setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura/BD Estadística SGR Santander/data")
datos5<-read.table("veinteboy.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)
library(emmeans)
#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'
# 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 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,na.action=na.exclude)
aov.patrodia<-aov(patrodia~semana+gen+bloque, na.action=na.exclude)
aov.injedia<-aov(injedia~semana+gen+bloque, na.action=na.exclude)
aov.coparea<-aov(coparea~semana+gen+bloque, na.action=na.exclude)
#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 23 2156.921 2276.803 -1055.460
## fit.ar1.alt 2 23 2091.477 2211.359 -1022.738
## fit.ar1het.alt 3 25 2094.943 2225.250 -1022.471 2 vs 3 0.533605 0.7658
anova(fit.ar1.alt)
## Denom. DF: 1356
## numDF F-value p-value
## (Intercept) 1 8866.626 <.0001
## semana 2 37.541 <.0001
## gen 16 16.271 <.0001
## bloque 2 7.103 9e-04
anova(fit.ar1het.alt)
## Denom. DF: 1356
## numDF F-value p-value
## (Intercept) 1 8812.211 <.0001
## semana 2 37.358 <.0001
## gen 16 16.200 <.0001
## bloque 2 7.166 8e-04
# 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 23 4234.514 4353.213 -2094.257
## fit.ar1.patrodia 2 23 4174.501 4293.200 -2064.251
## fit.ar1het.patrodia 3 25 4178.349 4307.371 -2064.175 2 vs 3 0.1514893
## p-value
## fit.compsym.patrodia
## fit.ar1.patrodia
## fit.ar1het.patrodia 0.9271
anova(fit.ar1.patrodia)
## Denom. DF: 1288
## numDF F-value p-value
## (Intercept) 1 14720.799 <.0001
## semana 2 49.763 <.0001
## gen 16 6.271 <.0001
## bloque 2 0.623 0.5364
anova(fit.ar1het.patrodia)
## Denom. DF: 1288
## numDF F-value p-value
## (Intercept) 1 14748.391 <.0001
## semana 2 49.291 <.0001
## gen 16 6.297 <.0001
## bloque 2 0.625 0.5352
# 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 23 3706.751 3825.451 -1830.376
## fit.ar1.injedia 2 23 3647.183 3765.883 -1800.592
## fit.ar1het.injedia 3 25 3623.453 3752.474 -1786.726 2 vs 3 27.73022
## p-value
## fit.compsym.injedia
## fit.ar1.injedia
## fit.ar1het.injedia <.0001
anova(fit.ar1.injedia)
## Denom. DF: 1288
## numDF F-value p-value
## (Intercept) 1 8485.612 <.0001
## semana 2 21.752 <.0001
## gen 16 9.624 <.0001
## bloque 2 10.472 <.0001
anova(fit.ar1het.injedia)
## Denom. DF: 1288
## numDF F-value p-value
## (Intercept) 1 9206.999 <.0001
## semana 2 21.507 <.0001
## gen 16 10.663 <.0001
## bloque 2 10.304 <.0001
# 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 23 4249.360 4368.060 -2101.680
## fit.ar1.coparea 2 23 4240.739 4359.438 -2097.369
## fit.ar1het.coparea 3 25 3780.911 3909.932 -1865.455 2 vs 3 463.8283
## p-value
## fit.compsym.coparea
## fit.ar1.coparea
## fit.ar1het.coparea <.0001
anova(fit.ar1.coparea)
## Denom. DF: 1288
## numDF F-value p-value
## (Intercept) 1 1630.9487 <.0001
## semana 2 36.4956 <.0001
## gen 16 7.8719 <.0001
## bloque 2 10.0542 <.0001
anova(fit.ar1het.coparea)
## Denom. DF: 1288
## numDF F-value p-value
## (Intercept) 1 2162.1154 <.0001
## semana 2 71.4173 <.0001
## gen 16 9.9428 <.0001
## bloque 2 12.6033 <.0001
#Tukey altura
library(multcompView)
gen.tuk.alt<-TukeyHSD(aov.alt, "gen", ordered = TRUE)
#Tukey diámetro del patrón
gen.tuk.patrodia<-TukeyHSD(aov.patrodia, "gen", ordered = TRUE)
#Tukey diámetro del injerto
gen.tuk.injedia<-TukeyHSD(aov.injedia, "gen", ordered = TRUE)
#Tukey área de la copa
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 d TCS01
## TCS02 a TCS02
## TCS03 a TCS03
## TCS04 abcd TCS04
## TCS05 ab TCS05
## TCS08 f TCS08
## TCS10 ab TCS10
## TCS11 ab TCS11
## TCS12 bcd TCS12
## TCS20 d TCS20
## TCS43 ab TCS43
## TCS44 abc TCS44
## TCS45 cd TCS45
## TCS46 e TCS46
## TCS47 bcd TCS47
## TCS48 cd TCS48
## TCS49 abc 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
#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 fg TCS01
## TCS02 acd TCS02
## TCS03 ab TCS03
## TCS04 cdef TCS04
## TCS05 ac TCS05
## TCS08 b TCS08
## TCS10 acde TCS10
## TCS11 ac TCS11
## TCS12 defg TCS12
## TCS20 efg TCS20
## TCS43 acde TCS43
## TCS44 acde TCS44
## TCS45 defg TCS45
## TCS46 g TCS46
## TCS47 cdef TCS47
## TCS48 cdef TCS48
## TCS49 defg 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
## Gráficas contrastes de medias Altura
#Gen
contrast <- emmeans(aov.alt, ~gen)
plot(contrast, comparisons = TRUE, xlab ="Altura")
medias.gen <- emmeans(aov.alt, pairwise ~ gen)
medias.gen
## $emmeans
## gen emmean SE df lower.CL upper.CL
## TCS01 1.908 0.0565 1356 1.797 2.018
## TCS02 1.401 0.0565 1356 1.290 1.512
## TCS03 1.408 0.0565 1356 1.297 1.519
## TCS04 1.631 0.0565 1356 1.520 1.742
## TCS05 1.503 0.0537 1356 1.397 1.608
## TCS08 0.872 0.0565 1356 0.762 0.983
## TCS10 1.508 0.0565 1356 1.397 1.619
## TCS11 1.515 0.0565 1356 1.404 1.626
## TCS12 1.732 0.0565 1356 1.621 1.843
## TCS20 1.895 0.0565 1356 1.784 2.006
## TCS43 1.497 0.0565 1356 1.386 1.608
## TCS44 1.542 0.0565 1356 1.431 1.653
## TCS45 1.811 0.0600 1356 1.693 1.929
## TCS46 2.173 0.0537 1356 2.068 2.279
## TCS47 1.738 0.0565 1356 1.627 1.849
## TCS48 1.825 0.0600 1356 1.707 1.943
## TCS49 1.546 0.0565 1356 1.435 1.657
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS01 - TCS02 0.50679 0.0799 1356 6.343 <.0001
