setwd("~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Fabricio/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':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, 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':
##
## 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':
##
## 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':
##
## 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 los rangos ambientales a evaluar (100-300, 500-2000)
clone.200 <- filter(clone, clone$env == "100" | clone$env=="300")
clone.400 <- filter(clone, clone$env == "300" | clone$env=="500")
clone.650 <- filter(clone, clone$env == "500" | clone$env=="800")
clone.850 <- filter(clone, clone$env == "800" | clone$env=="900")
clone.1050 <- filter(clone, clone$env == "900" | clone$env=="1200")
clone.1350 <- filter(clone, clone$env == "1200" | clone$env=="1500")
clone.1750 <- filter(clone, clone$env == "1500" | clone$env=="2000")
##Quitando las intensidades de luz no utilizadas
clone.200.fin <- droplevels(clone.200)
clone.400.fin <- droplevels(clone.400)
clone.650.fin <- droplevels(clone.650)
clone.850.fin <- droplevels(clone.850)
clone.1050.fin <- droplevels(clone.1050)
clone.1350.fin <- droplevels(clone.1350)
clone.1750.fin <- droplevels(clone.1750)
##Convirtiendo a factor los genotipos y ambientes
#genotipos
clone.200.fin$Clon<-as.factor(clone.200.fin$Clon)
clone.400.fin$Clon<-as.factor(clone.400.fin$Clon)
clone.650.fin$Clon<-as.factor(clone.650.fin$Clon)
clone.850.fin$Clon<-as.factor(clone.850.fin$Clon)
clone.1050.fin$Clon<-as.factor(clone.1050.fin$Clon)
clone.1350.fin$Clon<-as.factor(clone.1350.fin$Clon)
clone.1750.fin$Clon<-as.factor(clone.1750.fin$Clon)
#ambientes
clone.200.fin$env<-as.factor(clone.200.fin$env)
clone.400.fin$env<-as.factor(clone.400.fin$env)
clone.650.fin$env<-as.factor(clone.650.fin$env)
clone.850.fin$env<-as.factor(clone.850.fin$env)
clone.1050.fin$env<-as.factor(clone.1050.fin$env)
clone.1350.fin$env<-as.factor(clone.1350.fin$env)
clone.1750.fin$env<-as.factor(clone.1750.fin$env)
## Calculo del RDPI para todos los caracteres luz 200
A.200.rdpi<-rdpi(clone.200.fin, Clon, A, env)
##
## Attaching package: 'sciplot'
## The following object is masked from 'package:FSA':
##
## se
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.672 0.03535 1.946 0.011 *
## Residuals 300 5.450 0.01817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.268 0.0647 0.0162 a
## 2 TCS 02 0.359 0.0743 0.0186 a
## 3 TCS 03 0.388 0.208 0.0521 a
## 4 TCS 04 0.267 0.135 0.0339 a
## 5 TCS 05 0.226 0.0948 0.0237 a
## 6 TCS 08 0.278 0.0651 0.0163 a
## 7 TCS 10 0.243 0.173 0.0433 a
## 8 TCS 11 0.298 0.158 0.0396 a
## 9 TCS 12 0.327 0.220 0.0550 a
## 10 TCS 20 0.369 0.198 0.0495 a
## 11 TCS 40 0.336 0.129 0.0323 a
## 12 TCS 41 0.307 0.0739 0.0185 a
## 13 TCS 42 0.268 0.0545 0.0136 a
## 14 TCS 43 0.329 0.0990 0.0247 a
## 15 TCS 44 0.306 0.116 0.0289 a
## 16 TCS 45 0.271 0.0548 0.0137 a
## 17 TCS 46 0.303 0.0709 0.0177 a
## 18 TCS 47 0.253 0.0960 0.0240 a
## 19 TCS 48 0.325 0.108 0.0270 a
## 20 TCS 49 0.386 0.242 0.0605 a
E.200.rdpi<-rdpi(clone.200.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.230 0.11738 4.327 1.52e-08 ***
## Residuals 300 8.138 0.02713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.165 0.142 0.0356 abc
## 2 TCS 02 0.121 0.0954 0.0238 bc
## 3 TCS 03 0.364 0.346 0.0864 a
## 4 TCS 04 0.202 0.145 0.0363 abc
## 5 TCS 05 0.132 0.133 0.0332 bc
## 6 TCS 08 0.137 0.103 0.0258 bc
## 7 TCS 10 0.138 0.106 0.0266 bc
## 8 TCS 11 0.165 0.154 0.0386 abc
## 9 TCS 12 0.207 0.176 0.0439 abc
## 10 TCS 20 0.250 0.222 0.0554 ab
## 11 TCS 40 0.124 0.115 0.0289 bc
## 12 TCS 41 0.0325 0.0162 0.00404 c
## 13 TCS 42 0.0683 0.0511 0.0128 bc
## 14 TCS 43 0.212 0.176 0.0440 abc
## 15 TCS 44 0.190 0.167 0.0417 abc
## 16 TCS 45 0.0463 0.0330 0.00824 bc
## 17 TCS 46 0.146 0.101 0.0254 bc
## 18 TCS 47 0.128 0.122 0.0306 bc
## 19 TCS 48 0.139 0.121 0.0302 bc
## 20 TCS 49 0.357 0.336 0.0841 a
WUE.200.rdpi<-rdpi(clone.200.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.976 0.05138 3.21 1.12e-05 ***
## Residuals 300 4.802 0.01601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.248 0.146 0.0366 abc
## 2 TCS 02 0.363 0.0808 0.0202 a
## 3 TCS 03 0.335 0.200 0.0501 ab
## 4 TCS 04 0.222 0.105 0.0262 abc
## 5 TCS 05 0.235 0.130 0.0325 abc
## 6 TCS 08 0.259 0.108 0.0269 abc
## 7 TCS 10 0.150 0.100 0.0251 c
## 8 TCS 11 0.269 0.0719 0.0180 abc
## 9 TCS 12 0.200 0.114 0.0284 bc
## 10 TCS 20 0.241 0.165 0.0412 abc
## 11 TCS 40 0.344 0.110 0.0275 ab
## 12 TCS 41 0.327 0.0524 0.0131 ab
## 13 TCS 42 0.261 0.0848 0.0212 abc
## 14 TCS 43 0.366 0.221 0.0553 a
## 15 TCS 44 0.285 0.137 0.0343 abc
## 16 TCS 45 0.245 0.0918 0.0230 abc
## 17 TCS 46 0.285 0.143 0.0356 abc
## 18 TCS 47 0.249 0.116 0.0291 abc
## 19 TCS 48 0.314 0.0874 0.0219 ab
## 20 TCS 49 0.222 0.132 0.0331 abc
gsw.200.rdpi<-rdpi(clone.200.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.490 0.13105 5.802 2.32e-12 ***
## Residuals 300 6.776 0.02259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0977 0.0766 0.0192 d
## 2 TCS 02 0.0976 0.0753 0.0188 d
## 3 TCS 03 0.319 0.286 0.0714 ab
## 4 TCS 04 0.180 0.127 0.0317 bcd
## 5 TCS 05 0.204 0.159 0.0398 bcd
## 6 TCS 08 0.0963 0.0697 0.0174 d
## 7 TCS 10 0.201 0.140 0.0351 bcd
## 8 TCS 11 0.0829 0.0562 0.0140 d
## 9 TCS 12 0.301 0.253 0.0632 abc
## 10 TCS 20 0.219 0.181 0.0452 abcd
## 11 TCS 40 0.168 0.135 0.0337 bcd
## 12 TCS 41 0.0905 0.0560 0.0140 d
## 13 TCS 42 0.150 0.118 0.0295 bcd
## 14 TCS 43 0.0294 0.0197 0.00493 d
## 15 TCS 44 0.153 0.120 0.0300 bcd
## 16 TCS 45 0.154 0.132 0.0330 bcd
## 17 TCS 46 0.129 0.0897 0.0224 cd
## 18 TCS 47 0.160 0.130 0.0325 bcd
## 19 TCS 48 0.0928 0.0810 0.0202 d
## 20 TCS 49 0.402 0.306 0.0766 a
cica.200.rdpi<-rdpi(clone.200.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.1618 0.008518 3.029 3.17e-05 ***
## Residuals 300 0.8435 0.002812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.106 0.0368 0.00919 abcd
## 2 TCS 02 0.108 0.0272 0.00681 abcd
## 3 TCS 03 0.164 0.0651 0.0163 a
## 4 TCS 04 0.104 0.0334 0.00835 abcd
## 5 TCS 05 0.146 0.0971 0.0243 abc
## 6 TCS 08 0.121 0.0141 0.00352 abcd
## 7 TCS 10 0.100 0.0633 0.0158 abcd
## 8 TCS 11 0.0809 0.0455 0.0114 cd
## 9 TCS 12 0.111 0.0420 0.0105 abcd
## 10 TCS 20 0.0780 0.0710 0.0177 d
## 11 TCS 40 0.125 0.0303 0.00759 abcd
## 12 TCS 41 0.135 0.0612 0.0153 abcd
## 13 TCS 42 0.141 0.0803 0.0201 abcd
## 14 TCS 43 0.152 0.0307 0.00768 ab
## 15 TCS 44 0.0980 0.0271 0.00677 abcd
## 16 TCS 45 0.104 0.0614 0.0154 abcd
## 17 TCS 46 0.130 0.0442 0.0111 abcd
## 18 TCS 47 0.120 0.0577 0.0144 abcd
## 19 TCS 48 0.103 0.00849 0.00212 abcd
## 20 TCS 49 0.0957 0.0660 0.0165 bcd
dos.rdpi<-data.frame(A.200.rdpi$sp, A.200.rdpi$rdpi, E.200.rdpi$rdpi, WUE.200.rdpi$rdpi, gsw.200.rdpi$rdpi,
