setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura")
datos2<-read.table("paez.csv", header=T, sep=',')
datos2$gen<-as.factor(datos2$gen)
datos2$forestal<-as.factor(datos2$forestal)
datos2$bloque<-as.factor(datos2$bloque)
attach(datos2)
library(ggplot2)
library(emmeans)
#Gráfica diámetro
ggplot(datos2, 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 275 rows containing non-finite values (stat_smooth).
# Gráfica altura
ggplot(datos2, 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 275 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=datos2, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos2, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos2, 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 34 2325.655 2483.765 -1128.828
## fit.ar1.diam 2 34 2313.326 2471.436 -1122.663
## fit.ar1het.diam 3 37 2312.321 2484.381 -1119.160 2 vs 3 7.005676 0.0717
anova(fit.ar1.diam)
## Denom. DF: 773
## numDF F-value p-value
## (Intercept) 1 9933.582 <.0001
## semana 1 219.131 <.0001
## forestal 2 14.401 <.0001
## gen 4 1.644 0.1612
## bloque 2 33.825 <.0001
## semana:forestal 2 5.243 0.0055
## semana:gen 4 0.284 0.8886
## forestal:gen 8 2.082 0.0352
## semana:forestal:gen 8 1.055 0.3929
anova(fit.ar1het.diam)
## Denom. DF: 773
## numDF F-value p-value
## (Intercept) 1 10072.937 <.0001
## semana 1 258.211 <.0001
## forestal 2 12.715 <.0001
## gen 4 1.532 0.1911
## bloque 2 35.498 <.0001
## semana:forestal 2 5.858 0.0030
## semana:gen 4 0.302 0.8768
## forestal:gen 8 1.939 0.0515
## semana:forestal:gen 8 1.092 0.3662
# Análisis para altura
fit.compsym.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos2, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos2, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos2, 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 34 7958.484 8116.593 -3945.242
## fit.ar1.alt 2 34 7941.956 8100.066 -3936.978
## fit.ar1het.alt 3 37 7878.555 8050.615 -3902.277 2 vs 3 69.40135 <.0001
anova(fit.ar1het.alt)
## Denom. DF: 773
## numDF F-value p-value
## (Intercept) 1 7690.972 <.0001
## semana 1 542.437 <.0001
## forestal 2 30.987 <.0001
## gen 4 6.351 <.0001
## bloque 2 23.493 <.0001
## semana:forestal 2 11.574 <.0001
## semana:gen 4 4.154 0.0025
## forestal:gen 8 2.108 0.0329
## semana:forestal:gen 8 1.871 0.0615
#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 a TCS01
## TCS06 a TCS06
## TCS13 a 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 a Abarco
## Roble ab Roble
## Terminalia b 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 ac Abarco:CCN51
## Abarco:TCS01 ac Abarco:TCS01
## Abarco:TCS06 ac Abarco:TCS06
## Abarco:TCS13 ac Abarco:TCS13
## Abarco:TCS19 ac Abarco:TCS19
## Roble:CCN51 ac Roble:CCN51
## Roble:TCS01 abc Roble:TCS01
## Roble:TCS06 ab Roble:TCS06
## Roble:TCS13 b Roble:TCS13
## Roble:TCS19 ab Roble:TCS19
## Terminalia:CCN51 ac Terminalia:CCN51
## Terminalia:TCS01 ac Terminalia:TCS01
