setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura")
datos3<-read.table("sanluis.csv", header=T, sep=',')
datos3$gen<-as.factor(datos3$gen)
datos3$forestal<-as.factor(datos3$forestal)
datos3$bloque<-as.factor(datos3$bloque)
attach(datos3)
library(ggplot2)
library(emmeans)
#Gráfica diámetro
ggplot(datos3, 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 137 rows containing non-finite values (stat_smooth).
# Gráfica altura
ggplot(datos3, 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 137 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=datos3, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos3, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.diam <- gls(diam ~ semana*forestal*gen+bloque, data=datos3, 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 2341.345 2493.088 -1136.673
## fit.ar1.diam 2 34 2292.572 2444.315 -1112.286
## fit.ar1het.diam 3 36 2296.544 2457.213 -1112.272 2 vs 3 0.02815748 0.986
anova(fit.ar1.diam)
## Denom. DF: 641
## numDF F-value p-value
## (Intercept) 1 7420.471 <.0001
## semana 1 40.360 <.0001
## forestal 2 3.978 0.0192
## gen 4 1.139 0.3372
## bloque 2 2.319 0.0991
## semana:forestal 2 0.733 0.4810
## semana:gen 4 0.149 0.9634
## forestal:gen 8 3.223 0.0013
## semana:forestal:gen 8 0.120 0.9984
anova(fit.ar1het.diam)
## Denom. DF: 641
## numDF F-value p-value
## (Intercept) 1 7415.488 <.0001
## semana 1 40.093 <.0001
## forestal 2 3.992 0.0189
## gen 4 1.139 0.3371
## bloque 2 2.311 0.1000
## semana:forestal 2 0.728 0.4833
## semana:gen 4 0.148 0.9637
## forestal:gen 8 3.225 0.0013
## semana:forestal:gen 8 0.120 0.9985
# Análisis para altura
fit.compsym.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos3, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos3, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.alt <- gls(alt ~ semana*forestal*gen+bloque, data=datos3, 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 6999.878 7151.621 -3465.939
## fit.ar1.alt 2 34 6962.748 7114.491 -3447.374
## fit.ar1het.alt 3 36 6965.877 7126.546 -3446.939 2 vs 3 0.87086 0.647
anova(fit.ar1het.alt)
## Denom. DF: 641
## numDF F-value p-value
## (Intercept) 1 5993.385 <.0001
## semana 1 110.536 <.0001
## forestal 2 7.219 0.0008
## gen 4 6.155 0.0001
## bloque 2 1.822 0.1626
## semana:forestal 2 0.769 0.4637
## semana:gen 4 0.308 0.8725
## forestal:gen 8 7.771 <.0001
## semana:forestal:gen 8 0.190 0.9923
#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 b Abarco