cica.200.rdpi$rdpi)
colnames(dos.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
dos.rdpi$treatment <- "200"
dos.sum<-dos.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
dos.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.305404 0.1662431 0.2709382 0.1663682 0.1162209
## Calculo del RDPI para todos los caracteres luz 400
A.400.rdpi<-rdpi(clone.400.fin, Clon, A, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.502 0.1317 7.237 5.47e-16 ***
## Residuals 300 5.459 0.0182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0757 0.0550 0.0138 def
## 2 TCS 02 0.0679 0.0552 0.0138 ef
## 3 TCS 03 0.257 0.222 0.0555 abc
## 4 TCS 04 0.155 0.104 0.0259 bcdef
## 5 TCS 05 0.120 0.0934 0.0233 cdef
## 6 TCS 08 0.0595 0.0412 0.0103 ef
## 7 TCS 10 0.244 0.160 0.0401 abcd
## 8 TCS 11 0.127 0.105 0.0263 cdef
## 9 TCS 12 0.301 0.269 0.0672 ab
## 10 TCS 20 0.229 0.197 0.0491 abcde
## 11 TCS 40 0.128 0.0955 0.0239 cdef
## 12 TCS 41 0.0383 0.0222 0.00556 f
## 13 TCS 42 0.0664 0.0476 0.0119 ef
## 14 TCS 43 0.0502 0.0297 0.00742 f
## 15 TCS 44 0.134 0.0935 0.0234 bcdef
## 16 TCS 45 0.0648 0.0462 0.0116 ef
## 17 TCS 46 0.159 0.123 0.0306 bcdef
## 18 TCS 47 0.109 0.0866 0.0216 cdef
## 19 TCS 48 0.0998 0.0768 0.0192 cdef
## 20 TCS 49 0.361 0.296 0.0741 a
E.400.rdpi<-rdpi(clone.400.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.085 0.10974 4.339 1.41e-08 ***
## Residuals 300 7.587 0.02529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.168 0.144 0.0361 ab
## 2 TCS 02 0.127 0.0940 0.0235 b
## 3 TCS 03 0.367 0.347 0.0868 a
## 4 TCS 04 0.221 0.167 0.0416 ab
## 5 TCS 05 0.135 0.126 0.0316 b
## 6 TCS 08 0.118 0.0892 0.0223 b
## 7 TCS 10 0.136 0.0969 0.0242 b
## 8 TCS 11 0.141 0.0945 0.0236 b
## 9 TCS 12 0.225 0.185 0.0462 ab
## 10 TCS 20 0.182 0.160 0.0401 ab
## 11 TCS 40 0.123 0.103 0.0257 b
## 12 TCS 41 0.0416 0.0408 0.0102 b
## 13 TCS 42 0.0962 0.0732 0.0183 b
## 14 TCS 43 0.208 0.162 0.0406 ab
## 15 TCS 44 0.187 0.167 0.0418 ab
## 16 TCS 45 0.0442 0.0278 0.00695 b
## 17 TCS 46 0.196 0.136 0.0339 ab
## 18 TCS 47 0.138 0.114 0.0284 b
## 19 TCS 48 0.142 0.119 0.0297 b
## 20 TCS 49 0.354 0.328 0.0821 a
WUE.400.rdpi<-rdpi(clone.400.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.6955 0.03661 4.879 5.61e-10 ***
## Residuals 300 2.2509 0.00750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.124 0.0970 0.0243 ab
## 2 TCS 02 0.0887 0.0614 0.0154 b
## 3 TCS 03 0.224 0.165 0.0413 a
## 4 TCS 04 0.0864 0.0684 0.0171 b
## 5 TCS 05 0.114 0.0987 0.0247 b
## 6 TCS 08 0.0711 0.0538 0.0135 b
## 7 TCS 10 0.115 0.0830 0.0208 b
## 8 TCS 11 0.0679 0.0468 0.0117 b
## 9 TCS 12 0.118 0.112 0.0281 ab
## 10 TCS 20 0.0825 0.0540 0.0135 b
## 11 TCS 40 0.111 0.0787 0.0197 b
## 12 TCS 41 0.0474 0.0281 0.00702 b
## 13 TCS 42 0.0494 0.0359 0.00898 b
## 14 TCS 43 0.227 0.168 0.0420 a
## 15 TCS 44 0.0901 0.0659 0.0165 b
## 16 TCS 45 0.0684 0.0737 0.0184 b
## 17 TCS 46 0.110 0.0989 0.0247 b
## 18 TCS 47 0.106 0.0816 0.0204 b
## 19 TCS 48 0.0773 0.0531 0.0133 b
## 20 TCS 49 0.0676 0.0460 0.0115 b
gsw.400.rdpi<-rdpi(clone.400.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.484 0.13075 5.532 1.15e-11 ***
## Residuals 300 7.090 0.02363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.108 0.0832 0.0208 d
## 2 TCS 02 0.127 0.0976 0.0244 cd
## 3 TCS 03 0.326 0.285 0.0712 ab
## 4 TCS 04 0.208 0.149 0.0372 abcd
## 5 TCS 05 0.214 0.163 0.0407 abcd
## 6 TCS 08 0.0704 0.0551 0.0138 d
## 7 TCS 10 0.211 0.159 0.0397 abcd
## 8 TCS 11 0.0885 0.0642 0.0161 d
## 9 TCS 12 0.318 0.257 0.0642 abc
## 10 TCS 20 0.146 0.122 0.0305 bcd
## 11 TCS 40 0.162 0.124 0.0310 bcd
## 12 TCS 41 0.122 0.0936 0.0234 d
## 13 TCS 42 0.163 0.127 0.0318 bcd
## 14 TCS 43 0.0326 0.0238 0.00594 d
## 15 TCS 44 0.155 0.124 0.0309 bcd
## 16 TCS 45 0.155 0.131 0.0327 bcd
## 17 TCS 46 0.219 0.174 0.0435 abcd
## 18 TCS 47 0.181 0.131 0.0328 bcd
## 19 TCS 48 0.103 0.0742 0.0185 d
## 20 TCS 49 0.400 0.295 0.0738 a
cica.400.rdpi<-rdpi(clone.400.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.1602 0.008429 4.137 4.72e-08 ***
## Residuals 300 0.6113 0.002038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0256 0.0212 0.00529 d
## 2 TCS 02 0.0430 0.0315 0.00788 abcd
## 3 TCS 03 0.0893 0.0645 0.0161 abc
## 4 TCS 04 0.0386 0.0272 0.00680 bcd
## 5 TCS 05 0.0999 0.0736 0.0184 a
## 6 TCS 08 0.0260 0.0116 0.00289 d
## 7 TCS 10 0.0638 0.0480 0.0120 abcd
## 8 TCS 11 0.0704 0.0477 0.0119 abcd
## 9 TCS 12 0.0365 0.0259 0.00648 bcd
## 10 TCS 20 0.0930 0.0755 0.0189 ab
## 11 TCS 40 0.0471 0.0271 0.00677 abcd
## 12 TCS 41 0.0748 0.0548 0.0137 abcd
## 13 TCS 42 0.0835 0.0616 0.0154 abc
## 14 TCS 43 0.0487 0.0298 0.00746 abcd
## 15 TCS 44 0.0331 0.0251 0.00628 cd
## 16 TCS 45 0.0678 0.0527 0.0132 abcd
## 17 TCS 46 0.0610 0.0475 0.0119 abcd
## 18 TCS 47 0.0653 0.0470 0.0118 abcd
## 19 TCS 48 0.0325 0.0185 0.00462 cd
## 20 TCS 49 0.0453 0.0355 0.00888 abcd
tres.rdpi<-data.frame(A.400.rdpi$sp, A.400.rdpi$rdpi, E.400.rdpi$rdpi, WUE.400.rdpi$rdpi, gsw.400.rdpi$rdpi,
cica.400.rdpi$rdpi)
colnames(tres.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
tres.rdpi$treatment <- "400"
tres.sum<-tres.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
tres.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.1423913 0.1674835 0.1022347 0.1754807 0.05726504
## Calculo del RDPI para todos los caracteres luz 650
A.650.rdpi<-rdpi(clone.650.fin, Clon, A, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.750 0.14475 6.929 3.23e-15 ***
## Residuals 300 6.267 0.02089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0842 0.0615 0.0154 def
## 2 TCS 02 0.0940 0.0705 0.0176 def
## 3 TCS 03 0.262 0.234 0.0585 abcd
## 4 TCS 04 0.179 0.132 0.0331 bcdef
## 5 TCS 05 0.135 0.107 0.0268 bcdef
## 6 TCS 08 0.0542 0.0366 0.00916 ef
## 7 TCS 10 0.290 0.186 0.0464 abc
## 8 TCS 11 0.114 0.0795 0.0199 cdef
## 9 TCS 12 0.306 0.266 0.0665 ab
## 10 TCS 20 0.220 0.205 0.0514 abcde
## 11 TCS 40 0.122 0.0785 0.0196 cdef
## 12 TCS 41 0.0620 0.0437 0.0109 ef
## 13 TCS 42 0.0931 0.0674 0.0169 def
## 14 TCS 43 0.0251 0.0176 0.00441 f
## 15 TCS 44 0.133 0.107 0.0269 bcdef
## 16 TCS 45 0.0664 0.0557 0.0139 ef
## 17 TCS 46 0.258 0.219 0.0549 abcd
## 18 TCS 47 0.152 0.108 0.0270 bcdef
## 19 TCS 48 0.127 0.0987 0.0247 bcdef
## 20 TCS 49 0.364 0.270 0.0675 a
E.650.rdpi<-rdpi(clone.650.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.140 0.11264 4.292 1.87e-08 ***