## Terminalia:TCS06 c Terminalia:TCS06
## Terminalia:TCS13 ac Terminalia:TCS13
## Terminalia:TCS19 c 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 c TCS01
## TCS06 ab TCS06
## TCS13 b TCS13
## TCS19 ab 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 a Abarco
## Roble a 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 cde Abarco:CCN51
## Abarco:TCS01 de Abarco:TCS01
## Abarco:TCS06 cd Abarco:TCS06
## Abarco:TCS13 cd Abarco:TCS13
## Abarco:TCS19 acd Abarco:TCS19
## Roble:CCN51 acd Roble:CCN51
## Roble:TCS01 acd Roble:TCS01
## Roble:TCS06 ab Roble:TCS06
## Roble:TCS13 b Roble:TCS13
## Roble:TCS19 ac Roble:TCS19
## Terminalia:CCN51 de Terminalia:CCN51
## Terminalia:TCS01 e Terminalia:TCS01
## Terminalia:TCS06 de Terminalia:TCS06
## Terminalia:TCS13 de Terminalia:TCS13
## Terminalia:TCS19 acde 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 3.63 0.0715 773 3.49 3.77
## TCS01 3.72 0.0790 773 3.56 3.87
## TCS06 3.62 0.0718 773 3.48 3.76
## TCS13 3.43 0.0739 773 3.29 3.58
## TCS19 3.56 0.0763 773 3.41 3.71
##
## 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 -0.0849 0.107 773 -0.791 0.9332
## CCN51 - TCS06 0.0175 0.101 773 0.173 0.9998
## CCN51 - TCS13 0.1993 0.103 773 1.938 0.2983
## CCN51 - TCS19 0.0740 0.104 773 0.713 0.9535
## TCS01 - TCS06 0.1024 0.107 773 0.957 0.8742
## TCS01 - TCS13 0.2842 0.108 773 2.628 0.0664
## TCS01 - TCS19 0.1590 0.110 773 1.440 0.6020
## TCS06 - TCS13 0.1818 0.103 773 1.763 0.3960
## TCS06 - TCS19 0.0566 0.105 773 0.541 0.9831
## TCS13 - TCS19 -0.1252 0.106 773 -1.180 0.7632
##
## 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
## TCS01 3.72 0.0790 773 3.56 3.87 A
## CCN51 3.63 0.0715 773 3.49 3.77 A
## TCS06 3.62 0.0718 773 3.48 3.76 A
## TCS19 3.56 0.0763 773 3.41 3.71 A
## TCS13 3.43 0.0739 773 3.29 3.58 A
##
## 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 3.60 0.0531 773 3.50 3.71
## Roble 3.36 0.0625 773 3.24 3.49
## Terminalia 3.81 0.0575 773 3.70 3.92
##
## 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 0.241 0.0825 773 2.920 0.0101
## Abarco - Terminalia -0.205 0.0778 773 -2.637 0.0232
## Roble - Terminalia -0.446 0.0854 773 -5.225 <.0001
##
## 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
## Terminalia 3.81 0.0575 773 3.70 3.92 A
## Abarco 3.60 0.0531 773 3.50 3.71 B
## Roble 3.36 0.0625 773 3.24 3.49 C
##
## 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 3.50 0.129 773 3.25 3.75
## TCS01 Abarco 3.78 0.114 773 3.56 4.00
## TCS06 Abarco 3.65 0.113 773 3.42 3.87
## TCS13 Abarco 3.60 0.120 773 3.36 3.84
## TCS19 Abarco 3.50 0.114 773 3.27 3.72
## CCN51 Roble 3.72 0.115 773 3.49 3.94
## TCS01 Roble 3.56 0.169 773 3.23 3.89
## TCS06 Roble 3.27 0.138 773 3.00 3.54
## TCS13 Roble 2.99 0.147 773 2.70 3.28
## TCS19 Roble 3.29 0.124 773 3.05 3.53
## CCN51 Terminalia 3.69 0.126 773 3.44 3.93
## TCS01 Terminalia 3.82 0.120 773 3.58 4.06
## TCS06 Terminalia 3.93 0.122 773 3.70 4.17
