setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura/BD Estadística SGR Santander/data")
datos4<-read.table("sanjose.csv", header=T, sep=',')
datos4$gen<-as.factor(datos4$gen)
datos4$forestal<-as.factor(datos4$forestal)
datos4$bloque<-as.factor(datos4$bloque)
attach(datos4)
library(ggplot2)
library(emmeans)
#Gráfica diámetro
ggplot(datos4, aes(semana, diam, group = gen, colour = gen)) +
facet_grid(~forestal) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Diámetro") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 324 rows containing non-finite values (stat_smooth).
# Gráfica altura
ggplot(datos4, aes(semana, alt, group = gen, colour = gen)) +
facet_grid(~forestal) +
geom_smooth(method="lm", se=F) +
theme_classic() +
xlab ("Semana") +
ylab ("Altura") +
labs(colour = "Genotipo") +
theme_linedraw() +
theme(
plot.title = element_text(hjust = 0.5, size = 16),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14)
)
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 323 rows containing non-finite values (stat_smooth).
# Anova general
aov.diam<-aov(diam~semana+forestal+gen+bloque+forestal*gen, na.action=na.exclude)
aov.alt<-aov(alt~semana+forestal+gen+bloque+forestal*gen, na.action=na.exclude)
#Análisis para diámetro
library(nlme)
fit.compsym.diam <- gls(diam ~ semana+forestal+gen+bloque+forestal*gen, data=datos4, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.diam <- gls(diam ~ semana+forestal+gen+bloque+forestal*gen, data=datos4, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.diam <- gls(diam ~ semana+forestal+gen+bloque+forestal*gen, data=datos4, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.diam, fit.ar1.diam, fit.ar1het.diam) #compares the models
## Model df AIC BIC logLik Test L.Ratio p-value
## fit.compsym.diam 1 20 8163.675 8284.892 -4061.838
## fit.ar1.diam 2 20 7792.005 7913.222 -3876.002
## fit.ar1het.diam 3 32 7457.409 7651.356 -3696.705 2 vs 3 358.5956 <.0001
anova(fit.ar1.diam)
## Denom. DF: 3168
## numDF F-value p-value
## (Intercept) 1 28540.519 <.0001
## semana 1 2628.904 <.0001
## forestal 2 61.403 <.0001
## gen 4 14.295 <.0001
## bloque 2 318.469 <.0001
## forestal:gen 8 2.886 0.0033
anova(fit.ar1het.diam)
## Denom. DF: 3168
## numDF F-value p-value
## (Intercept) 1 26225.351 <.0001
## semana 1 3035.116 <.0001
## forestal 2 38.642 <.0001
## gen 4 6.158 0.0001
## bloque 2 207.556 <.0001
## forestal:gen 8 1.771 0.0779
# Análisis para altura
fit.compsym.alt <- gls(alt ~ semana+forestal+gen+bloque+forestal*gen, data=datos4, corr=corCompSymm(, form= ~ 1 | gen),na.action=na.exclude)
fit.ar1.alt <- gls(alt ~ semana+forestal+gen+bloque+forestal*gen, data=datos4, corr=corAR1(, form= ~ 1 | gen), na.action=na.exclude)
fit.ar1het.alt <- gls(alt ~ semana+forestal+gen+bloque+forestal*gen, data=datos4, corr=corAR1(, form= ~ 1 | gen), weights=varIdent(form = ~ 1 | semana), na.action=na.exclude)
anova(fit.compsym.alt, fit.ar1.alt, fit.ar1het.alt) #compares the models
## Model df AIC BIC logLik Test L.Ratio p-value
## fit.compsym.alt 1 20 31358.14 31479.36 -15659.07
## fit.ar1.alt 2 20 31038.88 31160.11 -15499.44
## fit.ar1het.alt 3 32 30620.78 30814.73 -15278.39 2 vs 3 442.1059 <.0001
anova(fit.ar1.alt)