## Residuals 300 7.874 0.02625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.169 0.142 0.0355 bcd
## 2 TCS 02 0.134 0.109 0.0271 cd
## 3 TCS 03 0.375 0.349 0.0872 a
## 4 TCS 04 0.249 0.189 0.0473 abc
## 5 TCS 05 0.139 0.121 0.0302 bcd
## 6 TCS 08 0.100 0.0855 0.0214 cd
## 7 TCS 10 0.228 0.139 0.0347 abcd
## 8 TCS 11 0.128 0.0945 0.0236 cd
## 9 TCS 12 0.240 0.195 0.0487 abcd
## 10 TCS 20 0.180 0.151 0.0379 abcd
## 11 TCS 40 0.125 0.0735 0.0184 cd
## 12 TCS 41 0.0739 0.0581 0.0145 cd
## 13 TCS 42 0.129 0.0987 0.0247 cd
## 14 TCS 43 0.198 0.145 0.0363 abcd
## 15 TCS 44 0.190 0.168 0.0419 abcd
## 16 TCS 45 0.0420 0.0260 0.00649 d
## 17 TCS 46 0.270 0.186 0.0466 abc
## 18 TCS 47 0.147 0.0968 0.0242 bcd
## 19 TCS 48 0.196 0.140 0.0350 abcd
## 20 TCS 49 0.343 0.304 0.0760 ab
WUE.650.rdpi<-rdpi(clone.650.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.6181 0.03253 4.389 1.05e-08 ***
## Residuals 300 2.2236 0.00741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.117 0.0913 0.0228 abc
## 2 TCS 02 0.0615 0.0471 0.0118 c
## 3 TCS 03 0.216 0.174 0.0435 a
## 4 TCS 04 0.0847 0.0641 0.0160 c
## 5 TCS 05 0.106 0.0968 0.0242 bc
## 6 TCS 08 0.0637 0.0512 0.0128 c
## 7 TCS 10 0.115 0.0884 0.0221 abc
## 8 TCS 11 0.0927 0.0740 0.0185 bc
## 9 TCS 12 0.112 0.0974 0.0243 abc
## 10 TCS 20 0.0755 0.0638 0.0160 c
## 11 TCS 40 0.106 0.0851 0.0213 bc
## 12 TCS 41 0.0282 0.0220 0.00550 c
## 13 TCS 42 0.0434 0.0346 0.00864 c
## 14 TCS 43 0.198 0.152 0.0380 ab
## 15 TCS 44 0.0728 0.0587 0.0147 c
## 16 TCS 45 0.0732 0.0680 0.0170 c
## 17 TCS 46 0.106 0.101 0.0253 bc
## 18 TCS 47 0.0985 0.0817 0.0204 bc
## 19 TCS 48 0.0759 0.0615 0.0154 c
## 20 TCS 49 0.0675 0.0603 0.0151 c
gsw.650.rdpi<-rdpi(clone.650.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.481 0.13056 4.683 1.81e-09 ***
## Residuals 300 8.363 0.02788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.113 0.0878 0.0219 cd
## 2 TCS 02 0.156 0.116 0.0289 bcd
## 3 TCS 03 0.337 0.290 0.0725 ab
## 4 TCS 04 0.248 0.177 0.0442 abcd
## 5 TCS 05 0.215 0.172 0.0430 abcd
## 6 TCS 08 0.0762 0.0493 0.0123 d
## 7 TCS 10 0.307 0.205 0.0512 abc
## 8 TCS 11 0.165 0.117 0.0293 bcd
## 9 TCS 12 0.332 0.265 0.0663 ab
## 10 TCS 20 0.157 0.127 0.0317 bcd
## 11 TCS 40 0.154 0.104 0.0260 bcd
## 12 TCS 41 0.152 0.116 0.0290 bcd
## 13 TCS 42 0.187 0.140 0.0350 abcd
## 14 TCS 43 0.0460 0.0334 0.00836 d
## 15 TCS 44 0.164 0.128 0.0321 bcd
## 16 TCS 45 0.162 0.129 0.0322 bcd
## 17 TCS 46 0.302 0.242 0.0605 abc
## 18 TCS 47 0.207 0.153 0.0382 abcd
## 19 TCS 48 0.173 0.114 0.0284 bcd
## 20 TCS 49 0.386 0.272 0.0680 a
cica.650.rdpi<-rdpi(clone.650.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.2073 0.010912 4.772 1.06e-09 ***
## Residuals 300 0.6859 0.002286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0171 0.0130 0.00325 d
## 2 TCS 02 0.0393 0.0263 0.00657 bcd
## 3 TCS 03 0.0902 0.0709 0.0177 ab
## 4 TCS 04 0.0417 0.0318 0.00796 bcd
## 5 TCS 05 0.0905 0.0708 0.0177 ab
## 6 TCS 08 0.0190 0.0115 0.00287 cd
## 7 TCS 10 0.0622 0.0470 0.0117 abcd
## 8 TCS 11 0.0896 0.0700 0.0175 ab
## 9 TCS 12 0.0517 0.0326 0.00816 abcd
## 10 TCS 20 0.105 0.0883 0.0221 a
## 11 TCS 40 0.0324 0.0246 0.00616 bcd
## 12 TCS 41 0.0739 0.0561 0.0140 abcd
## 13 TCS 42 0.0784 0.0698 0.0174 abc
## 14 TCS 43 0.0273 0.0212 0.00529 cd
## 15 TCS 44 0.0334 0.0268 0.00669 bcd
## 16 TCS 45 0.0702 0.0562 0.0140 abcd
## 17 TCS 46 0.0497 0.0333 0.00833 abcd
## 18 TCS 47 0.0610 0.0446 0.0112 abcd
## 19 TCS 48 0.0403 0.0314 0.00784 bcd
## 20 TCS 49 0.0366 0.0262 0.00656 bcd
cuatro.rdpi<-data.frame(A.650.rdpi$sp, A.650.rdpi$rdpi, E.650.rdpi$rdpi, WUE.650.rdpi$rdpi, gsw.650.rdpi$rdpi,
cica.650.rdpi$rdpi)
colnames(cuatro.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
cuatro.rdpi$treatment <- "650"
cuatro.sum<-cuatro.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
cuatro.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.1570272 0.1828684 0.0957263 0.2019889 0.05549307
## Calculo del RDPI para todos los caracteres luz 850
A.850.rdpi<-rdpi(clone.850.fin, Clon, A, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.788 0.14673 5.799 2.38e-12 ***
## Residuals 300 7.592 0.02531
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0890 0.0713 0.0178 de
## 2 TCS 02 0.112 0.0875 0.0219 cde
## 3 TCS 03 0.278 0.252 0.0629 abcd
## 4 TCS 04 0.201 0.168 0.0420 abcde
## 5 TCS 05 0.136 0.110 0.0276 abcde
## 6 TCS 08 0.0647 0.0527 0.0132 e
## 7 TCS 10 0.337 0.247 0.0618 a
## 8 TCS 11 0.104 0.0787 0.0197 cde
## 9 TCS 12 0.325 0.261 0.0653 ab
## 10 TCS 20 0.214 0.194 0.0484 abcde
## 11 TCS 40 0.109 0.0772 0.0193 cde
## 12 TCS 41 0.0819 0.0665 0.0166 de
## 13 TCS 42 0.111 0.0935 0.0234 cde
## 14 TCS 43 0.0297 0.0238 0.00596 e
## 15 TCS 44 0.136 0.120 0.0300 bcde
## 16 TCS 45 0.0764 0.0656 0.0164 e
## 17 TCS 46 0.298 0.272 0.0681 abc
## 18 TCS 47 0.215 0.171 0.0427 abcde
## 19 TCS 48 0.202 0.136 0.0341 abcde
## 20 TCS 49 0.301 0.231 0.0577 abc
E.850.rdpi<-rdpi(clone.850.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.257 0.11876 4.072 6.95e-08 ***
## Residuals 300 8.750 0.02917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.168 0.143 0.0358 bc
## 2 TCS 02 0.139 0.124 0.0310 bc
## 3 TCS 03 0.387 0.356 0.0891 a
## 4 TCS 04 0.268 0.219 0.0547 ab
## 5 TCS 05 0.141 0.116 0.0289 bc
## 6 TCS 08 0.105 0.0822 0.0205 bc
## 7 TCS 10 0.273 0.200 0.0501 ab
## 8 TCS 11 0.155 0.117 0.0292 bc
## 9 TCS 12 0.263 0.193 0.0482 ab
## 10 TCS 20 0.174 0.152 0.0379 abc
## 11 TCS 40 0.118 0.0865 0.0216 bc
## 12 TCS 41 0.0973 0.0786 0.0197 bc
## 13 TCS 42 0.151 0.120 0.0301 bc
## 14 TCS 43 0.183 0.135 0.0337 abc
## 15 TCS 44 0.194 0.171 0.0428 abc
## 16 TCS 45 0.0363 0.0310 0.00774 c
## 17 TCS 46 0.305 0.235 0.0587 ab
## 18 TCS 47 0.177 0.121 0.0302 abc
## 19 TCS 48 0.272 0.185 0.0462 ab
## 20 TCS 49 0.293 0.241 0.0602 ab
WUE.850.rdpi<-rdpi(clone.850.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.5564 0.029284 3.891 2.04e-07 ***
## Residuals 300 2.2581 0.007527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.114 0.0854 0.0214 abc
## 2 TCS 02 0.0509 0.0398 0.00995 c
## 3 TCS 03 0.219 0.177 0.0444 a
## 4 TCS 04 0.0849 0.0637 0.0159 bc
## 5 TCS 05 0.108 0.0934 0.0233 bc
## 6 TCS 08 0.0666 0.0484 0.0121 c
## 7 TCS 10 0.119 0.0904 0.0226 abc
## 8 TCS 11 0.0999 0.0843 0.0211 bc
## 9 TCS 12 0.116 0.105 0.0263 abc
## 10 TCS 20 0.0726 0.0592 0.0148 bc
## 11 TCS 40 0.118 0.0909 0.0227 abc
## 12 TCS 41 0.0359 0.0262 0.00656 c
## 13 TCS 42 0.0469 0.0343 0.00857 c
## 14 TCS 43 0.180 0.142 0.0354 ab
## 15 TCS 44 0.0685 0.0538 0.0135 c
## 16 TCS 45 0.0780 0.0601 0.0150 bc
## 17 TCS 46 0.101 0.103 0.0257 bc
## 18 TCS 47 0.106 0.0778 0.0195 bc
## 19 TCS 48 0.0867 0.0757 0.0189 bc
## 20 TCS 49 0.0967 0.0777 0.0194 bc
gsw.850.rdpi<-rdpi(clone.850.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.795 0.14709 4.368 1.19e-08 ***