## Roble b 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 abc Abarco:CCN51
## Abarco:TCS01 a Abarco:TCS01
## Abarco:TCS06 ab Abarco:TCS06
## Abarco:TCS13 abc Abarco:TCS13
## Abarco:TCS19 abc Abarco:TCS19
## Roble:CCN51 bc Roble:CCN51
## Roble:TCS01 abc Roble:TCS01
## Roble:TCS06 abc Roble:TCS06
## Roble:TCS13 abc Roble:TCS13
## Roble:TCS19 abc Roble:TCS19
## Terminalia:CCN51 abc Terminalia:CCN51
## Terminalia:TCS01 c Terminalia:TCS01
## Terminalia:TCS06 a Terminalia:TCS06
## Terminalia:TCS13 ab Terminalia:TCS13
## Terminalia:TCS19 abc 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 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 de Abarco:CCN51
## Abarco:TCS01 b Abarco:TCS01
## Abarco:TCS06 ab Abarco:TCS06
## Abarco:TCS13 acde Abarco:TCS13
## Abarco:TCS19 ab Abarco:TCS19
## Roble:CCN51 e Roble:CCN51
## Roble:TCS01 cde Roble:TCS01
## Roble:TCS06 abc Roble:TCS06
## Roble:TCS13 acd Roble:TCS13
## Roble:TCS19 acd Roble:TCS19
## Terminalia:CCN51 acd Terminalia:CCN51
## Terminalia:TCS01 cde Terminalia:TCS01
## Terminalia:TCS06 acd Terminalia:TCS06
## Terminalia:TCS13 ab Terminalia:TCS13
## Terminalia:TCS19 abc 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 5.67 0.100 641 5.48 5.87
## TCS01 5.57 0.101 641 5.37 5.77
## TCS06 5.27 0.116 641 5.04 5.49
## TCS13 5.42 0.101 641 5.22 5.62
## TCS19 5.50 0.112 641 5.28 5.72
##
## 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 0.1012 0.142 641 0.711 0.9540
## CCN51 - TCS06 0.4073 0.153 641 2.660 0.0612
## CCN51 - TCS13 0.2510 0.142 641 1.765 0.3953
## CCN51 - TCS19 0.1771 0.150 641 1.179 0.7635
## TCS01 - TCS06 0.3061 0.154 641 1.984 0.2751
## TCS01 - TCS13 0.1498 0.143 641 1.047 0.8333
## TCS01 - TCS19 0.0759 0.151 641 0.502 0.9871
## TCS06 - TCS13 -0.1563 0.154 641 -1.015 0.8485
## TCS06 - TCS19 -0.2303 0.161 641 -1.426 0.6108
## TCS13 - TCS19 -0.0739 0.151 641 -0.489 0.9883
##
## 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 5.67 0.100 641 5.48 5.87 A
## TCS01 5.57 0.101 641 5.37 5.77 A
## TCS19 5.50 0.112 641 5.28 5.72 A
## TCS13 5.42 0.101 641 5.22 5.62 A
## TCS06 5.27 0.116 641 5.04 5.49 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 5.37 0.0820 641 5.20 5.53
## Roble 5.72 0.0832 641 5.55 5.88
## Terminalia 5.38 0.0825 641 5.22 5.54
##
## 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 -0.3519 0.117 641 -3.005 0.0078
## Abarco - Terminalia -0.0119 0.116 641 -0.103 0.9942
## Roble - Terminalia 0.3400 0.117 641 2.902 0.0107
##
## 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
## Roble 5.72 0.0832 641 5.55 5.88 A
## Terminalia 5.38 0.0825 641 5.22 5.54 B
## Abarco 5.37 0.0820 641 5.20 5.53 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.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 5.66 0.171 641 5.32 5.99
## TCS01 Abarco 4.96 0.175 641 4.62 5.31
## TCS06 Abarco 5.03 0.204 641 4.63 5.43
## TCS13 Abarco 5.69 0.173 641 5.35 6.03
## TCS19 Abarco 5.49 0.189 641 5.12 5.86
## CCN51 Roble 5.90 0.166 641 5.57 6.23
## TCS01 Roble 5.70 0.185 641 5.34 6.07
## TCS06 Roble 5.77 0.201 641 5.37 6.16
## TCS13 Roble 5.46 0.176 641 5.12 5.81
## TCS19 Roble 5.75 0.196 641 5.37 6.14
## CCN51 Terminalia 5.46 0.182 641 5.11 5.82
## TCS01 Terminalia 6.05 0.166 641 5.72 6.38
## TCS06 Terminalia 5.01 0.198 641 4.62 5.39
## TCS13 Terminalia 5.12 