fin$madera~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.mad)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 0.389 0.1944 0.194 0.8243
## clones.6.fin$trat 1 2.778 2.7778 2.778 0.1060
## clones.6.fin$gen:clones.6.fin$trat 2 5.722 2.8611 2.861 0.0729 .
## Residuals 30 30.000 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.her<-aov(clones.6.fin$herbal~clones.6.fin$gen*clones.6.fin$trat)
summary(fit.her)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.6.fin$gen 2 7.056 3.528 3.451 0.0448 *
## clones.6.fin$trat 1 2.778 2.778 2.717 0.1097
## clones.6.fin$gen:clones.6.fin$trat 2 2.056 1.028 1.005 0.3779
## Residuals 30 30.667 1.022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#gen
kruskal.test(amargo ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: amargo by gen
## Kruskal-Wallis chi-squared = 17.683, df = 2, p-value = 0.0001446
kruskal.test(astringente ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: astringente by gen
## Kruskal-Wallis chi-squared = 11.724, df = 2, p-value = 0.002845
kruskal.test(acido ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: acido by gen
## Kruskal-Wallis chi-squared = 12.623, df = 2, p-value = 0.001816
kruskal.test(cacao ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: cacao by gen
## Kruskal-Wallis chi-squared = 20.531, df = 2, p-value = 3.481e-05
kruskal.test(frutal ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: frutal by gen
## Kruskal-Wallis chi-squared = 16.655, df = 2, p-value = 0.0002418
kruskal.test(floral ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: floral by gen
## Kruskal-Wallis chi-squared = 18.79, df = 2, p-value = 8.313e-05
kruskal.test(dulce ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: dulce by gen
## Kruskal-Wallis chi-squared = 22.647, df = 2, p-value = 1.209e-05
kruskal.test(nuez ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: nuez by gen
## Kruskal-Wallis chi-squared = 14.517, df = 2, p-value = 0.0007041
kruskal.test(madera ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: madera by gen
## Kruskal-Wallis chi-squared = 0.14021, df = 2, p-value = 0.9323
kruskal.test(herbal ~ gen, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: herbal by gen
## Kruskal-Wallis chi-squared = 4.8458, df = 2, p-value = 0.08867
#trat
kruskal.test(amargo ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: amargo by trat
## Kruskal-Wallis chi-squared = 1.8643, df = 1, p-value = 0.1721
kruskal.test(astringente ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: astringente by trat
## Kruskal-Wallis chi-squared = 0.11719, df = 1, p-value = 0.7321
kruskal.test(acido ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: acido by trat
## Kruskal-Wallis chi-squared = 0.34223, df = 1, p-value = 0.5585
kruskal.test(cacao ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: cacao by trat
## Kruskal-Wallis chi-squared = 3.1444, df = 1, p-value = 0.07619
kruskal.test(frutal ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: frutal by trat
## Kruskal-Wallis chi-squared = 0.33939, df = 1, p-value = 0.5602
kruskal.test(floral ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: floral by trat
## Kruskal-Wallis chi-squared = 0.23652, df = 1, p-value = 0.6267
kruskal.test(dulce ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: dulce by trat
## Kruskal-Wallis chi-squared = 0.1649, df = 1, p-value = 0.6847
kruskal.test(nuez ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: nuez by trat
## Kruskal-Wallis chi-squared = 0.37861, df = 1, p-value = 0.5383
kruskal.test(madera ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: madera by trat
## Kruskal-Wallis chi-squared = 1.9807, df = 1, p-value = 0.1593
kruskal.test(herbal ~ trat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: herbal by trat
## Kruskal-Wallis chi-squared = 1.5142, df = 1, p-value = 0.2185
#gentrat
kruskal.test(amargo ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: amargo by gentrat
## Kruskal-Wallis chi-squared = 24.003, df = 5, p-value = 0.0002169
kruskal.test(astringente ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: astringente by gentrat
## Kruskal-Wallis chi-squared = 20.485, df = 5, p-value = 0.001013
kruskal.test(acido ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: acido by gentrat
## Kruskal-Wallis chi-squared = 13.683, df = 5, p-value = 0.01775
kruskal.test(cacao ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: cacao by gentrat
## Kruskal-Wallis chi-squared = 26.464, df = 5, p-value = 7.252e-05
kruskal.test(frutal ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: frutal by gentrat
