setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Fabricio/Recharged_20C/data")
library(Plasticity)
library(agricolae)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
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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)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
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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.
##
## Attaching package: 'FSA'
## The following object is masked from 'package:psych':
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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
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:psych':
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## describe
## The following objects are masked from 'package:dplyr':
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## src, summarize
## The following objects are masked from 'package:plyr':
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## is.discrete, summarize
## The following objects are masked from 'package:base':
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## format.pval, units
library("PerformanceAnalytics")
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric
##
## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
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## first, last
##
## Attaching package: 'PerformanceAnalytics'
## The following objects are masked from 'package:agricolae':
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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
##
## 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)
clone <- read.table("res_fin.csv", header=T, sep=",")
clone$Repetición<-as.factor(clone$Repetición)
clone$Clon<-as.factor(clone$Clon)
clone$env<-as.factor(clone$env)
## Generando bases de datos para cada ambiente
clone.100 <- filter(clone, clone$env == "100")
clone.300 <- filter(clone, clone$env == "300")
clone.500 <- filter(clone, clone$env == "500")
clone.800 <- filter(clone, clone$env == "800")
clone.900 <- filter(clone, clone$env == "900")
clone.1200 <- filter(clone, clone$env == "1200")
clone.1500 <- filter(clone, clone$env == "1500")
clone.2000 <- filter(clone, clone$env == "2000")
##Quitando las intensidades de luz no utilizadas
clone.100.fin <- droplevels(clone.100)
clone.300.fin <- droplevels(clone.300)
clone.500.fin <- droplevels(clone.500)
clone.800.fin <- droplevels(clone.800)
clone.900.fin <- droplevels(clone.900)
clone.1200.fin <- droplevels(clone.1200)
clone.1500.fin <- droplevels(clone.1500)
clone.2000.fin <- droplevels(clone.2000)
##Convirtiendo a factor los genotipos y ambientes
#genotipos
clone.100.fin$Clon<-as.factor(clone.100.fin$Clon)
clone.300.fin$Clon<-as.factor(clone.300.fin$Clon)
clone.500.fin$Clon<-as.factor(clone.500.fin$Clon)
clone.800.fin$Clon<-as.factor(clone.800.fin$Clon)
clone.900.fin$Clon<-as.factor(clone.900.fin$Clon)
clone.1200.fin$Clon<-as.factor(clone.1200.fin$Clon)
clone.1500.fin$Clon<-as.factor(clone.1500.fin$Clon)
clone.2000.fin$Clon<-as.factor(clone.2000.fin$Clon)
#ambientes
clone.100.fin$env<-as.factor(clone.100.fin$env)
clone.300.fin$env<-as.factor(clone.300.fin$env)
clone.500.fin$env<-as.factor(clone.500.fin$env)
clone.800.fin$env<-as.factor(clone.800.fin$env)
clone.900.fin$env<-as.factor(clone.900.fin$env)
clone.1200.fin$env<-as.factor(clone.1200.fin$env)
clone.1500.fin$env<-as.factor(clone.1500.fin$env)
clone.2000.fin$env<-as.factor(clone.2000.fin$env)
## A
A.100<-aov(A~Clon, data = clone.100.fin)
A.300<-aov(A~Clon, data = clone.300.fin)