## TCS13 Terminalia 3.72 0.115 773 3.49 3.94
## TCS19 Terminalia 3.90 0.154 773 3.59 4.20
##
## 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 -2.80e-01 0.172 773 -1.627 0.9525
## CCN51 Abarco - TCS06 Abarco -1.45e-01 0.171 773 -0.848 0.9999
## CCN51 Abarco - TCS13 Abarco -1.01e-01 0.176 773 -0.573 1.0000
## CCN51 Abarco - TCS19 Abarco 2.94e-03 0.173 773 0.017 1.0000
## CCN51 Abarco - CCN51 Roble -2.16e-01 0.173 773 -1.249 0.9959
## CCN51 Abarco - TCS01 Roble -5.78e-02 0.215 773 -0.269 1.0000
## CCN51 Abarco - TCS06 Roble 2.30e-01 0.189 773 1.216 0.9968
## CCN51 Abarco - TCS13 Roble 5.13e-01 0.197 773 2.607 0.3667
## CCN51 Abarco - TCS19 Roble 2.12e-01 0.178 773 1.189 0.9975
## CCN51 Abarco - CCN51 Terminalia -1.86e-01 0.179 773 -1.038 0.9994
## CCN51 Abarco - TCS01 Terminalia -3.19e-01 0.177 773 -1.796 0.8993
## CCN51 Abarco - TCS06 Terminalia -4.34e-01 0.177 773 -2.449 0.4789
## CCN51 Abarco - TCS13 Terminalia -2.16e-01 0.173 773 -1.248 0.9959
## CCN51 Abarco - TCS19 Terminalia -3.95e-01 0.200 773 -1.975 0.8129
## TCS01 Abarco - TCS06 Abarco 1.35e-01 0.160 773 0.841 1.0000
## TCS01 Abarco - TCS13 Abarco 1.79e-01 0.166 773 1.083 0.9991
## TCS01 Abarco - TCS19 Abarco 2.83e-01 0.161 773 1.754 0.9153
## TCS01 Abarco - CCN51 Roble 6.39e-02 0.162 773 0.395 1.0000
## TCS01 Abarco - TCS01 Roble 2.22e-01 0.205 773 1.087 0.9991
## TCS01 Abarco - TCS06 Roble 5.10e-01 0.179 773 2.849 0.2231
## TCS01 Abarco - TCS13 Roble 7.93e-01 0.186 773 4.256 0.0022
## TCS01 Abarco - TCS19 Roble 4.92e-01 0.168 773 2.926 0.1865
## TCS01 Abarco - CCN51 Terminalia 9.45e-02 0.169 773 0.558 1.0000
## TCS01 Abarco - TCS01 Terminalia -3.85e-02 0.166 773 -0.232 1.0000
## TCS01 Abarco - TCS06 Terminalia -1.54e-01 0.167 773 -0.924 0.9999
## TCS01 Abarco - TCS13 Terminalia 6.38e-02 0.162 773 0.393 1.0000
## TCS01 Abarco - TCS19 Terminalia -1.15e-01 0.192 773 -0.598 1.0000
## TCS06 Abarco - TCS13 Abarco 4.47e-02 0.165 773 0.271 1.0000
## TCS06 Abarco - TCS19 Abarco 1.48e-01 0.160 773 0.925 0.9999
## TCS06 Abarco - CCN51 Roble -7.09e-02 0.161 773 -0.441 1.0000
## TCS06 Abarco - TCS01 Roble 8.75e-02 0.203 773 0.430 1.0000
## TCS06 Abarco - TCS06 Roble 3.75e-01 0.178 773 2.106 0.7307
## TCS06 Abarco - TCS13 Roble 6.58e-01 0.185 773 3.556 0.0304
## TCS06 Abarco - TCS19 Roble 3.57e-01 0.167 773 2.136 0.7101
## TCS06 Abarco - CCN51 Terminalia -4.03e-02 0.169 773 -0.239 1.0000
## TCS06 Abarco - TCS01 Terminalia -1.73e-01 0.165 773 -1.052 0.9993
## TCS06 Abarco - TCS06 Terminalia -2.89e-01 0.166 773 -1.743 0.9189
## TCS06 Abarco - TCS13 Terminalia -7.10e-02 0.161 773 -0.441 1.0000
## TCS06 Abarco - TCS19 Terminalia -2.49e-01 0.191 773 -1.307 0.9934
## TCS13 Abarco - TCS19 Abarco 1.04e-01 0.166 773 0.625 1.0000
## TCS13 Abarco - CCN51 Roble -1.16e-01 0.166 773 -0.694 1.0000
## TCS13 Abarco - TCS01 Roble 4.28e-02 0.209 773 0.205 1.0000
## TCS13 Abarco - TCS06 Roble 3.31e-01 0.183 773 1.806 0.8953
## TCS13 