setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura/BD Estadística SGR Santander/data")
datos5<-read.table("veinteclos4.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'
## Warning: Removed 76 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 80 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 77 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.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 24 20892.66 21027.80 -10422.33
## fit.ar1.alt 2 24 20831.94 20967.08 -10391.97
## fit.ar1het.alt 3 28 20786.56 20944.23 -10365.28 2 vs 3 53.37371 <.0001
anova(fit.ar1.alt)
## Denom. DF: 2061
## numDF F-value p-value
## (Intercept) 1 31594.600 <.0001
## semana 4 331.290 <.0001
## gen 15 18.393 <.0001
## bloque 2 171.668 <.0001
anova(fit.ar1het.alt)
## Denom. DF: 2061
## numDF F-value p-value
## (Intercept) 1 30388.268 <.0001
## semana 4 332.365 <.0001
## gen 15 17.125 <.0001
## bloque 2 166.764 <.0001
# 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 24 15594.89 15729.98 -7773.444
## fit.ar1.patrodia 2 24 15519.94 15655.03 -7735.969
## fit.ar1het.patrodia 3 28 15367.01 15524.62 -7655.504 2 vs 3 160.9304
## p-value
## fit.compsym.patrodia
## fit.ar1.patrodia
## fit.ar1het.patrodia <.0001
anova(fit.ar1.patrodia)
## Denom. DF: 2057
## numDF F-value p-value
## (Intercept) 1 25118.035 <.0001
## semana 4 404.220 <.0001
## gen 15 14.090 <.0001
## bloque 2 216.511 <.0001
anova(fit.ar1het.patrodia)
## Denom. DF: 2057
## numDF F-value p-value
## (Intercept) 1 25017.380 <.0001
## semana 4 424.272 <.0001
## gen 15 14.585 <.0001
## bloque 2 203.933 <.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 24 1350.424 1485.555 -651.2118
## fit.ar1.coparea 2 24 1255.607 1390.738 -603.8036
## fit.ar1het.coparea 3 28 1110.890 1268.543 -527.4450 2 vs 3 152.7172
## p-value
## fit.compsym.coparea
## fit.ar1.coparea
## fit.ar1het.coparea <.0001
anova(fit.ar1.coparea)
## Denom. DF: 2060
## numDF F-value p-value
## (Intercept) 1 16905.398 <.0001
## semana 4 254.961 <.0001
## gen 15 15.784 <.0001
## bloque 2 144.792 <.0001
anova(fit.ar1het.coparea)
## Denom. DF: 2060
## numDF F-value p-value
## (Intercept) 1 16555.224 <.0001
## semana 4 260.732 <.0001
## gen 15 15.275 <.0001
## bloque 2 140.240 <.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 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
## TCS02 b TCS02
## TCS03 acde TCS03
## TCS04 ac TCS04
## TCS05 def TCS05
## TCS08 ab TCS08
## TCS10 cdef TCS10
## TCS11 efg TCS11
## TCS12 cdef TCS12
## TCS20 h TCS20
## TCS43 acde TCS43
## TCS44 acde TCS44
## TCS45 acd TCS45
## TCS46 fg TCS46
## TCS47 acde TCS47
## TCS48 g TCS48
## TCS49 acde 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
## TCS02 abc TCS02
## TCS03 fgh TCS03
## TCS04 ab TCS04
## TCS05 fghi TCS05
## TCS08 b TCS08
## TCS10 cdef TCS10
## TCS11 ghi TCS11
## TCS12 ghi TCS12
## TCS20 i TCS20
## TCS43 defg TCS43
## TCS44 efgh TCS44
## TCS45 abcd TCS45
## TCS46 cdef TCS46
## TCS47 acde TCS47
## TCS48 hi 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
## TCS02 ab TCS02
## TCS03 cde TCS03
## TCS04 ab TCS04
## TCS05 def TCS05
## TCS08 b TCS08
## TCS10 cde TCS10
## TCS11 def TCS11
## TCS12 ef TCS12
## TCS20 f TCS20