0.176 641 4.77 5.46
## TCS19 Terminalia 5.25 0.198 641 4.86 5.64
##
## 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 0.693269 0.244 641 2.837 0.2299
## CCN51 Abarco - TCS06 Abarco 0.629313 0.266 641 2.364 0.5428
## CCN51 Abarco - TCS13 Abarco -0.029020 0.243 641 -0.119 1.0000
## CCN51 Abarco - TCS19 Abarco 0.168473 0.255 641 0.661 1.0000
## CCN51 Abarco - CCN51 Roble -0.242375 0.238 641 -1.017 0.9996
## CCN51 Abarco - TCS01 Roble -0.045174 0.252 641 -0.179 1.0000
## CCN51 Abarco - TCS06 Roble -0.108424 0.264 641 -0.411 1.0000
## CCN51 Abarco - TCS13 Roble 0.194395 0.246 641 0.792 1.0000
## CCN51 Abarco - TCS19 Roble -0.095986 0.261 641 -0.368 1.0000
## CCN51 Abarco - CCN51 Terminalia 0.193748 0.250 641 0.776 1.0000
## CCN51 Abarco - TCS01 Terminalia -0.393088 0.238 641 -1.649 0.9469
## CCN51 Abarco - TCS06 Terminalia 0.652490 0.262 641 2.491 0.4487
## CCN51 Abarco - TCS13 Terminalia 0.539041 0.246 641 2.194 0.6691
## CCN51 Abarco - TCS19 Terminalia 0.410130 0.262 641 1.567 0.9650
## TCS01 Abarco - TCS06 Abarco -0.063956 0.269 641 -0.238 1.0000
## TCS01 Abarco - TCS13 Abarco -0.722289 0.246 641 -2.940 0.1810
## TCS01 Abarco - TCS19 Abarco -0.524796 0.258 641 -2.036 0.7762
## TCS01 Abarco - CCN51 Roble -0.935644 0.241 641 -3.883 0.0097
## TCS01 Abarco - TCS01 Roble -0.738443 0.254 641 -2.910 0.1945
## TCS01 Abarco - TCS06 Roble -0.801693 0.266 641 -3.009 0.1528
## TCS01 Abarco - TCS13 Roble -0.498874 0.248 641 -2.012 0.7908
## TCS01 Abarco - TCS19 Roble -0.789255 0.262 641 -3.008 0.1530
## TCS01 Abarco - CCN51 Terminalia -0.499521 0.252 641 -1.980 0.8096
## TCS01 Abarco - TCS01 Terminalia -1.086357 0.241 641 -4.509 0.0007
## TCS01 Abarco - TCS06 Terminalia -0.040779 0.264 641 -0.154 1.0000
## TCS01 Abarco - TCS13 Terminalia -0.154228 0.248 641 -0.622 1.0000
## TCS01 Abarco - TCS19 Terminalia -0.283139 0.264 641 -1.073 0.9992
## TCS06 Abarco - TCS13 Abarco -0.658333 0.267 641 -2.462 0.4696
## TCS06 Abarco - TCS19 Abarco -0.460840 0.277 641 -1.661 0.9438
## TCS06 Abarco - CCN51 Roble -0.871688 0.263 641 -3.310 0.0668
## TCS06 Abarco - TCS01 Roble -0.674488 0.276 641 -2.440 0.4860
## TCS06 Abarco - TCS06 Roble -0.737737 0.287 641 -2.572 0.3909
## TCS06 Abarco - TCS13 Roble -0.434918 0.270 641 -1.610 0.9563
## TCS06 Abarco - TCS19 Roble -0.725299 0.285 641 -2.548 0.4075
## TCS06 Abarco - CCN51 Terminalia -0.435565 0.273 641 -1.593 0.9600
## TCS06 Abarco - TCS01 Terminalia -1.022401 0.263 641 -3.882 0.0097
## TCS06 Abarco - TCS06 Terminalia 0.023177 0.285 641 0.081 1.0000
## TCS06 Abarco - TCS13 Terminalia -0.090272 0.270 641 -0.334 1.0000
## TCS06 Abarco - TCS19 Terminalia -0.219183 0.285 641 -0.770 1.0000
## TCS13 Abarco - TCS19 Abarco 0.197493 0.256 641 0.771 1.0000
## TCS13 Abarco - CCN51 Roble -0.213355 0.240 641 -0.890 0.9999
## TCS13 Abarco - TCS01 Roble -0.016154 0.253 641 -0.064 1.0000
## TCS13 Abarco - TCS06 Roble -0.079404 