Abarco - TCS13 Roble 6.14e-01 0.191 773 3.221 0.0861
## TCS13 Abarco - TCS19 Roble 3.13e-01 0.172 773 1.816 0.8913
## TCS13 Abarco - CCN51 Terminalia -8.50e-02 0.173 773 -0.491 1.0000
## TCS13 Abarco - TCS01 Terminalia -2.18e-01 0.171 773 -1.277 0.9948
## TCS13 Abarco - TCS06 Terminalia -3.33e-01 0.171 773 -1.951 0.8262
## TCS13 Abarco - TCS13 Terminalia -1.16e-01 0.167 773 -0.694 1.0000
## TCS13 Abarco - TCS19 Terminalia -2.94e-01 0.195 773 -1.509 0.9748
## TCS19 Abarco - CCN51 Roble -2.19e-01 0.162 773 -1.355 0.9907
## TCS19 Abarco - TCS01 Roble -6.08e-02 0.204 773 -0.298 1.0000
## TCS19 Abarco - TCS06 Roble 2.27e-01 0.179 773 1.267 0.9952
## TCS19 Abarco - TCS13 Roble 5.10e-01 0.186 773 2.741 0.2821
## TCS19 Abarco - TCS19 Roble 2.09e-01 0.168 773 1.242 0.9961
## TCS19 Abarco - CCN51 Terminalia -1.89e-01 0.170 773 -1.112 0.9988
## TCS19 Abarco - TCS01 Terminalia -3.22e-01 0.166 773 -1.940 0.8323
## TCS19 Abarco - TCS06 Terminalia -4.37e-01 0.167 773 -2.621 0.3573
## TCS19 Abarco - TCS13 Terminalia -2.19e-01 0.162 773 -1.353 0.9908
## TCS19 Abarco - TCS19 Terminalia -3.98e-01 0.192 773 -2.074 0.7522
## CCN51 Roble - TCS01 Roble 1.58e-01 0.204 773 0.776 1.0000
## CCN51 Roble - TCS06 Roble 4.46e-01 0.179 773 2.486 0.4517
## CCN51 Roble - TCS13 Roble 7.29e-01 0.186 773 3.913 0.0085
## CCN51 Roble - TCS19 Roble 4.28e-01 0.169 773 2.536 0.4156
## CCN51 Roble - CCN51 Terminalia 3.06e-02 0.170 773 0.180 1.0000
## CCN51 Roble - TCS01 Terminalia -1.02e-01 0.166 773 -0.616 1.0000
## CCN51 Roble - TCS06 Terminalia -2.18e-01 0.167 773 -1.303 0.9936
## CCN51 Roble - TCS13 Terminalia -9.73e-05 0.162 773 -0.001 1.0000
## CCN51 Roble - TCS19 Terminalia -1.79e-01 0.192 773 -0.927 0.9998
## TCS01 Roble - TCS06 Roble 2.88e-01 0.219 773 1.317 0.9929
## TCS01 Roble - TCS13 Roble 5.71e-01 0.223 773 2.556 0.4018
## TCS01 Roble - TCS19 Roble 2.70e-01 0.211 773 1.282 0.9946
## TCS01 Roble - CCN51 Terminalia -1.28e-01 0.212 773 -0.602 1.0000
## TCS01 Roble - TCS01 Terminalia -2.61e-01 0.207 773 -1.258 0.9955
## TCS01 Roble - TCS06 Terminalia -3.76e-01 0.209 773 -1.802 0.8972
## TCS01 Roble - TCS13 Terminalia -1.59e-01 0.205 773 -0.775 1.0000
## TCS01 Roble - TCS19 Terminalia -3.37e-01 0.231 773 -1.460 0.9812
## TCS06 Roble - TCS13 Roble 2.83e-01 0.202 773 1.401 0.9872
## TCS06 Roble - TCS19 Roble -1.79e-02 0.185 773 -0.097 1.0000
## TCS06 Roble - CCN51 Terminalia -4.16e-01 0.187 773 -2.228 0.6444
## TCS06 Roble - TCS01 Terminalia -5.49e-01 0.184 773 -2.986 0.1613
## TCS06 Roble - TCS06 Terminalia -6.64e-01 0.184 773 -3.608 0.0255
## TCS06 Roble - TCS13 Terminalia -4.46e-01 0.180 773 -2.483 0.4543
## TCS06 Roble - TCS19 Terminalia -6.25e-01 0.207 773 -3.013 0.1506
## TCS13 Roble - TCS19 Roble -3.01e-01 0.193 773 -1.562 0.9661
## TCS13 Roble - CCN51 Terminalia -6.99e-01 0.194 773 -3.603 0.0260
## TCS13 Roble - TCS01 Terminalia -8.32e-01 0.190 773 -4.388 0.0012
## TCS13 Roble - TCS06 Terminalia -9.47e-01 0.191 773 -4.961 0.0001
## TCS13 Roble - TCS13 Terminalia -7.29e-01 0.187 773 -3.908 0.0087
## TCS13 Roble - TCS19 Terminalia -9.08e-01 0.214 773 -4.251 0.0022
## TCS19 Roble - CCN51 Terminalia -3.98e-01 0.176 773 -2.263 0.6184
## TCS19 Roble - TCS01 Terminalia -5.31e-01 0.173 773 -3.065 0.1316
## TCS19 Roble - TCS06 Terminalia -6.46e-01 0.173 773 -3.726 0.0169
## TCS19 Roble - TCS13 Terminalia -4.28e-01 0.169 773 -2.533 0.4176
## TCS19 Roble - TCS19 Terminalia -6.07e-01 0.197 773 -3.075 0.1283
## CCN51 Terminalia - TCS01 Terminalia -1.33e-01 0.174 773 -0.763 1.0000
## CCN51 Terminalia - TCS06 Terminalia -2.49e-01 0.175 773 -1.424 0.9851
## CCN51 Terminalia - TCS13 Terminalia -3.07e-02 0.170 773 -0.180 1.0000
## CCN51 Terminalia - TCS19 Terminalia -2.09e-01 0.198 773 -1.058 0.9993
## TCS01 Terminalia - TCS06 Terminalia -1.15e-01 0.171 773 -0.675 1.0000
## TCS01 Terminalia - TCS13 Terminalia 1.02e-01 0.166 773 0.614 1.0000
## TCS01 Terminalia - TCS19 Terminalia -7.61e-02 0.196 773 -0.389 1.0000
## TCS06 Terminalia - TCS13 Terminalia 2.18e-01 0.167 773 1.301 0.9938
## TCS06 Terminalia - TCS19 Terminalia 3.94e-02 0.196 773 0.201 1.0000
## TCS13 Terminalia - TCS19 Terminalia -1.78e-01 0.193 773 -0.927 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
## TCS06 Terminalia 3.93 0.122 773 3.70 4.17 A
## TCS19 Terminalia 3.90 0.154 773 3.59 4.20 AB
## TCS01 Terminalia 3.82 0.120 773 3.58 4.06 AB
## TCS01 Abarco 3.78 0.114 773 3.56 4.00 AB
## TCS13 Terminalia 3.72 0.115 773 3.49 3.94 AB
## CCN51 Roble 3.72 0.115 773 3.49 3.94 AB
## CCN51 Terminalia 3.69 0.126 773 3.44 3.93 AB
## TCS06 Abarco 3.65 0.113 773 3.42 3.87 AB
## TCS13 Abarco 3.60 0.120 773 3.36 3.84 ABC
## TCS01 Roble 3.56 0.169 773 3.23 3.89 ABC
## CCN51 Abarco 3.50 0.129 773 3.25 3.75 ABC
## TCS19 Abarco 3.50 0.114 773 3.27 3.72 ABC
## TCS19 Roble 3.29 0.124 773 3.05 3.53 BC
## TCS06 Roble 3.27 0.138 773 3.00 3.54 BC
## TCS13 Roble 2.99 0.147 773 2.70 3.28 C
##
## 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 123 2.73 773 118 128
## TCS01 131 3.02 773 125 136
## TCS06 117 2.75 773 112 123
## TCS13 110 2.83 773 105 116
## TCS19 111 2.92 773 105 117
##
## 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 -7.59 4.10 773 -1.851 0.3453
## CCN51 - TCS06 5.68 3.87 773 1.469 0.5827
## CCN51 - TCS13 12.90 3.93 773 3.282 0.0095
## CCN51 - TCS19 11.81 3.97 773 2.977 0.0249
## TCS01 - TCS06 13.28 4.09 773 3.247 0.0107
## TCS01 - TCS13 20.50 4.13 773 4.958 <.0001
## TCS01 - TCS19 19.40 4.22 773 4.599 <.0001
## TCS06 - TCS13 7.22 3.94 773 1.831 0.3561
## TCS06 - TCS19 6.13 4.00 773 1.531 0.5424
## TCS13 - TCS19 -1.09 4.06 773 -0.269 0.9989
##
## 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
## TCS01 131 3.02 773 125 136 A
## CCN51 123 2.73 773 118 128 AB
## TCS06 117 2.75 773 112 123 BC
## TCS19 111 2.92 773 105 117 C
## TCS13 110 2.83 773 105 116 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 