setwd("~/Google Drive/Agrosavia/colaboraciones/Laura")
datos5<-read.table("veinteclo_boy2.csv", header=T, sep=',')
datos5$gen<-as.factor(datos5$gen)
datos5$bloque<-as.factor(datos5$bloque)
datos5$semana<-as.factor(datos5$semana)
attach(datos5)
library(ggplot2)
#Gráfica altura
ggplot(datos5, aes(semana, alt, group = gen, colour = gen)) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Altura") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 68 rows containing non-finite values (stat_smooth).
# Gráfica diámetro patrón
ggplot(datos5, aes(semana, patrodia, group = gen, colour = gen)) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Diámetro del patrón") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 68 rows containing non-finite values (stat_smooth).
#Gráfica diámetro injerto
ggplot(datos5, aes(semana, injedia, group = gen, colour = gen)) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Diámetro del injerto") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 68 rows containing non-finite values (stat_smooth).
# Gráfica diámetro copa
ggplot(datos5, aes(semana, copdia, group = gen, colour = gen)) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Diámetro de la copa") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 68 rows containing non-finite values (stat_smooth).
# Gráfica área copa
ggplot(datos5, aes(semana, coparea, group = gen, colour = gen)) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Área de la copa") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 68 rows containing non-finite values (stat_smooth).
# Anova general
aov.alt<-aov(alt~semana*gen+bloque)
aov.patrodia<-aov(patrodia~semana*gen+bloque)
aov.injedia<-aov(injedia~semana*gen+bloque)
aov.copdia<-aov(copdia~semana*gen+bloque)
aov.coparea<-aov(coparea~semana*gen+bloque)
#Análisis para altura
library(nlme)
fit.compsym.alt <- gls(alt ~ semana*gen+bloque, data=datos5, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.alt <- gls(alt ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.alt <- gls(alt ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.alt, fit.ar1.alt, fit.ar1het.alt) #compares the models
## Model df AIC BIC logLik Test L.Ratio p-value
## fit.compsym.alt 1 55 14837.63 15120.09 -7363.814
## fit.ar1.alt 2 55 14832.74 15115.20 -7361.369
## fit.ar1het.alt 3 57 14121.39 14414.12 -7003.693 2 vs 3 715.3514 <.0001
anova(fit.ar1.alt)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 5342.808 <.0001
## semana 2 22.241 <.0001
## gen 16 5.767 <.0001
## bloque 2 1.541 0.2145
## semana:gen 32 1.288 0.1316
anova(fit.ar1het.alt)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 8103.217 <.0001
## semana 2 17.974 <.0001
## gen 16 8.514 <.0001
## bloque 2 0.197 0.8213
## semana:gen 32 0.820 0.7518
# Análisis para diámetro del patrón
fit.compsym.patrodia <- gls(patrodia ~ semana*gen+bloque, data=datos5, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.patrodia <- gls(patrodia ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.patrodia <- gls(patrodia ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.patrodia, fit.ar1.patrodia, fit.ar1het.patrodia) #compares the models
