setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura")
datos<-read.table("campohermoso.csv", header=T, sep=',')
datos$gen<-as.factor(datos$gen)
datos$forestal<-as.factor(datos$forestal)
datos$bloque<-as.factor(datos$bloque)
attach(datos)
library(ggplot2)
library(emmeans)
#Gráfica diámetro
ggplot(datos, 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 333 rows containing non-finite values (stat_smooth).
# Gráfica altura
ggplot(datos, 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 332 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=datos, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos, 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 1870.932 2026.389 -901.4659
## fit.ar1.diam 2 34 1858.949 2014.407 -895.4745
## fit.ar1het.diam 3 37 1857.064 2026.238 -891.5319 2 vs 3 7.885065 0.0484
anova(fit.ar1.diam)
## Denom. DF: 715
## numDF F-value p-value
## (Intercept) 1 10145.383 <.0001
## semana 1 110.023 <.0001
## forestal 2 47.989 <.0001
## gen 4 1.885 0.1113
## bloque 2 41.483 <.0001
## semana:forestal 2 0.833 0.4351
## semana:gen 4 0.476 0.7530
## forestal:gen 8 5.778 <.0001
## semana:forestal:gen 8 0.646 0.7389
anova(fit.ar1het.diam)
## Denom. DF: 715
## numDF F-value p-value
## (Intercept) 1 10189.583 <.0001
## semana 1 143.762 <.0001
## forestal 2 48.642 <.0001
## gen 4 1.778 0.1313
## bloque 2 39.717 <.0001
## semana:forestal 2 0.890 0.4110
## semana:gen 4 0.623 0.6461
## forestal:gen 8 6.062 <.0001
## semana:forestal:gen 8 0.808 0.5955
# Análisis para altura
fit.compsym.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos, 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 7321.417 7476.922 -3626.709
## fit.ar1.alt 2 34 7316.113 7471.618 -3624.056
## fit.ar1het.alt 3 37 7301.865 7471.091 -3613.932 2 vs 3 20.24765 2e-04
anova(fit.ar1het.alt)
## Denom. DF: 716
## numDF F-value p-value
## (Intercept) 1 6620.156 <.0001
## semana 1 191.354 <.0001
## forestal 2 38.975 <.0001
## gen 4 7.450 <.0001
## bloque 2 33.728 <.0001
## semana:forestal 2 0.831 0.4359
## semana:gen 4 1.823 0.1225
## forestal:gen 8 8.072 <.0001
## semana:forestal:gen 8 0.598 0.7796
#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 ab TCS06
## TCS13 ab TCS13
## TCS19 b 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 a 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 d Abarco:CCN51
## Abarco:TCS01 d Abarco:TCS01
## Abarco:TCS06 abcd Abarco:TCS06
## Abarco:TCS13 d Abarco:TCS13
## Abarco:TCS19 d Abarco:TCS19
## Roble:CCN51 acd Roble:CCN51
## Roble:TCS01 acd Roble:TCS01
## Roble:TCS06 d Roble:TCS06
## Roble:TCS13 d Roble:TCS13
## Roble:TCS19 abc Roble:TCS19
## Terminalia:CCN51 cd Terminalia:CCN51
## Terminalia:TCS01 abc Terminalia:TCS01
## Terminalia:TCS06 ab 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 b CCN51
## TCS01 a TCS01
## TCS06 a TCS06
## TCS13 a