## TCS43 ac TCS43
## TCS44 f TCS44
## TCS45 def TCS45
## TCS46 ef TCS46
## TCS47 cde TCS47
## TCS48 def TCS48
## TCS49 cd 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
## TCS02 143 3.18 2061 136 149
## TCS03 166 3.20 2061 160 173
## TCS04 158 3.22 2061 152 165
## TCS05 174 3.18 2061 168 181
## TCS08 147 4.04 2061 139 155
## TCS10 172 3.18 2061 166 178
## TCS11 177 3.18 2061 171 183
## TCS12 172 3.18 2061 165 178
## TCS20 215 3.18 2061 208 221
## TCS43 167 3.46 2061 160 174
## TCS44 167 3.20 2061 161 173
## TCS45 161 3.18 2061 155 167
## TCS46 186 3.18 2061 179 192
## TCS47 165 3.18 2061 159 172
## TCS48 191 3.20 2061 185 197
## TCS49 166 3.18 2061 160 172
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS02 - TCS03 -23.629 4.51 2061 -5.238 <.0001
## TCS02 - TCS04 -15.816 4.53 2061 -3.493 0.0409
## TCS02 - TCS05 -31.748 4.50 2061 -7.051 <.0001
## TCS02 - TCS08 -4.414 5.15 2061 -0.858 1.0000
## TCS02 - TCS10 -29.593 4.50 2061 -6.572 <.0001
## TCS02 - TCS11 -34.156 4.50 2061 -7.586 <.0001
## TCS02 - TCS12 -28.993 4.50 2061 -6.439 <.0001
## TCS02 - TCS20 -72.015 4.50 2061 -15.994 <.0001
## TCS02 - TCS43 -24.407 4.70 2061 -5.194 <.0001
## TCS02 - TCS44 -24.368 4.51 2061 -5.402 <.0001
## TCS02 - TCS45 -18.178 4.50 2061 -4.037 0.0057
## TCS02 - TCS46 -43.059 4.50 2061 -9.563 <.0001
## TCS02 - TCS47 -22.704 4.50 2061 -5.042 0.0001
## TCS02 - TCS48 -48.498 4.51 2061 -10.751 <.0001
## TCS02 - TCS49 -23.452 4.50 2061 -5.208 <.0001
## TCS03 - TCS04 7.813 4.54 2061 1.722 0.9411
## TCS03 - TCS05 -8.119 4.51 2061 -1.800 0.9167
## TCS03 - TCS08 19.215 5.15 2061 3.729 0.0182
## TCS03 - TCS10 -5.963 4.51 2061 -1.322 0.9951
## TCS03 - TCS11 -10.526 4.51 2061 -2.333 0.5986
## TCS03 - TCS12 -5.363 4.51 2061 -1.189 0.9985
## TCS03 - TCS20 -48.385 4.51 2061 -10.726 <.0001
## TCS03 - TCS43 -0.778 4.71 2061 -0.165 1.0000
## TCS03 - TCS44 -0.739 4.52 2061 -0.164 1.0000
## TCS03 - TCS45 5.452 4.51 2061 1.208 0.9982
## TCS03 - TCS46 -19.430 4.51 2061 -4.307 0.0019
## TCS03 - TCS47 0.926 4.51 2061 0.205 1.0000
## TCS03 - TCS48 -24.868 4.52 2061 -5.502 <.0001
## TCS03 - TCS49 0.178 4.51 2061 0.039 1.0000
## TCS04 - TCS05 -15.932 4.53 2061 -3.518 0.0376
## TCS04 - TCS08 11.402 5.17 2061 2.207 0.6926
## TCS04 - TCS10 -13.776 4.53 2061 -3.042 0.1524
## TCS04 - TCS11 -18.339 4.53 2061 -4.050 0.0054
## TCS04 - TCS12 -13.176 4.53 2061 -2.910 0.2109
## TCS04 - TCS20 -56.199 4.53 2061 -12.410 <.0001
## TCS04 - TCS43 -8.591 4.72 2061 -1.819 0.9097
## TCS04 - TCS44 -8.552 4.54 2061 -1.885 0.8828
## TCS04 - TCS45 -2.361 4.53 2061 -0.521 1.0000
## TCS04 - TCS46 -27.243 4.53 2061 -6.016 <.0001
## TCS04 - TCS47 -6.887 4.53 2061 -1.521 0.9802
## TCS04 - TCS48 -32.682 4.54 2061 -7.204 <.0001
## TCS04 - TCS49 -7.636 4.53 2061 -1.686 0.9505
## TCS05 - TCS08 27.334 5.15 2061 5.313 <.0001
## TCS05 - TCS10 2.156 4.50 2061 0.479 1.0000
## TCS05 - TCS11 -2.407 4.50 2061 -0.535 1.0000
## TCS05 - TCS12 2.756 4.50 2061 0.612 1.0000
## TCS05 - TCS20 -40.267 4.50 2061 -8.943 <.0001
## TCS05 - TCS43 7.341 4.70 2061 1.562 0.9746
## TCS05 - TCS44 7.380 4.51 2061 1.636 0.9618
## TCS05 - TCS45 13.570 4.50 2061 3.014 0.1638
## TCS05 - TCS46 -11.311 4.50 2061 -2.512 0.4629
## TCS05 - TCS47 9.044 4.50 2061 2.009 0.8206
## TCS05 - TCS48 -16.750 4.51 2061 -3.713 0.0193
## TCS05 - TCS49 8.296 4.50 2061 1.843 0.9007
## TCS08 - TCS10 -25.179 5.15 2061 -4.894 0.0001
## TCS08 - TCS11 -29.742 5.15 2061 -5.781 <.0001
## TCS08 - TCS12 -24.579 5.15 2061 -4.777 0.0002
## TCS08 - TCS20 -67.601 5.15 2061 -13.139 <.0001
## TCS08 - TCS43 -19.993 5.31 2061 -3.762 0.0162
## TCS08 - TCS44 -19.954 5.15 2061 -3.873 0.0107
## TCS08 - TCS45 -13.764 5.15 2061 -2.675 0.3474
## TCS08 - TCS46 -38.645 5.15 2061 -7.511 <.0001
## TCS08 - TCS47 -18.290 5.15 2061 -3.555 0.0333
## TCS08 - TCS48 -44.084 5.15 2061 -8.555 <.0001
## TCS08 - TCS49 -19.038 5.15 2061 -3.700 0.0202
## TCS10 - TCS11 -4.563 4.50 2061 -1.013 0.9998
## TCS10 - TCS12 0.600 4.50 2061 0.133 1.0000
## TCS10 - TCS20 -42.422 4.50 2061 -9.421 <.0001
## TCS10 - TCS43 5.185 4.70 2061 1.104 0.9994
## TCS10 - TCS44 5.224 4.51 2061 1.158 0.9989
## TCS10 - TCS45 11.415 4.50 2061 2.535 0.4459
## TCS10 - TCS46 -13.467 4.50 2061 -2.991 0.1735
## TCS10 - TCS47 6.889 4.50 2061 1.530 0.9791
## TCS10 - TCS48 -18.905 4.51 2061 -4.191 0.0030
## TCS10 - TCS49 6.141 4.50 2061 1.364 0.9933
## TCS11 - TCS12 5.163 4.50 2061 1.147 0.9990
## TCS11 - TCS20 -37.859 4.50 2061 -8.408 <.0001
## TCS11 - TCS43 9.748 4.70 2061 2.075 0.7814
## TCS11 - TCS44 9.787 4.51 2061 2.170 0.7187
## TCS11 - TCS45 15.978 4.50 2061 3.548 0.0340
## TCS11 - TCS46 -8.904 4.50 2061 -1.977 0.8378
## TCS11 - TCS47 11.452 4.50 2061 2.543 0.4398
## TCS11 - TCS48 -14.342 4.51 2061 -3.179 0.1056
## TCS11 - TCS49 10.704 4.50 2061 2.377 0.5652
## TCS12 - TCS20 -43.022 4.50 2061 -9.555 <.0001
## TCS12 - TCS43 4.585 4.70 2061 0.976 0.9999
## TCS12 - TCS44 4.624 4.51 2061 1.025 0.9997
## TCS12 - TCS45 10.815 4.50 2061 2.402 0.5464
## TCS12 - TCS46 -14.067 4.50 2061 -3.124 0.1229
## TCS12 - TCS47 6.289 4.50 2061 1.397 0.9914
## TCS12 - TCS48 -19.505 4.51 2061 -4.324 0.0017
## TCS12 - TCS49 5.541 4.50 2061 1.231 0.9978
## TCS20 - TCS43 47.608 4.70 2061 10.132 <.0001
## TCS20 - TCS44 47.646 4.51 2061 10.562 <.0001
## TCS20 - TCS45 53.837 4.50 2061 11.957 <.0001
## TCS20 - TCS46 28.956 4.50 2061 6.431 <.0001
## TCS20 - TCS47 49.311 4.50 2061 10.951 <.0001
## TCS20 - TCS48 23.517 4.51 2061 5.213 <.0001
## TCS20 - TCS49 48.563 4.50 2061 10.785 <.0001
## TCS43 - TCS44 0.039 4.71 2061 0.008 1.0000
## TCS43 - TCS45 6.230 4.70 2061 1.326 0.9950
## TCS43 - TCS46 -18.652 4.70 2061 -3.969 0.0074
## TCS43 - TCS47 1.704 4.70 2061 0.363 1.0000
## TCS43 - TCS48 -24.091 4.71 2061 -5.118 <.0001
## TCS43 - TCS49 0.955 4.70 2061 0.203 1.0000
## TCS44 - TCS45 6.191 4.51 2061 1.372 0.9928
## TCS44 - TCS46 -18.691 4.51 2061 -4.143 0.0037
## TCS44 - TCS47 1.665 4.51 2061 0.369 1.0000
## TCS44 - TCS48 -24.130 4.52 2061 -5.339 <.0001
## TCS44 - TCS49 0.916 4.51 2061 0.203 1.0000
## TCS45 - TCS46 -24.881 4.50 2061 -5.526 <.0001
## TCS45 - TCS47 -4.526 4.50 2061 -1.005 0.9998
## TCS45 - TCS48 -30.320 4.51 2061 -6.721 <.0001
## TCS45 - TCS49 -5.274 4.50 2061 -1.171 0.9987
## TCS46 - TCS47 20.356 4.50 2061 4.521 0.0007
## TCS46 - TCS48 -5.439 4.51 2061 -1.206 0.9982
## TCS46 - TCS49 19.607 4.50 2061 4.355 0.0015
## TCS47 - TCS48 -25.794 4.51 2061 -5.718 <.0001
## TCS47 - TCS49 -0.748 4.50 2061 -0.166 1.0000
## TCS48 - TCS49 25.046 4.51 2061 5.552 <.0001
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 16 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS20 215 3.18 2061 208 221 A