123 2.03 773 118.8 127
## Roble 104 2.39 773 98.9 108
## Terminalia 129 2.20 773 124.6 133
##
## 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 19.15 3.15 773 6.075 <.0001
## Abarco - Terminalia -6.14 2.98 773 -2.065 0.0979
## Roble - Terminalia -25.30 3.26 773 -7.752 <.0001
##
## 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
## Terminalia 129 2.20 773 124.6 133 A
## Abarco 123 2.03 773 118.8 127 A
## Roble 104 2.39 773 98.9 108 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")
## Warning: Comparison discrepancy in group "1", TCS13 Abarco - TCS01 Terminalia:
## Target overlap = 1e-04, overlap on graph = -0.0011
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 123.6 4.95 773 113.9 133.3
## TCS01 Abarco 133.3 4.36 773 124.8 141.9
## TCS06 Abarco 120.8 4.30 773 112.3 129.2
## TCS13 Abarco 120.2 4.60 773 111.2 129.3
## TCS19 Abarco 115.8 4.36 773 107.2 124.4
## CCN51 Roble 116.2 4.39 773 107.6 124.8
## TCS01 Roble 115.9 6.47 773 103.2 128.6
## TCS06 Roble 97.4 5.27 773 87.0 107.7
## TCS13 Roble 83.7 5.62 773 72.7 94.8
## TCS19 Roble 104.7 4.73 773 95.4 114.0
## CCN51 Terminalia 129.1 4.80 773 119.7 138.5
## TCS01 Terminalia 142.4 4.61 773 133.4 151.5
## TCS06 Terminalia 133.7 4.65 773 124.6 142.8
## TCS13 Terminalia 126.2 4.40 773 117.6 134.9
## TCS19 Terminalia 113.0 5.90 773 101.4 124.5
##
## 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 -9.743 6.58 773 -1.480 0.9788
## CCN51 Abarco - TCS06 Abarco 2.814 6.55 773 0.429 1.0000
## CCN51 Abarco - TCS13 Abarco 3.348 6.72 773 0.498 1.0000
## CCN51 Abarco - TCS19 Abarco 7.791 6.60 773 1.181 0.9977
## CCN51 Abarco - CCN51 Roble 7.388 6.62 773 1.116 0.9987
## CCN51 Abarco - TCS01 Roble 7.659 8.23 773 0.931 0.9998
## CCN51 Abarco - TCS06 Roble 26.212 7.23 773 3.628 0.0239
## CCN51 Abarco - TCS13 Roble 39.858 7.52 773 5.300 <.0001
## CCN51 Abarco - TCS19 Roble 18.859 6.81 773 2.767 0.2670
## CCN51 Abarco - CCN51 Terminalia -5.532 6.84 773 -0.809 1.0000
## CCN51 Abarco - TCS01 Terminalia -18.841 6.78 773 -2.779 0.2606
## CCN51 Abarco - TCS06 Terminalia -10.121 6.78 773 -1.494 0.9770
## CCN51 Abarco - TCS13 Terminalia -2.645 6.63 773 -0.399 1.0000
## CCN51 Abarco - TCS19 Terminalia 10.637 7.64 773 1.392 0.9879
## TCS01 Abarco - TCS06 Abarco 12.557 6.13 773 2.049 0.7677
## TCS01 Abarco - TCS13 Abarco 13.090 6.33 773 2.067 0.7565
## TCS01 Abarco - TCS19 Abarco 17.534 6.17 773 2.842 0.2271
## TCS01 Abarco - CCN51 Roble 17.130 6.19 773 2.768 0.2665
## TCS01 Abarco - TCS01 Roble 17.401 7.82 773 2.225 0.6466
## TCS01 Abarco - TCS06 Roble 35.954 6.84 773 5.254 <.0001
## TCS01 Abarco - TCS13 Roble 49.601 7.12 773 6.964 <.0001
## TCS01 Abarco - TCS19 Roble 28.602 6.43 773 4.449 0.0009
## TCS01 Abarco - CCN51 Terminalia 4.211 6.47 773 0.650 1.0000
## TCS01 Abarco - TCS01 Terminalia -9.099 6.35 773 -1.433 0.9842
## TCS01 Abarco - TCS06 Terminalia -0.379 6.37 773 -0.059 1.0000
## TCS01 Abarco - TCS13 Terminalia 