## Kruskal-Wallis chi-squared = 18.482, df = 5, p-value = 0.002399
kruskal.test(floral ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: floral by gentrat
## Kruskal-Wallis chi-squared = 19.234, df = 5, p-value = 0.001739
kruskal.test(dulce ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: dulce by gentrat
## Kruskal-Wallis chi-squared = 26.812, df = 5, p-value = 6.206e-05
kruskal.test(nuez ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: nuez by gentrat
## Kruskal-Wallis chi-squared = 18.271, df = 5, p-value = 0.002625
kruskal.test(madera ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: madera by gentrat
## Kruskal-Wallis chi-squared = 5.0097, df = 5, p-value = 0.4147
kruskal.test(herbal ~ gentrat, data = clones.6.fin)
##
## Kruskal-Wallis rank sum test
##
## data: herbal by gentrat
## Kruskal-Wallis chi-squared = 6.9723, df = 5, p-value = 0.2227
#a posteriori
pairwise.wilcox.test(clones.6.fin$amargo, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$amargo and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.00237 -
## TCS01 0.19032 0.00065
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$astringente, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$astringente and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.0283 -
## TCS01 0.4760 0.0023
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$acido, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$acido and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.5147 -
## TCS01 0.0049 0.0049
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$cacao, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$cacao and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.00035 -
## TCS01 0.34330 0.00020
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$frutal, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$frutal and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.02664 -
## TCS01 0.01270 0.00078
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$floral, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$floral and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.00084 -
## TCS01 0.04213 0.00084
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$dulce, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$dulce and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.06167 -
## TCS01 0.00036 0.00013
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$nuez, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$nuez and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.044 -
## TCS01 0.052 0.001
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$madera, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$madera and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 1 -
## TCS01 1 1
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.6.fin$herbal, clones.6.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.6.fin$herbal and clones.6.fin$gen
##
## EET8 ICS 95
## ICS 95 0.13 -
## TCS01 0.13 0.50
##
## P value adjustment method: BH
# 168 hours
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(amargo, na.rm = TRUE),
sd = sd(amargo, na.rm = TRUE),
median = median(amargo, na.rm = TRUE),
IQR = IQR(amargo, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 5.67 1.07 6 1.25
## 2 ICS 95 12 5.17 0.718 5 1
## 3 TCS01 12 3.5 0.674 4 1
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(astringente, na.rm = TRUE),
sd = sd(astringente, na.rm = TRUE),
median = median(astringente, na.rm = TRUE),
IQR = IQR(astringente, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 4.83 1.19 5 2
## 2 ICS 95 12 4.92 1.08 5 1.25
## 3 TCS01 12 2.67 0.651 3 1
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(acido, na.rm = TRUE),
sd = sd(acido, na.rm = TRUE),
median = median(acido, na.rm = TRUE),
IQR = IQR(acido, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 4.17 0.937 4 1.25
## 2 ICS 95 12 3.67 0.778 3.5 1
## 3 TCS01 12 3.83 0.937 3.5 2
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(cacao, na.rm = TRUE),
sd = sd(cacao, na.rm = TRUE),
median = median(cacao, na.rm = TRUE),
IQR = IQR(cacao, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 4.92 0.900 5 0.5
## 2 ICS 95 12 3.5 1 3 1.25
## 3 TCS01 12 4.5 1.17 4 2
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(frutal, na.rm = TRUE),
sd = sd(frutal, na.rm = TRUE),
median = median(frutal, na.rm = TRUE),
IQR = IQR(frutal, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 2.83 1.59 2.5 2.5
