setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura")
datos4<-read.table("santamaria.csv", header=T, sep=',')
datos4$gen<-as.factor(datos4$gen)
datos4$forestal<-as.factor(datos4$forestal)
datos4$bloque<-as.factor(datos4$bloque)
attach(datos4)
library(ggplot2)
library(emmeans)
#Gráfica diámetro
ggplot(datos4, aes(semana, diam, group = gen, colour = gen)) +
facet_grid(~forestal) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Diámetro") +
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 34 rows containing non-finite values (stat_smooth).
# Gráfica altura
ggplot(datos4, aes(semana, alt, group = gen, colour = gen)) +
facet_grid(~forestal) +
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 34 rows containing non-finite values (stat_smooth).
# Anova general
aov.diam<-aov(diam~semana*forestal*gen+bloque)
aov.alt<-aov(alt~semana*forestal*gen+bloque)
#Análisis para diámetro
library(nlme)
fit.compsym.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos4, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos4, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos4, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.diam, fit.ar1.diam, fit.ar1het.diam) #compares the models
## Model df AIC BIC logLik Test L.Ratio p-value
## fit.compsym.diam 1 33 1929.753 2077.746 -931.8766
## fit.ar1.diam 2 33 1884.634 2032.627 -909.3171
## fit.ar1het.diam 3 36 1799.179 1960.626 -863.5895 2 vs 3 91.45515 <.0001
anova(fit.ar1.diam)
## Denom. DF: 655
## numDF F-value p-value
## (Intercept) 1 9452.403 <.0001
## semana 1 448.364 <.0001
## forestal 2 0.699 0.4977
## gen 4 2.206 0.0669
## bloque 1 1.840 0.1754
## semana:forestal 2 0.201 0.8176
## semana:gen 4 0.162 0.9574
## forestal:gen 8 6.682 <.0001
## semana:forestal:gen 8 1.318 0.2312
anova(fit.ar1het.diam)
## Denom. DF: 655
## numDF F-value p-value
## (Intercept) 1 10600.425 <.0001
## semana 1 858.726 <.0001
## forestal 2 0.590 0.5547
## gen 4 2.961 0.0193
## bloque 1 2.649 0.1041
## semana:forestal 2 0.361 0.6969
## semana:gen 4 0.312 0.8697
## forestal:gen 8 5.407 <.0001
## semana:forestal:gen 8 2.315 0.0188
# Análisis para altura
fit.compsym.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos4, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos4, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos4, 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 33 6643.458 6791.451 -3288.729
## fit.ar1.alt 2 33 6635.769 6783.762 -3284.885
## fit.ar1het.alt 3 36 6615.120 6776.567 -3271.560 2 vs 3 26.64967 <.0001
anova(fit.ar1.alt)
## Denom. DF: 655
## numDF F-value p-value
## (Intercept) 1 20018.297 <.0001
## semana 1 1240.223 <.0001
## forestal 2 3.123 0.0447
## gen 4 5.110 0.0005
## bloque 1 0.131 0.7177
## semana:forestal 2 0.132 0.8766
## semana:gen 4 0.689 0.5999
## forestal:gen 8 11.487 <.0001
## semana:forestal:gen 8 1.065 0.3858
anova(fit.ar1het.alt)
## Denom. DF: 655
## numDF F-value p-value
## (Intercept) 1 18288.746 <.0001
## semana 1 1544.340 <.0001
## forestal 2 3.503 0.0307
## gen 4 6.449 <.0001
## bloque 1 0.006 0.9377
## semana:forestal 2 0.294 0.7457
## semana:gen 4 0.730 0.5715
## forestal:gen 8 10.470 <.0001
## semana:forestal:gen 8 1.374 0.2043
#Tukey diámetro
library(multcompView)
interac.tuk.diam<-TukeyHSD(aov.diam, "forestal:gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## gen
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal, gen
fores.tuk.diam<-TukeyHSD(aov.diam, "forestal", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## gen
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal, gen
gen.tuk.diam<-TukeyHSD(aov.diam, "gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## gen
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal, gen
#Tukey altura
interac.tuk.alt<-TukeyHSD(aov.alt, "forestal:gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## gen
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal, gen
fores.tuk.alt<-TukeyHSD(aov.alt, "forestal", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## gen
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal, gen
gen.tuk.alt<-TukeyHSD(aov.alt, "gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## gen
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana,
## forestal, gen
#Etiquetas Tukey diámetro
#Genotipos
generate_label_df_gen_diam <- function(gen.tuk.diam, variable){