A.500<-aov(A~Clon, data = clone.500.fin)
A.800<-aov(A~Clon, data = clone.800.fin)
A.900<-aov(A~Clon, data = clone.900.fin)
A.1200<-aov(A~Clon, data = clone.1200.fin)
A.1500<-aov(A~Clon, data = clone.1500.fin)
A.2000<-aov(A~Clon, data = clone.2000.fin)
# Anova A 100
anova(A.100)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 19.069 1.00364 3.7264 5.039e-05 ***
## Residuals 60 16.160 0.26933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(A.300)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 92.599 4.8736 4.5032 3.922e-06 ***
## Residuals 60 64.935 1.0823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(A.500)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 115.148 6.0604 3.6947 5.612e-05 ***
## Residuals 60 98.419 1.6403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(A.800)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 103.20 5.4314 2.4202 0.004945 **
## Residuals 60 134.65 2.2442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(A.900)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 74.638 3.9283 1.3748 0.1747
## Residuals 60 171.449 2.8575
anova(A.1200)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 61.12 3.2168 0.8795 0.6079
## Residuals 60 219.45 3.6575
anova(A.1500)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 79.918 4.2062 1.0063 0.4675
## Residuals 60 250.781 4.1797
anova(A.2000)
## Analysis of Variance Table
##
## Response: A
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 100.46 5.2876 1.2185 0.2741
## Residuals 60 260.37 4.3396
## E
E.100<-aov(E~Clon, data = clone.100.fin)
E.300<-aov(E~Clon, data = clone.300.fin)
E.500<-aov(E~Clon, data = clone.500.fin)
E.800<-aov(E~Clon, data = clone.800.fin)
E.900<-aov(E~Clon, data = clone.900.fin)
E.1200<-aov(E~Clon, data = clone.1200.fin)
E.1500<-aov(E~Clon, data = clone.1500.fin)
E.2000<-aov(E~Clon, data = clone.2000.fin)
# Anova E 100
anova(E.100)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 17.585 0.92552 2.9766 0.0006789 ***
## Residuals 60 18.656 0.31093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(E.300)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 18.242 0.96009 3.1226 0.0004056 ***
## Residuals 60 18.448 0.30747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(E.500)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 18.151 0.95529 2.7469 0.001536 **
## Residuals 60 20.866 0.34777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(E.800)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 16.536 0.87029 1.9334 0.02778 *
## Residuals 60 27.008 0.45014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(E.900)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 14.748 0.77621 1.3125 0.21
## Residuals 60 35.484 0.59140
anova(E.1200)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 13.882 0.73063 0.9264 0.5548
## Residuals 60 47.320 0.78866
anova(E.1500)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 22.495 1.1839 1.1316 0.3454
## Residuals 60 62.778 1.0463
anova(E.2000)
## Analysis of Variance Table
##
## Response: E
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 37.777 1.9883 1.4262 0.1494
## Residuals 60 83.648 1.3941
## WUE
WUE.100<-aov(WUE~Clon, data = clone.100.fin)
WUE.300<-aov(WUE~Clon, data = clone.300.fin)
WUE.500<-aov(WUE~Clon, data = clone.500.fin)
WUE.800<-aov(WUE~Clon, data = clone.800.fin)
WUE.900<-aov(WUE~Clon, data = clone.900.fin)
WUE.1200<-aov(WUE~Clon, data = clone.1200.fin)
WUE.1500<-aov(WUE~Clon, data = clone.1500.fin)
WUE.2000<-aov(WUE~Clon, data = clone.2000.fin)