## 2 ICS 95 12 2.17 0.718 2 1
## 3 TCS01 12 3.17 1.47 3 3
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(floral, na.rm = TRUE),
sd = sd(floral, na.rm = TRUE),
median = median(floral, na.rm = TRUE),
IQR = IQR(floral, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 1.08 0.900 1 0.5
## 2 ICS 95 12 0.833 0.389 1 0
## 3 TCS01 12 2.67 1.07 3 1.25
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(dulce, na.rm = TRUE),
sd = sd(dulce, na.rm = TRUE),
median = median(dulce, na.rm = TRUE),
IQR = IQR(dulce, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 2.08 1.83 2 2.5
## 2 ICS 95 12 0.583 0.669 0.5 1
## 3 TCS01 12 2.58 0.669 2.5 1
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(nuez, na.rm = TRUE),
sd = sd(nuez, na.rm = TRUE),
median = median(nuez, na.rm = TRUE),
IQR = IQR(nuez, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 1.83 1.27 1.5 1.25
## 2 ICS 95 12 1.58 1.08 2 1.25
## 3 TCS01 12 4.25 1.42 4 1.5
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(madera, na.rm = TRUE),
sd = sd(madera, na.rm = TRUE),
median = median(madera, na.rm = TRUE),
IQR = IQR(madera, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 1.42 0.793 2 1
## 2 ICS 95 12 1 0.426 1 0
## 3 TCS01 12 1.08 0.996 1 2
group_by(clones.7.fin, gen) %>%
summarise(
count = n(),
mean = mean(herbal, na.rm = TRUE),
sd = sd(herbal, na.rm = TRUE),
median = median(herbal, na.rm = TRUE),
IQR = IQR(herbal, na.rm = TRUE)
)
## # A tibble: 3 × 6
## gen count mean sd median IQR
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 EET8 12 0.833 0.835 1 1.25
## 2 ICS 95 12 0.583 0.515 1 1
## 3 TCS01 12 1.83 1.59 2 3
#Anova
fit.ama<-aov(clones.7.fin$amargo~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.ama)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 30.89 15.444 34.75 1.55e-08 ***
## clones.7.fin$trat 1 4.00 4.000 9.00 0.00539 **
## clones.7.fin$gen:clones.7.fin$trat 2 6.00 3.000 6.75 0.00380 **
## Residuals 30 13.33 0.444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.ast<-aov(clones.7.fin$astringente~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.ast)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 39.06 19.528 34.126 1.87e-08 ***
## clones.7.fin$trat 1 10.03 10.028 17.524 0.000228 ***
## clones.7.fin$gen:clones.7.fin$trat 2 6.06 3.028 5.291 0.010758 *
## Residuals 30 17.17 0.572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.acd<-aov(clones.7.fin$acido~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.acd)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 1.556 0.7778 0.959 0.395
## clones.7.fin$trat 1 0.111 0.1111 0.137 0.714
## clones.7.fin$gen:clones.7.fin$trat 2 1.556 0.7778 0.959 0.395
## Residuals 30 24.333 0.8111
fit.cac<-aov(clones.7.fin$cacao~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.cac)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 12.722 6.361 9.016 0.000859 ***
## clones.7.fin$trat 1 1.361 1.361 1.929 0.175079
## clones.7.fin$gen:clones.7.fin$trat 2 12.389 6.194 8.780 0.000996 ***
## Residuals 30 21.167 0.706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.frut<-aov(clones.7.fin$frutal~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.frut)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 6.22 3.111 4.375 0.0215 *
## clones.7.fin$trat 1 1.00 1.000 1.406 0.2450
## clones.7.fin$gen:clones.7.fin$trat 2 34.67 17.333 24.375 5.16e-07 ***
## Residuals 30 21.33 0.711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.flor<-aov(clones.7.fin$floral~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.flor)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 23.722 11.861 17.941 7.5e-06 ***
## clones.7.fin$trat 1 0.250 0.250 0.378 0.543
## clones.7.fin$gen:clones.7.fin$trat 2 3.167 1.583 2.395 0.108
## Residuals 30 19.833 0.661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.dul<-aov(clones.7.fin$dulce~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.dul)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 26.00 13.000 19.02 4.62e-06 ***
## clones.7.fin$trat 1 12.25 12.250 17.93 0.000200 ***
## clones.7.fin$gen:clones.7.fin$trat 2 14.00 7.000 10.24 0.000406 ***
## Residuals 30 20.50 0.683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.nuez<-aov(clones.7.fin$nuez~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.nuez)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 52.06 26.028 22.099 1.26e-06 ***
## clones.7.fin$trat 1 0.11 0.111 0.094 0.76085
## clones.7.fin$gen:clones.7.fin$trat 2 17.39 8.694 7.382 0.00247 **
## Residuals 30 35.33 1.178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit.mad<-aov(clones.7.fin$madera~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.mad)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 1.167 0.5833 0.921 0.409
## clones.7.fin$trat 1 0.444 0.4444 0.702 0.409
## clones.7.fin$gen:clones.7.fin$trat 2 0.389 0.1944 0.307 0.738
## Residuals 30 19.000 0.6333
fit.her<-aov(clones.7.fin$herbal~clones.7.fin$gen*clones.7.fin$trat)