## Residuals 300 10.102 0.03367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.120 0.0957 0.0239 cd
## 2 TCS 02 0.168 0.132 0.0329 abcd
## 3 TCS 03 0.355 0.303 0.0757 ab
## 4 TCS 04 0.280 0.214 0.0534 abcd
## 5 TCS 05 0.219 0.168 0.0420 abcd
## 6 TCS 08 0.0943 0.0749 0.0187 d
## 7 TCS 10 0.399 0.273 0.0682 a
## 8 TCS 11 0.205 0.161 0.0403 abcd
## 9 TCS 12 0.359 0.263 0.0657 ab
## 10 TCS 20 0.171 0.135 0.0339 abcd
## 11 TCS 40 0.137 0.0891 0.0223 bcd
## 12 TCS 41 0.174 0.134 0.0335 abcd
## 13 TCS 42 0.208 0.153 0.0383 abcd
## 14 TCS 43 0.0536 0.0385 0.00963 d
## 15 TCS 44 0.175 0.134 0.0335 abcd
## 16 TCS 45 0.177 0.130 0.0326 abcd
## 17 TCS 46 0.339 0.285 0.0712 abc
## 18 TCS 47 0.270 0.178 0.0446 abcd
## 19 TCS 48 0.263 0.177 0.0444 abcd
## 20 TCS 49 0.328 0.240 0.0601 abc
cica.850.rdpi<-rdpi(clone.850.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.2358 0.012412 5.008 2.6e-10 ***
## Residuals 300 0.7435 0.002478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0197 0.0148 0.00370 d
## 2 TCS 02 0.0341 0.0276 0.00691 bcd
## 3 TCS 03 0.0943 0.0769 0.0192 ab
## 4 TCS 04 0.0496 0.0352 0.00879 abcd
## 5 TCS 05 0.0884 0.0693 0.0173 abc
## 6 TCS 08 0.0220 0.0165 0.00412 d
## 7 TCS 10 0.0712 0.0538 0.0135 abcd
## 8 TCS 11 0.0955 0.0754 0.0188 ab
## 9 TCS 12 0.0617 0.0410 0.0102 abcd
## 10 TCS 20 0.108 0.0960 0.0240 a
## 11 TCS 40 0.0262 0.0226 0.00564 cd
## 12 TCS 41 0.0732 0.0571 0.0143 abcd
## 13 TCS 42 0.0809 0.0655 0.0164 abcd
## 14 TCS 43 0.0196 0.0140 0.00351 d
## 15 TCS 44 0.0356 0.0288 0.00719 bcd
## 16 TCS 45 0.0737 0.0548 0.0137 abcd
## 17 TCS 46 0.0384 0.0315 0.00787 bcd
## 18 TCS 47 0.0572 0.0425 0.0106 abcd
## 19 TCS 48 0.0580 0.0390 0.00976 abcd
## 20 TCS 49 0.0330 0.0220 0.00551 bcd
cinco.rdpi<-data.frame(A.850.rdpi$sp, A.850.rdpi$rdpi, E.850.rdpi$rdpi, WUE.850.rdpi$rdpi, gsw.850.rdpi$rdpi,
cica.850.rdpi$rdpi)
colnames(cinco.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
cinco.rdpi$treatment <- "850"
cinco.sum<-cinco.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
cinco.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.1709723 0.1949321 0.09843433 0.2248304 0.05702791
## Calculo del RDPI para todos los caracteres luz 1050
A.1050.rdpi<-rdpi(clone.1050.fin, Clon, A, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 3.082 0.16222 5.879 1.48e-12 ***
## Residuals 300 8.278 0.02759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.100 0.0737 0.0184 bcde
## 2 TCS 02 0.116 0.0937 0.0234 bcde
## 3 TCS 03 0.300 0.270 0.0675 abc
## 4 TCS 04 0.218 0.194 0.0485 abcde
## 5 TCS 05 0.138 0.0954 0.0238 bcde
## 6 TCS 08 0.0815 0.0645 0.0161 de
## 7 TCS 10 0.389 0.257 0.0641 a
## 8 TCS 11 0.103 0.0772 0.0193 bcde
## 9 TCS 12 0.366 0.268 0.0670 a
## 10 TCS 20 0.202 0.175 0.0437 abcde
## 11 TCS 40 0.121 0.0903 0.0226 bcde
## 12 TCS 41 0.0962 0.0755 0.0189 cde
## 13 TCS 42 0.129 0.111 0.0276 bcde
## 14 TCS 43 0.0453 0.0273 0.00683 e
## 15 TCS 44 0.145 0.124 0.0311 bcde
## 16 TCS 45 0.0860 0.0742 0.0185 de
## 17 TCS 46 0.306 0.281 0.0701 ab
## 18 TCS 47 0.271 0.234 0.0585 abcd
## 19 TCS 48 0.222 0.172 0.0431 abcde
## 20 TCS 49 0.237 0.170 0.0424 abcde
E.1050.rdpi<-rdpi(clone.1050.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.311 0.12165 4.007 1.02e-07 ***
## Residuals 300 9.108 0.03036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.184 0.143 0.0358 abc
## 2 TCS 02 0.150 0.112 0.0281 bc
## 3 TCS 03 0.399 0.367 0.0917 a
## 4 TCS 04 0.287 0.238 0.0594 ab
## 5 TCS 05 0.142 0.110 0.0276 bc
## 6 TCS 08 0.118 0.0856 0.0214 bc
## 7 TCS 10 0.326 0.206 0.0514 ab
## 8 TCS 11 0.167 0.119 0.0298 bc
## 9 TCS 12 0.327 0.208 0.0520 ab
## 10 TCS 20 0.185 0.149 0.0372 abc
## 11 TCS 40 0.148 0.103 0.0257 bc
## 12 TCS 41 0.119 0.0908 0.0227 bc
## 13 TCS 42 0.175 0.138 0.0345 bc
## 14 TCS 43 0.188 0.128 0.0321 abc
## 15 TCS 44 0.207 0.166 0.0414 abc
## 16 TCS 45 0.0636 0.0411 0.0103 c
## 17 TCS 46 0.318 0.245 0.0614 ab
## 18 TCS 47 0.237 0.154 0.0385 abc
## 19 TCS 48 0.300 0.216 0.0541 ab
## 20 TCS 49 0.230 0.165 0.0412 abc
WUE.1050.rdpi<-rdpi(clone.1050.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.5812 0.030590 4.309 1.69e-08 ***
## Residuals 300 2.1295 0.007098
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.119 0.0850 0.0212 abc
## 2 TCS 02 0.0481 0.0359 0.00898 c
## 3 TCS 03 0.224 0.175 0.0439 a
## 4 TCS 04 0.0906 0.0654 0.0164 bc
## 5 TCS 05 0.104 0.0871 0.0218 bc
## 6 TCS 08 0.0664 0.0503 0.0126 c
## 7 TCS 10 0.118 0.107 0.0267 abc
## 8 TCS 11 0.0941 0.0751 0.0188 bc
## 9 TCS 12 0.130 0.0932 0.0233 abc
## 10 TCS 20 0.0567 0.0397 0.00991 c
## 11 TCS 40 0.124 0.0880 0.0220 abc
## 12 TCS 41 0.0424 0.0324 0.00810 c
## 13 TCS 42 0.0531 0.0374 0.00936 c
## 14 TCS 43 0.173 0.130 0.0326 ab
## 15 TCS 44 0.0685 0.0511 0.0128 bc
## 16 TCS 45 0.0798 0.0560 0.0140 bc
## 17 TCS 46 0.0993 0.0916 0.0229 bc
## 18 TCS 47 0.120 0.0833 0.0208 abc
## 19 TCS 48 0.0953 0.0765 0.0191 bc
## 20 TCS 49 0.114 0.0830 0.0207 bc
gsw.1050.rdpi<-rdpi(clone.1050.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 3.393 0.17860 4.786 9.77e-10 ***
## Residuals 300 11.195 0.03732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.139 0.103 0.0257 cde
## 2 TCS 02 0.172 0.131 0.0328 cde
## 3 TCS 03 0.374 0.319 0.0796 abc
## 4 TCS 04 0.304 0.244 0.0610 abcde
## 5 TCS 05 0.220 0.161 0.0401 bcde
## 6 TCS 08 0.118 0.0940 0.0235 de
## 7 TCS 10 0.472 0.271 0.0677 a
## 8 TCS 11 0.220 0.156 0.0391 bcde
## 9 TCS 12 0.423 0.272 0.0679 ab
## 10 TCS 20 0.191 0.145 0.0364 bcde
## 11 TCS 40 0.154 0.126 0.0315 cde
## 12 TCS 41 0.189 0.135 0.0338 bcde
## 13 TCS 42 0.228 0.169 0.0423 abcde
## 14 TCS 43 0.0697 0.0466 0.0117 e
## 15 TCS 44 0.190 0.139 0.0347 bcde
## 16 TCS 45 0.203 0.146 0.0365 bcde
## 17 TCS 46 0.351 0.293 0.0734 abcd
## 18 TCS 47 0.329 0.227 0.0567 abcd
## 19 TCS 48 0.303 0.215 0.0538 abcde
## 20 TCS 49 0.303 0.202 0.0505 abcde
cica.1050.rdpi<-rdpi(clone.1050.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.1977 0.010403 4.165 4e-08 ***
## Residuals 300 0.7493 0.002498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0239 0.0195 0.00488 d
## 2 TCS 02 0.0316 0.0248 0.00621 cd
## 3 TCS 03 0.0953 0.0755 0.0189 ab
## 4 TCS 04 0.0520 0.0382 0.00955 abcd
## 5 TCS 05 0.0877 0.0692 0.0173 abc
## 6 TCS 08 0.0254 0.0197 0.00492 cd
## 7 TCS 10 0.0711 0.0539 0.0135 abcd
## 8 TCS 11 0.0877 0.0671 0.0168 abc
## 9 TCS 12 0.0625 0.0450 0.0112 abcd
## 10 TCS 20 0.106 0.0937 0.0234 a
## 11 TCS 40 0.0343 0.0318 0.00795 bcd
## 12 TCS 41 0.0700 0.0523 0.0131 abcd
## 13 TCS 42 0.0750 0.0630 0.0157 abcd
## 14 TCS 43 0.0175 0.0124 0.00309 d
## 15 TCS 44 0.0362 0.0285 0.00713 bcd
## 16 TCS 45 0.0748 0.0572 0.0143 abcd
## 17 TCS 46 0.0411 0.0331 0.00827 bcd
## 18 TCS 47 0.0542 0.0372 0.00930 abcd
## 19 TCS 48 0.0647 0.0530 0.0133 abcd
## 20 TCS 49 0.0513 0.0340 0.00849 abcd
seis.rdpi<-data.frame(A.1050.rdpi$sp, A.1050.rdpi$rdpi, E.1050.rdpi$rdpi, WUE.1050.rdpi$rdpi, gsw.1050.rdpi$rdpi,