# Anova WUE 100
anova(WUE.100)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 8.6667 0.45614 1.6975 0.06225 .
## Residuals 60 16.1226 0.26871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(WUE.300)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 12.900 0.67896 1.2814 0.2298
## Residuals 60 31.792 0.52987
anova(WUE.500)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 12.889 0.67835 1.2358 0.2613
## Residuals 60 32.935 0.54891
anova(WUE.800)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 11.101 0.58427 1.1782 0.3057
## Residuals 60 29.753 0.49588
anova(WUE.900)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 11.017 0.57983 1.1624 0.3188
## Residuals 60 29.930 0.49883
anova(WUE.1200)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 8.2202 0.43264 1.1442 0.3344
## Residuals 60 22.6876 0.37813
anova(WUE.1500)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 6.3643 0.33496 1.1784 0.3056
## Residuals 60 17.0554 0.28426
anova(WUE.2000)
## Analysis of Variance Table
##
## Response: WUE
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 5.2028 0.27383 1.4198 0.1523
## Residuals 60 11.5717 0.19286
## gsw
gsw.100<-aov(gsw~Clon, data = clone.100.fin)
gsw.300<-aov(gsw~Clon, data = clone.300.fin)
gsw.500<-aov(gsw~Clon, data = clone.500.fin)
gsw.800<-aov(gsw~Clon, data = clone.800.fin)
gsw.900<-aov(gsw~Clon, data = clone.900.fin)
gsw.1200<-aov(gsw~Clon, data = clone.1200.fin)
gsw.1500<-aov(gsw~Clon, data = clone.1500.fin)
gsw.2000<-aov(gsw~Clon, data = clone.2000.fin)
# Anova gsw 100
anova(gsw.100)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.028254 0.00148705 4.1244 1.336e-05 ***
## Residuals 60 0.021633 0.00036055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(gsw.300)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.029535 0.00155445 4.3783 5.851e-06 ***
## Residuals 60 0.021302 0.00035503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(gsw.500)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.029313 0.00154277 3.4495 0.0001298 ***
## Residuals 60 0.026835 0.00044724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(gsw.800)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.026563 0.00139807 2.3211 0.007048 **
## Residuals 60 0.036140 0.00060233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(gsw.900)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.025148 0.00132357 1.4879 0.1232
## Residuals 60 0.053372 0.00088953
anova(gsw.1200)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.029075 0.0015303 1.1085 0.3663
## Residuals 60 0.082830 0.0013805
anova(gsw.1500)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.050199 0.0026421 1.4386 0.1438
## Residuals 60 0.110195 0.0018366
anova(gsw.2000)
## Analysis of Variance Table
##
## Response: gsw
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.090158 0.0047452 2.1218 0.01433 *
## Residuals 60 0.134186 0.0022364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## cica
cica.100<-aov(cica~Clon, data = clone.100.fin)
cica.300<-aov(cica~Clon, data = clone.300.fin)
cica.500<-aov(cica~Clon, data = clone.500.fin)
cica.800<-aov(cica~Clon, data = clone.800.fin)
cica.900<-aov(cica~Clon, data = clone.900.fin)
cica.1200<-aov(cica~Clon, data = clone.1200.fin)
cica.1500<-aov(cica~Clon, data = clone.1500.fin)
cica.2000<-aov(cica~Clon, data = clone.2000.fin)
# Anova cica 100
anova(cica.100)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.19220 0.0101160 3.2955 0.0002215 ***