summary(fit.her)
## Df Sum Sq Mean Sq F value Pr(>F)
## clones.7.fin$gen 2 10.50 5.250 4.749 0.0162 *
## clones.7.fin$trat 1 0.03 0.028 0.025 0.8751
## clones.7.fin$gen:clones.7.fin$trat 2 5.06 2.528 2.286 0.1191
## Residuals 30 33.17 1.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#gen
kruskal.test(amargo ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: amargo by gen
## Kruskal-Wallis chi-squared = 20.87, df = 2, p-value = 2.938e-05
kruskal.test(astringente ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: astringente by gen
## Kruskal-Wallis chi-squared = 19.523, df = 2, p-value = 5.762e-05
kruskal.test(acido ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: acido by gen
## Kruskal-Wallis chi-squared = 1.8205, df = 2, p-value = 0.4024
kruskal.test(cacao ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: cacao by gen
## Kruskal-Wallis chi-squared = 9.4108, df = 2, p-value = 0.009046
kruskal.test(frutal ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: frutal by gen
## Kruskal-Wallis chi-squared = 2.5129, df = 2, p-value = 0.2847
kruskal.test(floral ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: floral by gen
## Kruskal-Wallis chi-squared = 17.219, df = 2, p-value = 0.0001824
kruskal.test(dulce ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: dulce by gen
## Kruskal-Wallis chi-squared = 15.136, df = 2, p-value = 0.0005167
kruskal.test(nuez ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: nuez by gen
## Kruskal-Wallis chi-squared = 16.248, df = 2, p-value = 0.0002964
kruskal.test(madera ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: madera by gen
## Kruskal-Wallis chi-squared = 2.5996, df = 2, p-value = 0.2726
kruskal.test(herbal ~ gen, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: herbal by gen
## Kruskal-Wallis chi-squared = 4.2588, df = 2, p-value = 0.1189
#trat
kruskal.test(amargo ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: amargo by trat
## Kruskal-Wallis chi-squared = 2.9798, df = 1, p-value = 0.08431
kruskal.test(astringente ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: astringente by trat
## Kruskal-Wallis chi-squared = 4.5403, df = 1, p-value = 0.03311
kruskal.test(acido ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: acido by trat
## Kruskal-Wallis chi-squared = 0.2219, df = 1, p-value = 0.6376
kruskal.test(cacao ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: cacao by trat
## Kruskal-Wallis chi-squared = 1.1942, df = 1, p-value = 0.2745
kruskal.test(frutal ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: frutal by trat
## Kruskal-Wallis chi-squared = 1.2131, df = 1, p-value = 0.2707
kruskal.test(floral ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: floral by trat
## Kruskal-Wallis chi-squared = 0.00028862, df = 1, p-value = 0.9864
kruskal.test(dulce ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: dulce by trat
## Kruskal-Wallis chi-squared = 5.9977, df = 1, p-value = 0.01432
kruskal.test(nuez ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: nuez by trat
## Kruskal-Wallis chi-squared = 0.46009, df = 1, p-value = 0.4976
kruskal.test(madera ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: madera by trat
## Kruskal-Wallis chi-squared = 0.9795, df = 1, p-value = 0.3223
kruskal.test(herbal ~ trat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: herbal by trat
## Kruskal-Wallis chi-squared = 0.18767, df = 1, p-value = 0.6649
#gentrat
kruskal.test(amargo ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: amargo by gentrat
## Kruskal-Wallis chi-squared = 27.004, df = 5, p-value = 5.693e-05
kruskal.test(astringente ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: astringente by gentrat
## Kruskal-Wallis chi-squared = 27.033, df = 5, p-value = 5.622e-05
kruskal.test(acido ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: acido by gentrat
## Kruskal-Wallis chi-squared = 4.2455, df = 5, p-value = 0.5146
kruskal.test(cacao ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: cacao by gentrat
## Kruskal-Wallis chi-squared = 19.682, df = 5, p-value = 0.001434
kruskal.test(frutal ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: frutal by gentrat