TCS13
## TCS19 a 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 f Abarco:CCN51
## Abarco:TCS01 cdef Abarco:TCS01
## Abarco:TCS06 abcd Abarco:TCS06
## Abarco:TCS13 acde Abarco:TCS13
## Abarco:TCS19 def Abarco:TCS19
## Roble:CCN51 abcd Roble:CCN51
## Roble:TCS01 cde Roble:TCS01
## Roble:TCS06 ef Roble:TCS06
## Roble:TCS13 cde Roble:TCS13
## Roble:TCS19 acd Roble:TCS19
## Terminalia:CCN51 cde Terminalia:CCN51
## Terminalia:TCS01 abc Terminalia:TCS01
## Terminalia:TCS06 ab Terminalia:TCS06
## Terminalia:TCS13 ab Terminalia:TCS13
## Terminalia:TCS19 b 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 3.25 0.0596 715 3.14 3.37
## TCS01 3.08 0.0630 715 2.95 3.20
## TCS06 3.04 0.0686 715 2.90 3.17
## TCS13 3.11 0.0601 715 2.99 3.22
## TCS19 3.06 0.0588 715 2.94 3.17
##
## 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 0.1764 0.0867 715 2.036 0.2499
## CCN51 - TCS06 0.2171 0.0908 715 2.390 0.1191
## CCN51 - TCS13 0.1462 0.0846 715 1.729 0.4168
## CCN51 - TCS19 0.1964 0.0837 715 2.345 0.1321
## TCS01 - TCS06 0.0406 0.0930 715 0.437 0.9924
## TCS01 - TCS13 -0.0302 0.0869 715 -0.348 0.9969
## TCS01 - TCS19 0.0199 0.0862 715 0.231 0.9994
## TCS06 - TCS13 -0.0709 0.0910 715 -0.779 0.9366
## TCS06 - TCS19 -0.0207 0.0905 715 -0.229 0.9994
## TCS13 - TCS19 0.0502 0.0841 715 0.597 0.9756
##
## 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
## CCN51 3.25 0.0596 715 3.14 3.37 A
## TCS13 3.11 0.0601 715 2.99 3.22 A
## TCS01 3.08 0.0630 715 2.95 3.20 A
## TCS19 3.06 0.0588 715 2.94 3.17 A
## TCS06 3.04 0.0686 715 2.90 3.17 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 3.37 0.0524 715 3.27 3.47
## Roble 3.18 0.0468 715 3.09 3.27
## Terminalia 2.76 0.0450 715 2.68 2.85
##
## 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 0.189 0.0700 715 2.705 0.0191
## Abarco - Terminalia 0.609 0.0690 715 8.815 <.0001
## Roble - Terminalia 0.419 0.0649 715 6.461 <.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
## Abarco 3.37 0.0524 715 3.27 3.47 A
## Roble 3.18 0.0468 715 3.09 3.27 B
## Terminalia 2.76 0.0450 715 2.68 2.85 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 3.41 0.0997 715 3.21 3.60
## TCS01 Abarco 3.43 0.1141 715 3.20 3.65
## TCS06 Abarco 3.02 0.1486 715 2.73 3.31
## TCS13 Abarco 3.42 0.1094 715 3.21 3.64
## TCS19 Abarco 3.59 0.1067 715 3.38 3.80
## CCN51 Roble 3.15 0.1135 715 2.92 3.37
## TCS01 Roble 3.07 0.1076 715 2.86 3.28
## TCS06 Roble 3.47 0.0928 715 3.29 3.66
## TCS13 Roble 3.33 0.1092 715 3.12 3.54
## TCS19 Roble 2.89 0.0982 715 2.70 3.09
## CCN51 Terminalia 3.21 0.0961 715 3.02 3.40
## TCS01 Terminalia 2.73 0.1055 715 2.53 2.94
## TCS06 Terminalia 2.62 0.1078 715 2.41 2.83
## TCS13 Terminalia 2.57 0.0928 715 2.39 2.75
## TCS19 Terminalia 2.69 0.1003 715 2.49 2.89
##
## 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 -0.01884 0.152 715 -0.124 