## Denom. DF: 3169
## numDF F-value p-value
## (Intercept) 1 28030.656 <.0001
## semana 1 2715.291 <.0001
## forestal 2 32.928 <.0001
## gen 4 12.439 <.0001
## bloque 2 185.253 <.0001
## forestal:gen 8 2.013 0.0413
anova(fit.ar1het.alt)
## Denom. DF: 3169
## numDF F-value p-value
## (Intercept) 1 27898.592 <.0001
## semana 1 3632.329 <.0001
## forestal 2 20.631 <.0001
## gen 4 9.074 <.0001
## bloque 2 114.012 <.0001
## forestal:gen 8 1.087 0.3686
#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
fores.tuk.diam<-TukeyHSD(aov.diam, "forestal", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
gen.tuk.diam<-TukeyHSD(aov.diam, "gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
#Tukey altura
interac.tuk.alt<-TukeyHSD(aov.alt, "forestal:gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
fores.tuk.alt<-TukeyHSD(aov.alt, "forestal", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
gen.tuk.alt<-TukeyHSD(aov.alt, "gen", ordered = TRUE)
## Warning in replications(paste("~", xx), data = mf): non-factors ignored: semana
#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 b TCS01
## TCS06 a TCS06
## TCS13 c 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 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 cd Abarco:CCN51
## Abarco:TCS01 ef Abarco:TCS01
## Abarco:TCS06 acd Abarco:TCS06
## Abarco:TCS13 f Abarco:TCS13
## Abarco:TCS19 cd Abarco:TCS19
## Roble:CCN51 ac Roble:CCN51
## Roble:TCS01 acd Roble:TCS01
## Roble:TCS06 ab Roble:TCS06
## Roble:TCS13 de Roble:TCS13
## Roble:TCS19 ac Roble:TCS19
## Terminalia:CCN51 b Terminalia:CCN51
## Terminalia:TCS01 ab Terminalia:TCS01
## Terminalia:TCS06 ab Terminalia:TCS06
## Terminalia:TCS13 acd 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 ab CCN51
## TCS01 b TCS01
## TCS06 a TCS06
## TCS13 b TCS13
## TCS19 c 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 de Abarco:CCN51
## Abarco:TCS01 e Abarco:TCS01
## Abarco:TCS06 cd Abarco:TCS06
## Abarco:TCS13 e Abarco:TCS13
## Abarco:TCS19 abc Abarco:TCS19
## Roble:CCN51 ac Roble:CCN51
## Roble:TCS01 cd Roble:TCS01
## Roble:TCS06 abc Roble:TCS06
## Roble:TCS13 cd Roble:TCS13
## Roble:TCS19 ab Roble:TCS19
## Terminalia:CCN51 ac Terminalia:CCN51
## Terminalia:TCS01 ac Terminalia:TCS01
## Terminalia:TCS06 ac Terminalia:TCS06
## Terminalia:TCS13 ac 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 3.44 0.0337 3168 3.38 3.51
## TCS01 3.69 0.0335 3168 3.62 3.75
## TCS06 3.44 0.0348 3168 3.37 3.51
## TCS13 3.88 0.0339 3168 3.82 3.95
## TCS19 3.53 0.0341 3168 3.46 3.60
##
## 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 -0.24456 0.0476 3168 -5.143 <.0001
## CCN51 - TCS06 0.00461 0.0484 3168 0.095 1.0000
## CCN51 - TCS13 -0.43814 0.0478 3168 -9.165 <.0001
## CCN51 - TCS19 -0.08448 0.0479 3168 -1.764 0.3952
## TCS01 - TCS06 0.24917 0.0484 3168 5.154 <.0001
## TCS01 - TCS13 -0.19358 0.0477 3168 -4.058 0.0005
## TCS01 - TCS19 0.16008 0.0478 3168 3.348 0.0073
## TCS06 - TCS13 -0.44275 0.0486 3168 -9.110 <.0001
## TCS06 - TCS19 -0.08910 0.0487 3168 -1.830 0.3565
## TCS13 - TCS19 0.35366 0.0481 3168 7.358 <.0001
##
## 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
## TCS13 3.88 0.0339 3168 3.82 3.95 A
## TCS01 3.69 0.0335 3168 3.62 3.75 B
## TCS19 3.53 0.0341 3168 3.46 3.60 C
## CCN51 3.44 0.0337 3168 3.38 3.51 C
## TCS06 3.44 0.0348 3168 3.37 3.51 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.