## Model df AIC BIC logLik Test L.Ratio
## fit.compsym.patrodia 1 55 4287.376 4569.839 -2088.688
## fit.ar1.patrodia 2 55 4221.886 4504.349 -2055.943
## fit.ar1het.patrodia 3 57 4225.812 4518.546 -2055.906 2 vs 3 0.07456518
## p-value
## fit.compsym.patrodia
## fit.ar1.patrodia
## fit.ar1het.patrodia 0.9634
anova(fit.ar1.patrodia)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 13914.439 <.0001
## semana 2 47.242 <.0001
## gen 16 5.928 <.0001
## bloque 2 0.606 0.5455
## semana:gen 32 0.394 0.9991
anova(fit.ar1het.patrodia)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 13911.844 <.0001
## semana 2 46.973 <.0001
## gen 16 5.931 <.0001
## bloque 2 0.607 0.5452
## semana:gen 32 0.393 0.9991
# Análisis para diámetro del injerto
fit.compsym.injedia <- gls(injedia ~ semana*gen+bloque, data=datos5, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.injedia <- gls(injedia ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.injedia <- gls(injedia ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.injedia, fit.ar1.injedia, fit.ar1het.injedia) #compares the models
## Model df AIC BIC logLik Test L.Ratio
## fit.compsym.injedia 1 55 3774.227 4056.689 -1832.113
## fit.ar1.injedia 2 55 3708.044 3990.507 -1799.022
## fit.ar1het.injedia 3 57 3686.006 3978.741 -1786.003 2 vs 3 26.03807
## p-value
## fit.compsym.injedia
## fit.ar1.injedia
## fit.ar1het.injedia <.0001
anova(fit.ar1.injedia)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 7976.138 <.0001
## semana 2 20.499 <.0001
## gen 16 9.046 <.0001
## bloque 2 9.995 <.0001
## semana:gen 32 0.381 0.9993
anova(fit.ar1het.injedia)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 8618.521 <.0001
## semana 2 20.217 <.0001
## gen 16 9.978 <.0001
## bloque 2 9.803 0.0001
## semana:gen 32 0.353 0.9997
# Análisis para diámetro de la copa
fit.compsym.copdia <- gls(copdia ~ semana*gen+bloque, data=datos5, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.copdia <- gls(copdia ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.copdia <- gls(copdia ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.copdia, fit.ar1.copdia, fit.ar1het.copdia) #compares the models
## Model df AIC BIC logLik Test L.Ratio p-value
## fit.compsym.copdia 1 55 13115.85 13398.31 -6502.925
## fit.ar1.copdia 2 55 13059.74 13342.20 -6474.871
## fit.ar1het.copdia 3 57 13008.16 13300.89 -6447.079 2 vs 3 55.58428 <.0001
anova(fit.ar1.copdia)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 8434.983 <.0001
## semana 2 49.503 <.0001
## gen 16 12.835 <.0001
## bloque 2 11.074 <.0001
## semana:gen 32 0.708 0.8872
anova(fit.ar1het.copdia)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 8413.016 <.0001
## semana 2 58.664 <.0001
## gen 16 12.238 <.0001
## bloque 2 11.879 <.0001
## semana:gen 32 0.774 0.8137
# Análisis para área de la copa
fit.compsym.coparea <- gls(coparea ~ semana*gen+bloque, data=datos5, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.coparea <- gls(coparea ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.coparea <- gls(coparea ~ semana*gen+bloque, data=datos5, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.coparea, fit.ar1.coparea, fit.ar1het.coparea) #compares the models