## TCS48 191 3.20 2061 185 197 B
## TCS46 186 3.18 2061 179 192 BC
## TCS11 177 3.18 2061 171 183 BCD
## TCS05 174 3.18 2061 168 181 CDE
## TCS10 172 3.18 2061 166 178 CDEF
## TCS12 172 3.18 2061 165 178 CDEF
## TCS43 167 3.46 2061 160 174 DEF
## TCS44 167 3.20 2061 161 173 DEF
## TCS03 166 3.20 2061 160 173 DEF
## TCS49 166 3.18 2061 160 172 DEF
## TCS47 165 3.18 2061 159 172 DEF
## TCS45 161 3.18 2061 155 167 EFG
## TCS04 158 3.22 2061 152 165 FG
## TCS08 147 4.04 2061 139 155 GH
## TCS02 143 3.18 2061 136 149 H
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 16 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
## TCS02 38.7 0.901 2057 36.9 40.5
## TCS03 45.8 0.890 2057 44.1 47.6
## TCS04 36.8 0.897 2057 35.1 38.6
## TCS05 46.1 0.887 2057 44.4 47.9
## TCS08 33.9 1.126 2057 31.7 36.1
## TCS10 42.0 0.887 2057 40.3 43.7
## TCS11 47.3 0.887 2057 45.5 49.0
## TCS12 46.7 0.887 2057 45.0 48.4
## TCS20 50.3 0.887 2057 48.6 52.0
## TCS43 42.7 0.963 2057 40.8 44.6
## TCS44 45.2 0.890 2057 43.5 47.0
## TCS45 40.0 0.887 2057 38.3 41.8
## TCS46 41.9 0.887 2057 40.2 43.6
## TCS47 41.0 0.887 2057 39.2 42.7
## TCS48 48.8 0.890 2057 47.0 50.5
## TCS49 43.4 0.887 2057 41.6 45.1
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS02 - TCS03 -7.144 1.27 2057 -5.641 <.0001
## TCS02 - TCS04 1.861 1.27 2057 1.464 0.9863
## TCS02 - TCS05 -7.445 1.26 2057 -5.890 <.0001
## TCS02 - TCS08 4.840 1.44 2057 3.356 0.0630
## TCS02 - TCS10 -3.308 1.26 2057 -2.617 0.3870
## TCS02 - TCS11 -8.576 1.26 2057 -6.784 <.0001
## TCS02 - TCS12 -7.999 1.26 2057 -6.328 <.0001
## TCS02 - TCS20 -11.590 1.26 2057 -9.168 <.0001
## TCS02 - TCS43 -3.997 1.32 2057 -3.031 0.1567
## TCS02 - TCS44 -6.536 1.27 2057 -5.161 <.0001
## TCS02 - TCS45 -1.332 1.26 2057 -1.054 0.9996
## TCS02 - TCS46 -3.201 1.26 2057 -2.532 0.4480
## TCS02 - TCS47 -2.280 1.26 2057 -1.803 0.9154
## TCS02 - TCS48 -10.069 1.27 2057 -7.950 <.0001
## TCS02 - TCS49 -4.678 1.26 2057 -3.701 0.0201
## TCS03 - TCS04 9.005 1.26 2057 7.124 <.0001
## TCS03 - TCS05 -0.301 1.26 2057 -0.240 1.0000
## TCS03 - TCS08 11.984 1.44 2057 8.348 <.0001
## TCS03 - TCS10 3.836 1.26 2057 3.052 0.1487
## TCS03 - TCS11 -1.432 1.26 2057 -1.140 0.9991
## TCS03 - TCS12 -0.855 1.26 2057 -0.680 1.0000
## TCS03 - TCS20 -4.446 1.26 2057 -3.537 0.0353
## TCS03 - TCS43 3.147 1.31 2057 2.399 0.5482
## TCS03 - TCS44 0.608 1.26 2057 0.483 1.0000
## TCS03 - TCS45 5.812 1.26 2057 4.624 0.0004
## TCS03 - TCS46 3.943 1.26 2057 3.137 0.1187
## TCS03 - TCS47 4.864 1.26 2057 3.870 0.0108
## TCS03 - TCS48 -2.925 1.26 2057 -2.323 0.6067
## TCS03 - TCS49 2.466 1.26 2057 1.962 0.8459
## TCS04 - TCS05 -9.306 1.26 2057 -7.376 <.0001
## TCS04 - TCS08 2.979 1.44 2057 2.069 0.7845
## TCS04 - TCS10 -5.169 1.26 2057 -4.097 0.0044
## TCS04 - TCS11 -10.437 1.26 2057 -8.273 <.0001
## TCS04 - TCS12 -9.860 1.26 2057 -7.815 <.0001
## TCS04 - TCS20 -13.451 1.26 2057 -10.662 <.0001
## TCS04 - TCS43 -5.858 1.32 2057 -4.452 0.0010
## TCS04 - TCS44 -8.397 1.26 2057 -6.644 <.0001
## TCS04 - TCS45 -3.193 1.26 2057 -2.531 0.4490
## TCS04 - TCS46 -5.062 1.26 2057 -4.012 0.0063
## TCS04 - TCS47 -4.141 1.26 2057 -3.282 0.0787
## TCS04 - TCS48 -11.930 1.26 2057 -9.438 <.0001
## TCS04 - TCS49 -6.539 1.26 2057 -5.183 <.0001
## TCS05 - TCS08 12.286 1.43 2057 8.571 <.0001
## TCS05 - TCS10 4.137 1.25 2057 3.298 0.0751
## TCS05 - TCS11 -1.131 1.25 2057 -0.902 0.9999
## TCS05 - TCS12 -0.554 1.25 2057 -0.441 1.0000
## TCS05 - TCS20 -4.145 1.25 2057 -3.304 0.0738
## TCS05 - TCS43 3.448 1.31 2057 2.634 0.3753
## TCS05 - TCS44 0.909 1.26 2057 0.723 1.0000
## TCS05 - TCS45 6.113 1.25 2057 4.873 0.0001
## TCS05 - TCS46 4.244 1.25 2057 3.383 0.0580
## TCS05 - TCS47 5.166 1.25 2057 4.118 0.0041
## TCS05 - TCS48 -2.623 1.26 2057 -2.087 0.7735
## TCS05 - TCS49 2.767 1.25 2057 2.206 0.6931
## TCS08 - TCS10 -8.149 1.43 2057 -5.685 <.0001
## TCS08 - TCS11 -13.417 1.43 2057 -9.360 <.0001
## TCS08 - TCS12 -12.839 1.43 2057 -8.957 <.0001
## TCS08 - TCS20 -16.430 1.43 2057 -11.462 <.0001
## TCS08 - TCS43 -8.838 1.48 2057 -5.969 <.0001
## TCS08 - TCS44 -11.377 1.44 2057 -7.925 <.0001
## TCS08 - TCS45 -6.172 1.43 2057 -4.306 0.0019
## TCS08 - TCS46 -8.042 1.43 2057 -5.610 <.0001
## TCS08 - TCS47 -7.120 1.43 2057 -4.967 0.0001
## TCS08 - TCS48 -14.909 1.44 2057 -10.385 <.0001
## TCS08 - TCS49 -9.519 1.43 2057 -6.640 <.0001
## TCS10 - TCS11 -5.268 1.25 2057 -4.199 0.0029
## TCS10 - TCS12 -4.691 1.25 2057 -3.739 0.0176
## TCS10 - TCS20 -8.282 1.25 2057 -6.602 <.0001
## TCS10 - TCS43 -0.689 1.31 2057 -0.526 1.0000
## TCS10 - TCS44 -3.228 1.26 2057 -2.568 0.4217
## TCS10 - TCS45 1.976 1.25 2057 1.575 0.9726
## TCS10 - TCS46 0.107 1.25 2057 0.085 1.0000
## TCS10 - TCS47 1.029 1.25 2057 0.820 1.0000
## TCS10 - TCS48 -6.760 1.26 2057 -5.379 <.0001
## TCS10 - TCS49 -1.370 1.25 2057 -1.092 0.9994
## TCS11 - TCS12 0.577 1.25 2057 0.460 1.0000
## TCS11 - TCS20 -3.014 1.25 2057 -2.402 0.5461
## TCS11 - TCS43 4.579 1.31 2057 3.498 0.0402
## TCS11 - TCS44 2.040 1.26 2057 1.623 0.9643
## TCS11 - TCS45 7.244 1.25 2057 5.775 <.0001
## TCS11 - TCS46 5.375 1.25 2057 4.285 0.0020
## TCS11 - TCS47 6.297 1.25 2057 5.019 0.0001
## TCS11 - TCS48 -1.492 1.26 2057 -1.187 0.9985
## TCS11 - TCS49 3.898 1.25 2057 3.107 0.1286
## TCS12 - TCS20 -3.591 1.25 2057 -2.862 0.2350
## TCS12 - TCS43 4.002 1.31 2057 3.057 0.1469
## TCS12 - TCS44 1.463 1.26 2057 1.164 0.9988
## TCS12 - TCS45 6.667 1.25 2057 5.314 <.0001
## TCS12 - TCS46 4.798 1.25 2057 3.824 0.0128
## TCS12 - TCS47 5.719 1.25 2057 4.559 0.0006
## TCS12 - TCS48 -2.070 1.26 2057 -1.647 0.9595
## TCS12 - TCS49 3.321 1.25 2057 2.647 0.3662
## TCS20 - TCS43 7.593 1.31 2057 5.800 <.0001
## TCS20 - TCS44 5.054 1.26 2057 4.021 0.0060
## TCS20 - TCS45 10.258 1.25 2057 8.177 <.0001
## TCS20 - TCS46 8.389 1.25 2057 6.687 <.0001
## TCS20 - TCS47 9.310 1.25 2057 7.422 <.0001
## TCS20 - TCS48 1.521 1.26 2057 1.210 0.9981
## TCS20 - TCS49 6.912 1.25 2057 5.510 <.0001
## TCS43 - TCS44 -2.539 1.31 2057 -1.936 0.8591
## TCS43 - TCS45 2.665 1.31 2057 2.036 0.8048
## TCS43 - TCS46 0.796 1.31 2057 0.608 1.0000
## TCS43 - TCS47 1.718 1.31 2057 1.312 0.9955
## TCS43 - TCS48 -6.071 1.31 2057 -4.629 0.0004