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 3.86 0.0258 3168 3.81 3.91
## Roble 3.56 0.0266 3168 3.51 3.61
## Terminalia 3.37 0.0267 3168 3.32 3.42
##
## 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 0.302 0.0370 3168 8.148 <.0001
## Abarco - Terminalia 0.490 0.0371 3168 13.197 <.0001
## Roble - Terminalia 0.188 0.0377 3168 4.994 <.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 3.86 0.0258 3168 3.81 3.91 A
## Roble 3.56 0.0266 3168 3.51 3.61 B
## Terminalia 3.37 0.0267 3168 3.32 3.42 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)
plot(contrast, comparisons = TRUE, xlab ="Diámetro")
medias.forestal.gen <- emmeans(aov.diam, pairwise ~ gen*forestal)
medias.forestal.gen
## $emmeans
## gen forestal emmean SE df lower.CL upper.CL
## CCN51 Abarco 3.72 0.0567 3168 3.61 3.83
## TCS01 Abarco 4.06 0.0564 3168 3.95 4.17
## TCS06 Abarco 3.62 0.0590 3168 3.50 3.73
## TCS13 Abarco 4.20 0.0576 3168 4.09 4.32
## TCS19 Abarco 3.70 0.0583 3168 3.59 3.82
## CCN51 Roble 3.48 0.0589 3168 3.37 3.60
## TCS01 Roble 3.59 0.0591 3168 3.48 3.71
## TCS06 Roble 3.33 0.0619 3168 3.21 3.45
## TCS13 Roble 3.86 0.0583 3168 3.74 3.97
## TCS19 Roble 3.53 0.0588 3168 3.42 3.65
## CCN51 Terminalia 3.13 0.0595 3168 3.02 3.25
## TCS01 Terminalia 3.41 0.0587 3168 3.30 3.53
## TCS06 Terminalia 3.37 0.0599 3168 3.26 3.49
## TCS13 Terminalia 3.59 0.0603 3168 3.47 3.70
## TCS19 Terminalia 3.35 0.0599 3168 3.23 3.47
##
## 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 -0.3428 0.0799 3168 -4.287 0.0017
## CCN51 Abarco - TCS06 Abarco 0.1024 0.0818 3168 1.251 0.9959
## CCN51 Abarco - TCS13 Abarco -0.4852 0.0808 3168 -6.009 <.0001
## CCN51 Abarco - TCS19 Abarco 0.0166 0.0813 3168 0.204 1.0000
## CCN51 Abarco - CCN51 Roble 0.2375 0.0817 3168 2.906 0.1940
## CCN51 Abarco - TCS01 Roble 0.1257 0.0818 3168 1.535 0.9711
## CCN51 Abarco - TCS06 Roble 0.3904 0.0839 3168 4.653 0.0003
## CCN51 Abarco - TCS13 Roble -0.1394 0.0813 3168 -1.713 0.9293
## CCN51 Abarco - TCS19 Roble 0.1843 0.0816 3168 2.258 0.6220
## CCN51 Abarco - CCN51 Terminalia 0.5858 0.0821 3168 7.131 <.0001
## CCN51 Abarco - TCS01 Terminalia 0.3066 0.0816 3168 3.757 0.0145
## CCN51 Abarco - TCS06 Terminalia 0.3443 0.0824 3168 4.176 0.0028
## CCN51 Abarco - TCS13 Terminalia 0.1334 0.0828 3168 1.611 0.9565
## CCN51 Abarco - TCS19 Terminalia 0.3689 0.0825 3168 4.473 0.0008
## TCS01 Abarco - TCS06 Abarco 0.4451 0.0816 3168 5.452 <.0001
## TCS01 Abarco - TCS13 Abarco -0.1425 0.0806 3168 -1.768 0.9108
## TCS01 Abarco - TCS19 Abarco 0.3593 0.0812 3168 4.428 0.0009
## TCS01 Abarco - CCN51 Roble 0.5802 0.0815 3168 7.115 <.0001
## TCS01 Abarco - TCS01 Roble 0.4684 0.0817 3168 5.735 <.0001
## TCS01 Abarco - TCS06 Roble 0.7332 0.0837 3168 8.754 <.0001
## TCS01 Abarco - TCS13 Roble 0.2034 0.0812 3168 2.506 0.4360
## TCS01 Abarco - TCS19 Roble 0.5271 0.0815 3168 6.471 <.0001
## TCS01 Abarco - CCN51 Terminalia 0.9285 0.0820 3168 11.327 <.0001