7.098 6.20 773 1.145 0.9983
## TCS01 Abarco - TCS19 Terminalia 20.379 7.33 773 2.782 0.2590
## TCS06 Abarco - TCS13 Abarco 0.533 6.30 773 0.085 1.0000
## TCS06 Abarco - TCS19 Abarco 4.977 6.13 773 0.812 1.0000
## TCS06 Abarco - CCN51 Roble 4.573 6.14 773 0.744 1.0000
## TCS06 Abarco - TCS01 Roble 4.844 7.77 773 0.623 1.0000
## TCS06 Abarco - TCS06 Roble 23.397 6.81 773 3.435 0.0451
## TCS06 Abarco - TCS13 Roble 37.044 7.08 773 5.235 <.0001
## TCS06 Abarco - TCS19 Roble 16.045 6.39 773 2.509 0.4351
## TCS06 Abarco - CCN51 Terminalia -8.347 6.44 773 -1.296 0.9940
## TCS06 Abarco - TCS01 Terminalia -21.656 6.30 773 -3.439 0.0446
## TCS06 Abarco - TCS06 Terminalia -12.936 6.33 773 -2.043 0.7720
## TCS06 Abarco - TCS13 Terminalia -5.459 6.15 773 -0.887 0.9999
## TCS06 Abarco - TCS19 Terminalia 7.822 7.29 773 1.073 0.9992
## TCS13 Abarco - TCS19 Abarco 4.444 6.34 773 0.701 1.0000
## TCS13 Abarco - CCN51 Roble 4.040 6.36 773 0.635 1.0000
## TCS13 Abarco - TCS01 Roble 4.311 7.99 773 0.540 1.0000
## TCS13 Abarco - TCS06 Roble 22.864 7.00 773 3.268 0.0751
## TCS13 Abarco - TCS13 Roble 36.511 7.28 773 5.013 0.0001
## TCS13 Abarco - TCS19 Roble 15.512 6.58 773 2.357 0.5481
## TCS13 Abarco - CCN51 Terminalia -8.880 6.62 773 -1.342 0.9915
## TCS13 Abarco - TCS01 Terminalia -22.189 6.52 773 -3.402 0.0500
## TCS13 Abarco - TCS06 Terminalia -13.469 6.53 773 -2.061 0.7601
## TCS13 Abarco - TCS13 Terminalia -5.992 6.37 773 -0.941 0.9998
## TCS13 Abarco - TCS19 Terminalia 7.289 7.45 773 0.979 0.9997
## TCS19 Abarco - CCN51 Roble -0.404 6.19 773 -0.065 1.0000
## TCS19 Abarco - TCS01 Roble -0.133 7.81 773 -0.017 1.0000
## TCS19 Abarco - TCS06 Roble 18.420 6.85 773 2.689 0.3136
## TCS19 Abarco - TCS13 Roble 32.067 7.11 773 4.509 0.0007
## TCS19 Abarco - TCS19 Roble 11.068 6.44 773 1.719 0.9269
## TCS19 Abarco - CCN51 Terminalia -13.324 6.48 773 -2.055 0.7644
## TCS19 Abarco - TCS01 Terminalia -26.633 6.34 773 -4.202 0.0027
## TCS19 Abarco - TCS06 Terminalia -17.913 6.37 773 -2.810 0.2435
## TCS19 Abarco - TCS13 Terminalia -10.436 6.19 773 -1.685 0.9374
## TCS19 Abarco - TCS19 Terminalia 2.845 7.33 773 0.388 1.0000
## CCN51 Roble - TCS01 Roble 0.271 7.81 773 0.035 1.0000
## CCN51 Roble - TCS06 Roble 18.824 6.86 773 2.744 0.2806
## CCN51 Roble - TCS13 Roble 32.471 7.12 773 4.558 0.0006
## CCN51 Roble - TCS19 Roble 11.472 6.46 773 1.777 0.9068
## CCN51 Roble - CCN51 Terminalia -12.920 6.51 773 -1.985 0.8067
## CCN51 Roble - TCS01 Terminalia -26.229 6.36 773 -4.127 0.0037
## CCN51 Roble - TCS06 Terminalia -17.509 6.39 773 -2.739 0.2835
## CCN51 Roble - TCS13 Terminalia -10.032 6.21 773 -1.615 0.9552
## CCN51 Roble - TCS19 Terminalia 3.249 7.36 773 0.442 1.0000
## TCS01 Roble - TCS06 Roble 18.553 8.35 773 2.221 0.6495
## TCS01 Roble - TCS13 Roble 32.200 8.54 773 3.772 0.0144
## TCS01 Roble - TCS19 Roble 11.201 8.05 773 1.391 0.9880
## TCS01 Roble - CCN51 Terminalia -13.191 8.12 773 -1.625 0.9529