cica.1050.rdpi$rdpi)
colnames(seis.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
seis.rdpi$treatment <- "1050"
seis.sum<-seis.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
seis.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.1836568 0.2135473 0.1010715 0.2476028 0.05809013
## Calculo del RDPI para todos los caracteres luz 1350
A.1350.rdpi<-rdpi(clone.1350.fin, Clon, A, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.691 0.14164 4.95 3.68e-10 ***
## Residuals 300 8.585 0.02862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0974 0.0779 0.0195 c
## 2 TCS 02 0.124 0.0843 0.0211 c
## 3 TCS 03 0.340 0.290 0.0726 ab
## 4 TCS 04 0.224 0.205 0.0512 abc
## 5 TCS 05 0.135 0.0834 0.0209 bc
## 6 TCS 08 0.0959 0.0722 0.0181 c
## 7 TCS 10 0.373 0.238 0.0594 a
## 8 TCS 11 0.130 0.0859 0.0215 bc
## 9 TCS 12 0.373 0.281 0.0703 a
## 10 TCS 20 0.189 0.155 0.0387 abc
## 11 TCS 40 0.139 0.101 0.0252 bc
## 12 TCS 41 0.160 0.0944 0.0236 abc
## 13 TCS 42 0.148 0.117 0.0292 bc
## 14 TCS 43 0.0990 0.0766 0.0191 c
## 15 TCS 44 0.149 0.125 0.0313 bc
## 16 TCS 45 0.0841 0.0754 0.0189 c
## 17 TCS 46 0.294 0.246 0.0616 abc
## 18 TCS 47 0.293 0.275 0.0687 abc
## 19 TCS 48 0.230 0.175 0.0437 abc
## 20 TCS 49 0.196 0.164 0.0411 abc
E.1350.rdpi<-rdpi(clone.1350.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.131 0.11216 3.627 9.75e-07 ***
## Residuals 300 9.278 0.03093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.181 0.142 0.0356 bc
## 2 TCS 02 0.167 0.106 0.0265 bc
## 3 TCS 03 0.431 0.361 0.0903 a
## 4 TCS 04 0.291 0.247 0.0617 abc
## 5 TCS 05 0.160 0.110 0.0274 bc
## 6 TCS 08 0.133 0.0886 0.0222 bc
## 7 TCS 10 0.305 0.201 0.0504 ab
## 8 TCS 11 0.196 0.127 0.0318 bc
## 9 TCS 12 0.348 0.229 0.0574 ab
## 10 TCS 20 0.201 0.149 0.0372 bc
## 11 TCS 40 0.155 0.123 0.0307 bc
## 12 TCS 41 0.226 0.128 0.0321 abc
## 13 TCS 42 0.203 0.144 0.0360 bc
## 14 TCS 43 0.239 0.152 0.0381 abc
## 15 TCS 44 0.209 0.161 0.0403 abc
## 16 TCS 45 0.0803 0.0571 0.0143 c
## 17 TCS 46 0.299 0.225 0.0562 abc
## 18 TCS 47 0.254 0.194 0.0484 abc
## 19 TCS 48 0.302 0.205 0.0513 abc
## 20 TCS 49 0.146 0.0976 0.0244 bc
WUE.1350.rdpi<-rdpi(clone.1350.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.5379 0.02831 4.444 7.55e-09 ***
## Residuals 300 1.9111 0.00637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.120 0.0918 0.0230 bc
## 2 TCS 02 0.0561 0.0310 0.00774 c
## 3 TCS 03 0.223 0.165 0.0413 a
## 4 TCS 04 0.0921 0.0641 0.0160 bc
## 5 TCS 05 0.102 0.0753 0.0188 bc
## 6 TCS 08 0.0685 0.0513 0.0128 c
## 7 TCS 10 0.108 0.0892 0.0223 bc
## 8 TCS 11 0.0830 0.0704 0.0176 bc
## 9 TCS 12 0.115 0.0817 0.0204 bc
## 10 TCS 20 0.0411 0.0284 0.00711 c
## 11 TCS 40 0.120 0.0888 0.0222 bc
## 12 TCS 41 0.0786 0.0511 0.0128 bc
## 13 TCS 42 0.0595 0.0400 0.00999 c
## 14 TCS 43 0.179 0.126 0.0314 ab
## 15 TCS 44 0.0665 0.0478 0.0119 c
## 16 TCS 45 0.0834 0.0572 0.0143 bc
## 17 TCS 46 0.0944 0.0815 0.0204 bc
## 18 TCS 47 0.126 0.0901 0.0225 abc
## 19 TCS 48 0.0874 0.0632 0.0158 bc
## 20 TCS 49 0.0965 0.0753 0.0188 bc
gsw.1350.rdpi<-rdpi(clone.1350.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.837 0.14933 3.703 6.2e-07 ***
## Residuals 300 12.098 0.04033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.156 0.119 0.0297 d
## 2 TCS 02 0.187 0.119 0.0296 cd
## 3 TCS 03 0.423 0.315 0.0787 abc
## 4 TCS 04 0.314 0.261 0.0651 abcd
## 5 TCS 05 0.225 0.143 0.0358 abcd
## 6 TCS 08 0.144 0.102 0.0256 d
## 7 TCS 10 0.471 0.272 0.0680 a
## 8 TCS 11 0.211 0.157 0.0392 bcd
## 9 TCS 12 0.443 0.284 0.0709 ab
## 10 TCS 20 0.217 0.156 0.0391 bcd
## 11 TCS 40 0.170 0.140 0.0350 cd
## 12 TCS 41 0.268 0.164 0.0409 abcd
## 13 TCS 42 0.254 0.176 0.0440 abcd
## 14 TCS 43 0.174 0.130 0.0324 cd
## 15 TCS 44 0.199 0.141 0.0352 bcd
## 16 TCS 45 0.239 0.165 0.0413 abcd
## 17 TCS 46 0.343 0.263 0.0659 abcd
## 18 TCS 47 0.338 0.278 0.0694 abcd
## 19 TCS 48 0.312 0.219 0.0548 abcd
## 20 TCS 49 0.256 0.203 0.0506 abcd
cica.1350.rdpi<-rdpi(clone.1350.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.1537 0.008089 3.375 4.28e-06 ***
## Residuals 300 0.7190 0.002397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0372 0.0271 0.00676 bc
## 2 TCS 02 0.0346 0.0229 0.00572 bc
## 3 TCS 03 0.0956 0.0654 0.0164 ab
## 4 TCS 04 0.0528 0.0393 0.00983 abc
## 5 TCS 05 0.0895 0.0623 0.0156 abc
## 6 TCS 08 0.0298 0.0220 0.00550 c
## 7 TCS 10 0.0726 0.0628 0.0157 abc
## 8 TCS 11 0.0750 0.0543 0.0136 abc
## 9 TCS 12 0.0739 0.0498 0.0124 abc
## 10 TCS 20 0.100 0.0889 0.0222 a
## 11 TCS 40 0.0345 0.0330 0.00825 bc
## 12 TCS 41 0.0778 0.0540 0.0135 abc
## 13 TCS 42 0.0761 0.0591 0.0148 abc
## 14 TCS 43 0.0503 0.0369 0.00922 abc
## 15 TCS 44 0.0392 0.0295 0.00737 abc
## 16 TCS 45 0.0807 0.0640 0.0160 abc
## 17 TCS 46 0.0444 0.0332 0.00829 abc
## 18 TCS 47 0.0402 0.0292 0.00730 abc
## 19 TCS 48 0.0655 0.0493 0.0123 abc
## 20 TCS 49 0.0428 0.0315 0.00787 abc
siete.rdpi<-data.frame(A.1350.rdpi$sp, A.1350.rdpi$rdpi, E.1350.rdpi$rdpi, WUE.1350.rdpi$rdpi, gsw.1350.rdpi$rdpi,
cica.1350.rdpi$rdpi)
colnames(siete.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
siete.rdpi$treatment <- "1350"
siete.sum<-siete.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
siete.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.1937137 0.2263364 0.09999357 0.2671455 0.06062948
## Calculo del RDPI para todos los caracteres luz 1750
A.1750.rdpi<-rdpi(clone.1750.fin, Clon, A, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.043 0.10751 3.999 1.07e-07 ***
## Residuals 300 8.066 0.02689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.133 0.0827 0.0207 bcde
## 2 TCS 02 0.125 0.0824 0.0206 bcde
## 3 TCS 03 0.369 0.332 0.0829 a
## 4 TCS 04 0.220 0.206 0.0516 abcde
## 5 TCS 05 0.154 0.0763 0.0191 bcde
## 6 TCS 08 0.151 0.0811 0.0203 bcde
## 7 TCS 10 0.314 0.212 0.0530 abc
## 8 TCS 11 0.142 0.0954 0.0239 bcde
## 9 TCS 12 0.328 0.279 0.0696 ab
## 10 TCS 20 0.195 0.130 0.0326 abcde
## 11 TCS 40 0.140 0.102 0.0255 bcde
## 12 TCS 41 0.0926 0.0687 0.0172 e
## 13 TCS 42 0.146 0.115 0.0287 bcde
## 14 TCS 43 0.111 0.0737 0.0184 cde
## 15 TCS 44 0.143 0.118 0.0295 bcde
## 16 TCS 45 0.0997 0.0807 0.0202 de
## 17 TCS 46 0.228 0.203 0.0506 abcde
## 18 TCS 47 0.303 0.266 0.0665 abcd
## 19 TCS 48 0.238 0.154 0.0386 abcde
## 20 TCS 49 0.166 0.143 0.0358 abcde
E.1750.rdpi<-rdpi(clone.1750.fin, Clon, E, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 1.562 0.08219 2.671 0.00024 ***