## Model df AIC BIC logLik Test L.Ratio
## fit.compsym.coparea 1 55 4286.012 4568.475 -2088.006
## fit.ar1.coparea 2 55 4278.074 4560.537 -2084.037
## fit.ar1het.coparea 3 57 3811.960 4104.694 -1848.980 2 vs 3 470.1137
## p-value
## fit.compsym.coparea
## fit.ar1.coparea
## fit.ar1het.coparea <.0001
anova(fit.ar1.coparea)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 1635.1608 <.0001
## semana 2 36.5863 <.0001
## gen 16 7.8921 <.0001
## bloque 2 10.0799 <.0001
## semana:gen 32 0.9952 0.4756
anova(fit.ar1het.coparea)
## Denom. DF: 1256
## numDF F-value p-value
## (Intercept) 1 2227.6517 <.0001
## semana 2 73.8807 <.0001
## gen 16 10.2599 <.0001
## bloque 2 12.9388 <.0001
## semana:gen 32 1.2593 0.153
#Tukey altura
library(multcompView)
interac.tuk.alt<-TukeyHSD(aov.alt, "semana:gen", ordered = TRUE)
semana.tuk.alt<-TukeyHSD(aov.alt, "semana", ordered = TRUE)
gen.tuk.alt<-TukeyHSD(aov.alt, "gen", ordered = TRUE)
#Tukey diámetro del patrón
interac.tuk.patrodia<-TukeyHSD(aov.patrodia, "semana:gen", ordered = TRUE)
semana.tuk.patrodia<-TukeyHSD(aov.patrodia, "semana", ordered = TRUE)
gen.tuk.patrodia<-TukeyHSD(aov.patrodia, "gen", ordered = TRUE)
#Tukey diámetro del injerto
interac.tuk.injedia<-TukeyHSD(aov.injedia, "semana:gen", ordered = TRUE)
semana.tuk.injedia<-TukeyHSD(aov.injedia, "semana", ordered = TRUE)
gen.tuk.injedia<-TukeyHSD(aov.injedia, "gen", ordered = TRUE)
#Tukey diámetro de la copa
interac.tuk.copdia<-TukeyHSD(aov.copdia, "semana:gen", ordered = TRUE)
semana.tuk.copdia<-TukeyHSD(aov.copdia, "semana", ordered = TRUE)
gen.tuk.copdia<-TukeyHSD(aov.copdia, "gen", ordered = TRUE)
#Tukey área de la copa
interac.tuk.coparea<-TukeyHSD(aov.coparea, "semana:gen", ordered = TRUE)
semana.tuk.coparea<-TukeyHSD(aov.coparea, "semana", ordered = TRUE)
gen.tuk.coparea<-TukeyHSD(aov.coparea, "gen", ordered = TRUE)
#Etiquetas Tukey altura
#Genotipos
generate_label_df_gen_alt <- function(gen.tuk.alt, variable){
Tukey.levels <- gen.tuk.alt[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.gen.alt <- generate_label_df_gen_alt(gen.tuk.alt, "gen")
labels.gen.alt
## Letters treatment
## TCS01 bcd TCS01
## TCS02 a TCS02
## TCS03 a TCS03
## TCS04 abc TCS04
## TCS05 abc TCS05
## TCS08 a TCS08
## TCS10 cd TCS10
## TCS11 abc TCS11
## TCS12 abcd TCS12
## TCS20 bcd TCS20
## TCS43 ab TCS43
## TCS44 ab TCS44
## TCS45 abcd TCS45
## TCS46 d TCS46
## TCS47 ab TCS47
## TCS48 abcd TCS48
## TCS49 abcd TCS49
# Semana
generate_label_df_semana_alt <- function(semana.tuk.alt, variable){
Tukey.levels <- semana.tuk.alt[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.semana.alt <- generate_label_df_semana_alt(semana.tuk.alt, "semana")
labels.semana.alt
## Letters treatment
## 100 a 100
## 112 a 112
## 123 a 123
# Interacción Semana:Genotipo
generate_label_df_interac_alt <- function(interac.tuk.alt, variable){
Tukey.levels <- interac.tuk.alt[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.interac.alt <- generate_label_df_interac_alt(interac.tuk.alt, "semana:gen")
labels.interac.alt