Tukey.levels <- gen.tuk.diam[[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.diam <- generate_label_df_gen_diam(gen.tuk.diam, "gen")
labels.gen.diam
## Letters treatment
## CCN51 a CCN51
## TCS01 ab TCS01
## TCS06 a TCS06
## TCS13 b TCS13
## TCS19 a TCS19
# Forestal
generate_label_df_forestal_diam <- function(fores.tuk.diam, variable){
Tukey.levels <- fores.tuk.diam[[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.forestal.diam <- generate_label_df_forestal_diam(fores.tuk.diam, "forestal")
labels.forestal.diam
## Letters treatment
## Abarco b Abarco
## Roble c Roble
## Terminalia a Terminalia
# Interacción Forestal:Genotipo
generate_label_df_interac_diam <- function(interac.tuk.diam, variable){
Tukey.levels <- interac.tuk.diam[[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.diam <- generate_label_df_interac_diam(interac.tuk.diam, "forestal:gen")
labels.interac.diam
## Letters treatment
## Abarco:CCN51 cdef Abarco:CCN51
## Abarco:TCS01 abcd Abarco:TCS01
## Abarco:TCS06 abc Abarco:TCS06
## Abarco:TCS13 acdef Abarco:TCS13
## Abarco:TCS19 f Abarco:TCS19
## Roble:CCN51 abcd Roble:CCN51
## Roble:TCS01 abcde Roble:TCS01
## Roble:TCS06 acdef Roble:TCS06
## Roble:TCS13 abcd Roble:TCS13
## Roble:TCS19 acdef Roble:TCS19
## Terminalia:CCN51 def Terminalia:CCN51
## Terminalia:TCS01 acdef Terminalia:TCS01
## Terminalia:TCS06 ef Terminalia:TCS06
## Terminalia:TCS13 b Terminalia:TCS13
## Terminalia:TCS19 ab Terminalia:TCS19
#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
## CCN51 ac CCN51
## TCS01 ab TCS01
## TCS06 c TCS06
## TCS13 ab TCS13
## TCS19 b TCS19
# Forestal
generate_label_df_forestal_alt <- function(fores.tuk.alt, variable){
Tukey.levels <- fores.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.forestal.alt <- generate_label_df_forestal_alt(fores.tuk.alt, "forestal")
labels.forestal.alt
## Letters treatment
## Abarco b Abarco
## Roble b Roble
## Terminalia a Terminalia
# Interacción Forestal: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, "forestal:gen")
labels.interac.alt
## Letters treatment
## Abarco:CCN51 f Abarco:CCN51
## Abarco:TCS01 abcde Abarco:TCS01
## Abarco:TCS06 abcd Abarco:TCS06
## Abarco:TCS13 cdef Abarco:TCS13
## Abarco:TCS19 acdef Abarco:TCS19
## Roble:CCN51 ab Roble:CCN51
## Roble:TCS01 abc Roble:TCS01
## Roble:TCS06 ef Roble:TCS06
## Roble:TCS13 cdef Roble:TCS13
## Roble:TCS19 abcde Roble:TCS19
## Terminalia:CCN51 def Terminalia:CCN51
## Terminalia:TCS01 def Terminalia:TCS01
## Terminalia:TCS06 f Terminalia:TCS06
## Terminalia:TCS13 b Terminalia:TCS13
## Terminalia:TCS19 ab Terminalia:TCS19
## Gráficas contrastes de medias diametro
#Gen
contrast <- emmeans(aov.diam, ~gen)
## NOTE: Results may be misleading due to involvement in interactions
plot(contrast, comparisons = TRUE, xlab ="Diámetro")
medias.gen <- emmeans(aov.diam, pairwise ~ gen)
## NOTE: Results may be misleading due to involvement in interactions
medias.gen
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CCN51 4.33 0.0712 655 4.19 4.47
## TCS01 4.12 0.0745 655 3.98 4.27
## TCS06 4.34 0.0756 655 4.19 4.49
## TCS13 4.02 0.0723 655 3.87 4.16
## TCS19 4.31 0.0712 655 4.17 4.45
##
## Results are averaged over the levels of: forestal, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CCN51 - TCS01 0.2029 0.103 655 1.968 0.2828
## CCN51 - TCS06 -0.0137 0.104 655 -0.132 0.9999
## CCN51 - TCS13 0.3098 0.101 655 3.053 0.0199
## CCN51 - TCS19 0.0136 0.101 655 0.135 0.9999
## TCS01 - TCS06 -0.2166 0.106 655 -2.041 0.2475
## TCS01 - TCS13 0.1069 0.104 655 1.030 0.8415
## TCS01 - TCS19 -0.1893 0.103 655 -1.836 0.3535
## TCS06 - TCS13 0.3235 0.105 655 3.092 0.0176
## TCS06 - TCS19 0.0273 0.104 655 0.263 0.9989
## TCS13 - TCS19 -0.2962 0.101 655 -2.919 0.0298
##
## Results are averaged over the levels of: forestal, bloque
## P value adjustment: tukey method for comparing a family of 5 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS06 4.34 0.0756 655 4.19 4.49 A
## CCN51 4.33 0.0712 655 4.19 4.47 A
## TCS19 4.31 0.0712 655 4.17 4.45 A
## TCS01 4.12 0.0745 655 3.98 4.27 AB
## TCS13 4.02 0.0723 655 3.87 4.16 B
##
## Results are averaged over the levels of: forestal, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 5 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.