## TCS01 - TCS03 0.49938 0.0799 1356 6.250 <.0001
## TCS01 - TCS04 0.27667 0.0799 1356 3.463 0.0504
## TCS01 - TCS05 0.40497 0.0779 1356 5.197 <.0001
## TCS01 - TCS08 1.03506 0.0799 1356 12.955 <.0001
## TCS01 - TCS10 0.39951 0.0799 1356 5.000 0.0001
## TCS01 - TCS11 0.39247 0.0799 1356 4.912 0.0001
## TCS01 - TCS12 0.17580 0.0799 1356 2.200 0.7265
## TCS01 - TCS20 0.01272 0.0799 1356 0.159 1.0000
## TCS01 - TCS43 0.41025 0.0799 1356 5.135 <.0001
## TCS01 - TCS44 0.36531 0.0799 1356 4.572 0.0007
## TCS01 - TCS45 0.09656 0.0824 1356 1.171 0.9992
## TCS01 - TCS46 -0.26580 0.0779 1356 -3.411 0.0593
## TCS01 - TCS47 0.16951 0.0799 1356 2.121 0.7787
## TCS01 - TCS48 0.08267 0.0824 1356 1.003 0.9999
## TCS01 - TCS49 0.36173 0.0799 1356 4.527 0.0008
## TCS02 - TCS03 -0.00741 0.0799 1356 -0.093 1.0000
## TCS02 - TCS04 -0.23012 0.0799 1356 -2.880 0.2457
## TCS02 - TCS05 -0.10182 0.0779 1356 -1.307 0.9971
## TCS02 - TCS08 0.52827 0.0799 1356 6.612 <.0001
## TCS02 - TCS10 -0.10728 0.0799 1356 -1.343 0.9961
## TCS02 - TCS11 -0.11432 0.0799 1356 -1.431 0.9922
## TCS02 - TCS12 -0.33099 0.0799 1356 -4.143 0.0042
## TCS02 - TCS20 -0.49407 0.0799 1356 -6.184 <.0001
## TCS02 - TCS43 -0.09654 0.0799 1356 -1.208 0.9989
## TCS02 - TCS44 -0.14148 0.0799 1356 -1.771 0.9402
## TCS02 - TCS45 -0.41023 0.0824 1356 -4.977 0.0001
## TCS02 - TCS46 -0.77259 0.0779 1356 -9.915 <.0001
## TCS02 - TCS47 -0.33728 0.0799 1356 -4.221 0.0031
## TCS02 - TCS48 -0.42412 0.0824 1356 -5.145 <.0001
## TCS02 - TCS49 -0.14506 0.0799 1356 -1.816 0.9265
## TCS03 - TCS04 -0.22272 0.0799 1356 -2.787 0.2999
## TCS03 - TCS05 -0.09441 0.0779 1356 -1.212 0.9988
## TCS03 - TCS08 0.53568 0.0799 1356 6.704 <.0001
## TCS03 - TCS10 -0.09988 0.0799 1356 -1.250 0.9983
## TCS03 - TCS11 -0.10691 0.0799 1356 -1.338 0.9962
## TCS03 - TCS12 -0.32358 0.0799 1356 -4.050 0.0061
## TCS03 - TCS20 -0.48667 0.0799 1356 -6.091 <.0001
## TCS03 - TCS43 -0.08914 0.0799 1356 -1.116 0.9996
## TCS03 - TCS44 -0.13407 0.0799 1356 -1.678 0.9625
## TCS03 - TCS45 -0.40282 0.0824 1356 -4.887 0.0001
## TCS03 - TCS46 -0.76519 0.0779 1356 -9.820 <.0001
## TCS03 - TCS47 -0.32988 0.0799 1356 -4.129 0.0045
## TCS03 - TCS48 -0.41671 0.0824 1356 -5.055 0.0001
## TCS03 - TCS49 -0.13765 0.0799 1356 -1.723 0.9527
## TCS04 - TCS05 0.12831 0.0779 1356 1.647 0.9684
## TCS04 - TCS08 0.75840 0.0799 1356 9.492 <.0001
## TCS04 - TCS10 0.12284 0.0799 1356 1.537 0.9836
## TCS04 - TCS11 0.11580 0.0799 1356 1.449 0.9910
## TCS04 - TCS12 -0.10086 0.0799 1356 -1.262 0.9981
## TCS04 - TCS20 -0.26395 0.0799 1356 -3.304 0.0820
## TCS04 - TCS43 0.13358 0.0799 1356 1.672 0.9637
## TCS04 - TCS44 0.08864 0.0799 1356 1.109 0.9996
## TCS04 - TCS45 -0.18011 0.0824 1356 -2.185 0.7370
## TCS04 - TCS46 -0.54247 0.0779 1356 -6.961 <.0001
## TCS04 - TCS47 -0.10716 0.0799 1356 -1.341 0.9961
## TCS04 - TCS48 -0.19399 0.0824 1356 -2.353 0.6145
## TCS04 - TCS49 0.08506 0.0799 1356 1.065 0.9998
## TCS05 - TCS08 0.63009 0.0779 1356 8.086 <.0001
## TCS05 - TCS10 -0.00547 0.0779 1356 -0.070 1.0000
## TCS05 - TCS11 -0.01250 0.0779 1356 -0.160 1.0000
## TCS05 - TCS12 -0.22917 0.0779 1356 -2.941 0.2139
## TCS05 - TCS20 -0.39226 0.0779 1356 -5.034 0.0001
## TCS05 - TCS43 0.00527 0.0779 1356 0.068 1.0000
## TCS05 - TCS44 -0.03966 0.0779 1356 -0.509 1.0000
## TCS05 - TCS45 -0.30841 0.0806 1356 -3.825 0.0145
## TCS05 - TCS46 -0.67078 0.0758 1356 -8.849 <.0001
## TCS05 - TCS47 -0.23547 0.0779 1356 -3.022 0.1761
## TCS05 - TCS48 -0.32230 0.0806 1356 -3.997 0.0076
## TCS05 - TCS49 -0.04324 0.0779 1356 -0.555 1.0000
## TCS08 - TCS10 -0.63556 0.0799 1356 -7.954 <.0001
## TCS08 - TCS11 -0.64259 0.0799 1356 -8.043 <.0001
## TCS08 - TCS12 -0.85926 0.0799 1356 -10.754 <.0001
## TCS08 - TCS20 -1.02235 0.0799 1356 -12.795 <.0001
## TCS08 - TCS43 -0.62481 0.0799 1356 -7.820 <.0001
## TCS08 - TCS44 -0.66975 0.0799 1356 -8.382 <.0001
## TCS08 - TCS45 -0.93850 0.0824 1356 -11.385 <.0001
## TCS08 - TCS46 -1.30087 0.0779 1356 -16.694 <.0001
## TCS08 - TCS47 -0.86556 0.0799 1356 -10.833 <.0001
## TCS08 - TCS48 -0.95239 0.0824 1356 -11.554 <.0001
## TCS08 - TCS49 -0.67333 0.0799 1356 -8.427 <.0001
## TCS10 - TCS11 -0.00704 0.0799 1356 -0.088 1.0000
## TCS10 - TCS12 -0.22370 0.0799 1356 -2.800 0.2923
## TCS10 - TCS20 -0.38679 0.0799 1356 -4.841 0.0002
## TCS10 - TCS43 0.01074 0.0799 1356 0.134 1.0000
## TCS10 - TCS44 -0.03420 0.0799 1356 -0.428 1.0000
## TCS10 - TCS45 -0.30294 0.0824 1356 -3.675 0.0248
## TCS10 - TCS46 -0.66531 0.0779 1356 -8.538 <.0001
## TCS10 - TCS47 -0.23000 0.0799 1356 -2.879 0.2466