## Kruskal-Wallis chi-squared = 22.773, df = 5, p-value = 0.000373
kruskal.test(floral ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: floral by gentrat
## Kruskal-Wallis chi-squared = 19.343, df = 5, p-value = 0.001659
kruskal.test(dulce ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: dulce by gentrat
## Kruskal-Wallis chi-squared = 26.789, df = 5, p-value = 6.27e-05
kruskal.test(nuez ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: nuez by gentrat
## Kruskal-Wallis chi-squared = 21.56, df = 5, p-value = 0.0006346
kruskal.test(madera ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: madera by gentrat
## Kruskal-Wallis chi-squared = 4.3152, df = 5, p-value = 0.505
kruskal.test(herbal ~ gentrat, data = clones.7.fin)
##
## Kruskal-Wallis rank sum test
##
## data: herbal by gentrat
## Kruskal-Wallis chi-squared = 9.3705, df = 5, p-value = 0.09517
#a posteriori
pairwise.wilcox.test(clones.7.fin$amargo, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$amargo and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.21350 -
## TCS01 0.00018 0.00018
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$astringente, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$astringente and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 1.00000 -
## TCS01 0.00034 0.00029
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$acido, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$acido and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.56 -
## TCS01 0.62 0.73
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$cacao, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$cacao and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.011 -
## TCS01 0.321 0.049
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$frutal, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$frutal and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.55 -
## TCS01 0.55 0.32
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$floral, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$floral and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.55615 -
## TCS01 0.00329 0.00041
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$dulce, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$dulce and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.04518 -
## TCS01 0.26794 0.00014
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$nuez, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$nuez and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.8808 -
## TCS01 0.0015 0.0010
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$madera, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$madera and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.25 -
## TCS01 0.49 1.00
##
## P value adjustment method: BH
pairwise.wilcox.test(clones.7.fin$herbal, clones.7.fin$gen,
p.adjust.method = "BH")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: clones.7.fin$herbal and clones.7.fin$gen
##
## EET8 ICS 95
## ICS 95 0.53 -
## TCS01 0.19 0.19
##
## P value adjustment method: BH
## Correlaciones para variables originales en tiempo 6 y 7
# Tiempo 6
phenom.6<-clones.6.fin %>%
select(trat, amargo, astringente, acido, cacao, frutal, floral, dulce, nuez, madera, herbal
) %>%
group_by(trat) %>%
correlation(method = "spearman")
phenom6<-phenom.6 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 90 × 11
## Group Parameter1 Parameter2 rho CI CI_low CI_high S p
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 "Large " amargo astringen… 0.718 0.95 3.65e-1 0.890 273. 3.18e-2
## 2 "Large " amargo acido 0.484 0.95 6.94e-3 0.781 500. 9.27e-1
## 3 "Large " amargo cacao -0.663 0.95 -8.66e-1 -0.270 1611. 9.37e-2
## 4 "Large " amargo frutal -0.667 0.95 -8.68e-1 -0.277 1616. 9.19e-2
## 5 "Large " amargo floral -0.724 0.95 -8.93e-1 -0.376 1671. 2.76e-2
## 6 "Large " amargo dulce -0.813 0.95 -9.30e-1 -0.548 1757. 1.80e-3
## 7 "Large " amargo nuez -0.700 0.95 -8.83e-1 -0.333 1647. 4.77e-2
## 8 "Large " amargo madera 0.286 0.95 -2.23e-1 0.672 692. 1 e+0
## 9 "Large " amargo herbal -0.102 0.95 -5.54e-1 0.395 1068. 1 e+0
## 10 "Large " astringente acido 0.667 0.95 2.77e-1 0.868 322. 9.19e-2
## 11 "Large " astringente cacao -0.619 0.95 -8.47e-1 -0.199 1569. 1.86e-1
## 12 "Large " astringente frutal -0.689 0.95 -8.78e-1 -0.314 1637. 5.95e-2
## 13 "Large " astringente floral -0.582 0.95 -8.30e-1 -0.144 1533. 3.26e-1
## 14 "Large " astringente dulce -0.850 0.95 -9.45e-1 -0.627 1793. 3.52e-4
## 15 "Large " astringente nuez -0.415 0.95 -7.46e-1 0.0788 1372. 1 e+0
## 16 "Large " astringente madera 0.252 0.95 -2.58e-1 0.652 725. 