1.0000
## CCN51 Abarco - TCS06 Abarco 0.38809 0.179 715 2.173 0.6844
## CCN51 Abarco - TCS13 Abarco -0.01587 0.148 715 -0.107 1.0000
## CCN51 Abarco - TCS19 Abarco -0.18011 0.146 715 -1.234 0.9963
## CCN51 Abarco - CCN51 Roble 0.26055 0.151 715 1.725 0.9251
## CCN51 Abarco - TCS01 Roble 0.33577 0.146 715 2.293 0.5962
## CCN51 Abarco - TCS06 Roble -0.06648 0.136 715 -0.488 1.0000
## CCN51 Abarco - TCS13 Roble 0.07628 0.148 715 0.516 1.0000
## CCN51 Abarco - TCS19 Roble 0.51441 0.140 715 3.676 0.0203
## CCN51 Abarco - CCN51 Terminalia 0.19989 0.138 715 1.444 0.9830
## CCN51 Abarco - TCS01 Terminalia 0.67276 0.145 715 4.642 0.0004
## CCN51 Abarco - TCS06 Terminalia 0.79002 0.147 715 5.375 <.0001
## CCN51 Abarco - TCS13 Terminalia 0.83858 0.136 715 6.157 <.0001
## CCN51 Abarco - TCS19 Terminalia 0.71521 0.141 715 5.058 0.0001
## TCS01 Abarco - TCS06 Abarco 0.40693 0.188 715 2.167 0.6888
## TCS01 Abarco - TCS13 Abarco 0.00298 0.158 715 0.019 1.0000
## TCS01 Abarco - TCS19 Abarco -0.16127 0.156 715 -1.032 0.9995
## TCS01 Abarco - CCN51 Roble 0.27940 0.161 715 1.735 0.9216
## TCS01 Abarco - TCS01 Roble 0.35462 0.157 715 2.259 0.6214
## TCS01 Abarco - TCS06 Roble -0.04763 0.147 715 -0.324 1.0000
## TCS01 Abarco - TCS13 Roble 0.09513 0.158 715 0.602 1.0000
## TCS01 Abarco - TCS19 Roble 0.53325 0.150 715 3.544 0.0318
## TCS01 Abarco - CCN51 Terminalia 0.21874 0.149 715 1.467 0.9803
## TCS01 Abarco - TCS01 Terminalia 0.69160 0.156 715 4.447 0.0010
## TCS01 Abarco - TCS06 Terminalia 0.80887 0.157 715 5.154 <.0001
## TCS01 Abarco - TCS13 Terminalia 0.85742 0.147 715 5.833 <.0001
## TCS01 Abarco - TCS19 Terminalia 0.73405 0.152 715 4.836 0.0002
## TCS06 Abarco - TCS13 Abarco -0.40395 0.184 715 -2.199 0.6657
## TCS06 Abarco - TCS19 Abarco -0.56820 0.183 715 -3.105 0.1188
## TCS06 Abarco - CCN51 Roble -0.12753 0.186 715 -0.684 1.0000
## TCS06 Abarco - TCS01 Roble -0.05231 0.183 715 -0.286 1.0000
## TCS06 Abarco - TCS06 Roble -0.45456 0.175 715 -2.598 0.3730
## TCS06 Abarco - TCS13 Roble -0.31180 0.184 715 -1.694 0.9347
## TCS06 Abarco - TCS19 Roble 0.12632 0.178 715 0.708 1.0000
## TCS06 Abarco - CCN51 Terminalia -0.18819 0.177 715 -1.061 0.9993
## TCS06 Abarco - TCS01 Terminalia 0.28467 0.182 715 1.568 0.9649
## TCS06 Abarco - TCS06 Terminalia 0.40194 0.184 715 2.190 0.6719
## TCS06 Abarco - TCS13 Terminalia 0.45049 0.175 715 2.570 0.3920
## TCS06 Abarco - TCS19 Terminalia 0.32712 0.180 715 1.822 0.8890
## TCS13 Abarco - TCS19 Abarco -0.16425 0.153 715 -1.075 0.9992
## TCS13 Abarco - CCN51 Roble 0.27642 0.158 715 1.754 0.9152
## TCS13 Abarco - TCS01 Roble 0.35164 0.153 715 2.299 0.5917
## TCS13 Abarco - TCS06 Roble -0.05061 0.143 715 -0.353 1.0000
## TCS13 Abarco - TCS13 Roble 0.09215 0.155 715 0.596 1.0000
## TCS13 Abarco - TCS19 Roble 0.53027 0.147 715 3.606 0.0258
## TCS13 Abarco - CCN51 Terminalia 