## Letters treatment
## 100:TCS01 abcd 100:TCS01
## 100:TCS02 ab 100:TCS02
## 100:TCS03 ab 100:TCS03
## 100:TCS04 abcd 100:TCS04
## 100:TCS05 ab 100:TCS05
## 100:TCS08 ab 100:TCS08
## 100:TCS10 abcd 100:TCS10
## 100:TCS11 abc 100:TCS11
## 100:TCS12 abcd 100:TCS12
## 100:TCS20 abcd 100:TCS20
## 100:TCS43 ab 100:TCS43
## 100:TCS44 ab 100:TCS44
## 100:TCS45 abcd 100:TCS45
## 100:TCS46 abcd 100:TCS46
## 100:TCS47 abc 100:TCS47
## 100:TCS48 abcd 100:TCS48
## 100:TCS49 abcd 100:TCS49
## 112:TCS01 abcd 112:TCS01
## 112:TCS02 abc 112:TCS02
## 112:TCS03 ab 112:TCS03
## 112:TCS04 abcd 112:TCS04
## 112:TCS05 abcd 112:TCS05
## 112:TCS08 ab 112:TCS08
## 112:TCS10 abcd 112:TCS10
## 112:TCS11 abcd 112:TCS11
## 112:TCS12 abcd 112:TCS12
## 112:TCS20 abcd 112:TCS20
## 112:TCS43 abcd 112:TCS43
## 112:TCS44 abcd 112:TCS44
## 112:TCS45 abcd 112:TCS45
## 112:TCS46 cde 112:TCS46
## 112:TCS47 a 112:TCS47
## 112:TCS48 abcd 112:TCS48
## 112:TCS49 abcd 112:TCS49
## 123:TCS01 bcd 123:TCS01
## 123:TCS02 abcd 123:TCS02
## 123:TCS03 abcd 123:TCS03
## 123:TCS04 abcd 123:TCS04
## 123:TCS05 abcd 123:TCS05
## 123:TCS08 abcd 123:TCS08
## 123:TCS10 e 123:TCS10
## 123:TCS11 abcd 123:TCS11
## 123:TCS12 abcd 123:TCS12
## 123:TCS20 bcde 123:TCS20
## 123:TCS43 abcd 123:TCS43
## 123:TCS44 abcd 123:TCS44
## 123:TCS45 abcd 123:TCS45
## 123:TCS46 de 123:TCS46
## 123:TCS47 abcd 123:TCS47
## 123:TCS48 abcd 123:TCS48
## 123:TCS49 abcd 123:TCS49
#Etiquetas Tukey diámetro del patrón
#Genotipos
generate_label_df_gen_patrodia <- function(gen.tuk.patrodia, variable){
Tukey.levels <- gen.tuk.patrodia[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.gen.patrodia <- generate_label_df_gen_patrodia(gen.tuk.patrodia, "gen")
labels.gen.patrodia
## Letters treatment
## TCS01 def TCS01
## TCS02 bcde TCS02
## TCS03 a TCS03
## TCS04 def TCS04
## TCS05 bde TCS05
## TCS08 ac TCS08
## TCS10 abcd TCS10
## TCS11 abc TCS11
## TCS12 def TCS12
## TCS20 ef TCS20
## TCS43 bcde TCS43
## TCS44 abc TCS44
## TCS45 def TCS45
## TCS46 f TCS46
## TCS47 bde TCS47
## TCS48 def TCS48
## TCS49 def TCS49
# Semana
generate_label_df_semana_patrodia <- function(semana.tuk.patrodia, variable){
Tukey.levels <- semana.tuk.patrodia[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.semana.patrodia <- generate_label_df_semana_patrodia(semana.tuk.patrodia, "semana")
labels.semana.patrodia
## Letters treatment
## 100 a 100
## 112 a 112
## 123 a 123
# Interacción Semana:Genotipo
generate_label_df_interac_patrodia <- function(interac.tuk.patrodia, variable){
Tukey.levels <- interac.tuk.patrodia[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.interac.patrodia <- generate_label_df_interac_patrodia(interac.tuk.patrodia, "semana:gen")
labels.interac.patrodia
## Letters treatment
## 100:TCS01 abcdefghijk 100:TCS01
## 100:TCS02 abcdefgh 100:TCS02
## 100:TCS03 ab 100:TCS03
## 100:TCS04 abcdefghi 100:TCS04
## 100:TCS05 abcdefghij 100:TCS05
## 100:TCS08 abce 100:TCS08
## 100:TCS10 abcdefghi 100:TCS10
## 100:TCS11 a 100:TCS11
## 100:TCS12 abcdefg 100:TCS12
## 100:TCS20 abcdefghijk 100:TCS20
## 100:TCS43 abcdefghij 100:TCS43
## 100:TCS44 ab 100:TCS44
## 100:TCS45 abcdefghijk 100:TCS45
## 100:TCS46 abcdefghijk 100:TCS46
## 100:TCS47 abc 100:TCS47
## 100:TCS48 abcdefghij 100:TCS48
## 100:TCS49 abcdefghij 100:TCS49
## 112:TCS01 cdefghijkl 112:TCS01
## 112:TCS02 abcdefghijkl 112:TCS02
## 112:TCS03 abcdef 112:TCS03
## 112:TCS04 defghijkl 