## TCS10 - TCS48 -0.31683 0.0824 1356 -3.844 0.0135
## TCS10 - TCS49 -0.03778 0.0799 1356 -0.473 1.0000
## TCS11 - TCS12 -0.21667 0.0799 1356 -2.712 0.3487
## TCS11 - TCS20 -0.37975 0.0799 1356 -4.753 0.0003
## TCS11 - TCS43 0.01778 0.0799 1356 0.223 1.0000
## TCS11 - TCS44 -0.02716 0.0799 1356 -0.340 1.0000
## TCS11 - TCS45 -0.29591 0.0824 1356 -3.590 0.0332
## TCS11 - TCS46 -0.65827 0.0779 1356 -8.448 <.0001
## TCS11 - TCS47 -0.22296 0.0799 1356 -2.791 0.2980
## TCS11 - TCS48 -0.30980 0.0824 1356 -3.758 0.0185
## TCS11 - TCS49 -0.03074 0.0799 1356 -0.385 1.0000
## TCS12 - TCS20 -0.16309 0.0799 1356 -2.041 0.8266
## TCS12 - TCS43 0.23444 0.0799 1356 2.934 0.2173
## TCS12 - TCS44 0.18951 0.0799 1356 2.372 0.6004
## TCS12 - TCS45 -0.07924 0.0824 1356 -0.961 0.9999
## TCS12 - TCS46 -0.44161 0.0779 1356 -5.667 <.0001
## TCS12 - TCS47 -0.00630 0.0799 1356 -0.079 1.0000
## TCS12 - TCS48 -0.09313 0.0824 1356 -1.130 0.9995
## TCS12 - TCS49 0.18593 0.0799 1356 2.327 0.6345
## TCS20 - TCS43 0.39753 0.0799 1356 4.975 0.0001
## TCS20 - TCS44 0.35259 0.0799 1356 4.413 0.0013
## TCS20 - TCS45 0.08385 0.0824 1356 1.017 0.9999
## TCS20 - TCS46 -0.27852 0.0779 1356 -3.574 0.0350
## TCS20 - TCS47 0.15679 0.0799 1356 1.962 0.8675
## TCS20 - TCS48 0.06996 0.0824 1356 0.849 1.0000
## TCS20 - TCS49 0.34901 0.0799 1356 4.368 0.0016
## TCS43 - TCS44 -0.04494 0.0799 1356 -0.562 1.0000
## TCS43 - TCS45 -0.31369 0.0824 1356 -3.805 0.0156
## TCS43 - TCS46 -0.67605 0.0779 1356 -8.676 <.0001
## TCS43 - TCS47 -0.24074 0.0799 1356 -3.013 0.1799
## TCS43 - TCS48 -0.32757 0.0824 1356 -3.974 0.0083
## TCS43 - TCS49 -0.04852 0.0799 1356 -0.607 1.0000
## TCS44 - TCS45 -0.26875 0.0824 1356 -3.260 0.0930
## TCS44 - TCS46 -0.63111 0.0779 1356 -8.099 <.0001
## TCS44 - TCS47 -0.19580 0.0799 1356 -2.451 0.5399
## TCS44 - TCS48 -0.28264 0.0824 1356 -3.429 0.0561
## TCS44 - TCS49 -0.00358 0.0799 1356 -0.045 1.0000
## TCS45 - TCS46 -0.36237 0.0806 1356 -4.494 0.0009
## TCS45 - TCS47 0.07294 0.0824 1356 0.885 1.0000
## TCS45 - TCS48 -0.01389 0.0847 1356 -0.164 1.0000
## TCS45 - TCS49 0.26517 0.0824 1356 3.217 0.1052
## TCS46 - TCS47 0.43531 0.0779 1356 5.586 <.0001
## TCS46 - TCS48 0.34848 0.0806 1356 4.322 0.0020
## TCS46 - TCS49 0.62753 0.0779 1356 8.053 <.0001
## TCS47 - TCS48 -0.08683 0.0824 1356 -1.053 0.9998
## TCS47 - TCS49 0.19222 0.0799 1356 2.406 0.5744
## TCS48 - TCS49 0.27906 0.0824 1356 3.385 0.0641
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 17 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS46 2.173 0.0537 1356 2.068 2.279 A
## TCS01 1.908 0.0565 1356 1.797 2.018 AB
## TCS20 1.895 0.0565 1356 1.784 2.006 B
## TCS48 1.825 0.0600 1356 1.707 1.943 BC
## TCS45 1.811 0.0600 1356 1.693 1.929 BC
## TCS47 1.738 0.0565 1356 1.627 1.849 BCD
## TCS12 1.732 0.0565 1356 1.621 1.843 BCD
## TCS04 1.631 0.0565 1356 1.520 1.742 BCDE
## TCS49 1.546 0.0565 1356 1.435 1.657 CDE
## TCS44 1.542 0.0565 1356 1.431 1.653 CDE
## TCS11 1.515 0.0565 1356 1.404 1.626 DE
## TCS10 1.508 0.0565 1356 1.397 1.619 DE
## TCS05 1.503 0.0537 1356 1.397 1.608 DE
## TCS43 1.497 0.0565 1356 1.386 1.608 DE
## TCS03 1.408 0.0565 1356 1.297 1.519 E
## TCS02 1.401 0.0565 1356 1.290 1.512 E
## TCS08 0.872 0.0565 1356 0.762 0.983 F
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 17 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
## Gráficas contrastes de medias diámetro del patrón
#Gen
contrast <- emmeans(aov.patrodia, ~gen)
plot(contrast, comparisons = TRUE, xlab ="Diámetro del patrón")
medias.gen <- emmeans(aov.patrodia, pairwise ~ gen)
medias.gen
## $emmeans
## gen emmean SE df lower.CL upper.CL
## TCS01 5.23 0.132 1288 4.98 5.49
## TCS02 4.91 0.133 1288 4.65 5.17
## TCS03 4.24 0.134 1288 3.98 4.50
## TCS04 5.14 0.133 1288 4.87 5.40
## TCS05 4.97 0.129 1288 4.72 5.22
## TCS08 4.19 0.163 1288 3.87 4.51
## TCS10 4.75 0.135 1288 4.48 5.01
## TCS11 4.34 0.134 1288 4.07 4.60
## TCS12 5.08 0.133 1288 4.82 5.34
## TCS20 5.42 0.133 1288 5.16 5.68
## TCS43 4.92 0.133 1288 4.66 5.18
## TCS44 4.37 0.133 1288 4.11 4.63
## TCS45 5.31 0.141 1288 5.03 5.59
## TCS46 5.64 0.125 1288 5.40 5.89
## TCS47 4.98 0.134 1288 4.72 5.25
## TCS48 5.25 0.140 1288 4.97 5.52
## TCS49 5.20 0.142 1288 4.92 5.48
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS01 - TCS02 0.32223 0.187 1288 1.725 0.9521