## Residuals 300 9.230 0.03077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.172 0.112 0.0280 b
## 2 TCS 02 0.170 0.114 0.0285 b
## 3 TCS 03 0.435 0.373 0.0932 a
## 4 TCS 04 0.283 0.242 0.0606 ab
## 5 TCS 05 0.210 0.115 0.0287 b
## 6 TCS 08 0.252 0.158 0.0395 ab
## 7 TCS 10 0.272 0.170 0.0425 ab
## 8 TCS 11 0.200 0.133 0.0331 b
## 9 TCS 12 0.289 0.227 0.0568 ab
## 10 TCS 20 0.245 0.154 0.0385 ab
## 11 TCS 40 0.147 0.128 0.0321 b
## 12 TCS 41 0.182 0.117 0.0293 b
## 13 TCS 42 0.217 0.154 0.0385 ab
## 14 TCS 43 0.219 0.159 0.0397 ab
## 15 TCS 44 0.205 0.145 0.0361 b
## 16 TCS 45 0.103 0.0651 0.0163 b
## 17 TCS 46 0.236 0.211 0.0527 ab
## 18 TCS 47 0.269 0.188 0.0471 ab
## 19 TCS 48 0.310 0.194 0.0485 ab
## 20 TCS 49 0.166 0.101 0.0253 b
WUE.1750.rdpi<-rdpi(clone.1750.fin, Clon, WUE, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.3744 0.019704 3.097 2.15e-05 ***
## Residuals 300 1.9088 0.006363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.109 0.0802 0.0200 abc
## 2 TCS 02 0.0698 0.0325 0.00812 c
## 3 TCS 03 0.199 0.151 0.0376 a
## 4 TCS 04 0.0886 0.0621 0.0155 bc
## 5 TCS 05 0.106 0.0667 0.0167 abc
## 6 TCS 08 0.121 0.105 0.0263 abc
## 7 TCS 10 0.0981 0.0761 0.0190 abc
## 8 TCS 11 0.0780 0.0501 0.0125 bc
## 9 TCS 12 0.0989 0.0779 0.0195 abc
## 10 TCS 20 0.0578 0.0398 0.00994 c
## 11 TCS 40 0.140 0.0946 0.0236 abc
## 12 TCS 41 0.0942 0.0560 0.0140 bc
## 13 TCS 42 0.0899 0.0530 0.0133 bc
## 14 TCS 43 0.179 0.125 0.0313 ab
## 15 TCS 44 0.0671 0.0488 0.0122 c
## 16 TCS 45 0.0928 0.0547 0.0137 bc
## 17 TCS 46 0.0981 0.0754 0.0189 abc
## 18 TCS 47 0.139 0.0943 0.0236 abc
## 19 TCS 48 0.109 0.0622 0.0155 abc
## 20 TCS 49 0.115 0.0861 0.0215 abc
gsw.1750.rdpi<-rdpi(clone.1750.fin, Clon, gsw, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 2.158 0.11360 2.903 6.54e-05 ***
## Residuals 300 11.742 0.03914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.196 0.0865 0.0216 abc
## 2 TCS 02 0.207 0.136 0.0339 abc
## 3 TCS 03 0.437 0.338 0.0845 a
## 4 TCS 04 0.302 0.269 0.0673 abc
## 5 TCS 05 0.236 0.133 0.0334 abc
## 6 TCS 08 0.287 0.161 0.0402 abc
## 7 TCS 10 0.420 0.263 0.0658 ab
## 8 TCS 11 0.181 0.0937 0.0234 bc
## 9 TCS 12 0.390 0.298 0.0745 abc
## 10 TCS 20 0.241 0.160 0.0401 abc
## 11 TCS 40 0.160 0.137 0.0341 c
## 12 TCS 41 0.192 0.121 0.0302 abc
## 13 TCS 42 0.255 0.178 0.0444 abc
## 14 TCS 43 0.173 0.120 0.0300 bc
## 15 TCS 44 0.190 0.134 0.0336 abc
## 16 TCS 45 0.248 0.174 0.0435 abc
## 17 TCS 46 0.288 0.233 0.0583 abc
## 18 TCS 47 0.365 0.269 0.0672 abc
## 19 TCS 48 0.327 0.209 0.0521 abc
## 20 TCS 49 0.218 0.190 0.0474 abc
cica.1750.rdpi<-rdpi(clone.1750.fin, Clon, cica, env)
## [1] "ANOVA test"
## Df Sum Sq Mean Sq F value Pr(>F)
## RDPI$sp 19 0.0963 0.005069 2.62 0.00032 ***
## Residuals 300 0.5805 0.001935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Joining, by = "sp"
## [1] "Tukey HSD test for differences accross groups"
## # A tibble: 20 × 5
## sp mean sd se groups
## <fct> <dbl> <dbl> <dbl> <chr>
## 1 TCS 01 0.0344 0.0239 0.00597 a
## 2 TCS 02 0.0394 0.0225 0.00562 a
## 3 TCS 03 0.0787 0.0561 0.0140 a
## 4 TCS 04 0.0476 0.0384 0.00960 a
## 5 TCS 05 0.0833 0.0559 0.0140 a
## 6 TCS 08 0.0641 0.0454 0.0113 a
## 7 TCS 10 0.0745 0.0602 0.0151 a
## 8 TCS 11 0.0521 0.0353 0.00882 a
## 9 TCS 12 0.0668 0.0439 0.0110 a
## 10 TCS 20 0.0875 0.0761 0.0190 a
## 11 TCS 40 0.0413 0.0275 0.00687 a
## 12 TCS 41 0.0396 0.0261 0.00651 a
## 13 TCS 42 0.0781 0.0509 0.0127 a
## 14 TCS 43 0.0472 0.0305 0.00764 a
## 15 TCS 44 0.0387 0.0299 0.00747 a
## 16 TCS 45 0.0801 0.0621 0.0155 a
## 17 TCS 46 0.0499 0.0359 0.00899 a
## 18 TCS 47 0.0425 0.0334 0.00836 a
## 19 TCS 48 0.0658 0.0476 0.0119 a
## 20 TCS 49 0.0410 0.0292 0.00730 a
ocho.rdpi<-data.frame(A.1750.rdpi$sp, A.1750.rdpi$rdpi, E.1750.rdpi$rdpi, WUE.1750.rdpi$rdpi, gsw.1750.rdpi$rdpi,
cica.1750.rdpi$rdpi)
colnames(ocho.rdpi)<-c('gen', 'Ardpi', 'Erdpi', 'WUErdpi', 'gswrdpi', 'cicardpi')
ocho.rdpi$treatment <- "1750"
ocho.sum<-ocho.rdpi %>%
select(Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi)%>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi))
ocho.sum
## Ardpi_m Erdpi_m WUErdpi_m gswrdpi_m cicardpi_m
## 1 0.1899071 0.2290522 0.1074696 0.2657023 0.05763213
# Creando una nueva base de datos de RDPi para los ocho intervalos de luz
dos.rdpi$ID <- seq.int(nrow(dos.rdpi))
tres.rdpi$ID <- seq.int(nrow(tres.rdpi))
cuatro.rdpi$ID <- seq.int(nrow(cuatro.rdpi))
cinco.rdpi$ID <- seq.int(nrow(cinco.rdpi))
seis.rdpi$ID <- seq.int(nrow(seis.rdpi))
siete.rdpi$ID <- seq.int(nrow(siete.rdpi))
ocho.rdpi$ID <- seq.int(nrow(ocho.rdpi))
total2 <- rbind (dos.rdpi, tres.rdpi)
total3 <- rbind (total2, cuatro.rdpi)
total4 <- rbind (total3, cinco.rdpi)
total5 <- rbind (total4, seis.rdpi)
total6 <- rbind (total5, siete.rdpi)
total7 <- rbind (total6, ocho.rdpi)
write.csv(total7, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Fabricio/data/total_7.csv")
## analysis of variance
anova_A <- aov(total7$Ardpi ~ total7$gen, data = total7)
anova(anova_A)
## Analysis of Variance Table
##
## Response: total7$Ardpi
## Df Sum Sq Mean Sq F value Pr(>F)
## total7$gen 19 12.413 0.65332 24.47 < 2.2e-16 ***
## Residuals 2220 59.272 0.02670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova_E <- aov(total7$Erdpi ~ total7$gen, data = total7)
anova(anova_E)
## Analysis of Variance Table
##
## Response: total7$Erdpi
## Df Sum Sq Mean Sq F value Pr(>F)
## total7$gen 19 11.524 0.60652 20.877 < 2.2e-16 ***
## Residuals 2220 64.496 0.02905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova_WUE <- aov(total7$WUErdpi ~ total7$gen, data = total7)
anova(anova_WUE)
## Analysis of Variance Table
##
## Response: total7$WUErdpi
## Df Sum Sq Mean Sq F value Pr(>F)
## total7$gen 19 3.0598 0.161040 13.376 < 2.2e-16 ***
## Residuals 2220 26.7268 0.012039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova_gSw <- aov(total7$gswrdpi ~ total7$gen, data = total7)
anova(anova_gSw)
## Analysis of Variance Table
##
## Response: total7$gswrdpi
## Df Sum Sq Mean Sq F value Pr(>F)
## total7$gen 19 14.845 0.78131 23.299 < 2.2e-16 ***
## Residuals 2220 74.445 0.03353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova_cica <- aov(total7$cicardpi ~ total7$gen, data = total7)
anova(anova_cica)
## Analysis of Variance Table
##
## Response: total7$cicardpi
## Df Sum Sq Mean Sq F value Pr(>F)
## total7$gen 19 0.8123 0.042752 15.118 < 2.2e-16 ***
## Residuals 2220 6.2780 0.002828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Mean contrast for A
A_emm <- emmeans(anova_A, "gen")
A_emm