112:TCS04
## 112:TCS05 bcdefghijkl 112:TCS05
## 112:TCS08 abcde 112:TCS08
## 112:TCS10 abcdefghijk 112:TCS10
## 112:TCS11 abcdefghi 112:TCS11
## 112:TCS12 cdefghijkl 112:TCS12
## 112:TCS20 fghijkl 112:TCS20
## 112:TCS43 abcdefghijkl 112:TCS43
## 112:TCS44 abcdefgh 112:TCS44
## 112:TCS45 abcdefghijkl 112:TCS45
## 112:TCS46 kl 112:TCS46
## 112:TCS47 defghijkl 112:TCS47
## 112:TCS48 abcdefghijkl 112:TCS48
## 112:TCS49 dfghijkl 112:TCS49
## 123:TCS01 hijkl 123:TCS01
## 123:TCS02 cdefghijkl 123:TCS02
## 123:TCS03 abcdefgh 123:TCS03
## 123:TCS04 fghijkl 123:TCS04
## 123:TCS05 bcdefghijkl 123:TCS05
## 123:TCS08 abcdefghijk 123:TCS08
## 123:TCS10 abcdefghijkl 123:TCS10
## 123:TCS11 abcdefghij 123:TCS11
## 123:TCS12 fghijkl 123:TCS12
## 123:TCS20 jkl 123:TCS20
## 123:TCS43 abcdefghijkl 123:TCS43
## 123:TCS44 abcdefghijk 123:TCS44
## 123:TCS45 jkl 123:TCS45
## 123:TCS46 l 123:TCS46
## 123:TCS47 ghijkl 123:TCS47
## 123:TCS48 ijkl 123:TCS48
## 123:TCS49 fghijkl 123:TCS49
#Etiquetas Tukey diámetro del injerto
#Genotipos
generate_label_df_gen_injedia <- function(gen.tuk.injedia, variable){
Tukey.levels <- gen.tuk.injedia[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.gen.injedia <- generate_label_df_gen_injedia(gen.tuk.injedia, "gen")
labels.gen.injedia
## Letters treatment
## TCS01 ef TCS01
## TCS02 acd TCS02
## TCS03 ab TCS03
## TCS04 cde TCS04
## TCS05 ac TCS05
## TCS08 b TCS08
## TCS10 acd TCS10
## TCS11 ac TCS11
## TCS12 def TCS12
## TCS20 def TCS20
## TCS43 acd TCS43
## TCS44 acd TCS44
## TCS45 def TCS45
## TCS46 f TCS46
## TCS47 cde TCS47
## TCS48 cde TCS48
## TCS49 def TCS49
# Semana
generate_label_df_semana_injedia <- function(semana.tuk.injedia, variable){
Tukey.levels <- semana.tuk.injedia[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.semana.injedia <- generate_label_df_semana_injedia(semana.tuk.injedia, "semana")
labels.semana.injedia
## Letters treatment
## 100 a 100
## 112 a 112
## 123 a 123
# Interacción Semana:Genotipo
generate_label_df_interac_injedia <- function(interac.tuk.injedia, variable){
Tukey.levels <- interac.tuk.injedia[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.interac.injedia <- generate_label_df_interac_injedia(interac.tuk.injedia, "semana:gen")
labels.interac.injedia
## Letters treatment
## 100:TCS01 cdefghij 100:TCS01
## 100:TCS02 abcdef 100:TCS02
## 100:TCS03 abc 100:TCS03
## 100:TCS04 cdefghi 100:TCS04
## 100:TCS05 abcd 100:TCS05
## 100:TCS08 b 100:TCS08
## 100:TCS10 abcdefg 100:TCS10
## 100:TCS11 abcd 100:TCS11
## 100:TCS12 abcdefg 100:TCS12
## 100:TCS20 cdefghij 100:TCS20
## 100:TCS43 abcdefg 100:TCS43
## 100:TCS44 abcdefg 100:TCS44
## 100:TCS45 cdefghij 100:TCS45
## 100:TCS46 cdefghij 100:TCS46
## 100:TCS47 abcd 100:TCS47
## 100:TCS48 cdefghi 100:TCS48
## 100:TCS49 cdefghij 100:TCS49
## 112:TCS01 defghij 112:TCS01
## 112:TCS02 abcdefg 112:TCS02
## 112:TCS03 abcd 112:TCS03
## 112:TCS04 cdefghi 112:TCS04
## 112:TCS05 abcdef 112:TCS05
## 112:TCS08 ab 112:TCS08
## 112:TCS10 abcdefg 112:TCS10
## 112:TCS11 abcdefg 112:TCS11
## 112:TCS12 defghij 112:TCS12
## 112:TCS20 cdefghij 112:TCS20
## 112:TCS43 abcdefg 112:TCS43
## 112:TCS44 abcdefg 112:TCS44
## 112:TCS45 cdefghij 112:TCS45
## 