## TCS01 - TCS03 0.99264 0.188 1288 5.280 <.0001
## TCS01 - TCS04 0.09680 0.187 1288 0.517 1.0000
## TCS01 - TCS05 0.26198 0.184 1288 1.422 0.9927
## TCS01 - TCS08 1.04685 0.210 1288 4.994 0.0001
## TCS01 - TCS10 0.48491 0.189 1288 2.571 0.4489
## TCS01 - TCS11 0.89509 0.188 1288 4.761 0.0003
## TCS01 - TCS12 0.15527 0.187 1288 0.829 1.0000
## TCS01 - TCS20 -0.18925 0.187 1288 -1.010 0.9999
## TCS01 - TCS43 0.31620 0.187 1288 1.687 0.9606
## TCS01 - TCS44 0.86252 0.187 1288 4.617 0.0005
## TCS01 - TCS45 -0.07580 0.193 1288 -0.393 1.0000
## TCS01 - TCS46 -0.40766 0.182 1288 -2.244 0.6953
## TCS01 - TCS47 0.25044 0.188 1288 1.332 0.9964
## TCS01 - TCS48 -0.01473 0.192 1288 -0.077 1.0000
## TCS01 - TCS49 0.03116 0.193 1288 0.161 1.0000
## TCS02 - TCS03 0.67041 0.189 1288 3.555 0.0374
## TCS02 - TCS04 -0.22543 0.188 1288 -1.199 0.9989
## TCS02 - TCS05 -0.06025 0.185 1288 -0.326 1.0000
## TCS02 - TCS08 0.72462 0.210 1288 3.449 0.0527
## TCS02 - TCS10 0.16268 0.189 1288 0.860 1.0000
## TCS02 - TCS11 0.57287 0.189 1288 3.038 0.1693
## TCS02 - TCS12 -0.16696 0.188 1288 -0.888 1.0000
## TCS02 - TCS20 -0.51147 0.188 1288 -2.721 0.3427
## TCS02 - TCS43 -0.00603 0.188 1288 -0.032 1.0000
## TCS02 - TCS44 0.54029 0.187 1288 2.883 0.2441
## TCS02 - TCS45 -0.39803 0.193 1288 -2.058 0.8171
## TCS02 - TCS46 -0.72988 0.182 1288 -4.006 0.0073
## TCS02 - TCS47 -0.07179 0.189 1288 -0.381 1.0000
## TCS02 - TCS48 -0.33696 0.193 1288 -1.749 0.9462
## TCS02 - TCS49 -0.29106 0.194 1288 -1.500 0.9872
## TCS03 - TCS04 -0.89584 0.189 1288 -4.735 0.0003
## TCS03 - TCS05 -0.73066 0.186 1288 -3.929 0.0099
## TCS03 - TCS08 0.05421 0.211 1288 0.257 1.0000
## TCS03 - TCS10 -0.50773 0.190 1288 -2.666 0.3798
## TCS03 - TCS11 -0.09754 0.190 1288 -0.514 1.0000
## TCS03 - TCS12 -0.83737 0.189 1288 -4.427 0.0013
## TCS03 - TCS20 -1.18188 0.189 1288 -6.247 <.0001
## TCS03 - TCS43 -0.67644 0.189 1288 -3.576 0.0349
## TCS03 - TCS44 -0.13011 0.189 1288 -0.690 1.0000
## TCS03 - TCS45 -1.06844 0.195 1288 -5.491 <.0001
## TCS03 - TCS46 -1.40029 0.183 1288 -7.634 <.0001
## TCS03 - TCS47 -0.74220 0.190 1288 -3.911 0.0106
## TCS03 - TCS48 -1.00737 0.194 1288 -5.196 <.0001
## TCS03 - TCS49 -0.96147 0.195 1288 -4.927 0.0001
## TCS04 - TCS05 0.16518 0.185 1288 0.891 1.0000
## TCS04 - TCS08 0.95005 0.211 1288 4.510 0.0009
## TCS04 - TCS10 0.38811 0.190 1288 2.045 0.8246
## TCS04 - TCS11 0.79830 0.189 1288 4.220 0.0031
## TCS04 - TCS12 0.05847 0.189 1288 0.310 1.0000
## TCS04 - TCS20 -0.28604 0.189 1288 -1.517 0.9857
## TCS04 - TCS43 0.21940 0.189 1288 1.163 0.9993
## TCS04 - TCS44 0.76572 0.188 1288 4.073 0.0056
## TCS04 - TCS45 -0.17260 0.194 1288 -0.890 1.0000
## TCS04 - TCS46 -0.50445 0.183 1288 -2.760 0.3175
## TCS04 - TCS47 0.15364 0.189 1288 0.812 1.0000
## TCS04 - TCS48 -0.11153 0.193 1288 -0.577 1.0000
## TCS04 - TCS49 -0.06564 0.195 1288 -0.337 1.0000
## TCS05 - TCS08 0.78487 0.208 1288 3.777 0.0173
## TCS05 - TCS10 0.22293 0.187 1288 1.194 0.9990
## TCS05 - TCS11 0.63311 0.186 1288 3.404 0.0607
## TCS05 - TCS12 -0.10671 0.185 1288 -0.576 1.0000
## TCS05 - TCS20 -0.45122 0.185 1288 -2.434 0.5528
## TCS05 - TCS43 0.05422 0.185 1288 0.292 1.0000
## TCS05 - TCS44 0.60054 0.185 1288 3.250 0.0958
## TCS05 - TCS45 -0.33778 0.191 1288 -1.767 0.9413
## TCS05 - TCS46 -0.66963 0.179 1288 -3.735 0.0201
## TCS05 - TCS47 -0.01154 0.186 1288 -0.062 1.0000
## TCS05 - TCS48 -0.27671 0.191 1288 -1.453 0.9908
## TCS05 - TCS49 -0.23082 0.191 1288 -1.206 0.9989
## TCS08 - TCS10 -0.56194 0.212 1288 -2.654 0.3884
## TCS08 - TCS11 -0.15175 0.211 1288 -0.719 1.0000
## TCS08 - TCS12 -0.89158 0.211 1288 -4.233 0.0029
## TCS08 - TCS20 -1.23609 0.211 1288 -5.866 <.0001
## TCS08 - TCS43 -0.73065 0.211 1288 -3.469 0.0495
## TCS08 - TCS44 -0.18433 0.210 1288 -0.877 1.0000
## TCS08 - TCS45 -1.12265 0.215 1288 -5.211 <.0001
## TCS08 - TCS46 -1.45450 0.206 1288 -7.074 <.0001
## TCS08 - TCS47 -0.79641 0.211 1288 -3.772 0.0176
## TCS08 - TCS48 -1.06158 0.215 1288 -4.942 0.0001
## TCS08 - TCS49 -1.01568 0.216 1288 -4.704 0.0004
## TCS10 - TCS11 0.41018 0.190 1288 2.154 0.7577
## TCS10 - TCS12 -0.32964 0.190 1288 -1.737 0.9492
## TCS10 - TCS20 -0.67416 0.190 1288 -3.552 0.0378
## TCS10 - TCS43 -0.16871 0.190 1288 -0.889 1.0000
## TCS10 - TCS44 0.37761 0.189 1288 1.996 0.8509
## TCS10 - TCS45 -0.56071 0.195 1288 -2.874 0.2493
## TCS10 - TCS46 -0.89256 0.184 1288 -4.847 0.0002
## TCS10 - TCS47 -0.23447 0.190 1288 -1.231 0.9986
## TCS10 - TCS48 -0.49964 0.194 1288 -2.570 0.4493
## TCS10 - TCS49 -0.45375 0.196 1288 -2.317 0.6420
## TCS11 - TCS12 -0.73983 0.189 1288 -3.911 0.0106
## TCS11 - TCS20 -1.08434 0.189 1288 -5.732 <.0001
## TCS11 - TCS43 -0.57890 0.189 1288 -3.060 0.1600
## TCS11 - TCS44 -0.03257 0.189 1288 -0.173 1.0000
## TCS11 - TCS45 -0.97089 0.195 1288 -4.990 0.0001
## TCS11 - TCS46 -1.30275 0.183 1288 -7.101 <.0001
## TCS11 - TCS47 -0.64465 0.190 1288 -3.397 0.0620
## TCS11 - TCS48 -0.90983 0.194 1288 -4.693 0.0004
## TCS11 - TCS49 -0.86393 0.195 1288 -4.427 0.0013
## TCS12 - TCS20 -0.34451 0.189 1288 -1.827 0.9227
## TCS12 - TCS43 0.16093 0.189 1288 0.853 1.0000
## TCS12 - TCS44 0.70725 0.188 1288 3.762 0.0183
## TCS12 - TCS45 -0.23107 0.194 1288 -1.191 0.9990
## TCS12 - TCS46 -0.56292 0.183 1288 -3.079 0.1523
## TCS12 - TCS47 0.09517 0.189 1288 0.503 1.0000
## TCS12 - TCS48 -0.17000 0.193 1288 -0.879 1.0000
## TCS12 - TCS49 -0.12411 0.195 1288 -0.638 1.0000
## TCS20 - TCS43 0.50544 0.189 1288 