1 e+0
## 17 "Large " astringente herbal -0.0880 0.95 -5.44e-1 0.408 1054. 1 e+0
## 18 "Large " acido cacao -0.277 0.95 -6.67e-1 0.232 1237. 1 e+0
## 19 "Large " acido frutal -0.339 0.95 -7.03e-1 0.167 1297. 1 e+0
## 20 "Large " acido floral -0.203 0.95 -6.21e-1 0.305 1166. 1 e+0
## 21 "Large " acido dulce -0.521 0.95 -8.00e-1 -0.0571 1474. 6.36e-1
## 22 "Large " acido nuez -0.0844 0.95 -5.41e-1 0.411 1051. 1 e+0
## 23 "Large " acido madera 0.0524 0.95 -4.37e-1 0.518 918. 1 e+0
## 24 "Large " acido herbal -0.259 0.95 -6.56e-1 0.251 1220. 1 e+0
## 25 "Large " cacao frutal 0.642 0.95 2.36e-1 0.857 347. 1.34e-1
## 26 "Large " cacao floral 0.641 0.95 2.34e-1 0.857 348. 1.34e-1
## 27 "Large " cacao dulce 0.735 0.95 3.97e-1 0.898 256. 2.12e-2
## 28 "Large " cacao nuez 0.540 0.95 8.32e-2 0.809 446. 5.17e-1
## 29 "Large " cacao madera -0.568 0.95 -8.23e-1 -0.123 1519. 3.63e-1
## 30 "Large " cacao herbal -0.0698 0.95 -5.31e-1 0.423 1037. 1 e+0
## 31 "Large " frutal floral 0.625 0.95 2.08e-1 0.849 364. 1.73e-1
## 32 "Large " frutal dulce 0.664 0.95 2.71e-1 0.867 326. 9.37e-2
## 33 "Large " frutal nuez 0.746 0.95 4.15e-1 0.902 246. 1.64e-2
## 34 "Large " frutal madera -0.318 0.95 -6.91e-1 0.189 1277. 1 e+0
## 35 "Large " frutal herbal 0.144 0.95 -3.59e-1 0.582 830. 1 e+0
## 36 "Large " floral dulce 0.579 0.95 1.39e-1 0.828 408. 3.31e-1
## 37 "Large " floral nuez 0.487 0.95 1.13e-2 0.783 497. 9.27e-1
## 38 "Large " floral madera -0.321 0.95 -6.93e-1 0.186 1280. 1 e+0
## 39 "Large " floral herbal -0.0467 0.95 -5.14e-1 0.442 1014. 1 e+0
## 40 "Large " dulce nuez 0.572 0.95 1.29e-1 0.825 415. 3.55e-1
## 41 "Large " dulce madera -0.355 0.95 -7.12e-1 0.149 1313. 1 e+0
## 42 "Large " dulce herbal 0.00114 0.95 -4.78e-1 0.479 968. 1 e+0
## 43 "Large " nuez madera -0.393 0.95 -7.34e-1 0.105 1350. 1 e+0
## 44 "Large " nuez herbal -0.0641 0.95 -5.26e-1 0.428 1031. 1 e+0
## 45 "Large " madera herbal 0.342 0.95 -1.64e-1 0.705 638. 1 e+0
## 46 "Small" amargo astringen… 0.478 0.95 -6.79e-4 0.778 506. 1 e+0
## 47 "Small" amargo acido -0.140 0.95 -5.80e-1 0.363 1105. 1 e+0
## 48 "Small" amargo cacao -0.490 0.95 -7.84e-1 -0.0148 1444. 1 e+0
## 49 "Small" amargo frutal -0.0956 0.95 -5.49e-1 0.401 1062. 1 e+0
## 50 "Small" amargo floral -0.497 0.95 -7.88e-1 -0.0242 1451. 1 e+0
## 51 "Small" amargo dulce -0.0555 0.95 -5.20e-1 0.435 1023. 1 e+0
## 52 "Small" amargo nuez -0.276 0.95 -6.66e-1 0.233 1236. 1 e+0
## 53 "Small" amargo madera 0.0650 0.95 -4.27e-1 0.527 906. 1 e+0
## 54 "Small" amargo herbal 0.216 0.95 -2.92e-1 0.630 759. 1 e+0
## 55 "Small" astringente acido 0.100 0.95 -3.97e-1 0.552 872. 1 e+0
## 56 "Small" astringente cacao -0.00413 0.95 -4.82e-1 0.475 973. 1 e+0
## 57 "Small" astringente frutal -0.0909 0.95 -5.46e-1 0.405 1057. 1 e+0
## 58 "Small" astringente floral -0.199 0.95 -6.18e-1 0.309 1162. 1 e+0
## 59 "Small" astringente dulce 0.251 0.95 -2.59e-1 0.651 726. 1 e+0
## 60 "Small" astringente nuez 0.251 0.95 -2.59e-1 0.651 726. 1 e+0
## 61 "Small" astringente madera 0.384 0.95 -1.16e-1 0.728 597. 1 e+0
## 62 "Small" astringente herbal 0.225 0.95 -2.84e-1 0.635 751. 1 e+0
## 63 "Small" acido cacao 0.0257 0.95 -4.58e-1 0.498 944. 1 e+0
## 64 "Small" acido frutal -0.553 0.95 -8.16e-1 -0.101 1505. 6.43e-1
## 65 "Small" acido floral -0.302 0.95 -6.82e-1 0.207 1261. 1 e+0
## 66 "Small" acido dulce -0.517 0.95 -7.98e-1 -0.0506 1470. 1 e+0
## 67 "Small" acido nuez -0.234 0.95 -6.41e-1 0.275 1196. 1 e+0
## 68 "Small" acido madera -0.284 0.95 -6.71e-1 0.225 1244. 1 e+0
## 69 "Small" acido herbal -0.408 0.95 -7.41e-1 0.0880 1364. 1 e+0
## 70 "Small" cacao frutal 0.516 0.95 4.94e-2 0.797 469. 1 e+0
## 71 "Small" cacao floral 0.650 0.95 2.49e-1 0.861 339. 1.50e-1
## 72 "Small" cacao dulce 0.454 0.95 -3.09e-2 0.766 529. 1 e+0
## 73 "Small" cacao nuez 0.619 0.95 1.99e-1 0.847 369. 2.54e-1
## 74 "Small" cacao madera 0.282 0.95 -2.27e-1 0.670 696. 1 e+0
## 75 "Small" cacao herbal 0.207 0.95 -3.02e-1 0.623 769. 1 e+0
## 76 "Small" frutal floral 0.819 0.95 5.60e-1 0.932 175. 1.44e-3
## 77 "Small" frutal dulce 0.569 0.95 1.24e-1 0.823 418. 5.38e-1
## 78 "Small" frutal nuez 0.697 0.95 3.27e-1 0.881 294. 5.81e-2
## 79 "Small" frutal madera 0.344 0.95 -1.61e-1 0.706 636. 1 e+0
## 80 "Small" frutal herbal 0.565 0.95 1.18e-1 0.821 422. 5.57e-1
## 81 "Small" floral dulce 0.356 0.95 -1.47e-1 0.713 624. 1 e+0
## 82 "Small" floral nuez 0.624 0.95 2.08e-1 0.849 364. 2.35e-1
## 83 "Small" floral madera 0.412 0.95 -8.32e-2 0.744 570. 1 e+0
## 84 "Small" floral herbal 0.446 0.95 -4.16e-2 0.762 537. 1 e+0
## 85 "Small" dulce nuez 0.616 0.95 1.95e-1 0.845 372. 2.60e-1
## 86 "Small" dulce madera 0.484 0.95 7.59e-3 0.782 500. 1 e+0
## 87 "Small" dulce herbal 0.262 0.95 -2.48e-1 0.658 715. 1 e+0
## 88 "Small" nuez madera 0.318 0.95 -1.89e-1 0.691 661. 1 e+0
## 89 "Small" nuez herbal 0.439 0.95 -4.98e-2 0.758 543. 1 e+0
## 90 "Small" madera herbal 0.489 0.95 1.44e-2 0.784 495. 1 e+0
## # … with 2 more variables: Method <chr>, n_Obs <int>
write.csv(phenom6, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Lucero/data/phenom6.csv")