0.265 641 -0.299 1.0000
## TCS13 Abarco - TCS13 Roble 0.223415 0.247 641 0.905 0.9999
## TCS13 Abarco - TCS19 Roble -0.066966 0.262 641 -0.256 1.0000
## TCS13 Abarco - CCN51 Terminalia 0.222768 0.251 641 0.888 0.9999
## TCS13 Abarco - TCS01 Terminalia -0.364068 0.240 641 -1.519 0.9732
## TCS13 Abarco - TCS06 Terminalia 0.681510 0.263 641 2.590 0.3786
## TCS13 Abarco - TCS13 Terminalia 0.568061 0.247 641 2.300 0.5908
## TCS13 Abarco - TCS19 Terminalia 0.439150 0.263 641 1.671 0.9412
## TCS19 Abarco - CCN51 Roble -0.410849 0.252 641 -1.631 0.9514
## TCS19 Abarco - TCS01 Roble -0.213648 0.265 641 -0.806 1.0000
## TCS19 Abarco - TCS06 Roble -0.276897 0.276 641 -1.004 0.9996
## TCS19 Abarco - TCS13 Roble 0.025922 0.259 641 0.100 1.0000
## TCS19 Abarco - TCS19 Roble -0.264459 0.274 641 -0.967 0.9997
## TCS19 Abarco - CCN51 Terminalia 0.025275 0.262 641 0.096 1.0000
## TCS19 Abarco - TCS01 Terminalia -0.561561 0.252 641 -2.229 0.6433
## TCS19 Abarco - TCS06 Terminalia 0.484017 0.275 641 1.762 0.9121
## TCS19 Abarco - TCS13 Terminalia 0.370567 0.259 641 1.431 0.9843
## TCS19 Abarco - TCS19 Terminalia 0.241656 0.274 641 0.882 0.9999
## CCN51 Roble - TCS01 Roble 0.197201 0.248 641 0.794 1.0000
## CCN51 Roble - TCS06 Roble 0.133951 0.261 641 0.513 1.0000
## CCN51 Roble - TCS13 Roble 0.436771 0.242 641 1.804 0.8963
## CCN51 Roble - TCS19 Roble 0.146390 0.257 641 0.569 1.0000
## CCN51 Roble - CCN51 Terminalia 0.436123 0.246 641 1.769 0.9095
## CCN51 Roble - TCS01 Terminalia -0.150713 0.235 641 -0.642 1.0000
## CCN51 Roble - TCS06 Terminalia 0.894865 0.259 641 3.459 0.0422
## CCN51 Roble - TCS13 Terminalia 0.781416 0.242 641 3.225 0.0854
## CCN51 Roble - TCS19 Terminalia 0.652505 0.259 641 2.524 0.4246
## TCS01 Roble - TCS06 Roble -0.063250 0.273 641 -0.232 1.0000
## TCS01 Roble - TCS13 Roble 0.239570 0.255 641 0.939 0.9998
## TCS01 Roble - TCS19 Roble -0.050811 0.269 641 -0.189 1.0000
## TCS01 Roble - CCN51 Terminalia 0.238923 0.259 641 0.921 0.9999
## TCS01 Roble - TCS01 Terminalia -0.347913 0.248 641 -1.401 0.9871
## TCS01 Roble - TCS06 Terminalia 0.697664 0.271 641 2.572 0.3905
## TCS01 Roble - TCS13 Terminalia 0.584215 0.255 641 2.289 0.5992
## TCS01 Roble - TCS19 Terminalia 0.455304 0.271 641 1.682 0.9381
## TCS06 Roble - TCS13 Roble 0.302819 0.268 641 1.131 0.9985
## TCS06 Roble - TCS19 Roble 0.012438 0.281 641 0.044 1.0000
## TCS06 Roble - CCN51 Terminalia 0.302172 0.271 641 1.114 0.9987
## TCS06 Roble - TCS01 Terminalia -0.284664 0.261 641 -1.090 0.9990
## TCS06 Roble - TCS06 Terminalia 0.760914 0.283 641 2.687 0.3153
## TCS06 Roble - TCS13 Terminalia 0.647465 0.268 641 2.418 0.5020
## TCS06 Roble - TCS19 Terminalia 0.518554 0.282 641 1.837 0.8823
## TCS13 Roble - TCS19 Roble -0.290381 0.264 641 -1.101 0.9989
## TCS13 Roble - CCN51 Terminalia -0.000647 0.254 641 -0.003 1.0000
## TCS13 Roble - TCS01 Terminalia -0.587483 0.242 641 -2.426 0.4963
## TCS13 Roble - TCS06 Terminalia 0.458095 0.265 641 1.727 0.9243
## TCS13 Roble - TCS13 Terminalia 0.344645 0.249 641 1.383 0.9886
## TCS13 Roble - TCS19 Terminalia 0.215734 0.265 641 0.814 1.0000
## TCS19 Roble - CCN51 Terminalia 0.289734 0.268 641 1.082 0.9991
## TCS19 Roble - TCS01 Terminalia -0.297102 0.257 641 -1.155 0.9982
## TCS19 Roble - TCS06 Terminalia 0.748476 0.279 641 2.680 0.3197
## TCS19 Roble - TCS13 Terminalia 0.635027 0.264 641 2.407 0.5103
## TCS19 Roble - TCS19 Terminalia 0.506116 0.279 641 1.815 0.8916
## CCN51 Terminalia - TCS01 Terminalia -0.586836 0.246 641 -2.381 0.5301
## CCN51 Terminalia - TCS06 Terminalia 0.458742 0.270 641 1.702 0.9322
## CCN51 Terminalia - TCS13 Terminalia 0.345293 0.254 641 1.362 0.9902
## CCN51 Terminalia - TCS19 Terminalia 0.216382 0.269 641 0.804 1.0000
## TCS01 Terminalia - TCS06 Terminalia 1.045578 0.259 641 4.041 0.0053
## TCS01 Terminalia - TCS13 Terminalia 0.932129 0.242 641 3.847 0.0110
## TCS01 Terminalia - TCS19 Terminalia 0.803218 0.259 641 3.107 0.1184
## TCS06 Terminalia - TCS13 Terminalia -0.113449 0.265 641 -0.427 1.0000
## TCS06 Terminalia - TCS19 Terminalia -0.242360 0.280 641 -0.864 0.9999
## TCS13 Terminalia - TCS19 Terminalia -0.128911 0.265 641 -0.486 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
## TCS01 Terminalia 6.05 0.166 641 5.72 6.38 A
## CCN51 Roble 5.90 0.166 641 5.57 6.23 AB
## TCS06 Roble 5.77 0.201 641 5.37 6.16 ABC
## TCS19 Roble 5.75 0.196 641 5.37 6.14 ABC
## TCS01 Roble 5.70 0.185 641 5.34 6.07 ABC
## TCS13 Abarco 5.69 0.173 641 5.35 6.03 ABC
## CCN51 Abarco 5.66 0.171 641 5.32 5.99 ABC
## TCS19 Abarco 5.49 0.189 641 5.12 5.86 ABC
## CCN51 Terminalia 5.46 0.182 641 5.11 5.82 ABC
## TCS13 Roble 5.46 0.176 641 5.12 5.81 ABC
## TCS19 Terminalia 5.25 0.198 641 4.86 5.64 ABC
## TCS13 Terminalia 5.12 0.176 641 4.77 5.46 BC
## TCS06 Abarco 5.03 0.204 641 4.63 5.43 BC
## TCS06 Terminalia 5.01 0.198 641 4.62 5.39 C
## TCS01 Abarco 4.96 0.175 641 4.62 5.31 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 201 3.79 641 193 208
## TCS01 184 3.83 641 177 192
## TCS06 171 4.40 641 162 180
## TCS13 178 3.83 641 171 186
## TCS19 170 4.25 641 162 178
##
## 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 16.478 5.39 641 3.057 0.0197
## CCN51 - TCS06 29.717 5.80 641 5.126 <.0001
## CCN51 - TCS13 22.340 5.38 641 4.149 0.0004
## CCN51 - TCS19 30.706 5.69 641 5.400 <.0001
## TCS01 - TCS06 13.239 5.84 641 2.266 0.1573
## TCS01 - TCS13 5.862 5.42 641 1.082 0.8157
## TCS01 - TCS19 14.228 5.72 641 2.488 0.0945
## TCS06 - TCS13 -7.377 5.83 641 -1.265 0.7127
## TCS06 - TCS19 0.989 6.11 641 0.162 0.9998
## TCS13 - TCS19 8.365 5.72 641 1.463 0.5872
##
## 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 201 3.79 641 193 208 A
## TCS01 184 3.83 641 177 192 B
## TCS13 178 3.83 641 171 186 B
## TCS06 171 4.40 641 162 180 B
## TCS19 170 4.25 