2.680 0.3701
## TCS20 - TCS44 1.05177 0.188 1288 5.595 <.0001
## TCS20 - TCS45 0.11345 0.194 1288 0.585 1.0000
## TCS20 - TCS46 -0.21841 0.183 1288 -1.195 0.9990
## TCS20 - TCS47 0.43968 0.189 1288 2.324 0.6367
## TCS20 - TCS48 0.17451 0.193 1288 0.903 1.0000
## TCS20 - TCS49 0.22041 0.195 1288 1.132 0.9995
## TCS43 - TCS44 0.54632 0.188 1288 2.906 0.2317
## TCS43 - TCS45 -0.39200 0.194 1288 -2.021 0.8377
## TCS43 - TCS46 -0.72385 0.183 1288 -3.959 0.0088
## TCS43 - TCS47 -0.06576 0.189 1288 -0.348 1.0000
## TCS43 - TCS48 -0.33093 0.193 1288 -1.712 0.9551
## TCS43 - TCS49 -0.28503 0.195 1288 -1.465 0.9900
## TCS44 - TCS45 -0.93832 0.193 1288 -4.852 0.0002
## TCS44 - TCS46 -1.27018 0.182 1288 -6.970 <.0001
## TCS44 - TCS47 -0.61208 0.189 1288 -3.245 0.0971
## TCS44 - TCS48 -0.87725 0.193 1288 -4.553 0.0007
## TCS44 - TCS49 -0.83136 0.194 1288 -4.284 0.0024
## TCS45 - TCS46 -0.33185 0.189 1288 -1.759 0.9434
## TCS45 - TCS47 0.32624 0.195 1288 1.676 0.9628
## TCS45 - TCS48 0.06107 0.198 1288 0.308 1.0000
## TCS45 - TCS49 0.10696 0.200 1288 0.535 1.0000
## TCS46 - TCS47 0.65809 0.183 1288 3.588 0.0335
## TCS46 - TCS48 0.39292 0.188 1288 2.090 0.7979
## TCS46 - TCS49 0.43882 0.189 1288 2.322 0.6386
## TCS47 - TCS48 -0.26517 0.194 1288 -1.367 0.9952
## TCS47 - TCS49 -0.21928 0.195 1288 -1.124 0.9995
## TCS48 - TCS49 0.04590 0.199 1288 0.230 1.0000
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 17 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS46 5.64 0.125 1288 5.40 5.89 A
## TCS20 5.42 0.133 1288 5.16 5.68 AB
## TCS45 5.31 0.141 1288 5.03 5.59 ABC
## TCS48 5.25 0.140 1288 4.97 5.52 ABC
## TCS01 5.23 0.132 1288 4.98 5.49 ABC
## TCS49 5.20 0.142 1288 4.92 5.48 ABC
## TCS04 5.14 0.133 1288 4.87 5.40 ABC
## TCS12 5.08 0.133 1288 4.82 5.34 ABC
## TCS47 4.98 0.134 1288 4.72 5.25 BCD
## TCS05 4.97 0.129 1288 4.72 5.22 BCD
## TCS43 4.92 0.133 1288 4.66 5.18 BCD
## TCS02 4.91 0.133 1288 4.65 5.17 BCDE
## TCS10 4.75 0.135 1288 4.48 5.01 CDEF
## TCS44 4.37 0.133 1288 4.11 4.63 DEF
## TCS11 4.34 0.134 1288 4.07 4.60 DEF
## TCS03 4.24 0.134 1288 3.98 4.50 F
## TCS08 4.19 0.163 1288 3.87 4.51 EF
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 17 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
## Gráficas contrastes de medias diámetro del injerto
#Gen
contrast <- emmeans(aov.injedia, ~gen)
plot(contrast, comparisons = TRUE, xlab ="Diámetro del injerto")
medias.gen <- emmeans(aov.injedia, pairwise ~ gen)
medias.gen
## $emmeans
## gen emmean SE df lower.CL upper.CL
## TCS01 3.57 0.107 1288 3.36 3.78
## TCS02 2.86 0.108 1288 2.65 3.08
## TCS03 2.47 0.109 1288 2.25 2.68
## TCS04 3.14 0.109 1288 2.93 3.35
## TCS05 2.71 0.105 1288 2.51 2.92
## TCS08 1.96 0.133 1288 1.70 2.22
## TCS10 2.88 0.110 1288 2.66 3.09
## TCS11 2.70 0.109 1288 2.48 2.91
## TCS12 3.34 0.109 1288 3.12 3.55
## TCS20 3.40 0.109 1288 3.19 3.62
## TCS43 2.89 0.109 1288 2.68 3.10
## TCS44 2.89 0.108 1288 2.68 3.11
## TCS45 3.40 0.115 1288 3.17 3.62
## TCS46 3.85 0.102 1288 3.65 4.05
## TCS47 3.11 0.109 1288 2.90 3.33
## TCS48 3.16 0.114 1288 2.93 3.38
## TCS49 3.30 0.115 1288 3.07 3.53
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS01 - TCS02 0.70349 0.152 1288 4.622 0.0005
## TCS01 - TCS03 1.10043 0.153 1288 7.184 <.0001
## TCS01 - TCS04 0.42577 0.153 1288 2.789 0.2993
## TCS01 - TCS05 0.85384 0.150 1288 5.689 <.0001
## TCS01 - TCS08 1.61114 0.171 1288 9.434 <.0001
## TCS01 - TCS10 0.69221 0.154 1288 4.504 0.0009
## TCS01 - TCS11 0.86911 0.153 1288 5.674 <.0001
## TCS01 - TCS12 0.23150 0.153 1288 1.516 0.9857
## TCS01 - TCS20 0.16442 0.153 1288 1.077 0.9997
## TCS01 - TCS43 0.67892 0.153 1288 4.447 0.0012
## TCS01 - TCS44 0.67383 0.152 1288 4.427 0.0013
## TCS01 - TCS45 0.17070 0.157 1288 1.087 0.9997
## TCS01 - TCS46 -0.28626 0.148 1288 -1.934 0.8804
## TCS01 - TCS47 0.45574 0.153 1288 2.975 0.1974
## TCS01 - TCS48 0.41019 0.157 1288 2.620 0.4124
## TCS01 - TCS49 0.26685 0.158 1288 1.693 0.9594
## TCS02 - TCS03 0.39694 0.154 1288 2.583 0.4394
## TCS02 - TCS04 -0.27772 0.153 1288 -1.813 0.9272
## TCS02 - TCS05 0.15034 0.151 1288 0.999 0.9999
## TCS02 - TCS08 0.90765 0.171 1288 5.302 <.0001
## TCS02 - TCS10 -0.01128 0.154 1288 -0.073 1.0000
## TCS02 - TCS11 0.16562 0.154 1288 1.078 0.9997
## TCS02 - TCS12 -0.47199 0.153 1288 -3.082 0.1513
## TCS02 - TCS20 -0.53907 0.153 1288 -3.520 0.0420
## TCS02 - TCS43 -0.02457 0.153 1288 -0.160 1.0000
## TCS02 - TCS44 -0.02966 0.153 1288 -0.194 1.0000
## TCS02 - TCS45 -0.53279 0.158 1288 -3.381 0.0651
## TCS02 - TCS46 -0.98975 0.148 1288 -6.667 <.0001
## TCS02 - TCS47 -0.24775 0.154 1288 -1.612 0.9740
## TCS02 - TCS48 -0.29330 0.157 1288 -1.868 0.9080
## TCS02 - TCS49 -0.43664 0.158 1288 -2.762 0.3158
## TCS03 - TCS04 -0.67466 0.154 1288 -4.377 0.0016
## TCS03 - TCS05 -0.24660 0.152 1288 -1.628 0.9716
## TCS03 - TCS08 0.51071 0.172 1288 2.969 0.2005
## TCS03 - TCS10 -0.40822 0.155 1288 -2.631 0.4046
## TCS03 - TCS11 -0.23133 0.155 1288 -1.496 0.9875
## TCS03 - TCS12 -0.86893 0.154 1288 -5.638 <.0001
## TCS03 - TCS20 -0.93601 0.154 1288 -6.073 <.0001
## TCS03 - TCS43 -0.42151 0.154 1288 -2.735 0.3335
## TCS03 - TCS44 -0.42661 0.154 1288 -2.776 0.3068
## TCS03 - TCS45 -0.92973 0.159 1288 -5.864 <.0001
## TCS03 - TCS46 -1.38670 0.149 1288 -9.278 <.0001
## TCS03 - TCS47 -0.64469 0.155 1288 -4.170 0.0038
## TCS03 - TCS48 -0.69024 0.158 1288 -4.369 0.0016
## TCS03 - TCS49 -0.83358 0.159 1288 -5.243 <.0001
## TCS04 - TCS05 0.42807 0.151 1288 2.835 0.2716
## TCS04 - TCS08 1.18537 0.172 1288 6.906 <.0001
## TCS04 - TCS10 0.26644 0.155 1288 1.723 0.9526
## TCS04 - TCS11 0.44334 0.154 1288 2.876 0.2480
## TCS04 - TCS12 -0.19427 0.154 1288 -1.264 0.9980
## TCS04 - TCS20 -0.26135 0.154 1288 -1.701 0.9576
## TCS04 - TCS43 0.25315 0.154 1288 1.648 0.9682
## TCS04 - TCS44 0.24806 0.153 1288 1.620 0.9729
## TCS04 - TCS45 -0.25506 0.158 1288 -1.614 0.9738
## TCS04 - TCS46 -0.71203 0.149 1288 -4.781 0.0002
## TCS04 - TCS47 0.02997 0.154 1288 0.194 1.0000
## TCS04 - TCS48 -0.01558 0.157 1288 -0.099 1.0000
## TCS04 - TCS49 -0.15892 0.159 1288 -1.002 0.9999
## TCS05 - TCS08 0.75730 0.169 1288 4.473 0.0010
## TCS05 - TCS10 -0.16162 0.152 1288 -1.063 0.9998
## TCS05 - TCS11 0.01527 0.152 1288 0.101 1.0000
## TCS05 - TCS12 -0.62233 0.151 1288 -4.121 0.0046
## TCS05 - TCS20 -0.68941 0.151 1288 -4.564 0.0007
## TCS05 - TCS43 -0.17491 0.151 1288 -1.158 0.9993
## TCS05 - TCS44 -0.18001 0.151 1288 -1.196 0.9990
## TCS05 - TCS45 -0.68313 0.156 1288 -4.385 0.0015
## TCS05 - TCS46 -1.14010 0.146 1288 -7.806 <.0001
## TCS05 - TCS47 -0.39809 0.151 1288 -2.628 0.4071
## TCS05 - TCS48 -0.44365 0.155 1288 -2.858 0.2580
## TCS05 - TCS49 -0.58698 0.156 1288 -3.763 0.0182
## TCS08 - TCS10 -0.91893 0.173 1288 -5.327 <.0001
## TCS08 - TCS11 -0.74203 0.172 1288 -4.313 0.0021
## TCS08 - TCS12 -1.37964 0.172 1288 -8.040 <.0001
## TCS08 - TCS20 -1.44672 0.172 1288 -8.427 <.0001
## TCS08 - TCS43 -0.93222 0.172 1288 -5.432 <.0001
## TCS08 - TCS44 -0.93731 0.171 1288 -5.475 <.0001
## TCS08 - TCS45 -1.44044 0.176 1288 -8.206 <.0001
## TCS08 - TCS46 -1.89740 0.168 1288 -11.327 <.0001
## TCS08 - TCS47 -1.15540 0.172 1288 -6.716 <.0001
## TCS08 - TCS48 -1.20095 0.175 1288 -6.862 <.0001
## TCS08 - TCS49 -1.34429 0.176 1288 -7.642 <.0001
## TCS10 - TCS11 0.17689 0.155 1288 1.140 0.9994
## TCS10 - TCS12 -0.46071 0.155 1288 -2.979 0.1955
## TCS10 - TCS20 -0.52779 0.155 1288 -3.413 0.0590
## TCS10 - TCS43 -0.01329 0.155 1288 -0.086 1.0000
## TCS10 - TCS44 -0.01839 0.154 1288 -0.119 1.0000
## TCS10 - TCS45 -0.52151 0.159 1288 -3.281 0.0877
## TCS10 - TCS46 -0.97848 0.150 1288 -6.521 <.0001
## TCS10 - TCS47 -0.23647 0.155 1288 -1.524 0.9850
## TCS10 - TCS48 -0.28202 0.158 1288 -1.781 0.9373
## TCS10 - TCS49 -0.42536 0.160 1288 -2.666 0.3801
## TCS11 - TCS12 -0.63761 0.154 1288 -4.137 0.0043
## TCS11 - TCS20 -0.70468 0.154 1288 -4.572 0.0007
## TCS11 - TCS43 -0.19018 0.154 1288 -1.234 0.9985
## TCS11 - TCS44 -0.19528 0.154 1288 -1.271 0.9979
## TCS11 - TCS45 -0.69840 0.159 1288 -4.406 0.0014
## TCS11 - TCS46 -1.15537 0.149 1288 -7.729 <.0001
## TCS11 - TCS47 -0.41336 0.155 1288 -2.673 0.3750
## TCS11 - TCS48 -0.45892 0.158 1288 -2.905 0.2323
## TCS11 - TCS49 -0.60225 0.159 1288 -3.788 0.0167
## TCS12 - TCS20 -0.06708 0.154 1288 -0.437 1.0000
## TCS12 - TCS43 0.44742 0.154 1288 2.912 0.2287
## TCS12 - TCS44 0.44233 0.153 1288 2.888 0.2415
## TCS12 - TCS45 -0.06080 0.158 1288 -0.385 1.0000
## TCS12 - TCS46 -0.51776 0.149 1288 -3.476 0.0483
## TCS12 - TCS47 0.22424 0.154 1288 1.455 0.9907
## TCS12 - TCS48 0.17869 0.158 1288 1.135 0.9995
## TCS12 - TCS49 0.03535 0.159 1288 0.223 1.0000
## TCS20 - TCS43 0.51450 0.154 1288 3.349 0.0718
## TCS20 - TCS44 0.50940 0.153 1288 3.326 0.0768
## TCS20 - TCS45 0.00628 0.158 1288 0.040 1.0000
## TCS20 - TCS46 -0.45068 0.149 1288 -3.026 0.1745
## TCS20 - TCS47 0.29132 0.154 1288 1.890 0.8994
## TCS20 - TCS48 0.24577 0.157 1288 1.561 0.9810
## TCS20 - TCS49 0.10243 0.159 1288 0.646 1.0000
## TCS43 - TCS44 -0.00510 0.153 1288 -0.033 1.0000
## TCS43 - TCS45 -0.50822 0.158 1288 -3.216 0.1056
## TCS43 - TCS46 -0.96518 0.149 1288 -6.480 <.0001
## TCS43 - TCS47 -0.22318 0.154 1288 -1.448 0.9911
## TCS43 - TCS48 -0.26873 0.157 1288 -1.707 0.9564
## TCS43 - TCS49 -0.41207 0.159 1288 -2.599 0.4280
## TCS44 - TCS45 -0.50312 0.158 1288 -3.193 0.1124
## TCS44 - TCS46 -0.96009 0.148 1288 -6.466 <.0001
## TCS44 - TCS47 -0.21808 0.154 1288 -1.419 0.9928
## TCS44 - TCS48 -0.26364 0.157 1288 -1.679 0.9622
## TCS44 - TCS49 -0.40697 0.158 1288 -2.574 0.4462
## TCS45 - TCS46 -0.45697 0.154 1288 -2.973 0.1985
## TCS45 - TCS47 0.28504 0.159 1288 1.798 0.9322
## TCS45 - TCS48 0.23948 0.161 1288 1.483 0.9886
## TCS45 - TCS49 0.09615 0.163 1288 0.590 1.0000
## TCS46 - TCS47 0.74200 0.149 1288 4.965 0.0001
## TCS46 - TCS48 0.69645 0.153 1288 4.548 0.0007
## TCS46 - TCS49 0.55311 0.154 1288 3.592 0.0331
## TCS47 - TCS48 -0.04555 0.158 1288 -0.288 1.0000
## TCS47 - TCS49 -0.18889 0.159 1288 -1.188 0.9991
## TCS48 - TCS49 -0.14334 0.162 1288 -0.883 1.0000
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 17 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS46 3.85 0.102 1288 3.65 4.05 A