## TCS01 Abarco - TCS01 Terminalia 0.6494 0.0814 3168 7.974 <.0001
## TCS01 Abarco - TCS06 Terminalia 0.6870 0.0823 3168 8.351 <.0001
## TCS01 Abarco - TCS13 Terminalia 0.4761 0.0826 3168 5.764 <.0001
## TCS01 Abarco - TCS19 Terminalia 0.7116 0.0823 3168 8.648 <.0001
## TCS06 Abarco - TCS13 Abarco -0.5876 0.0824 3168 -7.128 <.0001
## TCS06 Abarco - TCS19 Abarco -0.0858 0.0830 3168 -1.034 0.9995
## TCS06 Abarco - CCN51 Roble 0.1351 0.0834 3168 1.620 0.9545
## TCS06 Abarco - TCS01 Roble 0.0233 0.0835 3168 0.279 1.0000
## TCS06 Abarco - TCS06 Roble 0.2881 0.0855 3168 3.369 0.0541
## TCS06 Abarco - TCS13 Roble -0.2417 0.0830 3168 -2.912 0.1911
## TCS06 Abarco - TCS19 Roble 0.0820 0.0833 3168 0.984 0.9997
## TCS06 Abarco - CCN51 Terminalia 0.4834 0.0838 3168 5.770 <.0001
## TCS06 Abarco - TCS01 Terminalia 0.2043 0.0833 3168 2.453 0.4753
## TCS06 Abarco - TCS06 Terminalia 0.2419 0.0841 3168 2.877 0.2074
## TCS06 Abarco - TCS13 Terminalia 0.0310 0.0844 3168 0.367 1.0000
## TCS06 Abarco - TCS19 Terminalia 0.2665 0.0841 3168 3.170 0.0977
## TCS13 Abarco - TCS19 Abarco 0.5018 0.0820 3168 6.123 <.0001
## TCS13 Abarco - CCN51 Roble 0.7227 0.0823 3168 8.778 <.0001
## TCS13 Abarco - TCS01 Roble 0.6109 0.0825 3168 7.406 <.0001
## TCS13 Abarco - TCS06 Roble 0.8756 0.0845 3168 10.362 <.0001
## TCS13 Abarco - TCS13 Roble 0.3459 0.0820 3168 4.220 0.0023
## TCS13 Abarco - TCS19 Roble 0.6695 0.0822 3168 8.142 <.0001
## TCS13 Abarco - CCN51 Terminalia 1.0710 0.0827 3168 12.944 <.0001
## TCS13 Abarco - TCS01 Terminalia 0.7918 0.0822 3168 9.629 <.0001
## TCS13 Abarco - TCS06 Terminalia 0.8295 0.0830 3168 9.988 <.0001
## TCS13 Abarco - TCS13 Terminalia 0.6186 0.0834 3168 7.420 <.0001
## TCS13 Abarco - TCS19 Terminalia 0.8541 0.0831 3168 10.284 <.0001
## TCS19 Abarco - CCN51 Roble 0.2209 0.0829 3168 2.664 0.3274
## TCS19 Abarco - TCS01 Roble 0.1091 0.0830 3168 1.314 0.9932
## TCS19 Abarco - TCS06 Roble 0.3739 0.0851 3168 4.395 0.0011
## TCS19 Abarco - TCS13 Roble -0.1559 0.0825 3168 -1.890 0.8584
## TCS19 Abarco - TCS19 Roble 0.1677 0.0828 3168 2.026 0.7831
## TCS19 Abarco - CCN51 Terminalia 0.5692 0.0833 3168 6.830 <.0001
## TCS19 Abarco - TCS01 Terminalia 0.2901 0.0828 3168 3.504 0.0351
## TCS19 Abarco - TCS06 Terminalia 0.3277 0.0836 3168 3.919 0.0079
## TCS19 Abarco - TCS13 Terminalia 0.1168 0.0839 3168 1.391 0.9882
## TCS19 Abarco - TCS19 Terminalia 0.3523 0.0836 3168 4.212 0.0024
## CCN51 Roble - TCS01 Roble -0.1118 0.0834 3168 -1.340 0.9917
## CCN51 Roble - TCS06 Roble 0.1530 0.0854 3168 1.791 0.9021
## CCN51 Roble - TCS13 Roble -0.3768 0.0829 3168 -4.545 0.0006
## CCN51 Roble - TCS19 Roble -0.0531 0.0832 3168 -0.639 1.0000
## CCN51 Roble - CCN51 Terminalia 0.3483 0.0837 3168 4.162 0.0030
## CCN51 Roble - TCS01 Terminalia 0.0692 0.0832 3168 0.832 1.0000
## CCN51 Roble - TCS06 Terminalia 0.1068 0.0840 3168 1.272 0.9951
## CCN51 Roble - TCS13 Terminalia -0.1041 0.0843 3168 -1.235 0.9964
## CCN51 Roble - TCS19 Terminalia 0.1314 0.0840 3168 1.565 0.9659
## TCS01 Roble - TCS06 Roble 0.2648 0.0856 3168 3.095 0.1201
## TCS01 Roble - TCS13 Roble -0.2650 0.0830 3168 -3.193 0.0914
## TCS01 Roble - TCS19 Roble 0.0587 0.0833 3168 0.704 1.0000