112:TCS46 ij 112:TCS46
## 112:TCS47 cdefghij 112:TCS47
## 112:TCS48 abcdefghi 112:TCS48
## 112:TCS49 cdefghij 112:TCS49
## 123:TCS01 hij 123:TCS01
## 123:TCS02 cdefghij 123:TCS02
## 123:TCS03 abcdef 123:TCS03
## 123:TCS04 cdefghij 123:TCS04
## 123:TCS05 acdefghi 123:TCS05
## 123:TCS08 abcde 123:TCS08
## 123:TCS10 cdefghi 123:TCS10
## 123:TCS11 abcdefgh 123:TCS11
## 123:TCS12 ghij 123:TCS12
## 123:TCS20 fghij 123:TCS20
## 123:TCS43 cdefghij 123:TCS43
## 123:TCS44 cdefghi 123:TCS44
## 123:TCS45 fghij 123:TCS45
## 123:TCS46 j 123:TCS46
## 123:TCS47 efghij 123:TCS47
## 123:TCS48 cdefghij 123:TCS48
## 123:TCS49 defghij 123:TCS49
#Etiquetas Tukey diámetro de la copa
#Genotipos
generate_label_df_gen_copdia <- function(gen.tuk.copdia, variable){
Tukey.levels <- gen.tuk.copdia[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.gen.copdia <- generate_label_df_gen_copdia(gen.tuk.copdia, "gen")
labels.gen.copdia
## Letters treatment
## TCS01 de TCS01
## TCS02 abc TCS02
## TCS03 abc TCS03
## TCS04 bcd TCS04
## TCS05 ab TCS05
## TCS08 g TCS08
## TCS10 abc TCS10
## TCS11 abcd TCS11
## TCS12 ef TCS12
## TCS20 de TCS20
## TCS43 a TCS43
## TCS44 abcd TCS44
## TCS45 cde TCS45
## TCS46 f TCS46
## TCS47 cde TCS47
## TCS48 cde TCS48
## TCS49 cde TCS49
# Semana
generate_label_df_semana_copdia <- function(semana.tuk.copdia, variable){
Tukey.levels <- semana.tuk.copdia[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.semana.copdia <- generate_label_df_semana_copdia(semana.tuk.copdia, "semana")
labels.semana.copdia
## Letters treatment
## 100 a 100
## 112 a 112
## 123 a 123
# Interacción Semana:Genotipo
generate_label_df_interac_copdia <- function(interac.tuk.copdia, variable){
Tukey.levels <- interac.tuk.copdia[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.interac.copdia <- generate_label_df_interac_copdia(interac.tuk.copdia, "semana:gen")
labels.interac.copdia
## Letters treatment
## 100:TCS01 defghijklm 100:TCS01
## 100:TCS02 abcd 100:TCS02
## 100:TCS03 abcdefg 100:TCS03
## 100:TCS04 abcdefghij 100:TCS04
## 100:TCS05 abc 100:TCS05
## 100:TCS08 b 100:TCS08
## 100:TCS10 abcdefgh 100:TCS10
## 100:TCS11 abcdefgh 100:TCS11
## 100:TCS12 acdefghijk 100:TCS12
## 100:TCS20 cdefghijk 100:TCS20
## 100:TCS43 abcde 100:TCS43
## 100:TCS44 abcdefg 100:TCS44
## 100:TCS45 abcdefghij 100:TCS45
## 100:TCS46 fghijklmn 100:TCS46
## 100:TCS47 abcdefg 100:TCS47
## 100:TCS48 abcdefghi 100:TCS48
## 100:TCS49 abcdefghijk 100:TCS49
## 112:TCS01 ghijklmn 112:TCS01
## 112:TCS02 cdefghijklm 112:TCS02
## 112:TCS03 acdefghijk 112:TCS03
## 112:TCS04 cdefghijklm 112:TCS04
## 112:TCS05 abcdefgh 112:TCS05
## 112:TCS08 ab 112:TCS08
## 112:TCS10 abcdefghijk 112:TCS10
## 112:TCS11 cdefghijklm 112:TCS11
## 112:TCS12 klmno 112:TCS12
## 112:TCS20 ghijklmn 112:TCS20
## 112:TCS43 abcdef 112:TCS43
## 112:TCS44 cdefghijklm 112:TCS44
## 112:TCS45 defghijklmn 112:TCS45
## 112:TCS46 mno 112:TCS46
## 112:TCS47 efghijklmn 112:TCS47
## 112:TCS48 cdefghijklmn 112:TCS48
## 112:TCS49 cdefghijklmn 112:TCS49
## 123:TCS01 hijklmn 123:TCS01
## 123:TCS02 cdefghijkl 123:TCS02
## 123:TCS03 cdefghijklm 123:TCS03
## 123:TCS04 defghijklmn 123:TCS04
## 123:TCS05 acdefghijk 123:TCS05
## 123:TCS08 abcde 123:TCS08
## 123:TCS10 acdefghijk 123:TCS10
## 123:TCS11 cdefghijklm 123:TCS11
## 123:TCS12 no 123:TCS12
## 123:TCS20 lmno 123:TCS20
## 123:TCS43 acdefghijk 123:TCS43
## 123:TCS44 defghijklmn 123:TCS44
## 123:TCS45 jklmno 123:TCS45
## 123:TCS46 o 123:TCS46
## 123:TCS47 ijklmno 123:TCS47
## 123:TCS48 hijklmn 123:TCS48
## 123:TCS49 ghijklmn 123:TCS49
#Etiquetas Tukey área de la copa