## TCS01 3.57 0.107 1288 3.36 3.78 AB
## TCS20 3.40 0.109 1288 3.19 3.62 ABC
## TCS45 3.40 0.115 1288 3.17 3.62 ABCD
## TCS12 3.34 0.109 1288 3.12 3.55 BCD
## TCS49 3.30 0.115 1288 3.07 3.53 BCD
## TCS48 3.16 0.114 1288 2.93 3.38 BCDE
## TCS04 3.14 0.109 1288 2.93 3.35 BCDE
## TCS47 3.11 0.109 1288 2.90 3.33 BCDE
## TCS44 2.89 0.108 1288 2.68 3.11 CDEF
## TCS43 2.89 0.109 1288 2.68 3.10 CDEF
## TCS10 2.88 0.110 1288 2.66 3.09 CDEF
## TCS02 2.86 0.108 1288 2.65 3.08 DEF
## TCS05 2.71 0.105 1288 2.51 2.92 EF
## TCS11 2.70 0.109 1288 2.48 2.91 EF
## TCS03 2.47 0.109 1288 2.25 2.68 FG
## TCS08 1.96 0.133 1288 1.70 2.22 G
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 17 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
## Gráficas contrastes de medias área de la copa
#Gen
contrast <- emmeans(aov.coparea, ~gen)
plot(contrast, comparisons = TRUE, xlab ="Área de la copa")
medias.gen <- emmeans(aov.coparea, pairwise ~ gen)
medias.gen
## $emmeans
## gen emmean SE df lower.CL upper.CL
## TCS01 1.645 0.132 1288 1.386 1.905
## TCS02 1.428 0.133 1288 1.167 1.690
## TCS03 1.198 0.135 1288 0.933 1.462
## TCS04 1.377 0.134 1288 1.114 1.640
## TCS05 0.934 0.130 1288 0.679 1.188
## TCS08 0.486 0.164 1288 0.164 0.807
## TCS10 1.242 0.136 1288 0.975 1.508
## TCS11 1.336 0.135 1288 1.072 1.601
## TCS12 1.993 0.134 1288 1.730 2.256
## TCS20 1.736 0.134 1288 1.473 1.999
## TCS43 0.949 0.134 1288 0.686 1.212
## TCS44 1.279 0.133 1288 1.018 1.541
## TCS45 1.579 0.142 1288 1.301 1.857
## TCS46 2.341 0.126 1288 2.094 2.588
## TCS47 1.528 0.135 1288 1.264 1.793
## TCS48 1.550 0.141 1288 1.274 1.826
## TCS49 1.461 0.143 1288 1.182 1.741
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS01 - TCS02 0.2171 0.188 1288 1.156 0.9993
## TCS01 - TCS03 0.4478 0.189 1288 2.368 0.6032
## TCS01 - TCS04 0.2683 0.188 1288 1.424 0.9926
## TCS01 - TCS05 0.7119 0.185 1288 3.842 0.0136
## TCS01 - TCS08 1.1599 0.211 1288 5.501 <.0001
## TCS01 - TCS10 0.4038 0.190 1288 2.128 0.7745
## TCS01 - TCS11 0.3090 0.189 1288 1.634 0.9705
## TCS01 - TCS12 -0.3472 0.188 1288 -1.842 0.9174
## TCS01 - TCS20 -0.0910 0.188 1288 -0.483 1.0000
## TCS01 - TCS43 0.6968 0.188 1288 3.697 0.0230
## TCS01 - TCS44 0.3660 0.188 1288 1.948 0.8742
## TCS01 - TCS45 0.0666 0.194 1288 0.344 1.0000
## TCS01 - TCS46 -0.6953 0.183 1288 -3.806 0.0156
## TCS01 - TCS47 0.1170 0.189 1288 0.619 1.0000
## TCS01 - TCS48 0.0956 0.193 1288 0.495 1.0000
## TCS01 - TCS49 0.1841 0.195 1288 0.946 0.9999
## TCS02 - TCS03 0.2307 0.190 1288 1.216 0.9988
## TCS02 - TCS04 0.0512 0.189 1288 0.271 1.0000
## TCS02 - TCS05 0.4947 0.186 1288 2.662 0.3826
## TCS02 - TCS08 0.9427 0.211 1288 4.461 0.0011
## TCS02 - TCS10 0.1866 0.190 1288 0.981 0.9999
## TCS02 - TCS11 0.0919 0.190 1288 0.485 1.0000
## TCS02 - TCS12 -0.5644 0.189 1288 -2.985 0.1927
## TCS02 - TCS20 -0.3081 0.189 1288 -1.629 0.9713
## TCS02 - TCS43 0.4797 0.189 1288 2.537 0.4738
## TCS02 - TCS44 0.1489 0.188 1288 0.790 1.0000
## TCS02 - TCS45 -0.1505 0.195 1288 -0.774 1.0000
## TCS02 - TCS46 -0.9125 0.183 1288 -4.979 0.0001
## TCS02 - TCS47 -0.1001 0.190 1288 -0.528 1.0000
## TCS02 - TCS48 -0.1215 0.194 1288 -0.627 1.0000
## TCS02 - TCS49 -0.0330 0.195 1288 -0.169 1.0000
## TCS03 - TCS04 -0.1795 0.190 1288 -0.943 1.0000
## TCS03 - TCS05 0.2640 0.187 1288 1.412 0.9932
## TCS03 - TCS08 0.7120 0.212 1288 3.353 0.0709
## TCS03 - TCS10 -0.0441 0.192 1288 -0.230 1.0000
## TCS03 - TCS11 -0.1388 0.191 1288 -0.727 1.0000
## TCS03 - TCS12 -0.7951 0.190 1288 -4.179 0.0037
## TCS03 - TCS20 -0.5388 0.190 1288 -2.831 0.2734
## TCS03 - TCS43 0.2490 0.190 1288 1.309 0.9971
## TCS03 - TCS44 -0.0818 0.190 1288 -0.431 1.0000
## TCS03 - TCS45 -0.3812 0.196 1288 -1.948 0.8744
## TCS03 - TCS46 -1.1432 0.184 1288 -6.196 <.0001
## TCS03 - TCS47 -0.3308 0.191 1288 -1.733 0.9501
## TCS03 - TCS48 -0.3522 0.195 1288 -1.806 0.9295
## TCS03 - TCS49 -0.2637 0.196 1288 -1.344 0.9961
## TCS04 - TCS05 0.4435 0.186 1288 2.379 0.5948
## TCS04 - TCS08 0.8915 0.212 1288 4.208 0.0032
## TCS04 - TCS10 0.1354 0.191 1288 0.709 1.0000
## TCS04 - TCS11 0.0407 0.190 1288 0.214 1.0000
## TCS04 - TCS12 -0.6156 0.190 1288 -3.246 0.0970
## TCS04 - TCS20 -0.3593 0.190 1288 -1.894 0.8976
## TCS04 - TCS43 0.4285 0.190 1288 2.259 0.6847
## TCS04 - TCS44 0.0977 0.189 1288 0.517 1.0000
## TCS04 - TCS45 -0.2017 0.195 1288 -1.034 0.9998
## TCS04 - TCS46 -0.9637 0.184 1288 -5.241 <.0001
## TCS04 - TCS47 -0.1513 0.190 1288 -0.795 1.0000
## TCS04 - TCS48 -0.1728 0.194 1288 -0.889 1.0000
## TCS04 - TCS49 -0.0843 0.196 1288 -0.431 1.0000
## TCS05 - TCS08 0.4480 0.209 1288 2.144 0.7646
## TCS05 - TCS10 -0.3081 0.188 1288 -1.641 0.9694
## TCS05 - TCS11 -0.4028 0.187 1288 -2.153 0.7582
## TCS05 - TCS12 -1.0591 0.186 1288 -5.682 <.0001
## TCS05 - TCS20 -0.8028 0.186 1288 -4.306 0.0021
## TCS05 - TCS43 -0.0150 0.186 1288 -0.081 1.0000
## TCS05 - TCS44 -0.3458 0.186 1288 -1.861 0.9107
## TCS05 - TCS45 -0.6452 0.192 1288 -3.355 0.0704
## TCS05 - TCS46 -1.4072 0.180 1288 -7.805 <.0001
## TCS05 - TCS47 -0.5948 0.187 1288 -3.181 0.1163
## TCS05 - TCS48 -0.6163 0.192 1288 -3.216 0.1053
## TCS05 - TCS49 -0.5278 0.193 1288 -2.741 0.3296
## TCS08 - TCS10 -0.7561 0.213 1288 -3.551 0.0379
## TCS08 - TCS11 -0.8508 0.212 1288 -4.006 0.0073
## TCS08 - TCS12 -1.5071 0.212 1288 -7.114 <.0001
## TCS08 - TCS20 -1.2508 0.212 1288 -5.902 <.0001
## TCS08 - TCS43 -0.4630 0.212 1288 -2.186 0.7365
## TCS08 - TCS44 -0.7938 0.211 1288 -3.756 0.0187
## TCS08 - TCS45 -1.0932 0.217 1288 -5.045 0.0001
## TCS08 - TCS46 -1.8552 0.207 1288 -8.971 <.0001
## TCS08 - TCS47 -1.0428 0.212 1288 -4.911 0.0001
## TCS08 - TCS48 -1.0643 0.216 1288 -4.926 0.0001
## TCS08 - TCS49 -0.9758 0.217 1288 -4.494 0.0009
## TCS10 - TCS11 -0.0947 0.192 1288 -0.495 1.0000
## TCS10 - TCS12 -0.7510 0.191 1288 -3.934 0.0097
## TCS10 - TCS20 -0.4947 0.191 1288 -2.591 0.4335
## TCS10 - TCS43 0.2931 0.191 1288 1.535 0.9838
## TCS10 - TCS44 -0.0377 0.190 1288 -0.198 1.0000
## TCS10 - TCS45 -0.3371 0.196 1288 -1.718 0.9538
## TCS10 - TCS46 -1.0991 0.185 1288 -5.934 <.0001
## TCS10 - TCS47 -0.2868 0.192 1288 -1.497 0.9875
## TCS10 - TCS48 -0.3082 0.196 1288 -1.576 0.9791
## TCS10 - TCS49 -0.2197 0.197 1288 -1.115 0.9996
## TCS11 - TCS12 -0.6563 0.190 1288 -3.449 0.0527
## TCS11 - TCS20 -0.4000 0.190 1288 -2.102 0.7908
## TCS11 - TCS43 0.3878 0.190 1288 2.038 0.8282
## TCS11 - TCS44 0.0570 0.190 1288 0.300 1.0000
## TCS11 - TCS45 -0.2424 0.196 1288 -1.239 0.9985
## TCS11 - TCS46 -1.0044 0.185 1288 -5.443 <.0001
## TCS11 - TCS47 -0.1920 0.191 1288 -1.006 0.9999
## TCS11 - TCS48 -0.2135 0.195 1288 -1.095 0.9997
## TCS11 - TCS49 -0.1249 0.196 1288 -0.637 1.0000
## TCS12 - TCS20 0.2563 0.190 1288 1.351 0.9958
## TCS12 - TCS43 1.0441 0.190 1288 5.505 <.0001
## TCS12 - TCS44 0.7133 0.189 1288 3.773 0.0176
## TCS12 - TCS45 0.4139 0.195 1288 2.121 0.7790
## TCS12 - TCS46 -0.3481 0.184 1288 -1.893 0.8980
## TCS12 - TCS47 0.4643 0.190 1288 2.440 0.5481
## TCS12 - TCS48 0.4428 0.194 1288 2.278 0.6713
## TCS12 - TCS49 0.5313 0.196 1288 2.715 0.3467
## TCS20 - TCS43 0.7878 0.190 1288 4.154 0.0041
## TCS20 - TCS44 0.4570 0.189 1288 2.417 0.5658
## TCS20 - TCS45 0.1576 0.195 1288 0.808 1.0000
## TCS20 - TCS46 -0.6044 0.184 1288 -3.287 0.0862
## TCS20 - TCS47 0.2080 0.190 1288 1.093 0.9997
## TCS20 - TCS48 0.1865 0.194 1288 0.960 0.9999
## TCS20 - TCS49 0.2750 0.196 1288 1.405 0.9936
## TCS43 - TCS44 -0.3308 0.189 1288 -1.750 0.9459
## TCS43 - TCS45 -0.6302 0.195 1288 -3.230 0.1014
## TCS43 - TCS46 -1.3922 0.184 1288 -7.571 <.0001
## TCS43 - TCS47 -0.5798 0.190 1288 -3.047 0.1652
## TCS43 - TCS48 -0.6013 0.194 1288 -3.093 0.1470
## TCS43 - TCS49 -0.5128 0.196 1288 -2.620 0.4129
## TCS44 - TCS45 -0.2994 0.195 1288 -1.539 0.9834
## TCS44 - TCS46 -1.0614 0.183 1288 -5.791 <.0001
## TCS44 - TCS47 -0.2490 0.190 1288 -1.313 0.9970
## TCS44 - TCS48 -0.2704 0.194 1288 -1.396 0.9940
## TCS44 - TCS49 -0.1819 0.195 1288 -0.932 1.0000
## TCS45 - TCS46 -0.7620 0.190 1288 -4.016 0.0070
## TCS45 - TCS47 0.0504 0.196 1288 0.257 1.0000
## TCS45 - TCS48 0.0289 0.199 1288 0.145 1.0000
## TCS45 - TCS49 0.1174 0.201 1288 0.584 1.0000
## TCS46 - TCS47 0.8124 0.184 1288 4.403 0.0014
## TCS46 - TCS48 0.7909 0.189 1288 4.184 0.0036
## TCS46 - TCS49 0.8794 0.190 1288 4.626 0.0005
## TCS47 - TCS48 -0.0214 0.195 1288 -0.110 1.0000
## TCS47 - TCS49 0.0671 0.196 1288 0.342 1.0000
## TCS48 - TCS49 0.0885 0.200 1288 0.442 1.0000
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 17 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS46 2.341 0.126 1288 2.094 2.588 A
## TCS12 1.993 0.134 1288 1.730 2.256 AB
## TCS20 1.736 0.134 1288 1.473 1.999 ABC
## TCS01 1.645 0.132 1288 1.386 1.905 BC
## TCS45 1.579 0.142 1288 1.301 1.857 BCD
## TCS48 1.550 0.141 1288 1.274 1.826 BCD
## TCS47 1.528 0.135 1288 1.264 1.793 BCD
## TCS49 1.461 0.143 1288 1.182 1.741 BCD
## TCS02 1.428 0.133 1288 1.167 1.690 BCD
## TCS04 1.377 0.134 1288 1.114 1.640 BCD
## TCS11 1.336 0.135 1288 1.072 1.601 BCD
## TCS44 1.279 0.133 1288 1.018 1.541 CD
## TCS10 1.242 0.136 1288 0.975 1.508 CD
## TCS03 1.198 0.135 1288 0.933 1.462 CDE
## TCS43 0.949 0.134 1288 0.686 1.212 DE
## TCS05 0.934 0.130 1288 0.679 1.188 DE
## TCS08 0.486 0.164 1288 0.164 0.807 E
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 17 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
detach(datos5)