# Tiempo 7
phenom.7<-clones.7.fin %>%
select(trat, amargo, astringente, acido, cacao, frutal, floral, dulce, nuez, madera, herbal
) %>%
group_by(trat) %>%
correlation(method = "spearman")
phenom7<-phenom.7 %>% as_tibble() %>% print(n=Inf)
## # A tibble: 90 × 11
## Group Parameter1 Parameter2 rho CI CI_low CI_high S p
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 "Large… amargo astringen… 0.788 0.95 4.98e-1 0.920 205. 4.16e-3
## 2 "Large… amargo acido 0.00850 0.95 -4.72e-1 0.485 961. 1 e+0
## 3 "Large… amargo cacao -0.409 0.95 -7.42e-1 0.0863 1365. 1 e+0
## 4 "Large… amargo frutal -0.805 0.95 -9.26e-1 -0.530 1749. 2.39e-3
## 5 "Large… amargo floral -0.731 0.95 -8.96e-1 -0.389 1677. 1.98e-2
## 6 "Large… amargo dulce -0.568 0.95 -8.23e-1 -0.123 1519. 3.62e-1
## 7 "Large… amargo nuez -0.747 0.95 -9.03e-1 -0.417 1692. 1.45e-2
## 8 "Large… amargo madera 0.260 0.95 -2.49e-1 0.657 717. 1 e+0
## 9 "Large… amargo herbal -0.113 0.95 -5.61e-1 0.386 1079. 1 e+0
## 10 "Large… astringen… acido -0.0574 0.95 -5.22e-1 0.433 1025. 1 e+0
## 11 "Large… astringen… cacao -0.710 0.95 -8.87e-1 -0.351 1657. 3.07e-2
## 12 "Large… astringen… frutal -0.782 0.95 -9.17e-1 -0.485 1727. 5.05e-3
## 13 "Large… astringen… floral -0.714 0.95 -8.89e-1 -0.357 1661. 2.91e-2
## 14 "Large… astringen… dulce -0.737 0.95 -8.99e-1 -0.399 1683. 1.74e-2
## 15 "Large… astringen… nuez -0.887 0.95 -9.59e-1 -0.711 1829. 3.98e-5
## 16 "Large… astringen… madera 0.167 0.95 -3.38e-1 0.598 807. 1 e+0
## 17 "Large… astringen… herbal 0.0957 0.95 -4.01e-1 0.549 876. 1 e+0
## 18 "Large… acido cacao 0.154 0.95 -3.50e-1 0.589 820. 1 e+0
## 19 "Large… acido frutal 0.0997 0.95 -3.98e-1 0.552 872. 1 e+0
## 20 "Large… acido floral 0.207 0.95 -3.02e-1 0.623 769. 1 e+0
## 21 "Large… acido dulce 0.238 0.95 -2.71e-1 0.643 738. 1 e+0
## 22 "Large… acido nuez 0.118 0.95 -3.82e-1 0.564 855. 1 e+0
## 23 "Large… acido madera -0.173 0.95 -6.02e-1 0.333 1136. 1 e+0
## 24 "Large… acido herbal -0.213 0.95 -6.28e-1 0.295 1176. 1 e+0
## 25 "Large… cacao frutal 0.625 0.95 2.10e-1 0.850 363. 1.60e-1
## 26 "Large… cacao floral 0.498 0.95 2.57e-2 0.789 486. 8.85e-1
## 27 "Large… cacao dulce 0.624 0.95 2.07e-1 0.849 365. 1.60e-1
## 28 "Large… cacao nuez 0.695 0.95 3.24e-1 0.880 296. 4.28e-2
## 29 "Large… cacao madera -0.0193 0.95 -4.93e-1 0.463 988. 1 e+0
## 30 "Large… cacao herbal -0.273 0.95 -6.65e-1 0.236 1234. 1 e+0
## 31 "Large… frutal floral 0.859 0.95 6.45e-1 0.948 137. 2.19e-4
## 32 "Large… frutal dulce 0.729 0.95 3.85e-1 0.895 263. 2.04e-2
## 33 "Large… frutal nuez 0.898 0.95 7.35e-1 0.963 98.9 1.93e-5
## 34 "Large… frutal madera -0.0382 0.95 -5.07e-1 0.448 1006. 1 e+0
## 35 "Large… frutal herbal -0.0777 0.95 -5.36e-1 0.416 1044. 1 e+0
## 36 "Large… floral dulce 0.680 0.95 2.99e-1 0.874 310. 5.67e-2
## 37 "Large… floral nuez 0.743 0.95 4.10e-1 0.901 249. 1.53e-2
## 38 "Large… floral madera -0.0560 0.95 -5.21e-1 0.434 1023. 1 e+0
## 39 "Large… floral herbal 0.0401 0.95 -4.47e-1 0.509 930. 1 e+0
## 40 "Large… dulce nuez 0.744 0.95 4.12e-1 0.901 248. 1.53e-2
## 41 "Large… dulce madera -0.266 0.95 -6.60e-1 0.243 1227. 1 e+0
## 42 "Large… dulce herbal -0.0147 0.95 -4.90e-1 0.467 983. 1 e+0
## 43 "Large… nuez madera -0.0914 0.95 -5.46e-1 0.405 1058. 1 e+0
## 44 "Large… nuez herbal -0.0285 0.95 -5.00e-1 0.456 997. 1 e+0
## 45 "Large… madera herbal -0.571 0.95 -8.24e-1 -0.128 1523. 3.57e-1
## 46 "Small" amargo astringen… 0.478 0.95 -1.97e-5 0.779 505. 1 e+0
## 47 "Small" amargo acido 0.151 0.95 -3.53e-1 0.587 822. 1 e+0
## 48 "Small" amargo cacao 0.0115 0.95 -4.70e-1 0.487 958. 1 e+0
## 49 "Small" amargo frutal 0.268 0.95 -2.41e-1 0.662 709. 1 e+0
## 50 "Small" amargo floral -0.325 0.95 -6.96e-1 0.181 1284. 1 e+0
## 51 "Small" amargo dulce -0.0557 0.95 -5.20e-1 0.434 1023. 1 e+0
## 52 "Small" amargo nuez -0.143 0.95 -5.82e-1 0.360 1108. 1 e+0
## 53 "Small" amargo madera -0.0134 0.95 -4.89e-1 0.468 982. 1 e+0
## 54 "Small" amargo herbal -0.141 0.95 -5.80e-1 0.362 1106. 1 e+0
## 55 "Small" astringen… acido 0.0831 0.95 -4.12e-1 0.540 889. 