setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura/BD Estadística SGR Santander/data")
datos4<-read.table("libano.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 196 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 196 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 8265.595 8385.895 -4112.797
## fit.ar1.diam 2 20 8104.670 8224.970 -4032.335
## fit.ar1het.diam 3 31 7013.025 7199.490 -3475.513 2 vs 3 1113.645 <.0001
anova(fit.ar1.diam)
## Denom. DF: 3026
## numDF F-value p-value
## (Intercept) 1 20502.442 <.0001
## semana 1 3456.396 <.0001
## forestal 2 0.049 0.9523
## gen 4 0.829 0.5068
## bloque 2 54.821 <.0001
## forestal:gen 8 5.678 <.0001
anova(fit.ar1het.diam)
## Denom. DF: 3026
## numDF F-value p-value
## (Intercept) 1 25856.498 <.0001
## semana 1 4824.702 <.0001
## forestal 2 1.272 0.2804
## gen 4 2.688 0.0297
## bloque 2 66.890 <.0001
## forestal:gen 8 5.331 <.0001
# 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 30203.20 30323.50 -15081.60
## fit.ar1.alt 2 20 30094.19 30214.49 -15027.10
## fit.ar1het.alt 3 31 29262.16 29448.62 -14600.08 2 vs 3 854.0366 <.0001
anova(fit.ar1.alt)
## Denom. DF: 3026
## numDF F-value p-value
## (Intercept) 1 26144.186 <.0001
## semana 1 3218.840 <.0001
## forestal 2 1.769 0.1707
## gen 4 5.751 0.0001
## bloque 2 62.379 <.0001
## forestal:gen 8 6.328 <.0001
anova(fit.ar1het.alt)
## Denom. DF: 3026
## numDF F-value p-value
## (Intercept) 1 31708.88 <.0001
## semana 1 3719.82 <.0001
## forestal 2 3.62 0.0269
## gen 4 5.05 0.0005
## bloque 2 49.27 <.0001
## forestal:gen 8 5.25 <.0001
#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 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 c 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 abcd Abarco:CCN51
## Abarco:TCS01 abcd Abarco:TCS01
## Abarco:TCS06 d Abarco:TCS06
## Abarco:TCS13 b Abarco:TCS13
## Abarco:TCS19 abcd Abarco:TCS19
## Roble:CCN51 acd Roble:CCN51
## Roble:TCS01 ab Roble:TCS01
## Roble:TCS06 abcd Roble:TCS06
## Roble:TCS13 abcd Roble:TCS13
## Roble:TCS19 cd Roble:TCS19
## Terminalia:CCN51 abc Terminalia:CCN51
## Terminalia:TCS01 acd Terminalia:TCS01
## Terminalia:TCS06 ab Terminalia:TCS06
## Terminalia:TCS13 cd Terminalia:TCS13
## Terminalia:TCS19 acd 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 c CCN51
## TCS01 b TCS01
## TCS06 ac TCS06
## TCS13 ac TCS13
## TCS19 ab TCS19
# Forestal
generate_label_df_forestal_alt <- function(fores.tuk.alt, variable){
Tukey.levels <- fores.tuk.alt[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.forestal.alt <- generate_label_df_forestal_alt(fores.tuk.alt, "forestal")
labels.forestal.alt