## Residuals 60 0.18418 0.0030697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.300)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.23260 0.0122420 2.9 0.0008905 ***
## Residuals 60 0.25328 0.0042213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.500)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.22562 0.011875 2.5298 0.003339 **
## Residuals 60 0.28164 0.004694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.800)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.22883 0.012044 2.2436 0.009294 **
## Residuals 60 0.32208 0.005368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.900)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.22306 0.0117402 2.122 0.01432 *
## Residuals 60 0.33195 0.0055325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.1200)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.24215 0.0127447 2.2525 0.009003 **
## Residuals 60 0.33948 0.0056579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.1500)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.25063 0.013191 2.6147 0.002465 **
## Residuals 60 0.30270 0.005045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(cica.2000)
## Analysis of Variance Table
##
## Response: cica
## Df Sum Sq Mean Sq F value Pr(>F)
## Clon 19 0.21030 0.011069 2.9135 0.0008488 ***
## Residuals 60 0.22794 0.003799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##Genotype means
# Genotype mean 100
A.100.mean <- clone.100.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 100
A.100.mean.pond <- clone.100.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 300
A.300.mean <- clone.300.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 300
A.300.mean.pond <- clone.300.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 500
A.500.mean <- clone.500.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 500
A.500.mean.pond <- clone.500.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 800
A.800.mean <- clone.800.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 800
A.800.mean.pond <- clone.800.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 900
A.900.mean <- clone.900.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 900
A.900.mean.pond <- clone.900.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 1200
A.1200.mean <- clone.1200.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 1200
A.1200.mean.pond <- clone.1200.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 1500
A.1500.mean <- clone.1500.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 1500
A.1500.mean.pond <- clone.1500.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype mean 2000
A.2000.mean <- clone.2000.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
group_by(Clon) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
# Genotype pooled mean 2000
A.2000.mean.pond <- clone.2000.fin %>%
select(Clon, A, E, WUE, gsw, cica) %>%
summarise(
A_m = mean(A),
E_m = mean(E),
WUE_m = mean(WUE),
gsw_m = mean(gsw),
cica_m = mean(cica))
## Mean and grand mean tables
# Grand mean tables
# A
A.100.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 2.291955 1.577324 1.654262 0.05578251 0.7533489
A.300.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 4.219721 1.61974 2.801827 0.05621585 0.601851
A.500.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 4.553467 1.642263 2.939552 0.05601538 0.5720501
A.800.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 4.676854 1.703282 2.90732 0.05640295 0.5574663
A.900.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 4.759056 1.794922 2.817201 0.06010203 0.5689107
A.1200.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 5.102638 2.047754 2.623097 0.06900316 0.5853469