## TCS01 Roble - CCN51 Terminalia 0.4601 0.0839 3168 5.486 <.0001
## TCS01 Roble - TCS01 Terminalia 0.1810 0.0833 3168 2.173 0.6846
## TCS01 Roble - TCS06 Terminalia 0.2186 0.0841 3168 2.599 0.3709
## TCS01 Roble - TCS13 Terminalia 0.0077 0.0844 3168 0.091 1.0000
## TCS01 Roble - TCS19 Terminalia 0.2432 0.0842 3168 2.890 0.2013
## TCS06 Roble - TCS13 Roble -0.5298 0.0851 3168 -6.228 <.0001
## TCS06 Roble - TCS19 Roble -0.2061 0.0853 3168 -2.415 0.5033
## TCS06 Roble - CCN51 Terminalia 0.1953 0.0858 3168 2.276 0.6087
## TCS06 Roble - TCS01 Terminalia -0.0838 0.0853 3168 -0.982 0.9997
## TCS06 Roble - TCS06 Terminalia -0.0461 0.0861 3168 -0.536 1.0000
## TCS06 Roble - TCS13 Terminalia -0.2571 0.0864 3168 -2.975 0.1640
## TCS06 Roble - TCS19 Terminalia -0.0215 0.0861 3168 -0.250 1.0000
## TCS13 Roble - TCS19 Roble 0.3237 0.0828 3168 3.908 0.0082
## TCS13 Roble - CCN51 Terminalia 0.7251 0.0833 3168 8.701 <.0001
## TCS13 Roble - TCS01 Terminalia 0.4460 0.0828 3168 5.387 <.0001
## TCS13 Roble - TCS06 Terminalia 0.4836 0.0836 3168 5.784 <.0001
## TCS13 Roble - TCS13 Terminalia 0.2727 0.0839 3168 3.249 0.0778
## TCS13 Roble - TCS19 Terminalia 0.5082 0.0836 3168 6.076 <.0001
## TCS19 Roble - CCN51 Terminalia 0.4014 0.0836 3168 4.803 0.0002
## TCS19 Roble - TCS01 Terminalia 0.1223 0.0831 3168 1.472 0.9800
## TCS19 Roble - TCS06 Terminalia 0.1600 0.0839 3168 1.907 0.8501
## TCS19 Roble - TCS13 Terminalia -0.0510 0.0842 3168 -0.605 1.0000
## TCS19 Roble - TCS19 Terminalia 0.1846 0.0839 3168 2.200 0.6649
## CCN51 Terminalia - TCS01 Terminalia -0.2791 0.0836 3168 -3.339 0.0594
## CCN51 Terminalia - TCS06 Terminalia -0.2415 0.0844 3168 -2.862 0.2151
## CCN51 Terminalia - TCS13 Terminalia -0.4524 0.0847 3168 -5.341 <.0001
## CCN51 Terminalia - TCS19 Terminalia -0.2169 0.0844 3168 -2.570 0.3904
## TCS01 Terminalia - TCS06 Terminalia 0.0376 0.0839 3168 0.449 1.0000
## TCS01 Terminalia - TCS13 Terminalia -0.1733 0.0842 3168 -2.058 0.7629
## TCS01 Terminalia - TCS19 Terminalia 0.0622 0.0839 3168 0.742 1.0000
## TCS06 Terminalia - TCS13 Terminalia -0.2109 0.0850 3168 -2.482 0.4541
## TCS06 Terminalia - TCS19 Terminalia 0.0246 0.0847 3168 0.291 1.0000
## TCS13 Terminalia - TCS19 Terminalia 0.2355 0.0850 3168 2.771 0.2632
##
## 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
## TCS13 Abarco 4.20 0.0576 3168 4.09 4.32 A
## TCS01 Abarco 4.06 0.0564 3168 3.95 4.17 AB
## TCS13 Roble 3.86 0.0583 3168 3.74 3.97 BC
## CCN51 Abarco 3.72 0.0567 3168 3.61 3.83 CD
## TCS19 Abarco 3.70 0.0583 3168 3.59 3.82 CD
## TCS06 Abarco 3.62 0.0590 3168 3.50 3.73 CDE
## TCS01 Roble 3.59 0.0591 3168 3.48 3.71 CDE
## TCS13 Terminalia 3.59 0.0603 3168 3.47 3.70 CDE
## TCS19 Roble 3.53 0.0588 3168 3.42 3.65 DE
## CCN51 Roble 3.48 0.0589 3168 3.37 3.60 DE
## TCS01 Terminalia 3.41 0.0587 3168 3.30 3.53 EF
## TCS06 Terminalia 3.37 0.0599 3168 3.26 3.49 EF
## TCS19 Terminalia 3.35 0.0599 3168 3.23 3.47 EF
## TCS06 Roble 3.33 0.0619 3168 3.21 3.45 EF
## CCN51 Terminalia 3.13 0.0595 3168 3.02 3.25 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 136 1.31 3169 133 139
## TCS01 140 1.30 3169 138 143
## TCS06 134 1.35 3169 131 136
## TCS13 141 1.32 3169 