## gen emmean SE df lower.CL upper.CL
## TCS 01 0.1211 0.0154 2220 0.0908 0.151
## TCS 02 0.1424 0.0154 2220 0.1121 0.173
## TCS 03 0.3134 0.0154 2220 0.2831 0.344
## TCS 04 0.2091 0.0154 2220 0.1788 0.239
## TCS 05 0.1491 0.0154 2220 0.1188 0.179
## TCS 08 0.1121 0.0154 2220 0.0819 0.142
## TCS 10 0.3127 0.0154 2220 0.2824 0.343
## TCS 11 0.1455 0.0154 2220 0.1152 0.176
## TCS 12 0.3325 0.0154 2220 0.3022 0.363
## TCS 20 0.2313 0.0154 2220 0.2010 0.262
## TCS 40 0.1564 0.0154 2220 0.1262 0.187
## TCS 41 0.1198 0.0154 2220 0.0895 0.150
## TCS 42 0.1372 0.0154 2220 0.1070 0.168
## TCS 43 0.0984 0.0154 2220 0.0682 0.129
## TCS 44 0.1638 0.0154 2220 0.1335 0.194
## TCS 45 0.1069 0.0154 2220 0.0766 0.137
## TCS 46 0.2639 0.0154 2220 0.2336 0.294
## TCS 47 0.2281 0.0154 2220 0.1978 0.258
## TCS 48 0.2062 0.0154 2220 0.1759 0.236
## TCS 49 0.2875 0.0154 2220 0.2572 0.318
##
## Confidence level used: 0.95
pwpp(A_emm)
cld_A <-multcomp::cld(A_emm, alpha = 0.05, Letters = LETTERS, reversed=T)
plot(A_emm, comparisons = TRUE, xlab = expression(RDPI[A]))
## Mean contrast for E
E_emm <- emmeans(anova_E, "gen")
E_emm
## gen emmean SE df lower.CL upper.CL
## TCS 01 0.1723 0.0161 2220 0.1407 0.2038
## TCS 02 0.1439 0.0161 2220 0.1123 0.1755
## TCS 03 0.3940 0.0161 2220 0.3624 0.4256
## TCS 04 0.2573 0.0161 2220 0.2257 0.2889
## TCS 05 0.1514 0.0161 2220 0.1198 0.1830
## TCS 08 0.1374 0.0161 2220 0.1058 0.1690
## TCS 10 0.2396 0.0161 2220 0.2080 0.2712
## TCS 11 0.1646 0.0161 2220 0.1330 0.1962
## TCS 12 0.2713 0.0161 2220 0.2397 0.3029
## TCS 20 0.2025 0.0161 2220 0.1709 0.2341
## TCS 40 0.1343 0.0161 2220 0.1028 0.1659
## TCS 41 0.1103 0.0161 2220 0.0787 0.1418
## TCS 42 0.1485 0.0161 2220 0.1170 0.1801
## TCS 43 0.2067 0.0161 2220 0.1751 0.2383
## TCS 44 0.1974 0.0161 2220 0.1658 0.2290
## TCS 45 0.0593 0.0161 2220 0.0278 0.0909
## TCS 46 0.2530 0.0161 2220 0.2214 0.2846
## TCS 47 0.1928 0.0161 2220 0.1613 0.2244
## TCS 48 0.2374 0.0161 2220 0.2058 0.2689
## TCS 49 0.2700 0.0161 2220 0.2384 0.3016
##
## Confidence level used: 0.95
pwpp(E_emm)
cld_E <-multcomp::cld(E_emm, alpha = 0.05, Letters = LETTERS, reversed=T)
plot(E_emm, comparisons = TRUE, xlab = expression(RDPI[E]))
## Mean contrast for WUE
WUE_emm <- emmeans(anova_WUE, "gen")
WUE_emm
## gen emmean SE df lower.CL upper.CL
## TCS 01 0.1361 0.0104 2220 0.1157 0.156
## TCS 02 0.1054 0.0104 2220 0.0851 0.126
## TCS 03 0.2343 0.0104 2220 0.2140 0.255
## TCS 04 0.1070 0.0104 2220 0.0867 0.127
## TCS 05 0.1250 0.0104 2220 0.1047 0.145
## TCS 08 0.1023 0.0104 2220 0.0820 0.123
## TCS 10 0.1175 0.0104 2220 0.0972 0.138
## TCS 11 0.1121 0.0104 2220 0.0918 0.132
## TCS 12 0.1270 0.0104 2220 0.1067 0.147
## TCS 20 0.0896 0.0104 2220 0.0693 0.110
## TCS 40 0.1520 0.0104 2220 0.1317 0.172
## TCS 41 0.0933 0.0104 2220 0.0730 0.114
## TCS 42 0.0861 0.0104 2220 0.0658 0.106
## TCS 43 0.2144 0.0104 2220 0.1941 0.235
## TCS 44 0.1026 0.0104 2220 0.0822 0.123
## TCS 45 0.1029 0.0104 2220 0.0826 0.123
## TCS 46 0.1277 0.0104 2220 0.1073 0.148
## TCS 47 0.1350 0.0104 2220 0.1146 0.155
## TCS 48 0.1208 0.0104 2220 0.1005 0.141
## TCS 49 0.1113 0.0104 2220 0.0910 0.132
##
## Confidence level used: 0.95
pwpp(WUE_emm)
cld_WUE <-multcomp::cld(WUE_emm, alpha = 0.05, Letters = LETTERS, reversed=T)
plot(WUE_emm, comparisons = TRUE, xlab =expression(RDPI[WUE]))
## Mean contrast for gSw
gSw_emm <- emmeans(anova_gSw, "gen")
gSw_emm
## gen emmean SE df lower.CL upper.CL
## TCS 01 0.1328 0.0173 2220 0.0988 0.167
## TCS 02 0.1595 0.0173 2220 0.1255 0.193
## TCS 03 0.3675 0.0173 2220 0.3336 0.401
## TCS 04 0.2623 0.0173 2220 0.2284 0.296
## TCS 05 0.2190 0.0173 2220 0.1851 0.253
## TCS 08 0.1265 0.0173 2220 0.0926 0.160
## TCS 10 0.3543 0.0173 2220 0.3204 0.388
## TCS 11 0.1648 0.0173 2220 0.1309 0.199
## TCS 12 0.3667 0.0173 2220 0.3328 0.401
## TCS 20 0.1917 0.0173 2220 0.1578 0.226
## TCS 40 0.1579 0.0173 2220 0.1240 0.192
## TCS 41 0.1697 0.0173 2220 0.1357 0.204
## TCS 42 0.2064 0.0173 2220 0.1725 0.240
## TCS 43 0.0825 0.0173 2220 0.0486 0.116
## TCS 44 0.1750 0.0173 2220 0.1410 0.209
## TCS 45 0.1914 0.0173 2220 0.1575 0.225
## TCS 46 0.2815 0.0173 2220 0.2476 0.315
## TCS 47 0.2642 0.0173 2220 0.2303 0.298
## TCS 48 0.2249 0.0173 2220 0.1909 0.259
## TCS 49 0.3274 0.0173 2220 0.2935 0.361
##
## Confidence level used: 0.95
pwpp(gSw_emm)
cld_gSw <-multcomp::cld(gSw_emm, alpha = 0.05, Letters = LETTERS, reversed=T)
plot(gSw_emm, comparisons = TRUE, xlab =expression(RDPI[gSw]))
## Mean contrast for cica
cica_emm <- emmeans(anova_cica, "gen")
cica_emm
## gen emmean SE df lower.CL upper.CL
## TCS 01 0.0377 0.00502 2220 0.0279 0.0476
## TCS 02 0.0471 0.00502 2220 0.0372 0.0569
## TCS 03 0.1010 0.00502 2220 0.0912 0.1109
## TCS 04 0.0552 0.00502 2220 0.0453 0.0651
## TCS 05 0.0980 0.00502 2220 0.0881 0.1078
## TCS 08 0.0439 0.00502 2220 0.0340 0.0537
## TCS 10 0.0737 0.00502 2220 0.0638 0.0835
## TCS 11 0.0787 0.00502 2220 0.0689 0.0886
## TCS 12 0.0663 0.00502 2220 0.0565 0.0762
## TCS 20 0.0969 0.00502 2220 0.0870 0.1067
## TCS 40 0.0487 0.00502 2220 0.0388 0.0585
## TCS 41 0.0777 0.00502 2220 0.0679 0.0876
## TCS 42 0.0876 0.00502 2220 0.0777 0.0974
## TCS 43 0.0519 0.00502 2220 0.0420 0.0617
## TCS 44 0.0449 0.00502 2220 0.0350 0.0547
## TCS 45 0.0788 0.00502 2220 0.0690 0.0887
## TCS 46 0.0592 0.00502 2220 0.0493 0.0690
## TCS 47 0.0630 0.00502 2220 0.0531 0.0728
## TCS 48 0.0614 0.00502 2220 0.0516 0.0713
## TCS 49 0.0494 0.00502 2220 0.0395 0.0592
##
## Confidence level used: 0.95
pwpp(cica_emm)
cld_cica <-multcomp::cld(cica_emm, alpha = 0.05, Letters = LETTERS, reversed=T)
plot(cica_emm, comparisons = TRUE, xlab = expression(RDPI[Ci/Ca]))
## table with factors and 3rd quantile
dt_A <- group_by(total7, gen) %>%
summarise(w=mean(Ardpi)) %>%
arrange(desc(w))
dt_E <- group_by(total7, gen) %>%
summarise(w=mean(Erdpi)) %>%
arrange(desc(w))
dt_WUE <- group_by(total7, gen) %>%
summarise(w=mean(WUErdpi)) %>%
arrange(desc(w))
dt_gsw <- group_by(total7, gen) %>%
summarise(w=mean(gswrdpi)) %>%
arrange(desc(w))
dt_cica <- group_by(total7, gen) %>%
summarise(w=mean(cicardpi)) %>%
arrange(desc(w))
## extracting the compact letter display and adding to the Tk table
dt_A$cldA <- cld_A$.group
print(dt_A)
## # A tibble: 20 × 3
## gen w cldA
## <fct> <dbl> <chr>
## 1 TCS 12 0.333 " A "
## 2 TCS 03 0.313 " A "
## 3 TCS 10 0.313 " A "
## 4 TCS 49 0.287 " AB "
## 5 TCS 46 0.264 " ABC "
## 6 TCS 20 0.231 " BCD "
## 7 TCS 47 0.228 " BCD "
## 8 TCS 04 0.209 " CDE "
## 9 TCS 48 0.206 " CDE "
## 10 TCS 44 0.164 " DEF"
## 11 TCS 40 0.156 " DEF"
## 12 TCS 05 0.149 " EF"
## 13 TCS 11 0.145 " EF"
## 14 TCS 02 0.142 " EF"
## 15 TCS 42 0.137 " EF"
## 16 TCS 01 0.121 " F"
## 17 TCS 41 0.120 " F"
## 18 TCS 08 0.112 " F"
## 19 TCS 45 0.107 " F"
## 20 TCS 43 0.0984 " F"
dt_E$cldE <- cld_E$.group