0.21576 0.146 715 1.482 0.9785
## TCS13 Abarco - TCS01 Terminalia 0.68862 0.152 715 4.545 0.0006
## TCS13 Abarco - TCS06 Terminalia 0.80589 0.154 715 5.239 <.0001
## TCS13 Abarco - TCS13 Terminalia 0.85444 0.144 715 5.954 <.0001
## TCS13 Abarco - TCS19 Terminalia 0.73108 0.148 715 4.924 0.0001
## TCS19 Abarco - CCN51 Roble 0.44067 0.156 715 2.828 0.2344
## TCS19 Abarco - TCS01 Roble 0.51589 0.152 715 3.405 0.0497
## TCS19 Abarco - TCS06 Roble 0.11364 0.141 715 0.804 1.0000
## TCS19 Abarco - TCS13 Roble 0.25640 0.153 715 1.679 0.9390
## TCS19 Abarco - TCS19 Roble 0.69452 0.145 715 4.789 0.0002
## TCS19 Abarco - CCN51 Terminalia 0.38001 0.144 715 2.647 0.3406
## TCS19 Abarco - TCS01 Terminalia 0.85287 0.150 715 5.685 <.0001
## TCS19 Abarco - TCS06 Terminalia 0.97014 0.152 715 6.393 <.0001
## TCS19 Abarco - TCS13 Terminalia 1.01869 0.141 715 7.204 <.0001
## TCS19 Abarco - TCS19 Terminalia 0.89532 0.146 715 6.116 <.0001
## CCN51 Roble - TCS01 Roble 0.07522 0.156 715 0.481 1.0000
## CCN51 Roble - TCS06 Roble -0.32703 0.147 715 -2.231 0.6419
## CCN51 Roble - TCS13 Roble -0.18427 0.157 715 -1.171 0.9979
## CCN51 Roble - TCS19 Roble 0.25385 0.150 715 1.691 0.9356
## CCN51 Roble - CCN51 Terminalia -0.06066 0.149 715 -0.407 1.0000
## CCN51 Roble - TCS01 Terminalia 0.41220 0.155 715 2.663 0.3302
## CCN51 Roble - TCS06 Terminalia 0.52947 0.156 715 3.385 0.0529
## CCN51 Roble - TCS13 Terminalia 0.57802 0.147 715 3.943 0.0076
## CCN51 Roble - TCS19 Terminalia 0.45466 0.152 715 3.000 0.1558
## TCS01 Roble - TCS06 Roble -0.40225 0.142 715 -2.833 0.2316
## TCS01 Roble - TCS13 Roble -0.25949 0.153 715 -1.693 0.9350
## TCS01 Roble - TCS19 Roble 0.17864 0.146 715 1.226 0.9966
## TCS01 Roble - CCN51 Terminalia -0.13588 0.144 715 -0.942 0.9998
## TCS01 Roble - TCS01 Terminalia 0.33699 0.150 715 2.241 0.6350
## TCS01 Roble - TCS06 Terminalia 0.45425 0.152 715 2.979 0.1642
## TCS01 Roble - TCS13 Terminalia 0.50280 0.142 715 3.538 0.0324
## TCS01 Roble - TCS19 Terminalia 0.37944 0.147 715 2.579 0.3856
## TCS06 Roble - TCS13 Roble 0.14276 0.143 715 0.997 0.9996
## TCS06 Roble - TCS19 Roble 0.58089 0.135 715 4.300 0.0018
## TCS06 Roble - CCN51 Terminalia 0.26637 0.134 715 1.994 0.8017
## TCS06 Roble - TCS01 Terminalia 0.73924 0.140 715 5.267 <.0001
## TCS06 Roble - TCS06 Terminalia 0.85650 0.142 715 6.020 <.0001
## TCS06 Roble - TCS13 Terminalia 0.90505 0.131 715 6.898 <.0001
## TCS06 Roble - TCS19 Terminalia 0.78169 0.137 715 5.722 <.0001
## TCS13 Roble - TCS19 Roble 0.43812 0.147 715 2.983 0.1627
## TCS13 Roble - CCN51 Terminalia 0.12361 0.146 715 0.849 0.9999
## TCS13 Roble - TCS01 Terminalia 0.59647 0.152 715 3.931 0.0080
## TCS13 Roble - TCS06 Terminalia 0.71374 0.153 715 4.654 0.0004
## TCS13 Roble - TCS13 Terminalia 0.76229 0.143 715 5.321 <.0001
## TCS13 Roble - TCS19 Terminalia 0.63892 0.148 715 