#Genotipos
generate_label_df_gen_coparea <- function(gen.tuk.coparea, variable){
Tukey.levels <- gen.tuk.coparea[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.gen.coparea <- generate_label_df_gen_coparea(gen.tuk.coparea, "gen")
labels.gen.coparea
## Letters treatment
## TCS01 cd TCS01
## TCS02 acd TCS02
## TCS03 abc TCS03
## TCS04 acd TCS04
## TCS05 ab TCS05
## TCS08 b TCS08
## TCS10 ac TCS10
## TCS11 acd TCS11
## TCS12 de TCS12
## TCS20 cde TCS20
## TCS43 ab TCS43
## TCS44 ac TCS44
## TCS45 acd TCS45
## TCS46 e TCS46
## TCS47 acd TCS47
## TCS48 acd TCS48
## TCS49 acd TCS49
# Semana
generate_label_df_semana_coparea <- function(semana.tuk.coparea, variable){
Tukey.levels <- semana.tuk.coparea[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.semana.coparea <- generate_label_df_semana_coparea(semana.tuk.coparea, "semana")
labels.semana.coparea
## Letters treatment
## 100 a 100
## 112 a 112
## 123 a 123
# Interacción Semana:Genotipo
generate_label_df_interac_coparea <- function(interac.tuk.coparea, variable){
Tukey.levels <- interac.tuk.coparea[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.interac.coparea <- generate_label_df_interac_coparea(interac.tuk.coparea, "semana:gen")
labels.interac.coparea
## Letters treatment
## 100:TCS01 abcdefgh 100:TCS01
## 100:TCS02 abc 100:TCS02
## 100:TCS03 abcd 100:TCS03
## 100:TCS04 abcdefg 100:TCS04
## 100:TCS05 ab 100:TCS05
## 100:TCS08 a 100:TCS08
## 100:TCS10 abcdef 100:TCS10
## 100:TCS11 abcdef 100:TCS11
## 100:TCS12 abcdefg 100:TCS12
## 100:TCS20 abcdefg 100:TCS20
## 100:TCS43 abcd 100:TCS43
## 100:TCS44 abcde 100:TCS44
## 100:TCS45 abcdef 100:TCS45
## 100:TCS46 abcdefgh 100:TCS46
## 100:TCS47 abcdef 100:TCS47
## 100:TCS48 abcdef 100:TCS48
## 100:TCS49 abcdefg 100:TCS49
## 112:TCS01 abcdefgh 112:TCS01
## 112:TCS02 efghi 112:TCS02
## 112:TCS03 abcdefg 112:TCS03
## 112:TCS04 abcdefgh 112:TCS04
## 112:TCS05 abcdef 112:TCS05
## 112:TCS08 a 112:TCS08
## 112:TCS10 abcdefg 112:TCS10
## 112:TCS11 abcdefgh 112:TCS11
## 112:TCS12 defghi 112:TCS12
## 112:TCS20 abcdefgh 112:TCS20
## 112:TCS43 abcd 112:TCS43
## 112:TCS44 abcdefgh 112:TCS44
## 112:TCS45 abcdefgh 112:TCS45
## 112:TCS46 ghi 112:TCS46
## 112:TCS47 abcdefgh 112:TCS47
## 112:TCS48 abcdefgh 112:TCS48
## 112:TCS49 abcdefgh 112:TCS49
## 123:TCS01 abcdefgh 123:TCS01
## 123:TCS02 abcdefg 123:TCS02
## 123:TCS03 abcdefgh 123:TCS03
## 123:TCS04 abcdefgh 123:TCS04
## 123:TCS05 abcdefg 123:TCS05
## 123:TCS08 abcd 123:TCS08
## 123:TCS10 abcdefgh 123:TCS10
## 123:TCS11 abcdefgh 123:TCS11
## 123:TCS12 hi 123:TCS12
## 123:TCS20 fghi 123:TCS20
## 123:TCS43 abcdefg 123:TCS43
## 123:TCS44 abcdefgh 123:TCS44
## 123:TCS45 cdefghi 123:TCS45
## 123:TCS46 i 123:TCS46
## 123:TCS47 defghi 123:TCS47
## 123:TCS48 bcdefghi 123:TCS48
## 123:TCS49 abcdefghi 123:TCS49
detach(datos5)