641 162 178 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 172 3.10 641 166 178
## Roble 190 3.15 641 184 196
## Terminalia 181 3.12 641 175 187
##
## 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 -18.24 4.43 641 -4.114 0.0001
## Abarco - Terminalia -9.08 4.40 641 -2.061 0.0989
## Roble - Terminalia 9.16 4.44 641 2.065 0.0979
##
## 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
## Roble 190 3.15 641 184 196 A
## Terminalia 181 3.12 641 175 187 AB
## Abarco 172 3.10 641 166 178 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", TCS06 Roble - TCS01 Terminalia:
## Target overlap = 2e-04, overlap on graph = -7e-04
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 205 6.47 641 192 218
## TCS01 Abarco 142 6.61 641 129 155
## TCS06 Abarco 160 7.74 641 145 175
## TCS13 Abarco 191 6.54 641 178 204
## TCS19 Abarco 160 7.17 641 146 174
## CCN51 Roble 217 6.29 641 204 229
## TCS01 Roble 205 6.99 641 192 219
## TCS06 Roble 171 7.62 641 156 186
## TCS13 Roble 178 6.67 641 165 192
## TCS19 Roble 178 7.43 641 163 192
## CCN51 Terminalia 180 6.89 641 167 194
## TCS01 Terminalia 205 6.29 641 193 217
## TCS06 Terminalia 181 7.51 641 166 196
## TCS13 Terminalia 165 6.68 641 152 178
## TCS19 Terminalia 172 7.50 641 157 187
##
## 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 62.804 9.25 641 6.789 <.0001
## CCN51 Abarco - TCS06 Abarco 44.647 10.08 641 4.430 0.0011
## CCN51 Abarco - TCS13 Abarco 13.769 9.20 641 1.497 0.9765
## CCN51 Abarco - TCS19 Abarco 44.763 9.65 641 4.641 0.0004
## CCN51 Abarco - CCN51 Roble -11.681 9.02 641 -1.294 0.9940
## CCN51 Abarco - TCS01 Roble -0.480 9.53 641 -0.050 1.0000
## CCN51 Abarco - TCS06 Roble 33.483 10.00 641 3.350 0.0592
## CCN51 Abarco - TCS13 Roble 26.357 9.30 641 2.835 0.2310
## CCN51 Abarco - TCS19 Roble 27.120 9.86 641 2.750 0.2776
## CCN51 Abarco - CCN51 Terminalia 24.413 9.45 641 2.583 0.3835
## CCN51 Abarco - TCS01 Terminalia -0.158 9.02 641 -0.018 1.0000
## CCN51 Abarco - TCS06 Terminalia 23.754 9.92 641 2.395 0.5193
## CCN51 Abarco - TCS13 Terminalia 39.627 9.30 641 4.261 0.0022
## CCN51 Abarco - TCS19 Terminalia 32.967 9.91 641 3.328 0.0632
## TCS01 Abarco - TCS06 Abarco -18.157 10.19 641 -1.782 0.9047
## TCS01 Abarco - TCS13 Abarco -49.035 9.30 641 -5.272 <.0001
## TCS01 Abarco - TCS19 Abarco -18.041 9.76 641 -1.849 0.8770
## TCS01 Abarco - CCN51 Roble -74.485 9.12 641 -8.167 <.0001
## TCS01 Abarco - TCS01 Roble -63.284 9.61 641 -6.587 <.0001
## TCS01 Abarco - TCS06 Roble -29.322 10.09 641 -2.907 0.1958
## TCS01 Abarco - TCS13 Roble -36.447 9.39 641 -3.883 0.0097
## TCS01 Abarco - TCS19 Roble -35.685 9.93 641 -3.593 0.0272
## TCS01 Abarco - CCN51 Terminalia -38.392 9.55 641 -4.020 0.0057
## TCS01 Abarco - TCS01 Terminalia -62.962 9.12 641 -6.903 <.0001
## TCS01 Abarco - TCS06 Terminalia -39.050 10.00 641 -3.904 0.0090
## TCS01 Abarco - TCS13 Terminalia -23.178 