4.309 0.0017
## TCS19 Roble - CCN51 Terminalia -0.31452 0.137 715 -2.290 0.5984
## TCS19 Roble - TCS01 Terminalia 0.15835 0.144 715 1.099 0.9989
## TCS19 Roble - TCS06 Terminalia 0.27561 0.146 715 1.890 0.8579
## TCS19 Roble - TCS13 Terminalia 0.32417 0.135 715 2.400 0.5155
## TCS19 Roble - TCS19 Terminalia 0.20080 0.140 715 1.431 0.9843
## CCN51 Terminalia - TCS01 Terminalia 0.47287 0.143 715 3.312 0.0661
## CCN51 Terminalia - TCS06 Terminalia 0.59013 0.145 715 4.082 0.0044
## CCN51 Terminalia - TCS13 Terminalia 0.63868 0.134 715 4.782 0.0002
## CCN51 Terminalia - TCS19 Terminalia 0.51532 0.139 715 3.713 0.0178
## TCS01 Terminalia - TCS06 Terminalia 0.11726 0.151 715 0.777 1.0000
## TCS01 Terminalia - TCS13 Terminalia 0.16582 0.140 715 1.180 0.9977
## TCS01 Terminalia - TCS19 Terminalia 0.04245 0.146 715 0.292 1.0000
## TCS06 Terminalia - TCS13 Terminalia 0.04856 0.142 715 0.341 1.0000
## TCS06 Terminalia - TCS19 Terminalia -0.07481 0.147 715 -0.508 1.0000
## TCS13 Terminalia - TCS19 Terminalia -0.12337 0.137 715 -0.903 0.9999
##
## 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 3.59 0.1067 715 3.38 3.80 A
## TCS06 Roble 3.47 0.0928 715 3.29 3.66 AB
## TCS01 Abarco 3.43 0.1141 715 3.20 3.65 AB
## TCS13 Abarco 3.42 0.1094 715 3.21 3.64 AB
## CCN51 Abarco 3.41 0.0997 715 3.21 3.60 AB
## TCS13 Roble 3.33 0.1092 715 3.12 3.54 ABC
## CCN51 Terminalia 3.21 0.0961 715 3.02 3.40 ABCD
## CCN51 Roble 3.15 0.1135 715 2.92 3.37 ABCDE
## TCS01 Roble 3.07 0.1076 715 2.86 3.28 BCDE
## TCS06 Abarco 3.02 0.1486 715 2.73 3.31 ABCDEF
## TCS19 Roble 2.89 0.0982 715 2.70 3.09 CDEF
## TCS01 Terminalia 2.73 0.1055 715 2.53 2.94 DEF
## TCS19 Terminalia 2.69 0.1003 715 2.49 2.89 EF
## TCS06 Terminalia 2.62 0.1078 715 2.41 2.83 EF
## TCS13 Terminalia 2.57 0.0928 715 2.39 2.75 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 115.5 2.68 716 110.3 121
## TCS01 105.5 2.83 716 100.0 111
## TCS06 99.2 3.08 716 93.1 105
## TCS13 101.2 2.70 716 95.9 107
## TCS19 98.5 2.63 716 93.3 104
##
## 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 10.001 3.89 716 2.570 0.0771
## CCN51 - TCS06 16.370 4.08 716 4.014 0.0006
## CCN51 - TCS13 14.303 3.80 716 3.767 0.0017
## CCN51 - TCS19 17.026 3.76 716 4.534 0.0001
## TCS01 - TCS06 6.369 4.18 716 1.525 0.5466
## TCS01 - TCS13 4.302 3.90 716 1.102 0.8054
## TCS01 - TCS19 7.024 3.86 716 1.818 0.3639
## TCS06 - TCS13 -2.067 4.08 716 -0.506 0.9868
## TCS06 - TCS19 0.656 4.06 716 0.162 0.9998
## TCS13 - TCS19 2.723 3.77 716 0.722 0.9514
##
## 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
## CCN51 115.5 2.68 716 110.3 121 A
## TCS01 105.5 2.83 716 100.0 111 AB
## TCS13 101.2 2.70 716 95.9 107 B
## TCS06 99.2 3.08 716 93.1 105 B
## TCS19 98.5 2.63 716 93.3 104 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.