138 144
## TCS19 125 1.32 3169 122 127
##
## 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 -4.438 1.85 3169 -2.403 0.1146
## CCN51 - TCS06 2.411 1.88 3169 1.282 0.7026
## CCN51 - TCS13 -4.956 1.86 3169 -2.671 0.0585
## CCN51 - TCS19 11.514 1.86 3169 6.190 <.0001
## TCS01 - TCS06 6.848 1.88 3169 3.648 0.0025
## TCS01 - TCS13 -0.518 1.85 3169 -0.280 0.9987
## TCS01 - TCS19 15.952 1.86 3169 8.593 <.0001
## TCS06 - TCS13 -7.366 1.89 3169 -3.905 0.0009
## TCS06 - TCS19 9.104 1.89 3169 4.815 <.0001
## TCS13 - TCS19 16.470 1.87 3169 8.828 <.0001
##
## 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
## TCS13 141 1.32 3169 138 144 A
## TCS01 140 1.30 3169 138 143 A
## CCN51 136 1.31 3169 133 139 AB
## TCS06 134 1.35 3169 131 136 B
## TCS19 125 1.32 3169 122 127 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 143 1.00 3169 141 145
## Roble 133 1.03 3169 131 135
## Terminalia 129 1.04 3169 127 131
##
## 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 10.27 1.44 3169 7.150 <.0001
## Abarco - Terminalia 14.12 1.44 3169 9.803 <.0001
## Roble - Terminalia 3.84 1.46 3169 2.629 0.0234
##
## 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 143 1.00 3169 141 145 A
## Roble 133 1.03 3169 131 135 B
## Terminalia 129 1.04 3169 127 131 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.alt, ~gen*forestal)
plot(contrast, comparisons = TRUE, xlab ="Altura")
medias.forestal.gen <- emmeans(aov.alt, pairwise ~ gen*forestal)
medias.forestal.gen
## $emmeans
## gen forestal emmean SE df lower.CL upper.CL
## CCN51 Abarco 145 2.20 3169 141 150
## TCS01 Abarco 152 2.19 3169 147 156
## TCS06 Abarco 139 2.29 3169 134 143
## TCS13 Abarco 151 2.23 3169 147 155
## TCS19 Abarco 130 2.27 3169 125 134
## CCN51 Roble 133 2.29 3169 128 137
## TCS01 Roble 138 2.29 3169 134 143
## TCS06 Roble 129 2.40 3169 124 134
## TCS13 Roble 139 2.27 3169 135 144
## TCS19 Roble 126 2.28 3169 122 131
## CCN51 Terminalia 130 2.31 3169 126 135
## TCS01 Terminalia 132 2.28 3169 127 136
## TCS06 Terminalia 133 2.33 3169 128 138
## TCS13 Terminalia 133 2.34 3169 128 137
## TCS19 Terminalia 118 2.33 3169 114 123
##
## 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 -6.281 3.10 3169 -2.023 0.7846
## CCN51 Abarco - TCS06 Abarco 6.578 3.18 3169 2.071 0.7546
## CCN51 Abarco - TCS13 Abarco -5.700 3.14 3169 -1.818 0.8912
## CCN51 Abarco - TCS19 Abarco 15.844 3.16 3169 5.017 0.0001
## CCN51 Abarco - CCN51 Roble 12.812 3.17 3169 4.038 0.0049
## CCN51 Abarco - TCS01 Roble 7.310 3.18 3169 2.300 0.5904
## CCN51 Abarco - TCS06 Roble 16.269 3.26 3169 4.993 0.0001
## CCN51 Abarco - TCS13 Roble 6.081 3.16 3169 1.926 0.8404
## CCN51 Abarco - TCS19 Roble 19.336 3.17 3169 6.101 <.0001
## CCN51 Abarco - CCN51 Terminalia 15.124 3.19 3169 4.742 0.0002
## CCN51 Abarco - TCS01 Terminalia 13.593 3.17 3169 4.289 0.0017
## CCN51 Abarco - TCS06 Terminalia 12.321 3.20 3169 3.849 0.0103
## CCN51 Abarco - TCS13 Terminalia 12.688 3.21 3169 3.953 0.0069
## CCN51 Abarco - TCS19 Terminalia 27.300 3.20 3169 8.526 <.0001
## TCS01 Abarco - TCS06 Abarco 12.859 3.17 3169 4.056 0.0046
## TCS01 Abarco - TCS13 Abarco 0.580 3.13 3169 0.186 1.0000
## TCS01 Abarco - TCS19 Abarco 