print(dt_E)
## # A tibble: 20 × 3
## gen w cldE
## <fct> <dbl> <chr>
## 1 TCS 03 0.394 " A "
## 2 TCS 12 0.271 " B "
## 3 TCS 49 0.270 " B "
## 4 TCS 04 0.257 " B "
## 5 TCS 46 0.253 " BC "
## 6 TCS 10 0.240 " BCD "
## 7 TCS 48 0.237 " BCD "
## 8 TCS 43 0.207 " BCDE "
## 9 TCS 20 0.203 " BCDE "
## 10 TCS 44 0.197 " BCDE "
## 11 TCS 47 0.193 " BCDE "
## 12 TCS 01 0.172 " CDEF "
## 13 TCS 11 0.165 " DEF "
## 14 TCS 05 0.151 " EF "
## 15 TCS 42 0.149 " EF "
## 16 TCS 02 0.144 " EF "
## 17 TCS 08 0.137 " EFG"
## 18 TCS 40 0.134 " EFG"
## 19 TCS 41 0.110 " FG"
## 20 TCS 45 0.0593 " G"
dt_WUE$cldWUE <- cld_WUE$.group
print(dt_WUE)
## # A tibble: 20 × 3
## gen w cldWUE
## <fct> <dbl> <chr>
## 1 TCS 03 0.234 " A "
## 2 TCS 43 0.214 " A "
## 3 TCS 40 0.152 " B "
## 4 TCS 01 0.136 " BC"
## 5 TCS 47 0.135 " BC"
## 6 TCS 46 0.128 " BC"
## 7 TCS 12 0.127 " BC"
## 8 TCS 05 0.125 " BC"
## 9 TCS 48 0.121 " BC"
## 10 TCS 10 0.118 " BC"
## 11 TCS 11 0.112 " BC"
## 12 TCS 49 0.111 " BC"
## 13 TCS 04 0.107 " BC"
## 14 TCS 02 0.105 " BC"
## 15 TCS 45 0.103 " BC"
## 16 TCS 44 0.103 " BC"
## 17 TCS 08 0.102 " BC"
## 18 TCS 41 0.0933 " C"
## 19 TCS 20 0.0896 " C"
## 20 TCS 42 0.0861 " C"
dt_gsw$cldgsw <- cld_gSw$.group
print(dt_gsw)
## # A tibble: 20 × 3
## gen w cldgsw
## <fct> <dbl> <chr>
## 1 TCS 03 0.368 " A "
## 2 TCS 12 0.367 " A "
## 3 TCS 10 0.354 " A "
## 4 TCS 49 0.327 " AB "
## 5 TCS 46 0.281 " ABC "
## 6 TCS 47 0.264 " BCD "
## 7 TCS 04 0.262 " BCD "
## 8 TCS 48 0.225 " CDE "
## 9 TCS 05 0.219 " CDEF "
## 10 TCS 42 0.206 " CDEFG "
## 11 TCS 20 0.192 " DEFG "
## 12 TCS 45 0.191 " DEFG "
## 13 TCS 44 0.175 " EFG "
## 14 TCS 41 0.170 " EFG "
## 15 TCS 11 0.165 " EFGH"
## 16 TCS 02 0.159 " EFGH"
## 17 TCS 40 0.158 " EFGH"
## 18 TCS 01 0.133 " FGH"
## 19 TCS 08 0.127 " GH"
## 20 TCS 43 0.0825 " H"
dt_cica$cldcica <- cld_cica$.group
print(dt_cica)
## # A tibble: 20 × 3
## gen w cldcica
## <fct> <dbl> <chr>
## 1 TCS 03 0.101 " A "
## 2 TCS 05 0.0980 " AB "
## 3 TCS 20 0.0969 " AB "
## 4 TCS 42 0.0876 " ABC "
## 5 TCS 45 0.0788 " ABCD "
## 6 TCS 11 0.0787 " ABCD "
## 7 TCS 41 0.0777 " ABCD "
## 8 TCS 10 0.0737 " BCDE "
## 9 TCS 12 0.0663 " CDEF "
## 10 TCS 47 0.0630 " CDEFG"
## 11 TCS 48 0.0614 " DEFG"
## 12 TCS 46 0.0592 " DEFG"
## 13 TCS 04 0.0552 " DEFG"
## 14 TCS 43 0.0519 " EFG"
## 15 TCS 49 0.0494 " EFG"
## 16 TCS 40 0.0487 " EFG"
## 17 TCS 02 0.0471 " FG"
## 18 TCS 44 0.0449 " FG"
## 19 TCS 08 0.0439 " FG"
## 20 TCS 01 0.0377 " G"
### group total7 by gen and return some averages and estandar errors
total8 <- total7 %>%
select(gen, Ardpi, Erdpi, WUErdpi, gswrdpi, cicardpi) %>%
group_by(gen) %>%
summarise(
Ardpi_m = mean(Ardpi),
Erdpi_m = mean(Erdpi),
WUErdpi_m = mean(WUErdpi),
gswrdpi_m = mean(gswrdpi),
cicardpi_m = mean(cicardpi),
n=n(),
sd_A=sd(Ardpi),
sd_E=sd(Erdpi),
sd_WUE=sd(WUErdpi),
sd_gsw=sd(gswrdpi),
sd_cica=sd(cicardpi)
) %>%
mutate(se_A=sd_A/sqrt(n)) %>%
mutate(se_E=sd_E/sqrt(n)) %>%
mutate(se_WUE=sd_WUE/sqrt(n)) %>%
mutate(se_gsw=sd_gsw/sqrt(n)) %>%
mutate(se_cica=sd_cica/sqrt(n))
## Adding Tukey letters to low
total8<-merge(dt_A, total8, by="gen")
total8<-merge(dt_E, total8, by="gen")
total8<-merge(dt_WUE, total8, by="gen")
total8<-merge(dt_gsw, total8, by="gen")
## Warning in merge.data.frame(dt_gsw, total8, by = "gen"): column names 'w.x',
## 'w.y' are duplicated in the result
total8<-merge(dt_cica, total8, by="gen")
## saving the data frames
write.csv(total8, "~/Library/CloudStorage/GoogleDrive-icarounam@gmail.com/Mi unidad/Agrosavia/Colaboraciones/Fabricio/data/total_8.csv")
totales <- read.table("total_8.csv", header=T, sep=",")
attach(totales)
## Own color palette
mycolors = c(brewer.pal(name="Dark2", n = 8), brewer.pal(name="Paired", n = 12))
##Barplots
Aplot=totales %>%
arrange(Ardpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=Ardpi_m, fill=cldA)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_color_manual(values = mycolors) +
geom_errorbar(aes(ymin=Ardpi_m-se_E, ymax=Ardpi_m+se_E), width=0.2)+
geom_text(aes(label = cldA, y = Ardpi_m), nudge_x =0, nudge_y = 0.1, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[A])) +
theme_classic()
Aplot + theme(axis.text = element_text(size = 12))
ggsave("Aplot.tiff", plot= Aplot, width = 4, height = 3, dpi = 1000)
Eplot=totales %>%
arrange(Erdpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=Erdpi_m, fill=cldE)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=Erdpi_m-se_E, ymax=Erdpi_m+se_E), width=0.2)+
geom_text(aes(label = cldE, y = Erdpi_m), nudge_x =0, nudge_y = 0.1, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[E])) +
theme_classic()
Eplot + theme(axis.text = element_text(size = 12))
ggsave("Eplot.tiff", plot= Eplot, width = 4, height = 3, dpi = 1000)
WUEplot=totales %>%
arrange(WUErdpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=WUErdpi_m, fill=cldWUE)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=WUErdpi_m-se_WUE, ymax=WUErdpi_m+se_WUE), width=0.2)+
geom_text(aes(label = cldWUE, y = WUErdpi_m), nudge_x =0, nudge_y = 0.09, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[WUE])) +
theme_classic()
WUEplot + theme(axis.text = element_text(size = 12))
ggsave("WUEplot.tiff", plot= WUEplot, width = 4, height = 3, dpi = 1000)
gswplot=totales %>%
arrange(gswrdpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=gswrdpi_m, fill = cldgsw)) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=gswrdpi_m-se_gsw, ymax=gswrdpi_m+se_gsw), width=0.2)+
geom_text(aes(label = cldgsw, y = gswrdpi_m), nudge_x =0, nudge_y = 0.09, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[gSw])) +
theme_classic()
gswplot + theme(axis.text = element_text(size = 12))
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Paired is 12
## Returning the palette you asked for with that many colors
ggsave("gswplot.tiff", plot= gswplot, width = 4, height = 3, dpi = 1000)
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Paired is 12
## Returning the palette you asked for with that many colors
cicaplot=totales %>%
arrange(cicardpi_m) %>%
mutate(gen=factor(gen, levels = gen)) %>%
ggplot( aes(x=gen, y=cicardpi_m, fill = cldcica )) +
geom_col(alpha=.9, width=.7, show.legend = FALSE) +
scale_fill_brewer(palette = "Paired") +
geom_errorbar(aes(ymin=cicardpi_m-se_cica, ymax=cicardpi_m+se_cica), width=0.2)+
geom_text(aes(label = cldcica, y = cicardpi_m), nudge_x =0, nudge_y = 0.07, size = 3.5)+
coord_flip() +
xlab("") +
ylab(expression(RDPI[Ci/Ca])) +
theme_classic()
cicaplot + theme(axis.text = element_text(size = 12))
ggsave("cicaplot.tiff", plot= cicaplot, width = 4, height = 3, dpi = 1000)