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 113.1 2.35 716 108.4 117.7
## Roble 108.1 2.10 716 104.0 112.2
## Terminalia 90.8 2.02 716 86.8 94.8
##
## 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 4.95 3.15 716 1.574 0.2574
## Abarco - Terminalia 22.27 3.10 716 7.189 <.0001
## Roble - Terminalia 17.32 2.91 716 5.951 <.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
## Abarco 113.1 2.35 716 108.4 117.7 A
## Roble 108.1 2.10 716 104.0 112.2 A
## Terminalia 90.8 2.02 716 86.8 94.8 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 136.2 4.48 716 127.4 145.0
## TCS01 Abarco 113.2 5.12 716 103.1 123.2
## TCS06 Abarco 92.9 6.67 716 79.8 106.0
## TCS13 Abarco 104.6 4.91 716 94.9 114.2
## TCS19 Abarco 118.5 4.79 716 109.1 127.9
## CCN51 Roble 97.1 5.09 716 87.1 107.1
## TCS01 Roble 111.4 4.83 716 101.9 120.9
## TCS06 Roble 121.3 4.17 716 113.1 129.4
## TCS13 Roble 111.3 4.90 716 101.7 120.9
## TCS19 Roble 99.5 4.41 716 90.8 108.2
## CCN51 Terminalia 113.3 4.32 716 104.8 121.8
## TCS01 Terminalia 92.0 4.74 716 82.7 101.3
## TCS06 Terminalia 83.3 4.84 716 73.8 92.8
## TCS13 Terminalia 87.8 4.17 716 79.6 96.0
## TCS19 Terminalia 77.5 4.46 716 68.7 86.3
##
## 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 22.987 6.81 716 3.376 0.0544
## CCN51 Abarco - TCS06 Abarco 43.277 8.02 716 5.396 <.0001
## CCN51 Abarco - TCS13 Abarco 31.602 6.63 716 4.767 0.0002
## CCN51 Abarco - TCS19 Abarco 17.640 6.56 716 2.690 0.3131
## CCN51 Abarco - CCN51 Roble 39.068 6.78 716 5.758 <.0001
## CCN51 Abarco - TCS01 Roble 24.760 6.58 716 3.765 0.0148
## CCN51 Abarco - TCS06 Roble 14.897 6.11 716 2.437 0.4881
## CCN51 Abarco - TCS13 Roble 24.872 6.64 716 3.745 0.0159
## CCN51 Abarco - TCS19 Roble 36.668 6.29 716 5.833 <.0001
## CCN51 Abarco - CCN51 Terminalia 22.840 6.22 716 3.673 0.0205
## CCN51 Abarco - TCS01 Terminalia 44.165 6.51 716 6.786 <.0001
## CCN51 Abarco - TCS06 Terminalia 52.843 6.60 716 8.007 <.0001
## CCN51 Abarco - TCS13 Terminalia 48.343 6.12 716 7.903 <.0001
## CCN51 Abarco - TCS19 Terminalia 58.677 6.32 716 9.281 <.0001
## TCS01 Abarco - TCS06 Abarco 20.291 8.43 716 2.406 0.5111
## TCS01 Abarco - TCS13 Abarco 8.615 7.11 716 1.212 0.9970
## TCS01 Abarco - TCS19 Abarco -5.347 7.02 716 -0.762 1.0000
## TCS01 Abarco - CCN51 Roble 16.081 7.23 716 2.224 0.6474
## TCS01 Abarco - TCS01 Roble 1.774 7.05 716 0.252 1.0000
## TCS01 Abarco - TCS06 Roble -8.090 6.61 716 -1.225 0.9966
## TCS01 Abarco - TCS13 Roble 1.885 7.09 716 0.266 1.0000
## TCS01 Abarco - TCS19 Roble 13.681 6.76 716 2.025 0.7833
## TCS01 Abarco - CCN51 Terminalia -0.147 6.70 716 -0.022 1.0000
## TCS01 Abarco - TCS01 Terminalia 21.178 6.98 716 3.032 0.1438
## TCS01 Abarco - TCS06 Terminalia 29.856 7.05 716 4.237 0.0024
## TCS01 Abarco - TCS13 Terminalia 25.356 6.60 716 3.841 0.0112
## TCS01 Abarco - TCS19 Terminalia 35.690 6.79 716 5.256 <.0001
## TCS06 Abarco - TCS13 Abarco -11.675 8.25 716 -1.415 0.9858
## TCS06 Abarco - TCS19 Abarco -25.637 8.22 716 -3.120 0.1140
## TCS06 Abarco - CCN51 Roble -4.210 8.37 716 -0.503 1.0000
## TCS06 Abarco - TCS01 Roble -18.517 8.22 716 -2.254 0.6252
## TCS06 Abarco - TCS06 Roble -28.381 7.86 716 -3.612 0.0253
## TCS06 Abarco - TCS13 Roble -18.406 8.26 716 -2.227 0.6452
## TCS06 Abarco - TCS19 Roble -6.609 8.01 716 -0.826 1.0000
## TCS06 Abarco - CCN51 Terminalia -20.437 7.97 716 -2.565 0.3953
## TCS06 Abarco - TCS01 Terminalia 0.887 8.15 716 0.109 1.0000
## TCS06 Abarco - TCS06 Terminalia 9.566 8.24 716 1.161 0.9981
## TCS06 Abarco - TCS13 Terminalia 5.065 7.87 716 0.644 1.0000
## TCS06 Abarco - TCS19 Terminalia 15.400 8.04 716 1.916 0.8447
## TCS13 Abarco - TCS19 Abarco -13.962 6.86 716 -2.035 0.7766
## TCS13 Abarco - CCN51 Roble 7.466 7.08 716 1.055 0.9993
## TCS13 Abarco - TCS01 Roble -6.842 6.87 716 -0.996 0.9996
## TCS13 Abarco - TCS06 Roble -16.705 6.44 716 -2.596 0.3742
## TCS13 Abarco - TCS13 Roble -6.730 6.94 716 -0.970 0.9997
## TCS13 Abarco - TCS19 Roble 5.066 6.60 716 0.767 1.0000
## TCS13 Abarco - CCN51 Terminalia -8.762 6.54 716 -1.340 0.9916
## TCS13 Abarco - TCS01 Terminalia 12.563 6.80 716 1.846 0.8782
## TCS13 Abarco - TCS06 Terminalia 21.241 6.91 716 3.076 0.1284
## TCS13 Abarco - TCS13 Terminalia 16.741 6.44 716 2.598 0.3729
## TCS13 Abarco - TCS19 Terminalia 27.075 6.64 716 4.078 0.0045
## TCS19 Abarco - CCN51 Roble 21.428 7.00 716 3.062 0.1331
## TCS19 Abarco - TCS01 Roble 7.120 6.80 716 1.046 0.9994
## TCS19 Abarco - TCS06 Roble -2.743 6.35 716 -0.432 1.0000
## TCS19 Abarco - TCS13 Roble 7.232 6.86 716 1.055 0.9993
## TCS19 Abarco - TCS19 Roble 19.028 6.51 716 2.921 0.1889
## TCS19 Abarco - CCN51 Terminalia 5.200 6.45 716 0.806 1.0000
## TCS19 Abarco - TCS01 Terminalia 26.525 6.74 716 3.937 0.0078
## TCS19 Abarco - TCS06 Terminalia 35.203 6.81 716 5.166 <.0001
## TCS19 Abarco - TCS13 Terminalia 30.703 6.35 716 4.835 0.0002
## TCS19 Abarco - TCS19 Terminalia 41.037 6.55 716 6.267 <.0001
## CCN51 Roble - TCS01 Roble -14.307 7.02 716 -2.037 0.7752
## CCN51 Roble - TCS06 Roble -24.171 6.58 716 -3.673 0.0205
## CCN51 Roble - TCS13 Roble -14.196 7.06 716 -2.009 0.7924
## CCN51 Roble - TCS19 Roble -2.400 6.74 716 -0.356 1.0000
## CCN51 Roble - CCN51 Terminalia -16.228 6.69 716 -2.427 0.4958
## CCN51 Roble - TCS01 Terminalia 5.097 6.95 716 0.733 1.0000
## CCN51 Roble - TCS06 Terminalia 13.776 7.02 716 1.962 0.8201
## CCN51 Roble - TCS13 Terminalia 9.275 6.58 716 1.409 0.9864
## CCN51 Roble - TCS19 Terminalia 19.609 6.78 716 2.893 0.2018
## TCS01 Roble - TCS06 Roble -9.864 6.38 716 -1.547 0.9687
## TCS01 Roble - TCS13 Roble 0.112 6.88 716 0.016 1.0000
## TCS01 Roble - TCS19 Roble 11.908 6.54 716 1.820 0.8897
## TCS01 Roble - CCN51 Terminalia -1.920 6.48 716 -0.296 1.0000
## TCS01 Roble - TCS01 Terminalia 19.404 6.75 716 2.873 0.2116
## TCS01 Roble - TCS06 Terminalia 28.083 6.85 716 4.102 0.0041
## TCS01 Roble - TCS13 Terminalia 23.583 6.38 716 3.695 0.0190
## TCS01 Roble - TCS19 Terminalia 33.917 6.58 716 5.155 <.0001
## TCS06 Roble - TCS13 Roble 9.975 6.43 716 1.551 0.9681
## TCS06 Roble - TCS19 Roble 21.771 6.07 716 3.588 0.0274
## TCS06 Roble - CCN51 Terminalia 7.943 6.00 716 1.324 0.9925
## TCS06 Roble - TCS01 Terminalia 29.268 6.30 716 4.643 0.0004
## TCS06 Roble - TCS06 Terminalia 37.947 6.39 716 5.941 <.0001
## TCS06 Roble - TCS13 Terminalia 33.446 5.89 716 5.676 <.0001
## TCS06 Roble - TCS19 Terminalia 43.780 6.11 716 7.171 <.0001
## TCS13 Roble - TCS19 Roble 11.796 6.60 716 1.788 0.9024
## TCS13 Roble - CCN51 Terminalia -2.032 6.54 716 -0.311 1.0000
## TCS13 Roble - TCS01 Terminalia 19.293 6.81 716 2.831 0.2325
## TCS13 Roble - TCS06 Terminalia 27.971 6.89 716 4.062 0.0048
## TCS13 Roble - TCS13 Terminalia 23.471 6.43 716 3.648 0.0224
## TCS13 Roble - TCS19 Terminalia 33.805 6.63 716 5.098 <.0001
## TCS19 Roble - CCN51 Terminalia -13.828 6.17 716 -2.242 0.6344
## TCS19 Roble - TCS01 Terminalia 7.497 6.47 716 1.158 0.9981
## TCS19 Roble - TCS06 Terminalia 16.175 6.55 716 2.470 0.4637
## TCS19 Roble - TCS13 Terminalia 11.675 6.07 716 1.925 0.8402
## TCS19 Roble - TCS19 Terminalia 22.009 6.27 716 3.509 0.0357
## CCN51 Terminalia - TCS01 Terminalia 21.325 6.41 716 3.326 0.0634
## CCN51 Terminalia - TCS06 Terminalia 30.003 6.49 716 4.623 0.0004
## CCN51 Terminalia - TCS13 Terminalia 25.503 6.00 716 4.252 0.0022
## CCN51 Terminalia - TCS19 Terminalia 35.837 6.20 716 5.776 <.0001
## TCS01 Terminalia - TCS06 Terminalia 8.679 6.77 716 1.281 0.9946
## TCS01 Terminalia - TCS13 Terminalia 4.178 6.31 716 0.662 1.0000
## TCS01 Terminalia - TCS19 Terminalia 14.512 6.51 716 2.229 0.6436
## TCS06 Terminalia - TCS13 Terminalia -4.500 6.39 716 -0.705 1.0000
## TCS06 Terminalia - TCS19 Terminalia 5.834 6.58 716 0.886 0.9999
## TCS13 Terminalia - TCS19 Terminalia 10.334 6.10 716 1.693 0.9349
##
## 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 136.2 4.48 716 127.4 145.0 A
## TCS06 Roble 121.3 4.17 716 113.1 129.4 AB
## TCS19 Abarco 118.5 4.79 716 109.1 127.9 ABC
## CCN51 Terminalia 113.3 4.32 716 104.8 121.8 BCD
## TCS01 Abarco 113.2 5.12 716 103.1 123.2 ABCD
## TCS01 Roble 111.4 4.83 716 101.9 120.9 BCD
## TCS13 Roble 111.3 4.90 716 101.7 120.9 BCD
## TCS13 Abarco 104.6 4.91 716 94.9 114.2 BCDE
## TCS19 Roble 99.5 4.41 716 90.8 108.2 CDE
## CCN51 Roble 97.1 5.09 716 87.1 107.1 CDEF
## TCS06 Abarco 92.9 6.67 716 79.8 106.0 CDEF
## TCS01 Terminalia 92.0 4.74 716 82.7 101.3 DEF
## TCS13 Terminalia 87.8 4.17 716 79.6 96.0 EF
## TCS06 Terminalia 83.3 4.84 716 73.8 92.8 EF
## TCS19 Terminalia 77.5 4.46 716 68.7 86.3 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(datos)