A.1500.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 5.783435 2.528369 2.403089 0.08625527 0.6151746
A.2000.mean.pond
## A_m E_m WUE_m gsw_m cica_m
## 1 6.91928 3.392319 2.133834 0.1161241 0.654072
# Mean tables
# A
A.100.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 2.66 1.32 2.25 0.0563 0.753
## 2 TCS 02 2.50 2.29 1.12 0.0857 0.830
## 3 TCS 03 1.82 1.62 1.49 0.0438 0.755
## 4 TCS 04 2.49 1.50 1.77 0.0494 0.730
## 5 TCS 05 1.97 1.04 2.05 0.0332 0.662
## 6 TCS 08 2.17 1.82 1.27 0.0466 0.738
## 7 TCS 10 1.23 0.590 1.98 0.0186 0.671
## 8 TCS 11 2.52 1.94 1.35 0.0770 0.821
## 9 TCS 12 1.51 0.805 1.79 0.0252 0.674
## 10 TCS 20 1.84 1.59 1.47 0.0630 0.814
## 11 TCS 40 2.38 2.07 1.18 0.0690 0.798
## 12 TCS 41 2.77 1.91 1.44 0.0707 0.783
## 13 TCS 42 1.98 1.21 1.67 0.0391 0.714
## 14 TCS 43 2.34 1.66 1.78 0.0496 0.747
## 15 TCS 44 2.58 2.29 1.27 0.0720 0.788
## 16 TCS 45 3.20 1.68 1.92 0.0770 0.770
## 17 TCS 46 2.63 1.64 1.70 0.0603 0.766
## 18 TCS 47 2.47 1.29 2.00 0.0518 0.743
## 19 TCS 48 3.02 2.20 1.43 0.0859 0.807
## 20 TCS 49 1.74 1.07 2.16 0.0415 0.704
A.300.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 4.62 1.41 3.60 0.0576 0.608
## 2 TCS 02 5.29 2.27 2.42 0.0826 0.669
## 3 TCS 03 3.54 1.67 3.10 0.0439 0.545
## 4 TCS 04 4.37 1.68 2.80 0.0548 0.593
## 5 TCS 05 3.19 1.08 3.07 0.0337 0.514
## 6 TCS 08 3.85 1.86 2.15 0.0459 0.579
## 7 TCS 10 1.61 0.576 2.64 0.0175 0.566
## 8 TCS 11 4.66 2.01 2.35 0.0788 0.702
## 9 TCS 12 2.31 0.800 2.67 0.0247 0.539
## 10 TCS 20 4.06 1.81 2.19 0.0741 0.716
## 11 TCS 40 4.88 2.04 2.44 0.0658 0.621
## 12 TCS 41 5.19 1.83 2.83 0.0660 0.599
## 13 TCS 42 3.43 1.22 2.84 0.0382 0.540
## 14 TCS 43 4.59 1.71 3.20 0.0497 0.549
## 15 TCS 44 4.98 2.37 2.26 0.0736 0.648
## 16 TCS 45 5.59 1.77 3.19 0.0791 0.632
## 17 TCS 46 4.98 1.69 3.09 0.0609 0.591
## 18 TCS 47 4.21 1.31 3.36 0.0512 0.585
## 19 TCS 48 5.95 2.24 2.74 0.0864 0.656
## 20 TCS 49 3.08 1.05 3.10 0.0399 0.585
A.500.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 5.08 1.51 3.64 0.0602 0.589
## 2 TCS 02 5.70 2.18 2.70 0.0770 0.620
## 3 TCS 03 3.94 1.73 3.37 0.0443 0.499
## 4 TCS 04 5.03 1.89 2.87 0.0618 0.579
## 5 TCS 05 3.43 1.12 3.12 0.0343 0.492
## 6 TCS 08 4.11 1.89 2.24 0.0454 0.550
## 7 TCS 10 1.61 0.587 2.57 0.0176 0.571
## 8 TCS 11 4.81 2.03 2.40 0.0817 0.679
## 9 TCS 12 2.36 0.813 2.65 0.0249 0.538
## 10 TCS 20 4.65 1.89 2.38 0.0754 0.682
## 11 TCS 40 5.31 1.96 2.74 0.0614 0.568
## 12 TCS 41 5.45 1.77 3.09 0.0623 0.552
## 13 TCS 42 3.55 1.21 2.97 0.0372 0.509
## 14 TCS 43 5.06 1.74 3.37 0.0490 0.499
## 15 TCS 44 5.52 2.41 2.43 0.0743 0.618
## 16 TCS 45 6.04 1.89 3.22 0.0823 0.618
## 17 TCS 46 5.12 1.61 3.30 0.0572 0.543
## 18 TCS 47 4.49 1.31 3.55 0.0510 0.553
## 19 TCS 48 6.52 2.26 3.01 0.0845 0.615
## 20 TCS 49 3.30 1.04 3.16 0.0385 0.569
A.800.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 5.38 1.68 3.44 0.0637 0.587
## 2 TCS 02 5.74 2.15 2.75 0.0727 0.593
## 3 TCS 03 4.18 1.83 3.36 0.0456 0.483
## 4 TCS 04 5.54 2.19 2.75 0.0713 0.586
## 5 TCS 05 3.59 1.19 3.08 0.0349 0.479
## 6 TCS 08 4.21 1.95 2.21 0.0453 0.536
## 7 TCS 10 2.25 0.871 2.50 0.0276 0.565
## 8 TCS 11 4.72 2.06 2.40 0.0839 0.663
## 9 TCS 12 2.39 0.834 2.68 0.0252 0.511
## 10 TCS 20 4.79 1.97 2.36 0.0755 0.665
## 11 TCS 40 4.95 1.78 2.84 0.0522 0.535
## 12 TCS 41 5.58 1.80 3.12 0.0616 0.532
## 13 TCS 42 3.63 1.29 2.88 0.0380 0.502
## 14 TCS 43 5.23 1.77 3.32 0.0484 0.478
## 15 TCS 44 5.76 2.49 2.45 0.0754 0.605
## 16 TCS 45 6.29 2.03 3.10 0.0861 0.616
## 17 TCS 46 5.14 1.64 3.16 0.0567 0.541
## 18 TCS 47 4.46 1.29 3.47 0.0496 0.543
## 19 TCS 48 6.31 2.18 3.12 0.0765 0.578
## 20 TCS 49 3.38 1.07 3.17 0.0378 0.553
A.900.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 5.49 1.82 3.23 0.0684 0.604