## TCS01 Roble - TCS01 Terminalia -26.500 7.92 773 -3.345 0.0597
## TCS01 Roble - TCS06 Terminalia -17.780 7.98 773 -2.227 0.6453
## TCS01 Roble - TCS13 Terminalia -10.303 7.82 773 -1.318 0.9929
## TCS01 Roble - TCS19 Terminalia 2.978 8.82 773 0.338 1.0000
## TCS06 Roble - TCS13 Roble 13.647 7.72 773 1.767 0.9105
## TCS06 Roble - TCS19 Roble -7.352 7.08 773 -1.039 0.9994
## TCS06 Roble - CCN51 Terminalia -31.744 7.13 773 -4.452 0.0009
## TCS06 Roble - TCS01 Terminalia -45.053 7.02 773 -6.415 <.0001
## TCS06 Roble - TCS06 Terminalia -36.333 7.04 773 -5.164 <.0001
## TCS06 Roble - TCS13 Terminalia -28.856 6.87 773 -4.199 0.0027
## TCS06 Roble - TCS19 Terminalia -15.575 7.93 773 -1.965 0.8182
## TCS13 Roble - TCS19 Roble -20.999 7.36 773 -2.852 0.2220
## TCS13 Roble - CCN51 Terminalia -45.390 7.41 773 -6.124 <.0001
## TCS13 Roble - TCS01 Terminalia -58.700 7.24 773 -8.103 <.0001
## TCS13 Roble - TCS06 Terminalia -49.980 7.30 773 -6.849 <.0001
## TCS13 Roble - TCS13 Terminalia -42.503 7.13 773 -5.959 <.0001
## TCS13 Roble - TCS19 Terminalia -29.222 8.16 773 -3.580 0.0281
## TCS19 Roble - CCN51 Terminalia -24.391 6.72 773 -3.631 0.0236
## TCS19 Roble - TCS01 Terminalia -37.701 6.62 773 -5.697 <.0001
## TCS19 Roble - TCS06 Terminalia -28.981 6.63 773 -4.372 0.0013
## TCS19 Roble - TCS13 Terminalia -21.504 6.46 773 -3.327 0.0630
## TCS19 Roble - TCS19 Terminalia -8.223 7.54 773 -1.090 0.9990
## CCN51 Terminalia - TCS01 Terminalia -13.309 6.66 773 -1.998 0.7994
## CCN51 Terminalia - TCS06 Terminalia -4.589 6.67 773 -0.688 1.0000
## CCN51 Terminalia - TCS13 Terminalia 2.887 6.51 773 0.443 1.0000
## CCN51 Terminalia - TCS19 Terminalia 16.169 7.56 773 2.139 0.7080
## TCS01 Terminalia - TCS06 Terminalia 8.720 6.54 773 1.333 0.9920
## TCS01 Terminalia - TCS13 Terminalia 16.197 6.36 773 2.545 0.4092
## TCS01 Terminalia - TCS19 Terminalia 29.478 7.48 773 3.941 0.0077
## TCS06 Terminalia - TCS13 Terminalia 7.477 6.40 773 1.168 0.9979
## TCS06 Terminalia - TCS19 Terminalia 20.758 7.49 773 2.770 0.2657
## TCS13 Terminalia - TCS19 Terminalia 13.281 7.36 773 1.804 0.8961
##
## 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
## TCS01 Terminalia 142.4 4.61 773 133.4 151.5 A
## TCS06 Terminalia 133.7 4.65 773 124.6 142.8 AB
## TCS01 Abarco 133.3 4.36 773 124.8 141.9 AB
## CCN51 Terminalia 129.1 4.80 773 119.7 138.5 AB
## TCS13 Terminalia 126.2 4.40 773 117.6 134.9 ABC
## CCN51 Abarco 123.6 4.95 773 113.9 133.3 ABC
## TCS06 Abarco 120.8 4.30 773 112.3 129.2 BC
## TCS13 Abarco 120.2 4.60 773 111.2 129.3 ABCD
## CCN51 Roble 116.2 4.39 773 107.6 124.8 BCD
## TCS01 Roble 115.9 6.47 773 103.2 128.6 ABCD
## TCS19 Abarco 115.8 4.36 773 107.2 124.4 BCD
## TCS19 Terminalia 113.0 5.90 773 101.4 124.5 BCD
## TCS19 Roble 104.7 4.73 773 95.4 114.0 CDE
## TCS06 Roble 97.4 5.27 773 87.0 107.7 DE
## TCS13 Roble 83.7 5.62 773 72.7 94.8 E
##
## 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(datos2)