## TCS43 - TCS49 -0.681 1.31 2057 -0.520 1.0000
## TCS44 - TCS45 5.204 1.26 2057 4.141 0.0037
## TCS44 - TCS46 3.335 1.26 2057 2.653 0.3619
## TCS44 - TCS47 4.256 1.26 2057 3.387 0.0573
## TCS44 - TCS48 -3.532 1.26 2057 -2.805 0.2665
## TCS44 - TCS49 1.858 1.26 2057 1.478 0.9849
## TCS45 - TCS46 -1.869 1.25 2057 -1.490 0.9837
## TCS45 - TCS47 -0.948 1.25 2057 -0.755 1.0000
## TCS45 - TCS48 -8.737 1.26 2057 -6.951 <.0001
## TCS45 - TCS49 -3.346 1.25 2057 -2.667 0.3525
## TCS46 - TCS47 0.921 1.25 2057 0.735 1.0000
## TCS46 - TCS48 -6.867 1.26 2057 -5.464 <.0001
## TCS46 - TCS49 -1.477 1.25 2057 -1.177 0.9986
## TCS47 - TCS48 -7.789 1.26 2057 -6.197 <.0001
## TCS47 - TCS49 -2.399 1.25 2057 -1.912 0.8706
## TCS48 - TCS49 5.390 1.26 2057 4.289 0.0020
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 16 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS20 50.3 0.887 2057 48.6 52.0 A
## TCS48 48.8 0.890 2057 47.0 50.5 AB
## TCS11 47.3 0.887 2057 45.5 49.0 ABC
## TCS12 46.7 0.887 2057 45.0 48.4 ABCD
## TCS05 46.1 0.887 2057 44.4 47.9 ABCDE
## TCS03 45.8 0.890 2057 44.1 47.6 BCDE
## TCS44 45.2 0.890 2057 43.5 47.0 BCDEF
## TCS49 43.4 0.887 2057 41.6 45.1 CDEFG
## TCS43 42.7 0.963 2057 40.8 44.6 DEFGH
## TCS10 42.0 0.887 2057 40.3 43.7 EFGH
## TCS46 41.9 0.887 2057 40.2 43.6 EFGH
## TCS47 41.0 0.887 2057 39.2 42.7 FGHI
## TCS45 40.0 0.887 2057 38.3 41.8 GHI
## TCS02 38.7 0.901 2057 36.9 40.5 HIJ
## TCS04 36.8 0.897 2057 35.1 38.6 IJ
## TCS08 33.9 1.126 2057 31.7 36.1 J
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 16 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
## TCS02 0.889 0.0278 2060 0.835 0.944
## TCS03 1.145 0.0279 2060 1.090 1.200
## TCS04 0.941 0.0281 2060 0.886 0.996
## TCS05 1.205 0.0278 2060 1.151 1.260
## TCS08 0.767 0.0353 2060 0.698 0.836
## TCS10 1.146 0.0278 2060 1.091 1.200
## TCS11 1.204 0.0279 2060 1.149 1.259
## TCS12 1.267 0.0278 2060 1.212 1.321
## TCS20 1.299 0.0278 2060 1.244 1.353
## TCS43 1.007 0.0302 2060 0.948 1.066
## TCS44 1.293 0.0279 2060 1.238 1.348
## TCS45 1.186 0.0278 2060 1.132 1.241
## TCS46 1.257 0.0278 2060 1.202 1.311
## TCS47 1.149 0.0278 2060 1.095 1.204
## TCS48 1.234 0.0279 2060 1.179 1.289
## TCS49 1.098 0.0278 2060 1.043 1.152
##
## Results are averaged over the levels of: semana, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## TCS02 - TCS03 -0.255543 0.0394 2060 -6.488 <.0001
## TCS02 - TCS04 -0.051351 0.0395 2060 -1.299 0.9960
## TCS02 - TCS05 -0.315926 0.0393 2060 -8.036 <.0001
## TCS02 - TCS08 0.122679 0.0449 2060 2.731 0.3114
## TCS02 - TCS10 -0.256296 0.0393 2060 -6.519 <.0001
## TCS02 - TCS11 -0.314785 0.0394 2060 -7.992 <.0001
## TCS02 - TCS12 -0.377333 0.0393 2060 -9.598 <.0001
## TCS02 - TCS20 -0.409111 0.0393 2060 -10.406 <.0001
## TCS02 - TCS43 -0.117624 0.0410 2060 -2.867 0.2327
## TCS02 - TCS44 -0.403705 0.0394 2060 -10.249 <.0001
## TCS02 - TCS45 -0.296741 0.0393 2060 -7.548 <.0001
## TCS02 - TCS46 -0.367407 0.0393 2060 -9.345 <.0001
## TCS02 - TCS47 -0.259778 0.0393 2060 -6.608 <.0001
## TCS02 - TCS48 -0.344540 0.0394 2060 -8.747 <.0001
## TCS02 - TCS49 -0.208148 0.0393 2060 -5.294 <.0001
## TCS03 - TCS04 0.204192 0.0396 2060 5.155 <.0001
## TCS03 - TCS05 -0.060383 0.0394 2060 -1.533 0.9787
## TCS03 - TCS08 0.378221 0.0450 2060 8.406 <.0001
## TCS03 - TCS10 -0.000754 0.0394 2060 -0.019 1.0000
## TCS03 - TCS11 -0.059242 0.0395 2060 -1.501 0.9825
## TCS03 - TCS12 -0.121791 0.0394 2060 -3.092 0.1339
## TCS03 - TCS20 -0.153568 0.0394 2060 -3.899 0.0097
## TCS03 - TCS43 0.137919 0.0411 2060 3.356 0.0631
## TCS03 - TCS44 -0.148162 0.0395 2060 -3.755 0.0166
## TCS03 - TCS45 -0.041198 0.0394 2060 -1.046 0.9997
## TCS03 - TCS46 -0.111865 0.0394 2060 -2.840 0.2471
## TCS03 - TCS47 -0.004235 0.0394 2060 -0.108 1.0000
## TCS03 - TCS48 -0.088997 0.0395 2060 -2.255 0.6572
## TCS03 - TCS49 0.047395 0.0394 2060 1.203 0.9983
## TCS04 - TCS05 -0.264575 0.0395 2060 -6.691 <.0001
## TCS04 - TCS08 0.174030 0.0451 2060 3.857 0.0114
## TCS04 - TCS10 -0.204945 0.0395 2060 -5.183 <.0001
## TCS04 - TCS11 -0.263434 0.0396 2060 -6.650 <.0001
## TCS04 - TCS12 -0.325982 0.0395 2060 -8.245 <.0001
## TCS04 - TCS20 -0.357760 0.0395 2060 -9.048 <.0001
## TCS04 - TCS43 -0.066272 0.0412 2060 -1.607 0.9673
## TCS04 - TCS44 -0.352354 0.0396 2060 -8.895 <.0001
## TCS04 - TCS45 -0.245390 0.0395 2060 -6.206 <.0001
## TCS04 - TCS46 -0.316057 0.0395 2060 -7.994 <.0001
## TCS04 - TCS47 -0.208427 0.0395 2060 -5.271 <.0001
## TCS04 - TCS48 -0.293189 0.0396 2060 -7.402 <.0001
## TCS04 - TCS49 -0.156797 0.0395 2060 -3.966 0.0075
## TCS05 - TCS08 0.438605 0.0449 2060 9.763 <.0001
## TCS05 - TCS10 0.059630 0.0393 2060 1.517 0.9807
## TCS05 - TCS11 0.001141 0.0394 2060 0.029 1.0000
## TCS05 - TCS12 -0.061407 0.0393 2060 -1.562 0.9747
## TCS05 - TCS20 -0.093185 0.0393 2060 -2.370 0.5705
## TCS05 - TCS43 0.198302 0.0410 2060 4.833 0.0002
## TCS05 - TCS44 -0.087779 0.0394 2060 -2.229 0.6768
## TCS05 - TCS45 0.019185 0.0393 2060 0.488 1.0000
## TCS05 - TCS46 -0.051481 0.0393 2060 -1.309 0.9956
## TCS05 - TCS47 0.056148 0.0393 2060 1.428 0.9892
## TCS05 - TCS48 -0.028614 0.0394 2060 -0.726 1.0000
## TCS05 - TCS49 0.107778 0.0393 2060 2.741 0.3048
## TCS08 - TCS10 -0.378975 0.0449 2060 -8.436 <.0001
## TCS08 - TCS11 -0.437464 0.0450 2060 -9.724 <.0001
## TCS08 - TCS12 -0.500012 0.0449 2060 -11.130 <.0001
## TCS08 - TCS20 -0.531790 0.0449 2060 -11.837 <.0001
## TCS08 - TCS43 -0.240302 0.0464 2060 -5.178 <.0001
## TCS08 - TCS44 -0.526384 0.0450 2060 -11.701 <.0001
## TCS08 - TCS45 -0.419419 0.0449 2060 -9.336 <.0001
## TCS08 - TCS46 -0.490086 0.0449 2060 -10.909 <.0001
## TCS08 - TCS47 -0.382456 0.0449 2060 -8.513 <.0001
## TCS08 - TCS48 -0.467219 0.0450 2060 -10.385 <.0001
## TCS08 - TCS49 -0.330827 0.0449 2060 -7.364 <.0001
## TCS10 - TCS11 -0.058489 0.0394 2060 -1.485 0.9842
## TCS10 - TCS12 -0.121037 0.0393 2060 -3.079 0.1387
## TCS10 - TCS20 -0.152815 0.0393 2060 -3.887 0.0102
## TCS10 - TCS43 0.138673 0.0410 2060 3.380 0.0586
## TCS10 - TCS44 -0.147409 0.0394 2060 -3.742 0.0174
## TCS10 - TCS45 -0.040444 0.0393 2060 -1.029 0.9997
## TCS10 - TCS46 -0.111111 0.0393 2060 -2.826 0.2548
## TCS10 - TCS47 -0.003481 0.0393 2060 -0.089 1.0000
## TCS10 - TCS48 -0.088244 0.0394 2060 -2.240 0.6682
## TCS10 - TCS49 0.048148 0.0393 2060 1.225 0.9979
## TCS11 - TCS12 -0.062548 0.0394 2060 -1.588 0.9706
## TCS11 - TCS20 -0.094326 0.0394 2060 -2.395 0.5518
## TCS11 - TCS43 0.197161 0.0411 2060 4.798 0.0002
## TCS11 - TCS44 -0.088920 0.0395 2060 -2.253 0.6586
## TCS11 - TCS45 0.018044 0.0394 2060 0.458 1.0000
## TCS11 - TCS46 -0.052623 0.0394 2060 -1.336 0.9946
## TCS11 - TCS47 0.055007 0.0394 2060 1.397 0.9914
## TCS11 - TCS48 -0.029755 0.0395 2060 -0.754 1.0000
## TCS11 - TCS49 0.106637 0.0394 2060 2.707 0.3263
## TCS12 - TCS20 -0.031778 0.0393 2060 -0.808 1.0000
## TCS12 - TCS43 0.259710 0.0410 2060 6.330 <.0001
## TCS12 - TCS44 -0.026372 0.0394 2060 -0.670 1.0000
## TCS12 - TCS45 0.080593 0.0393 2060 2.050 0.7965
## TCS12 - TCS46 0.009926 0.0393 2060 0.252 1.0000
## TCS12 - TCS47 0.117556 0.0393 2060 2.990 0.1738
## TCS12 - TCS48 0.032793 0.0394 2060 0.833 1.0000
## TCS12 - TCS49 0.169185 0.0393 2060 4.303 0.0019
## TCS20 - TCS43 0.291488 0.0410 2060 7.105 <.0001
## TCS20 - TCS44 0.005406 0.0394 2060 0.137 1.0000
## TCS20 - TCS45 0.112370 0.0393 2060 2.858 0.2373
## TCS20 - TCS46 0.041704 0.0393 2060 1.061 0.9996
## TCS20 - TCS47 0.149333 0.0393 2060 3.798 0.0142
## TCS20 - TCS48 0.064571 0.0394 2060 1.639 0.9611
## TCS20 - TCS49 0.200963 0.0393 2060 5.112 <.0001
## TCS43 - TCS44 -0.286082 0.0411 2060 -6.960 <.0001
## TCS43 - TCS45 -0.179117 0.0410 2060 -4.366 0.0014
## TCS43 - TCS46 -0.249784 0.0410 2060 -6.088 <.0001
## TCS43 - TCS47 -0.142154 0.0410 2060 -3.465 0.0448
## TCS43 - TCS48 -0.226917 0.0411 2060 -5.521 <.0001
## TCS43 - TCS49 -0.090525 0.0410 2060 -2.206 0.6927
## TCS44 - TCS45 0.106964 0.0394 2060 2.716 0.3210
## TCS44 - TCS46 0.036298 0.0394 2060 0.922 0.9999
## TCS44 - TCS47 0.143927 0.0394 2060 3.654 0.0237
## TCS44 - TCS48 0.059165 0.0395 2060 1.499 0.9827
## TCS44 - TCS49 0.195557 0.0394 2060 4.965 0.0001
## TCS45 - TCS46 -0.070667 0.0393 2060 -1.797 0.9175
## TCS45 - TCS47 0.036963 0.0393 2060 0.940 0.9999
## TCS45 - TCS48 -0.047800 0.0394 2060 -1.214 0.9981
## TCS45 - TCS49 0.088593 0.0393 2060 2.253 0.6586
## TCS46 - TCS47 0.107630 0.0393 2060 2.738 0.3071
## TCS46 - TCS48 0.022867 0.0394 2060 0.581 1.0000
## TCS46 - TCS49 0.159259 0.0393 2060 4.051 0.0054
## TCS47 - TCS48 -0.084762 0.0394 2060 -2.152 0.7308
## TCS47 - TCS49 0.051630 0.0393 2060 1.313 0.9955
## TCS48 - TCS49 0.136392 0.0394 2060 3.463 0.0451
##
## Results are averaged over the levels of: semana, bloque
## P value adjustment: tukey method for comparing a family of 16 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS20 1.299 0.0278 2060 1.244 1.353 A
## TCS44 1.293 0.0279 2060 1.238 1.348 A
## TCS12 1.267 0.0278 2060 1.212 1.321 AB
## TCS46 1.257 0.0278 2060 1.202 1.311 AB
## TCS48 1.234 0.0279 2060 1.179 1.289 AB
## TCS05 1.205 0.0278 2060 1.151 1.260 ABC
## TCS11 1.204 0.0279 2060 1.149 1.259 ABC
## TCS45 1.186 0.0278 2060 1.132 1.241 ABC
## TCS47 1.149 0.0278 2060 1.095 1.204 BC
## TCS10 1.146 0.0278 2060 1.091 1.200 BCD
## TCS03 1.145 0.0279 2060 1.090 1.200 BCD
## TCS49 1.098 0.0278 2060 1.043 1.152 CD
## TCS43 1.007 0.0302 2060 0.948 1.066 DE
## TCS04 0.941 0.0281 2060 0.886 0.996 E
## TCS02 0.889 0.0278 2060 0.835 0.944 EF
## TCS08 0.767 0.0353 2060 0.698 0.836 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 16 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)