9.39 641 -2.468 0.4652
## TCS01 Abarco - TCS19 Terminalia -29.838 9.99 641 -2.986 0.1619
## TCS06 Abarco - TCS13 Abarco -30.878 10.12 641 -3.050 0.1374
## TCS06 Abarco - TCS19 Abarco 0.116 10.50 641 0.011 1.0000
## TCS06 Abarco - CCN51 Roble -56.328 9.97 641 -5.649 <.0001
## TCS06 Abarco - TCS01 Roble -45.127 10.47 641 -4.312 0.0017
## TCS06 Abarco - TCS06 Roble -11.165 10.86 641 -1.028 0.9995
## TCS06 Abarco - TCS13 Roble -18.290 10.23 641 -1.788 0.9023
## TCS06 Abarco - TCS19 Roble -17.528 10.78 641 -1.627 0.9524
## TCS06 Abarco - CCN51 Terminalia -20.235 10.35 641 -1.954 0.8240
## TCS06 Abarco - TCS01 Terminalia -44.805 9.97 641 -4.494 0.0008
## TCS06 Abarco - TCS06 Terminalia -20.893 10.79 641 -1.937 0.8337
## TCS06 Abarco - TCS13 Terminalia -5.021 10.23 641 -0.491 1.0000
## TCS06 Abarco - TCS19 Terminalia -11.681 10.78 641 -1.084 0.9991
## TCS13 Abarco - TCS19 Abarco 30.994 9.69 641 3.197 0.0925
## TCS13 Abarco - CCN51 Roble -25.450 9.07 641 -2.805 0.2469
## TCS13 Abarco - TCS01 Roble -14.249 9.58 641 -1.487 0.9778
## TCS13 Abarco - TCS06 Roble 19.713 10.04 641 1.963 0.8194
## TCS13 Abarco - TCS13 Roble 12.588 9.35 641 1.347 0.9912
## TCS13 Abarco - TCS19 Roble 13.350 9.91 641 1.347 0.9911
## TCS13 Abarco - CCN51 Terminalia 10.643 9.50 641 1.120 0.9987
## TCS13 Abarco - TCS01 Terminalia -13.928 9.07 641 -1.535 0.9707
## TCS13 Abarco - TCS06 Terminalia 9.985 9.96 641 1.002 0.9996
## TCS13 Abarco - TCS13 Terminalia 25.857 9.35 641 2.766 0.2684
## TCS13 Abarco - TCS19 Terminalia 19.197 9.95 641 1.929 0.8378
## TCS19 Abarco - CCN51 Roble -56.444 9.54 641 -5.919 <.0001
## TCS19 Abarco - TCS01 Roble -45.243 10.04 641 -4.508 0.0007
## TCS19 Abarco - TCS06 Roble -11.281 10.44 641 -1.080 0.9991
## TCS19 Abarco - TCS13 Roble -18.406 9.80 641 -1.877 0.8638
## TCS19 Abarco - TCS19 Roble -17.643 10.36 641 -1.703 0.9317
## TCS19 Abarco - CCN51 Terminalia -20.350 9.93 641 -2.050 0.7672
## TCS19 Abarco - TCS01 Terminalia -44.921 9.54 641 -4.711 0.0003
## TCS19 Abarco - TCS06 Terminalia -21.009 10.40 641 -2.020 0.7856
## TCS19 Abarco - TCS13 Terminalia -5.136 9.80 641 -0.524 1.0000
## TCS19 Abarco - TCS19 Terminalia -11.796 10.37 641 -1.138 0.9984
## CCN51 Roble - TCS01 Roble 11.201 9.40 641 1.191 0.9974
## CCN51 Roble - TCS06 Roble 45.163 9.88 641 4.570 0.0006
## CCN51 Roble - TCS13 Roble 38.038 9.17 641 4.149 0.0034
## CCN51 Roble - TCS19 Roble 38.800 9.74 641 3.985 0.0066
## CCN51 Roble - CCN51 Terminalia 36.093 9.33 641 3.868 0.0102
## CCN51 Roble - TCS01 Terminalia 11.523 8.89 641 1.296 0.9939
## CCN51 Roble - TCS06 Terminalia 35.435 9.79 641 3.618 0.0250
## CCN51 Roble - TCS13 Terminalia 51.307 9.17 641 5.594 <.0001
## CCN51 Roble - TCS19 Terminalia 44.647 9.79 641 4.562 0.0006
## TCS01 Roble - TCS06 Roble 33.963 10.33 641 3.287 0.0713
## TCS01 Roble - TCS13 Roble 26.837 9.66 641 2.779 0.2611
## TCS01 Roble - TCS19 Roble 27.600 10.16 