#Forestal
contrast <- emmeans(aov.diam, ~forestal)
## NOTE: Results may be misleading due to involvement in interactions
plot(contrast, comparisons = TRUE, xlab ="Diámetro")
medias.forestal <- emmeans(aov.diam, pairwise ~ forestal)
## NOTE: Results may be misleading due to involvement in interactions
medias.forestal
## $emmeans
## forestal emmean SE df lower.CL upper.CL
## Abarco 4.29 0.0552 655 4.18 4.4
## Roble 4.19 0.0573 655 4.08 4.3
## Terminalia 4.19 0.0571 655 4.08 4.3
##
## Results are averaged over the levels of: gen, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Abarco - Roble 0.09832 0.0795 655 1.236 0.4322
## Abarco - Terminalia 0.09535 0.0794 655 1.201 0.4533
## Roble - Terminalia -0.00297 0.0809 655 -0.037 0.9993
##
## Results are averaged over the levels of: gen, bloque
## P value adjustment: tukey method for comparing a family of 3 estimates
cld_forestal <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_forestal
## forestal emmean SE df lower.CL upper.CL .group
## Abarco 4.29 0.0552 655 4.18 4.4 A
## Terminalia 4.19 0.0571 655 4.08 4.3 A
## Roble 4.19 0.0573 655 4.08 4.3 A
##
## Results are averaged over the levels of: gen, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 3 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.
#Forestal*Gen
contrast <- emmeans(aov.diam, ~gen*forestal)
## NOTE: Results may be misleading due to involvement in interactions
plot(contrast, comparisons = TRUE, xlab ="Diámetro")
medias.forestal.gen <- emmeans(aov.diam, pairwise ~ gen*forestal)
## NOTE: Results may be misleading due to involvement in interactions
medias.forestal.gen
## $emmeans
## gen forestal emmean SE df lower.CL upper.CL
## CCN51 Abarco 4.46 0.123 655 4.22 4.70
## TCS01 Abarco 3.98 0.123 655 3.74 4.22
## TCS06 Abarco 3.91 0.126 655 3.66 4.15
## TCS13 Abarco 4.40 0.123 655 4.16 4.64
## TCS19 Abarco 4.68 0.123 655 4.44 4.93
## CCN51 Roble 3.99 0.124 655 3.75 4.24
## TCS01 Roble 4.08 0.126 655 3.83 4.33
## TCS06 Roble 4.47 0.142 655 4.19 4.75
## TCS13 Roble 3.99 0.123 655 3.75 4.23
## TCS19 Roble 4.42 0.124 655 4.18 4.66
## CCN51 Terminalia 4.52 0.123 655 4.28 4.76
## TCS01 Terminalia 4.30 0.138 655 4.03 4.58
## TCS06 Terminalia 4.64 0.124 655 4.40 4.89
## TCS13 Terminalia 3.66 0.130 655 3.40 3.91
## TCS19 Terminalia 3.83 0.123 655 3.59 4.07
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CCN51 Abarco - TCS01 Abarco 0.47960 0.174 655 2.760 0.2716
## CCN51 Abarco - TCS06 Abarco 0.55477 0.176 655 3.158 0.1030
## CCN51 Abarco - TCS13 Abarco 0.06238 0.174 655 0.359 1.0000
## CCN51 Abarco - TCS19 Abarco -0.22205 0.174 655 -1.278 0.9947
## CCN51 Abarco - CCN51 Roble 0.47126 0.175 655 2.697 0.3087
## CCN51 Abarco - TCS01 Roble 0.38138 0.176 655 2.169 0.6868
## CCN51 Abarco - TCS06 Roble -0.00450 0.188 655 -0.024 1.0000
## CCN51 Abarco - TCS13 Roble 0.47489 0.174 655 2.733 0.2874
## CCN51 Abarco - TCS19 Roble 0.04327 0.175 655 0.248 1.0000
## CCN51 Abarco - CCN51 Terminalia -0.06029 0.174 655 -0.347 1.0000
## CCN51 Abarco - TCS01 Terminalia 0.15857 0.185 655 0.858 0.9999
## CCN51 Abarco - TCS06 Terminalia -0.18047 0.175 655 -1.033 0.9995
## CCN51 Abarco - TCS13 Terminalia 0.80312 0.179 655 4.493 0.0008
## CCN51 Abarco - TCS19 Terminalia 0.63053 0.174 655 3.628 0.0240
## TCS01 Abarco - TCS06 Abarco 0.07517 0.176 655 0.428 1.0000
## TCS01 Abarco - TCS13 Abarco -0.41722 0.174 655 -2.401 0.5149
## TCS01 Abarco - TCS19 Abarco -0.70165 0.174 655 -4.038 0.0053
## TCS01 Abarco - CCN51 Roble -0.00834 0.175 655 -0.048 1.0000
## TCS01 Abarco - TCS01 Roble -0.09822 0.176 655 -0.559 1.0000
## TCS01 Abarco - TCS06 Roble -0.48410 0.188 655 -2.575 0.3888
## TCS01 Abarco - TCS13 Roble -0.00471 0.174 655 -0.027 1.0000
## TCS01 Abarco - TCS19 Roble -0.43633 0.175 655 -2.497 0.4436
## TCS01 Abarco - CCN51 Terminalia -0.53989 0.174 655 -3.107 0.1184
## TCS01 Abarco - TCS01 Terminalia -0.32103 0.185 655 -1.736 0.9212
## TCS01 Abarco - TCS06 Terminalia -0.66006 0.175 655 -3.778 0.0142
## TCS01 