## 2 TCS 02 5.56 2.14 2.67 0.0717 0.598
## 3 TCS 03 4.25 1.94 3.34 0.0483 0.490
## 4 TCS 04 5.98 2.49 2.63 0.0844 0.609
## 5 TCS 05 3.72 1.27 2.98 0.0370 0.489
## 6 TCS 08 4.23 2.06 2.10 0.0472 0.545
## 7 TCS 10 3.41 1.28 2.43 0.0506 0.595
## 8 TCS 11 4.78 2.18 2.32 0.0905 0.676
## 9 TCS 12 2.58 0.953 2.46 0.0298 0.558
## 10 TCS 20 4.77 1.97 2.38 0.0756 0.661
## 11 TCS 40 4.71 1.75 2.78 0.0503 0.537
## 12 TCS 41 5.61 1.89 2.98 0.0647 0.546
## 13 TCS 42 3.59 1.35 2.73 0.0394 0.518
## 14 TCS 43 5.31 1.85 3.17 0.0505 0.490
## 15 TCS 44 5.77 2.54 2.40 0.0777 0.614
## 16 TCS 45 6.41 2.19 2.92 0.0932 0.634
## 17 TCS 46 5.10 1.69 3.06 0.0582 0.546
## 18 TCS 47 4.42 1.31 3.28 0.0523 0.566
## 19 TCS 48 5.59 2.02 3.13 0.0694 0.558
## 20 TCS 49 3.92 1.20 3.35 0.0431 0.543
A.1200.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 5.85 2.15 2.92 0.0786 0.625
## 2 TCS 02 5.63 2.31 2.48 0.0745 0.608
## 3 TCS 03 4.43 2.14 3.13 0.0526 0.506
## 4 TCS 04 6.45 2.91 2.43 0.101 0.633
## 5 TCS 05 4.05 1.45 2.83 0.0409 0.491
## 6 TCS 08 4.38 2.26 1.98 0.0505 0.553
## 7 TCS 10 4.29 1.65 2.34 0.0743 0.608
## 8 TCS 11 5.06 2.49 2.13 0.104 0.702
## 9 TCS 12 3.34 1.38 2.29 0.0467 0.578
## 10 TCS 20 4.87 2.08 2.33 0.0786 0.660
## 11 TCS 40 4.79 1.92 2.57 0.0532 0.547
## 12 TCS 41 5.95 2.14 2.80 0.0715 0.563
## 13 TCS 42 3.60 1.46 2.55 0.0412 0.529
## 14 TCS 43 5.74 2.14 2.95 0.0568 0.504
## 15 TCS 44 5.94 2.74 2.29 0.0839 0.626
## 16 TCS 45 6.59 2.47 2.66 0.106 0.656
## 17 TCS 46 5.29 1.84 2.88 0.0616 0.553
## 18 TCS 47 4.91 1.55 2.98 0.0642 0.606
## 19 TCS 48 5.60 2.16 2.92 0.0736 0.567
## 20 TCS 49 5.29 1.71 2.99 0.0662 0.593
A.1500.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 6.73 2.78 2.54 0.104 0.667
## 2 TCS 02 6.28 2.85 2.22 0.0925 0.647
## 3 TCS 03 5.15 2.79 2.74 0.0711 0.562
## 4 TCS 04 6.81 3.33 2.25 0.117 0.655
## 5 TCS 05 4.85 1.88 2.64 0.0517 0.517
## 6 TCS 08 4.79 2.64 1.84 0.0588 0.573
## 7 TCS 10 5.00 2.02 2.26 0.0902 0.612
## 8 TCS 11 6.05 3.38 1.82 0.142 0.756
## 9 TCS 12 4.53 1.97 2.12 0.0674 0.618
## 10 TCS 20 5.21 2.31 2.31 0.0867 0.660
## 11 TCS 40 5.07 2.19 2.37 0.0585 0.561
## 12 TCS 41 8.16 3.40 2.44 0.121 0.646
## 13 TCS 42 3.83 1.68 2.37 0.0471 0.549
## 14 TCS 43 7.07 3.03 2.62 0.0831 0.558
## 15 TCS 44 6.15 3.00 2.15 0.0923 0.642
## 16 TCS 45 6.72 2.84 2.38 0.126 0.682
## 17 TCS 46 5.78 2.13 2.75 0.0686 0.558
## 18 TCS 47 5.48 1.86 2.76 0.0793 0.634
## 19 TCS 48 5.81 2.37 2.67 0.0795 0.585
## 20 TCS 49 6.21 2.13 2.81 0.0877 0.621
A.2000.mean
## # A tibble: 20 × 6
## Clon A_m E_m WUE_m gsw_m cica_m
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCS 01 8.70 3.86 2.26 0.155 0.703
## 2 TCS 02 7.36 3.83 1.93 0.135 0.700
## 3 TCS 03 6.10 3.69 2.14 0.0992 0.637
## 4 TCS 04 7.05 3.72 2.04 0.127 0.668
## 5 TCS 05 6.58 2.88 2.33 0.0788 0.571
## 6 TCS 08 6.36 4.51 1.56 0.107 0.647
## 7 TCS 10 6.59 2.88 2.18 0.114 0.626
## 8 TCS 11 7.09 4.62 1.58 0.203 0.799
## 9 TCS 12 5.64 2.59 2.04 0.0915 0.635
## 10 TCS 20 6.39 3.10 2.16 0.115 0.687
## 11 TCS 40 5.66 2.76 2.08 0.0721 0.598
## 12 TCS 41 9.65 4.71 2.12 0.178 0.699
## 13 TCS 42 4.55 2.32 2.00 0.0620 0.600
## 14 TCS 43 8.45 3.88 2.35 0.110 0.593
## 15 TCS 44 6.45 3.42 1.95 0.103 0.661
## 16 TCS 45 7.71 3.44 2.25 0.150 0.691
## 17 TCS 46 7.43 2.97 2.51 0.0951 0.590
## 18 TCS 47 6.40 2.44 2.50 0.108 0.661
## 19 TCS 48 6.85 3.28 2.17 0.108 0.652
## 20 TCS 49 7.39 2.94 2.54 0.112 0.660