1 e+0
## 56 "Small" astringen… cacao -0.259 0.95 -6.56e-1 0.251 1220. 1 e+0
## 57 "Small" astringen… frutal 0.302 0.95 -2.06e-1 0.682 676. 1 e+0
## 58 "Small" astringen… floral -0.612 0.95 -8.44e-1 -0.189 1562. 2.97e-1
## 59 "Small" astringen… dulce -0.481 0.95 -7.80e-1 -0.00324 1435. 1 e+0
## 60 "Small" astringen… nuez -0.343 0.95 -7.05e-1 0.163 1301. 1 e+0
## 61 "Small" astringen… madera 0.210 0.95 -2.98e-1 0.626 765. 1 e+0
## 62 "Small" astringen… herbal -0.424 0.95 -7.50e-1 0.0679 1380. 1 e+0
## 63 "Small" acido cacao 0.129 0.95 -3.73e-1 0.572 844. 1 e+0
## 64 "Small" acido frutal 0.160 0.95 -3.45e-1 0.593 814. 1 e+0
## 65 "Small" acido floral -0.317 0.95 -6.91e-1 0.190 1276. 1 e+0
## 66 "Small" acido dulce -0.00847 0.95 -4.85e-1 0.472 977. 1 e+0
## 67 "Small" acido nuez 0.325 0.95 -1.82e-1 0.695 654. 1 e+0
## 68 "Small" acido madera 0.302 0.95 -2.06e-1 0.682 676. 1 e+0
## 69 "Small" acido herbal -0.548 0.95 -8.13e-1 -0.0938 1500. 7.82e-1
## 70 "Small" cacao frutal 0.454 0.95 -3.17e-2 0.766 529. 1 e+0
## 71 "Small" cacao floral 0.0901 0.95 -4.06e-1 0.545 882. 1 e+0
## 72 "Small" cacao dulce 0.669 0.95 2.80e-1 0.869 321. 1.06e-1
## 73 "Small" cacao nuez 0.187 0.95 -3.20e-1 0.611 788. 1 e+0
## 74 "Small" cacao madera 0.117 0.95 -3.83e-1 0.564 856. 1 e+0
## 75 "Small" cacao herbal 0.0592 0.95 -4.32e-1 0.523 912. 1 e+0
## 76 "Small" frutal floral -0.313 0.95 -6.88e-1 0.194 1272. 1 e+0
## 77 "Small" frutal dulce 0.0744 0.95 -4.19e-1 0.534 897. 1 e+0
## 78 "Small" frutal nuez -0.200 0.95 -6.19e-1 0.308 1163. 1 e+0
## 79 "Small" frutal madera 0.206 0.95 -3.02e-1 0.623 769. 1 e+0
## 80 "Small" frutal herbal -0.108 0.95 -5.58e-1 0.391 1074. 1 e+0
## 81 "Small" floral dulce 0.214 0.95 -2.95e-1 0.628 762. 1 e+0
## 82 "Small" floral nuez 0.306 0.95 -2.02e-1 0.685 672. 1 e+0
## 83 "Small" floral madera -0.164 0.95 -5.96e-1 0.341 1128. 1 e+0
## 84 "Small" floral herbal 0.672 0.95 2.85e-1 0.870 318. 1.02e-1
## 85 "Small" dulce nuez 0.197 0.95 -3.11e-1 0.617 778. 1 e+0
## 86 "Small" dulce madera 0.172 0.95 -3.34e-1 0.601 803. 1 e+0
## 87 "Small" dulce herbal 0.392 0.95 -1.07e-1 0.733 589. 1 e+0
## 88 "Small" nuez madera 0.205 0.95 -3.03e-1 0.623 770. 1 e+0
## 89 "Small" nuez herbal 0.0651 0.95 -4.27e-1 0.527 906. 1 e+0
## 90 "Small" madera herbal -0.00858 0.95 -4.85e-1 0.472 977. 1 e+0
## # … with 2 more variables: Method <chr>, n_Obs <int>
write.csv(phenom7, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Lucero/data/phenom7.csv")
##Gráficos radar
# 144 hours
radar6<-group_by(clones.6.fin, gen) %>%
summarise_at(vars(amargo:herbal), median, na.rm = TRUE) %>%
rename(Bitter=amargo, Astringent=astringente, Acid=acido, Cocoa=cacao,Fruity=frutal,
Floral=floral, Sweet=dulce, Nut=nuez, Wood=madera, Herbal=herbal) %>%
column_to_rownames(var = "gen") %>% head()
radar6 <-rbind(rep(7,10) , rep(0,10) , radar6)
radar6
## Bitter Astringent Acid Cocoa Fruity Floral Sweet Nut Wood Herbal
## 1 7.0 7.0 7.0 7 7.0 7 7 7.0 7.0 7.0
## 2 0.0 0.0 0.0 0 0.0 0 0 0.0 0.0 0.0
## EET8 5.0 4.5 4.5 6 3.0 2 1 2.0 1.5 1.0
## ICS 95 6.5 6.0 4.0 3 1.5 0 0 1.0 1.5 1.5
## TCS01 5.0 4.0 3.0 6 5.0 3 4 3.5 1.5 2.0
# Color vector
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart(radar6, axistype=1, seg = 7,
#custom polygon
pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
#custom the grid
cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,7,1), calcex=0.8, cglwd=0.8,
#custom labels
vlcex=0.8, )
legend(x=1.5, y=1, legend = rownames(radar6[-c(1,2),]), bty = "n", pch=20 , col=colors_in , cex=0.8, pt.cex=1)
# 168 hours
radar7<-group_by(clones.7.fin, gen) %>%
summarise_at(vars(amargo:herbal), median, na.rm = TRUE) %>%
rename(Bitter=amargo, Astringent=astringente, Acid=acido, Cocoa=cacao,Fruity=frutal,
Floral=floral, Sweet=dulce, Nut=nuez, Wood=madera, Herbal=herbal) %>%
column_to_rownames(var = "gen") %>% head()
radar7 <-rbind(rep(7,10) , rep(0,10) , radar7)
radar7
## Bitter Astringent Acid Cocoa Fruity Floral Sweet Nut Wood Herbal
## 1 7 7 7.0 7 7.0 7 7.0 7.0 7 7
## 2 0 0 0.0 0 0.0 0 0.0 0.0 0 0
## EET8 6 5 4.0 5 2.5 1 2.0 1.5 2 1
## ICS 95 5 5 3.5 3 2.0 1 0.5 2.0 1 1
## TCS01 4 3 3.5 4 3.0 3 2.5 4.0 1 2
# Color vector
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart(radar7, axistype=1, seg = 7,
#custom polygon
pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1,
#custom the grid
cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,7,1), calcex=0.8, cglwd=0.8,
#custom labels
vlcex=0.8)
legend(x=1.5, y=1, legend = rownames(radar7[-c(1,2),]), bty = "n", pch=20 , col=colors_in , cex=0.8, pt.cex=1)