Abarco - TCS13 Terminalia 0.32352 0.179 655 1.810 0.8937
## TCS01 Abarco - TCS19 Terminalia 0.15093 0.174 655 0.869 0.9999
## TCS06 Abarco - TCS13 Abarco -0.49239 0.176 655 -2.803 0.2476
## TCS06 Abarco - TCS19 Abarco -0.77682 0.176 655 -4.423 0.0011
## TCS06 Abarco - CCN51 Roble -0.08352 0.177 655 -0.473 1.0000
## TCS06 Abarco - TCS01 Roble -0.17340 0.178 655 -0.976 0.9997
## TCS06 Abarco - TCS06 Roble -0.55928 0.190 655 -2.947 0.1777
## TCS06 Abarco - TCS13 Roble -0.07989 0.176 655 -0.455 1.0000
## TCS06 Abarco - TCS19 Roble -0.51150 0.177 655 -2.897 0.2005
## TCS06 Abarco - CCN51 Terminalia -0.61507 0.176 655 -3.502 0.0367
## TCS06 Abarco - TCS01 Terminalia -0.39621 0.187 655 -2.122 0.7196
## TCS06 Abarco - TCS06 Terminalia -0.73524 0.177 655 -4.164 0.0032
## TCS06 Abarco - TCS13 Terminalia 0.24834 0.181 655 1.375 0.9892
## TCS06 Abarco - TCS19 Terminalia 0.07575 0.176 655 0.431 1.0000
## TCS13 Abarco - TCS19 Abarco -0.28443 0.174 655 -1.637 0.9500
## TCS13 Abarco - CCN51 Roble 0.40888 0.175 655 2.340 0.5606
## TCS13 Abarco - TCS01 Roble 0.31899 0.176 655 1.814 0.8918
## TCS13 Abarco - TCS06 Roble -0.06689 0.188 655 -0.356 1.0000
## TCS13 Abarco - TCS13 Roble 0.41251 0.174 655 2.374 0.5353
## TCS13 Abarco - TCS19 Roble -0.01911 0.175 655 -0.109 1.0000
## TCS13 Abarco - CCN51 Terminalia -0.12268 0.174 655 -0.706 1.0000
## TCS13 Abarco - TCS01 Terminalia 0.09618 0.185 655 0.520 1.0000
## TCS13 Abarco - TCS06 Terminalia -0.24285 0.175 655 -1.390 0.9880
## TCS13 Abarco - TCS13 Terminalia 0.74074 0.179 655 4.144 0.0035
## TCS13 Abarco - TCS19 Terminalia 0.56815 0.174 655 3.269 0.0751
## TCS19 Abarco - CCN51 Roble 0.69330 0.175 655 3.968 0.0070
## TCS19 Abarco - TCS01 Roble 0.60342 0.176 655 3.432 0.0458
## TCS19 Abarco - TCS06 Roble 0.21754 0.188 655 1.157 0.9981
## TCS19 Abarco - TCS13 Roble 0.69694 0.174 655 4.011 0.0059
## TCS19 Abarco - TCS19 Roble 0.26532 0.175 655 1.519 0.9733
## TCS19 Abarco - CCN51 Terminalia 0.16175 0.174 655 0.931 0.9998
## TCS19 Abarco - TCS01 Terminalia 0.38061 0.185 655 2.058 0.7620
## TCS19 Abarco - TCS06 Terminalia 0.04158 0.175 655 0.238 1.0000
## TCS19 Abarco - TCS13 Terminalia 1.02517 0.179 655 5.735 <.0001
## TCS19 Abarco - TCS19 Terminalia 0.85257 0.174 655 4.906 0.0001
## CCN51 Roble - TCS01 Roble -0.08988 0.177 655 -0.509 1.0000
## CCN51 Roble - TCS06 Roble -0.47576 0.189 655 -2.518 0.4286
## CCN51 Roble - TCS13 Roble 0.00363 0.175 655 0.021 1.0000
## CCN51 Roble - TCS19 Roble -0.42799 0.176 655 -2.437 0.4883
## CCN51 Roble - CCN51 Terminalia -0.53155 0.175 655 -3.042 0.1402
## CCN51 Roble - TCS01 Terminalia -0.31269 0.186 655 -1.683 0.9378
## CCN51 Roble - TCS06 Terminalia -0.65172 0.176 655 -3.711 0.0181
## CCN51 Roble - TCS13 Terminalia 0.33186 0.180 655 1.847 0.8778
## CCN51 Roble - TCS19 Terminalia 0.15927 0.175 655 0.912 0.9999
## TCS01 Roble - TCS06 Roble -0.38588 0.190 655 -2.031 0.7791
## TCS01 Roble - TCS13 Roble 0.09351 0.176 655 0.532 1.0000
## TCS01 Roble - TCS19 Roble -0.33811 0.177 655 -1.913 0.8460
## TCS01 Roble - CCN51 Terminalia -0.44167 0.176 655 -2.512 0.4330
## TCS01 Roble - TCS01 Terminalia -0.22281 0.187 655 -1.192 0.9974
## TCS01 Roble - TCS06 Terminalia -0.56184 0.177 655 -3.179 0.0972
## TCS01 Roble - TCS13 Terminalia 0.42174 0.181 655 2.334 0.5656
## TCS01 Roble - TCS19 Terminalia 0.24915 0.176 655 1.417 0.9857
## TCS06 Roble - TCS13 Roble 0.47939 0.188 655 2.550 0.4063
## TCS06 Roble - TCS19 Roble 0.04777 0.189 655 0.253 1.0000
## TCS06 Roble - CCN51 Terminalia -0.05579 0.188 655 -0.297 1.0000
## TCS06 Roble - TCS01 Terminalia 0.16307 0.198 655 0.823 1.0000
## TCS06 Roble - TCS06 Terminalia -0.17596 0.189 655 -0.931 0.9998
## TCS06 Roble - TCS13 Terminalia 0.80762 0.193 655 4.190 0.0029
## TCS06 Roble - TCS19 Terminalia 0.63503 0.188 655 3.378 0.0543
## TCS13 Roble - TCS19 Roble -0.43162 0.175 655 -2.470 0.4634
## TCS13 Roble - CCN51 Terminalia -0.53518 0.174 655 -3.080 0.1272
## TCS13 Roble - TCS01 Terminalia -0.31632 0.185 