641 2.715 0.2979
## TCS01 Roble - CCN51 Terminalia 24.893 9.82 641 2.535 0.4167
## TCS01 Roble - TCS01 Terminalia 0.322 9.40 641 0.034 1.0000
## TCS01 Roble - TCS06 Terminalia 24.234 10.27 641 2.360 0.5454
## TCS01 Roble - TCS13 Terminalia 40.107 9.66 641 4.151 0.0034
## TCS01 Roble - TCS19 Terminalia 33.447 10.25 641 3.263 0.0765
## TCS06 Roble - TCS13 Roble -7.126 10.14 641 -0.703 1.0000
## TCS06 Roble - TCS19 Roble -6.363 10.64 641 -0.598 1.0000
## TCS06 Roble - CCN51 Terminalia -9.070 10.26 641 -0.884 0.9999
## TCS06 Roble - TCS01 Terminalia -33.641 9.88 641 -3.404 0.0501
## TCS06 Roble - TCS06 Terminalia -9.728 10.72 641 -0.907 0.9999
## TCS06 Roble - TCS13 Terminalia 6.144 10.14 641 0.606 1.0000
## TCS06 Roble - TCS19 Terminalia -0.516 10.69 641 -0.048 1.0000
## TCS13 Roble - TCS19 Roble 0.763 9.98 641 0.076 1.0000
## TCS13 Roble - CCN51 Terminalia -1.944 9.60 641 -0.203 1.0000
## TCS13 Roble - TCS01 Terminalia -26.515 9.17 641 -2.892 0.2027
## TCS13 Roble - TCS06 Terminalia -2.603 10.04 641 -0.259 1.0000
## TCS13 Roble - TCS13 Terminalia 13.270 9.44 641 1.406 0.9867
## TCS13 Roble - TCS19 Terminalia 6.610 10.04 641 0.658 1.0000
## TCS19 Roble - CCN51 Terminalia -2.707 10.14 641 -0.267 1.0000
## TCS19 Roble - TCS01 Terminalia -27.278 9.74 641 -2.801 0.2486
## TCS19 Roble - TCS06 Terminalia -3.365 10.57 641 -0.318 1.0000
## TCS19 Roble - TCS13 Terminalia 12.507 9.99 641 1.252 0.9957
## TCS19 Roble - TCS19 Terminalia 5.847 10.56 641 0.554 1.0000
## CCN51 Terminalia - TCS01 Terminalia -24.571 9.33 641 -2.633 0.3496
## CCN51 Terminalia - TCS06 Terminalia -0.659 10.20 641 -0.065 1.0000
## CCN51 Terminalia - TCS13 Terminalia 15.214 9.60 641 1.585 0.9616
## CCN51 Terminalia - TCS19 Terminalia 8.554 10.18 641 0.840 1.0000
## TCS01 Terminalia - TCS06 Terminalia 23.912 9.79 641 2.441 0.4849
## TCS01 Terminalia - TCS13 Terminalia 39.785 9.17 641 4.338 0.0016
## TCS01 Terminalia - TCS19 Terminalia 33.125 9.79 641 3.385 0.0532
## TCS06 Terminalia - TCS13 Terminalia 15.873 10.05 641 1.580 0.9626
## TCS06 Terminalia - TCS19 Terminalia 9.212 10.62 641 0.868 0.9999
## TCS13 Terminalia - TCS19 Terminalia -6.660 10.04 641 -0.663 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 Roble 217 6.29 641 204 229 A
## TCS01 Roble 205 6.99 641 192 219 AB
## TCS01 Terminalia 205 6.29 641 193 217 AB
## CCN51 Abarco 205 6.47 641 192 218 AB
## TCS13 Abarco 191 6.54 641 178 204 ABC
## TCS06 Terminalia 181 7.51 641 166 196 BC
## CCN51 Terminalia 180 6.89 641 167 194 BC
## TCS13 Roble 178 6.67 641 165 192 BC
## TCS19 Roble 178 7.43 641 163 192 BC
## TCS19 Terminalia 172 7.50 641 157 187 BCD
## TCS06 Roble 171 7.62 641 156 186 BCD
## TCS13 Terminalia 165 6.68 641 152 178 CD
## TCS06 Abarco 160 7.74 641 145 175 CD
## TCS19 Abarco 160 7.17 641 146 174 CD
## TCS01 Abarco 142 6.61 641 129 155 D
##
## 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(datos3)