22.125 3.15 3169 7.021 <.0001
## TCS01 Abarco - CCN51 Roble 19.093 3.17 3169 6.030 <.0001
## TCS01 Abarco - TCS01 Roble 13.591 3.17 3169 4.286 0.0018
## TCS01 Abarco - TCS06 Roble 22.549 3.25 3169 6.934 <.0001
## TCS01 Abarco - TCS13 Roble 12.362 3.15 3169 3.923 0.0078
## TCS01 Abarco - TCS19 Roble 25.617 3.16 3169 8.100 <.0001
## TCS01 Abarco - CCN51 Terminalia 21.405 3.18 3169 6.725 <.0001
## TCS01 Abarco - TCS01 Terminalia 19.874 3.16 3169 6.284 <.0001
## TCS01 Abarco - TCS06 Terminalia 18.602 3.19 3169 5.823 <.0001
## TCS01 Abarco - TCS13 Terminalia 18.969 3.20 3169 5.922 <.0001
## TCS01 Abarco - TCS19 Terminalia 33.580 3.20 3169 10.509 <.0001
## TCS06 Abarco - TCS13 Abarco -12.278 3.20 3169 -3.836 0.0108
## TCS06 Abarco - TCS19 Abarco 9.266 3.22 3169 2.875 0.2086
## TCS06 Abarco - CCN51 Roble 6.234 3.24 3169 1.926 0.8403
## TCS06 Abarco - TCS01 Roble 0.732 3.24 3169 0.226 1.0000
## TCS06 Abarco - TCS06 Roble 9.690 3.32 3169 2.918 0.1882
## TCS06 Abarco - TCS13 Roble -0.497 3.22 3169 -0.154 1.0000
## TCS06 Abarco - TCS19 Roble 12.758 3.23 3169 3.945 0.0071
## TCS06 Abarco - CCN51 Terminalia 8.546 3.25 3169 2.627 0.3518
## TCS06 Abarco - TCS01 Terminalia 7.015 3.23 3169 2.169 0.6871
## TCS06 Abarco - TCS06 Terminalia 5.743 3.26 3169 1.759 0.9140
## TCS06 Abarco - TCS13 Terminalia 6.110 3.27 3169 1.867 0.8695
## TCS06 Abarco - TCS19 Terminalia 20.721 3.27 3169 6.346 <.0001
## TCS13 Abarco - TCS19 Abarco 21.544 3.18 3169 6.770 <.0001
## TCS13 Abarco - CCN51 Roble 18.512 3.20 3169 5.791 <.0001
## TCS13 Abarco - TCS01 Roble 13.011 3.20 3169 4.062 0.0045
## TCS13 Abarco - TCS06 Roble 21.969 3.28 3169 6.695 <.0001
## TCS13 Abarco - TCS13 Roble 11.781 3.18 3169 3.702 0.0177
## TCS13 Abarco - TCS19 Roble 25.036 3.19 3169 7.841 <.0001
## TCS13 Abarco - CCN51 Terminalia 20.825 3.21 3169 6.482 <.0001
## TCS13 Abarco - TCS01 Terminalia 19.293 3.19 3169 6.042 <.0001
## TCS13 Abarco - TCS06 Terminalia 18.021 3.22 3169 5.589 <.0001
## TCS13 Abarco - TCS13 Terminalia 18.389 3.23 3169 5.688 <.0001
## TCS13 Abarco - TCS19 Terminalia 33.000 3.22 3169 10.233 <.0001
## TCS19 Abarco - CCN51 Roble -3.032 3.22 3169 -0.942 0.9998
## TCS19 Abarco - TCS01 Roble -8.533 3.22 3169 -2.648 0.3380
## TCS19 Abarco - TCS06 Roble 0.425 3.30 3169 0.129 1.0000
## TCS19 Abarco - TCS13 Roble -9.763 3.20 3169 -3.048 0.1360
## TCS19 Abarco - TCS19 Roble 3.492 3.22 3169 1.086 0.9991
## TCS19 Abarco - CCN51 Terminalia -0.719 3.24 3169 -0.222 1.0000
## TCS19 Abarco - TCS01 Terminalia -2.251 3.21 3169 -0.700 1.0000
## TCS19 Abarco - TCS06 Terminalia -3.523 3.25 3169 -1.085 0.9991
## TCS19 Abarco - TCS13 Terminalia -3.156 3.26 3169 -0.969 0.9997
## TCS19 Abarco - TCS19 Terminalia 11.456 3.25 3169 3.528 0.0324
## CCN51 Roble - TCS01 Roble -5.502 3.24 3169 -1.699 0.9339
## CCN51 Roble - TCS06 Roble 3.457 3.32 3169 1.042 0.9994
## CCN51 Roble - TCS13 Roble -6.731 3.22 3169 -2.091 0.7412
## CCN51 Roble - TCS19 Roble 6.524 3.23 3169 2.020 0.7866
## CCN51 Roble - CCN51 Terminalia 2.312 3.25 3169 0.712 1.0000
## CCN51 Roble - TCS01 Terminalia 0.781 3.23 3169 0.242 1.0000
## CCN51 Roble - TCS06 Terminalia -0.491 3.26 3169 -0.151 1.0000
## CCN51 Roble - TCS13 