655 -1.711 0.9295
## TCS13 Roble - TCS06 Terminalia -0.65535 0.175 655 -3.751 0.0157
## TCS13 Roble - TCS13 Terminalia 0.32823 0.179 655 1.836 0.8826
## TCS13 Roble - TCS19 Terminalia 0.15564 0.174 655 0.896 0.9999
## TCS19 Roble - CCN51 Terminalia -0.10356 0.175 655 -0.593 1.0000
## TCS19 Roble - TCS01 Terminalia 0.11530 0.186 655 0.620 1.0000
## TCS19 Roble - TCS06 Terminalia -0.22374 0.176 655 -1.274 0.9949
## TCS19 Roble - TCS13 Terminalia 0.75985 0.180 655 4.229 0.0025
## TCS19 Roble - TCS19 Terminalia 0.58726 0.175 655 3.361 0.0571
## CCN51 Terminalia - TCS01 Terminalia 0.21886 0.185 655 1.184 0.9976
## CCN51 Terminalia - TCS06 Terminalia -0.12017 0.175 655 -0.688 1.0000
## CCN51 Terminalia - TCS13 Terminalia 0.86341 0.179 655 4.830 0.0002
## CCN51 Terminalia - TCS19 Terminalia 0.69082 0.174 655 3.975 0.0068
## TCS01 Terminalia - TCS06 Terminalia -0.33903 0.186 655 -1.825 0.8876
## TCS01 Terminalia - TCS13 Terminalia 0.64455 0.190 655 3.398 0.0510
## TCS01 Terminalia - TCS19 Terminalia 0.47196 0.185 655 2.552 0.4046
## TCS06 Terminalia - TCS13 Terminalia 0.98358 0.180 655 5.475 <.0001
## TCS06 Terminalia - TCS19 Terminalia 0.81099 0.175 655 4.642 0.0004
## TCS13 Terminalia - TCS19 Terminalia -0.17259 0.179 655 -0.965 0.9998
##
## Results are averaged over the levels of: bloque
## P value adjustment: tukey method for comparing a family of 15 estimates
cld_forestal_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_forestal_gen
## gen forestal emmean SE df lower.CL upper.CL .group
## TCS19 Abarco 4.68 0.123 655 4.44 4.93 A
## TCS06 Terminalia 4.64 0.124 655 4.40 4.89 AB
## CCN51 Terminalia 4.52 0.123 655 4.28 4.76 ABC
## TCS06 Roble 4.47 0.142 655 4.19 4.75 ABCDE
## CCN51 Abarco 4.46 0.123 655 4.22 4.70 ABCD
## TCS19 Roble 4.42 0.124 655 4.18 4.66 ABCDE
## TCS13 Abarco 4.40 0.123 655 4.16 4.64 ABCDE
## TCS01 Terminalia 4.30 0.138 655 4.03 4.58 ABCDEF
## TCS01 Roble 4.08 0.126 655 3.83 4.33 BCDEF
## CCN51 Roble 3.99 0.124 655 3.75 4.24 CDEF
## TCS13 Roble 3.99 0.123 655 3.75 4.23 CDEF
## TCS01 Abarco 3.98 0.123 655 3.74 4.22 CDEF
## TCS06 Abarco 3.91 0.126 655 3.66 4.15 DEF
## TCS19 Terminalia 3.83 0.123 655 3.59 4.07 EF
## TCS13 Terminalia 3.66 0.130 655 3.40 3.91 F
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 15 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 altura
#Gen
contrast <- emmeans(aov.alt, ~gen)
## NOTE: Results may be misleading due to involvement in interactions
plot(contrast, comparisons = TRUE, xlab ="Altura")
medias.gen <- emmeans(aov.alt, pairwise ~ gen)
## NOTE: Results may be misleading due to involvement in interactions
medias.gen
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CCN51 193 2.60 655 188 198
## TCS01 185 2.72 655 180 191
## TCS06 199 2.76 655 194 204
## TCS13 185 2.64 655 180 190
## TCS19 182 2.60 655 177 187
##
## Results are averaged over the levels of: forestal, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CCN51 - TCS01 7.897 3.77 655 2.097 0.2225
## CCN51 - TCS06 -5.810 3.79 655 -1.531 0.5423
## CCN51 - TCS13 7.826 3.71 655 2.111 0.2165
## CCN51 - TCS19 11.300 3.68 655 3.072 0.0188
## TCS01 - TCS06 -13.707 3.88 655 -3.536 0.0040
## TCS01 - TCS13 -0.071 3.79 655 -0.019 1.0000
## TCS01 - TCS19 3.403 3.77 655 0.904 0.8955
## TCS06 - TCS13 13.636 3.82 655 3.568 0.0035
## TCS06 - TCS19 17.110 3.79 655 4.510 0.0001
## TCS13 - TCS19 3.474 3.71 655 0.937 0.8824
##
## Results are averaged over the levels of: forestal, bloque
## P value adjustment: tukey method for comparing a family of 5 estimates
cld_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_gen
## gen emmean SE df lower.CL upper.CL .group
## TCS06 199 2.76 655 194 204 A
## CCN51 193 2.60 655 188 198 AB
## TCS13 185 2.64 655 180 190 BC
## TCS01 185 2.72 655 180 191 BC
## TCS19 182 2.60 655 177 187 C
##
## Results are averaged over the levels of: forestal, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 5 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.
#Forestal
contrast <- emmeans(aov.alt, ~forestal)
## NOTE: Results may be misleading due to involvement in interactions
plot(contrast, comparisons = TRUE, xlab ="Altura")
medias.forestal <- emmeans(aov.alt, pairwise ~ forestal)
## NOTE: Results may be misleading due to involvement in interactions
medias.forestal
## $emmeans
## forestal emmean SE df lower.CL upper.CL
## Abarco 192 2.02 655 188 196
## Roble 185 2.09 655 181 189
## Terminalia 189 2.09 655 185 193
##
## Results are averaged over the levels of: gen, bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Abarco - Roble 6.96 2.91 655 2.396 0.0444
## Abarco - Terminalia 3.15 2.90 655 1.084 0.5241
## Roble - Terminalia -3.81 2.95 655 -1.291 0.4006
##
## Results are averaged over the levels of: gen, bloque
## P value adjustment: tukey method for comparing a family of 3 estimates
cld_forestal <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_forestal
## forestal emmean SE df lower.CL upper.CL .group
## Abarco 192 2.02 655 188 196 A
## Terminalia 189 2.09 655 185 193 AB
## Roble 185 2.09 655 181 189 B
##
## Results are averaged over the levels of: gen, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 3 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.
#Forestal*Gen
contrast <- emmeans(aov.alt, ~gen*forestal)
## NOTE: Results may be misleading due to involvement in interactions
plot(contrast, comparisons = TRUE, xlab ="Altura")
medias.forestal.gen <- emmeans(aov.alt, pairwise ~ gen*forestal)
## NOTE: Results may be misleading due to involvement in interactions
medias.forestal.gen
## $emmeans
## gen forestal emmean SE df lower.CL upper.CL
## CCN51 Abarco 212 4.49 655 203 220
## TCS01 Abarco 185 4.49 655 176 193
## TCS06 Abarco 180 4.59 655 171 189
## TCS13 Abarco 194 4.49 655 185 203
## TCS19 Abarco 191 4.49 655 182 199
## CCN51 Roble 170 4.54 655 161 179
## TCS01 Roble 173 4.59 655 164 182
## TCS06 Roble 206 5.20 655 196 216
## TCS13 Roble 194 4.49 655 185 202
## TCS19 Roble 184 4.54 655 175 193
## CCN51 Terminalia 198 4.49 655 189 207
## TCS01 Terminalia 198 5.05 655 188 208
## TCS06 Terminalia 211 4.54 655 202 219
## TCS13 Terminalia 168 4.74 655 159 177
## TCS19 Terminalia 171 4.49 655 162 179
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CCN51 Abarco - TCS01 Abarco 26.93640 6.35 655 4.243 0.0023
## CCN51 Abarco - TCS06 Abarco 31.32756 6.42 655 4.882 0.0001
## CCN51 Abarco - TCS13 Abarco 17.33122 6.35 655 2.730 0.2891
## CCN51 Abarco - TCS19 Abarco 20.96155 6.35 655 3.302 0.0683
## CCN51 Abarco - CCN51 Roble 41.93194 6.38 655 6.569 <.0001
## CCN51 Abarco - TCS01 Roble 38.67897 6.42 655 6.022 <.0001
## CCN51 Abarco - TCS06 Roble 5.53475 6.87 655 0.806 1.0000
## CCN51 Abarco - TCS13 Roble 17.89140 6.35 655 2.818 0.2396
## CCN51 Abarco - TCS19 Roble 27.32503 6.38 655 4.281 0.0020
## CCN51 Abarco - CCN51 Terminalia 13.38179 6.35 655 2.108 0.7294
## CCN51 Abarco - TCS01 Terminalia 13.38954 6.76 655 1.982 0.8085
## CCN51 Abarco - TCS06 Terminalia 1.02082 6.38 655 0.160 1.0000
## CCN51 Abarco - TCS13 Terminalia 43.56916 6.53 655 6.671 <.0001
## CCN51 Abarco - TCS19 Terminalia 40.92743 6.35 655 6.447 <.0001
## TCS01 Abarco - TCS06 Abarco 4.39116 6.42 655 0.684 1.0000
## TCS01 Abarco - TCS13 Abarco -9.60518 6.35 655 -1.513 0.9741
## TCS01 Abarco - TCS19 Abarco -5.97485 6.35 655 -0.941 0.9998
## TCS01 Abarco - CCN51 Roble 14.99554 6.38 