Terminalia -0.124 3.27 3169 -0.038 1.0000
## CCN51 Roble - TCS19 Terminalia 14.488 3.26 3169 4.442 0.0009
## TCS01 Roble - TCS06 Roble 8.958 3.32 3169 2.696 0.3073
## TCS01 Roble - TCS13 Roble -1.229 3.22 3169 -0.382 1.0000
## TCS01 Roble - TCS19 Roble 12.026 3.24 3169 3.716 0.0168
## TCS01 Roble - CCN51 Terminalia 7.814 3.26 3169 2.400 0.5152
## TCS01 Roble - TCS01 Terminalia 6.282 3.23 3169 1.943 0.8313
## TCS01 Roble - TCS06 Terminalia 5.011 3.27 3169 1.534 0.9713
## TCS01 Roble - TCS13 Terminalia 5.378 3.28 3169 1.642 0.9493
## TCS01 Roble - TCS19 Terminalia 19.989 3.27 3169 6.117 <.0001
## TCS06 Roble - TCS13 Roble -10.188 3.30 3169 -3.084 0.1235
## TCS06 Roble - TCS19 Roble 3.067 3.31 3169 0.926 0.9999
## TCS06 Roble - CCN51 Terminalia -1.144 3.33 3169 -0.343 1.0000
## TCS06 Roble - TCS01 Terminalia -2.676 3.31 3169 -0.808 1.0000
## TCS06 Roble - TCS06 Terminalia -3.948 3.34 3169 -1.181 0.9977
## TCS06 Roble - TCS13 Terminalia -3.580 3.35 3169 -1.068 0.9992
## TCS06 Roble - TCS19 Terminalia 11.031 3.34 3169 3.299 0.0670
## TCS13 Roble - TCS19 Roble 13.255 3.22 3169 4.122 0.0035
## TCS13 Roble - CCN51 Terminalia 9.044 3.24 3169 2.795 0.2500
## TCS13 Roble - TCS01 Terminalia 7.512 3.21 3169 2.337 0.5628
## TCS13 Roble - TCS06 Terminalia 6.240 3.25 3169 1.922 0.8423
## TCS13 Roble - TCS13 Terminalia 6.607 3.26 3169 2.030 0.7806
## TCS13 Roble - TCS19 Terminalia 21.219 3.25 3169 6.533 <.0001
## TCS19 Roble - CCN51 Terminalia -4.211 3.25 3169 -1.298 0.9940
## TCS19 Roble - TCS01 Terminalia -5.743 3.23 3169 -1.780 0.9062
## TCS19 Roble - TCS06 Terminalia -7.015 3.26 3169 -2.154 0.6982
## TCS19 Roble - TCS13 Terminalia -6.648 3.27 3169 -2.036 0.7768
## TCS19 Roble - TCS19 Terminalia 7.964 3.26 3169 2.445 0.4812
## CCN51 Terminalia - TCS01 Terminalia -1.532 3.25 3169 -0.472 1.0000
## CCN51 Terminalia - TCS06 Terminalia -2.803 3.28 3169 -0.856 0.9999
## CCN51 Terminalia - TCS13 Terminalia -2.436 3.28 3169 -0.742 1.0000
## CCN51 Terminalia - TCS19 Terminalia 12.175 3.28 3169 3.716 0.0168
## TCS01 Terminalia - TCS06 Terminalia -1.272 3.26 3169 -0.390 1.0000
## TCS01 Terminalia - TCS13 Terminalia -0.905 3.27 3169 -0.277 1.0000
## TCS01 Terminalia - TCS19 Terminalia 13.707 3.26 3169 4.207 0.0025
## TCS06 Terminalia - TCS13 Terminalia 0.367 3.30 3169 0.111 1.0000
## TCS06 Terminalia - TCS19 Terminalia 14.979 3.29 3169 4.555 0.0005
## TCS13 Terminalia - TCS19 Terminalia 14.611 3.30 3169 4.433 0.0009
##
## 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 Abarco 152 2.19 3169 147 156 A
## TCS13 Abarco 151 2.23 3169 147 155 A
## CCN51 Abarco 145 2.20 3169 141 150 AB
## TCS13 Roble 139 2.27 3169 135 144 BC
## TCS06 Abarco 139 2.29 3169 134 143 BC
## TCS01 Roble 138 2.29 3169 134 143 BC
## TCS06 Terminalia 133 2.33 3169 128 138 CD
## TCS13 Terminalia 133 2.34 3169 128 137 CD
## CCN51 Roble 133 2.29 3169 128 137 CD
## TCS01 Terminalia 132 2.28 3169 127 136 CD
## CCN51 Terminalia 130 2.31 3169 126 135 CD
## TCS19 Abarco 130 2.27 3169 125 134 CD
## TCS06 Roble 129 2.40 3169 124 134 CDE
## TCS19 Roble 126 2.28 3169 122 131 DE
## TCS19 Terminalia 118 2.33 3169 114 123 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(datos4)