655 2.349 0.5537
## TCS01 Abarco - TCS01 Roble 11.74257 6.42 655 1.828 0.8860
## TCS01 Abarco - TCS06 Roble -21.40165 6.87 655 -3.116 0.1156
## TCS01 Abarco - TCS13 Roble -9.04500 6.35 655 -1.425 0.9849
## TCS01 Abarco - TCS19 Roble 0.38863 6.38 655 0.061 1.0000
## TCS01 Abarco - CCN51 Terminalia -13.55461 6.35 655 -2.135 0.7108
## TCS01 Abarco - TCS01 Terminalia -13.54686 6.76 655 -2.005 0.7948
## TCS01 Abarco - TCS06 Terminalia -25.91558 6.38 655 -4.060 0.0049
## TCS01 Abarco - TCS13 Terminalia 16.63276 6.53 655 2.547 0.4084
## TCS01 Abarco - TCS19 Terminalia 13.99102 6.35 655 2.204 0.6620
## TCS06 Abarco - TCS13 Abarco -13.99634 6.42 655 -2.181 0.6785
## TCS06 Abarco - TCS19 Abarco -10.36601 6.42 655 -1.615 0.9551
## TCS06 Abarco - CCN51 Roble 10.60438 6.45 655 1.644 0.9483
## TCS06 Abarco - TCS01 Roble 7.35141 6.49 655 1.133 0.9985
## TCS06 Abarco - TCS06 Roble -25.79280 6.93 655 -3.721 0.0174
## TCS06 Abarco - TCS13 Roble -13.43615 6.42 655 -2.094 0.7389
## TCS06 Abarco - TCS19 Roble -4.00253 6.45 655 -0.620 1.0000
## TCS06 Abarco - CCN51 Terminalia -17.94577 6.42 655 -2.796 0.2512
## TCS06 Abarco - TCS01 Terminalia -17.93802 6.82 655 -2.630 0.3516
## TCS06 Abarco - TCS06 Terminalia -30.30674 6.45 655 -4.698 0.0003
## TCS06 Abarco - TCS13 Terminalia 12.24161 6.60 655 1.855 0.8740
## TCS06 Abarco - TCS19 Terminalia 9.59987 6.42 655 1.496 0.9766
## TCS13 Abarco - TCS19 Abarco 3.63033 6.35 655 0.572 1.0000
## TCS13 Abarco - CCN51 Roble 24.60072 6.38 655 3.854 0.0108
## TCS13 Abarco - TCS01 Roble 21.34775 6.42 655 3.324 0.0640
## TCS13 Abarco - TCS06 Roble -11.79646 6.87 655 -1.717 0.9274
## TCS13 Abarco - TCS13 Roble 0.56019 6.35 655 0.088 1.0000
## TCS13 Abarco - TCS19 Roble 9.99381 6.38 655 1.566 0.9653
## TCS13 Abarco - CCN51 Terminalia -3.94943 6.35 655 -0.622 1.0000
## TCS13 Abarco - TCS01 Terminalia -3.94168 6.76 655 -0.583 1.0000
## TCS13 Abarco - TCS06 Terminalia -16.31040 6.38 655 -2.555 0.4024
## TCS13 Abarco - TCS13 Terminalia 26.23795 6.53 655 4.018 0.0058
## TCS13 Abarco - TCS19 Terminalia 23.59621 6.35 655 3.717 0.0177
## TCS19 Abarco - CCN51 Roble 20.97039 6.38 655 3.285 0.0717
## TCS19 Abarco - TCS01 Roble 17.71742 6.42 655 2.758 0.2724
## TCS19 Abarco - TCS06 Roble -15.42680 6.87 655 -2.246 0.6312
## TCS19 Abarco - TCS13 Roble -3.07015 6.35 655 -0.484 1.0000
## TCS19 Abarco - TCS19 Roble 6.36348 6.38 655 0.997 0.9996
## TCS19 Abarco - CCN51 Terminalia -7.57976 6.35 655 -1.194 0.9974
## TCS19 Abarco - TCS01 Terminalia -7.57201 6.76 655 -1.121 0.9987
## TCS19 Abarco - TCS06 Terminalia -19.94073 6.38 655 -3.124 0.1131
## TCS19 Abarco - TCS13 Terminalia 22.60761 6.53 655 3.462 0.0417
## TCS19 Abarco - TCS19 Terminalia 19.96587 6.35 655 3.145 0.1068
## CCN51 Roble - TCS01 Roble -3.25297 6.46 655 -0.504 1.0000
## CCN51 Roble - TCS06 Roble -36.39719 6.90 655 -5.273 <.0001
## CCN51 Roble - TCS13 Roble -24.04054 6.38 655 -3.766 0.0148
## CCN51 Roble - TCS19 Roble -14.60691 6.42 655 -2.276 0.6087
## CCN51 Roble - CCN51 Terminalia -28.55015 6.38 655 -4.473 0.0009
## CCN51 Roble - TCS01 Terminalia -28.54240 6.79 655 -4.204 0.0027
## CCN51 Roble - TCS06 Terminalia -40.91112 6.42 655 -6.376 <.0001
## CCN51 Roble - TCS13 Terminalia 1.63722 6.56 655 0.249 1.0000
## CCN51 Roble - TCS19 Terminalia -1.00452 6.38 655 -0.157 1.0000
## TCS01 Roble - TCS06 Roble -33.14422 6.94 655 -4.775 0.0002
## TCS01 Roble - TCS13 Roble -20.78757 6.42 655 -3.236 0.0827
## TCS01 Roble - TCS19 Roble -11.35394 6.46 655 -1.759 0.9135
## TCS01 Roble - CCN51 Terminalia -25.29718 6.42 655 -3.939 0.0078
## TCS01 Roble - TCS01 Terminalia -25.28943 6.83 655 -3.704 0.0185
## TCS01 Roble - TCS06 Terminalia -37.65815 6.46 655 -5.833 <.0001
## TCS01 Roble - TCS13 Terminalia 4.89019 6.60 655 0.741 1.0000
## TCS01 Roble - TCS19 Terminalia 2.24845 6.42 655 0.350 1.0000
## TCS06 Roble - TCS13 Roble 12.35665 6.87 655 1.799 0.8982
## TCS06 Roble - TCS19 Roble 21.79027 6.90 655 3.157 0.1033
## TCS06 Roble - CCN51 Terminalia 7.84703 6.87 655 1.142 0.9984
## TCS06 Roble - TCS01 Terminalia 7.85479 7.24 655 1.085 0.9991
## TCS06 Roble - TCS06 Terminalia -4.51393 6.90 655 -0.654 1.0000
## TCS06 Roble - TCS13 Terminalia 38.03441 7.04 655 5.401 <.0001
## TCS06 Roble - TCS19 Terminalia 35.39267 6.87 655 5.153 <.0001
## TCS13 Roble - TCS19 Roble 9.43362 6.38 655 1.478 0.9790
## TCS13 Roble - CCN51 Terminalia -4.50962 6.35 655 -0.710 1.0000
## TCS13 Roble - TCS01 Terminalia -4.50186 6.76 655 -0.666 1.0000
## TCS13 Roble - TCS06 Terminalia -16.87058 6.38 655 -2.643 0.3431
## TCS13 Roble - TCS13 Terminalia 25.67776 6.53 655 3.932 0.0080
## TCS13 Roble - TCS19 Terminalia 23.03602 6.35 655 3.628 0.0240
## TCS19 Roble - CCN51 Terminalia -13.94324 6.38 655 -2.184 0.6760
## TCS19 Roble - TCS01 Terminalia -13.93549 6.79 655 -2.053 0.7656
## TCS19 Roble - TCS06 Terminalia -26.30421 6.42 655 -4.099 0.0042
## TCS19 Roble - TCS13 Terminalia 16.24414 6.56 655 2.475 0.4602
## TCS19 Roble - TCS19 Terminalia 13.60240 6.38 655 2.131 0.7136
## CCN51 Terminalia - TCS01 Terminalia 0.00775 6.76 655 0.001 1.0000
## CCN51 Terminalia - TCS06 Terminalia -12.36097 6.38 655 -1.937 0.8338
## CCN51 Terminalia - TCS13 Terminalia 30.18738 6.53 655 4.622 0.0004
## CCN51 Terminalia - TCS19 Terminalia 27.54564 6.35 655 4.339 0.0016
## TCS01 Terminalia - TCS06 Terminalia -12.36872 6.79 655 -1.822 0.8887
## TCS01 Terminalia - TCS13 Terminalia 30.17962 6.93 655 4.355 0.0015
## TCS01 Terminalia - TCS19 Terminalia 27.53788 6.76 655 4.076 0.0046
## TCS06 Terminalia - TCS13 Terminalia 42.54834 6.56 655 6.482 <.0001
## TCS06 Terminalia - TCS19 Terminalia 39.90660 6.38 655 6.252 <.0001
## TCS13 Terminalia - TCS19 Terminalia -2.64174 6.53 655 -0.404 1.0000
##
## Results are averaged over the levels of: bloque
## P value adjustment: tukey method for comparing a family of 15 estimates
cld_forestal_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_forestal_gen
## gen forestal emmean SE df lower.CL upper.CL .group
## CCN51 Abarco 212 4.49 655 203 220 A
## TCS06 Terminalia 211 4.54 655 202 219 A
## TCS06 Roble 206 5.20 655 196 216 AB
## CCN51 Terminalia 198 4.49 655 189 207 ABC
## TCS01 Terminalia 198 5.05 655 188 208 ABC
## TCS13 Abarco 194 4.49 655 185 203 ABCD
## TCS13 Roble 194 4.49 655 185 202 ABCD
## TCS19 Abarco 191 4.49 655 182 199 ABCDE
## TCS01 Abarco 185 4.49 655 176 193 BCDEF
## TCS19 Roble 184 4.54 655 175 193 BCDEF
## TCS06 Abarco 180 4.59 655 171 189 CDEF
## TCS01 Roble 173 4.59 655 164 182 DEF
## TCS19 Terminalia 171 4.49 655 162 179 EF
## CCN51 Roble 170 4.54 655 161 179 EF
## TCS13 Terminalia 168 4.74 655 159 177 F
##
## Results are averaged over the levels of: bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 15 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(datos4)