## Letters treatment
## Abarco a Abarco
## Roble b Roble
## Terminalia c 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 abcde Abarco:CCN51
## Abarco:TCS01 abc Abarco:TCS01
## Abarco:TCS06 de Abarco:TCS06
## Abarco:TCS13 abc Abarco:TCS13
## Abarco:TCS19 ab Abarco:TCS19
## Roble:CCN51 e Roble:CCN51
## Roble:TCS01 b Roble:TCS01
## Roble:TCS06 abcde Roble:TCS06
## Roble:TCS13 acde Roble:TCS13
## Roble:TCS19 cde Roble:TCS19
## Terminalia:CCN51 abcd Terminalia:CCN51
## Terminalia:TCS01 abc Terminalia:TCS01
## Terminalia:TCS06 abc Terminalia:TCS06
## Terminalia:TCS13 de 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 3.04 0.0372 3026 2.97 3.12
## TCS01 3.02 0.0386 3026 2.94 3.10
## TCS06 3.04 0.0369 3026 2.97 3.12
## TCS13 2.99 0.0382 3026 2.92 3.07
## TCS19 3.11 0.0370 3026 3.04 3.18
##
## 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 0.024023 0.0536 3026 0.448 0.9917
## CCN51 - TCS06 0.000902 0.0524 3026 0.017 1.0000
## CCN51 - TCS13 0.050348 0.0533 3026 0.945 0.8792
## CCN51 - TCS19 -0.066752 0.0524 3026 -1.273 0.7077
## TCS01 - TCS06 -0.023121 0.0534 3026 -0.433 0.9927
## TCS01 - TCS13 0.026324 0.0543 3026 0.485 0.9888
## TCS01 - TCS19 -0.090775 0.0535 3026 -1.698 0.4352
## TCS06 - TCS13 0.049445 0.0531 3026 0.932 0.8846
## TCS06 - TCS19 -0.067654 0.0522 3026 -1.295 0.6942
## TCS13 - TCS19 -0.117099 0.0531 3026 -2.204 0.1783
##
## 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
## TCS19 3.11 0.0370 3026 3.04 3.18 A
## CCN51 3.04 0.0372 3026 2.97 3.12 A
## TCS06 3.04 0.0369 3026 2.97 3.12 A
## TCS01 3.02 0.0386 3026 2.94 3.10 A
## TCS13 2.99 0.0382 3026 2.92 3.07 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 3.01 0.0290 3026 2.96 3.07
## Roble 3.05 0.0288 3026 3.00 3.11
## Terminalia 3.06 0.0295 3026 3.00 3.12
##
## 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 -0.04084 0.0409 3026 -0.999 0.5774
## Abarco - Terminalia -0.04827 0.0414 3026 -1.166 0.4734
## Roble - Terminalia -0.00742 0.0412 3026 -0.180 0.9822
##
## Results are averaged over the levels of: gen, bloque
## P value adjustment: tukey method for comparing a family of 3 estimates
cld_forestal <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_forestal
## forestal emmean SE df lower.CL upper.CL .group
## Terminalia 3.06 0.0295 3026 3.00 3.12 A
## Roble 3.05 0.0288 3026 3.00 3.11 A
## Abarco 3.01 0.0290 3026 2.96 3.07 A
##
## Results are averaged over the levels of: gen, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
#Forestal*Gen
contrast <- emmeans(aov.diam, ~gen*forestal)
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 3.05 0.0650 3026 2.92 3.18
## TCS01 Abarco 3.05 0.0651 3026 2.92 3.18
## TCS06 Abarco 3.25 0.0634 3026 3.12 3.37
## TCS13 Abarco 2.76 0.0666 3026 2.63 2.89
## TCS19 Abarco 2.95 0.0646 3026 2.82 3.08
## CCN51 Roble 3.14 0.0642 3026 3.02 3.27
## TCS01 Roble 2.87 0.0645 3026 2.75 3.00
## TCS06 Roble 3.01 0.0640 3026 2.89 3.14
## TCS13 Roble 3.00 0.0658 3026 2.87 3.13
## TCS19 Roble 3.24 0.0631 3026 3.11 3.36
## CCN51 Terminalia 2.94 0.0640 3026 2.81 3.06
## TCS01 Terminalia 3.13 0.0709 3026 3.00 3.27
## TCS06 Terminalia 2.87 0.0643 3026 2.74 2.99
## TCS13 Terminalia 3.22 0.0658 3026 3.09 3.35
## TCS19 Terminalia 3.15 0.0643 3026 3.02 3.27
##
## 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 -0.000158 0.0920 3026 -0.002 1.0000
## CCN51 Abarco - TCS06 Abarco -0.196836 0.0908 3026 -2.168 0.6877
## CCN51 Abarco - TCS13 Abarco 0.290262 0.0931 3026 3.119 0.1125
## CCN51 Abarco - TCS19 Abarco 0.100799 0.0916 3026 1.100 0.9989
## CCN51 Abarco - CCN51 Roble -0.090287 0.0913 3026 -0.989 0.9997
## CCN51 Abarco - TCS01 Roble 0.177225 0.0915 3026 1.937 0.8346
## CCN51 Abarco - TCS06 Roble 0.038397 0.0912 3026 0.421 1.0000
## CCN51 Abarco - TCS13 Roble 0.049007 0.0925 3026 0.530 1.0000
## CCN51 Abarco - TCS19 Roble -0.184496 0.0906 3026 -2.037 0.7760
## CCN51 Abarco - CCN51 Terminalia 0.112500 0.0912 3026 1.234 0.9964
## CCN51 Abarco - TCS01 Terminalia -0.082783 0.0962 3026 -0.861 0.9999
## CCN51 Abarco - TCS06 Terminalia 0.183360 0.0914 3026 2.006 0.7953
## CCN51 Abarco - TCS13 Terminalia -0.166012 0.0924 3026 -1.796 0.9001
## CCN51 Abarco - TCS19 Terminalia -0.094344 0.0914 3026 -1.032 0.9995
## TCS01 Abarco - TCS06 Abarco -0.196678 0.0909 3026 -2.164 0.6908
## TCS01 Abarco - TCS13 Abarco 0.290420 0.0932 3026 3.117 0.1129
## TCS01 Abarco - TCS19 Abarco 0.100957 0.0918 3026 1.100 0.9989
## TCS01 Abarco - CCN51 Roble -0.090129 0.0914 3026 -0.986 0.9997
## TCS01 Abarco - TCS01 Roble 0.177383 0.0916 3026 1.936 0.8349
## TCS01 Abarco - TCS06 Roble 0.038555 0.0913 3026 0.422 1.0000
## TCS01 Abarco - TCS13 Roble 0.049165 0.0926 3026 0.531 1.0000
## TCS01 Abarco - TCS19 Roble -0.184338 0.0907 3026 -2.033 0.7787
## TCS01 Abarco - CCN51 Terminalia 0.112659 0.0913 3026 1.234 0.9964
## TCS01 Abarco - TCS01 Terminalia -0.082625 0.0963 3026 -0.858 0.9999
## TCS01 Abarco - TCS06 Terminalia 0.183518 0.0915 3026 2.005 0.7956
## TCS01 Abarco - TCS13 Terminalia -0.165853 0.0925 3026 -1.792 0.9016
## TCS01 Abarco - TCS19 Terminalia -0.094186 0.0915 3026 -1.029 0.9995
## TCS06 Abarco - TCS13 Abarco 0.487098 0.0920 3026 5.296 <.0001
## TCS06 Abarco - TCS19 Abarco 0.297635 0.0905 3026 3.287 0.0695
## TCS06 Abarco - CCN51 Roble 0.106549 0.0902 3026 1.181 0.9977
## TCS06 Abarco - TCS01 Roble 0.374061 0.0904 3026 4.136 0.0033
## TCS06 Abarco - TCS06 Roble 0.235233 0.0901 3026 2.611 0.3628
## TCS06 Abarco - TCS13 Roble 0.245843 0.0914 3026 2.691 0.3110
## TCS06 Abarco - TCS19 Roble 0.012340 0.0895 3026 0.138 1.0000
## TCS06 Abarco - CCN51 Terminalia 0.309337 0.0901 3026 3.433 0.0441
## TCS06 Abarco - TCS01 Terminalia 0.114053 0.0951 3026 1.199 0.9973
## TCS06 Abarco - TCS06 Terminalia 0.380196 0.0903 3026 4.209 0.0024
## TCS06 Abarco - TCS13 Terminalia 0.030825 0.0914 3026 0.337 1.0000
## TCS06 Abarco - TCS19 Terminalia 0.102492 0.0903 3026 1.135 0.9985
## TCS13 Abarco - TCS19 Abarco -0.189463 0.0928 3026 -2.041 0.7738
## TCS13 Abarco - CCN51 Roble -0.380549 0.0925 3026 -4.113 0.0036
## TCS13 Abarco - TCS01 Roble -0.113037 0.0927 3026 -1.219 0.9968
## TCS13 Abarco - TCS06 Roble -0.251865 0.0924 3026 -2.726 0.2892
## TCS13 Abarco - TCS13 Roble -0.241255 0.0936 3026 -2.577 0.3855
## TCS13 Abarco - TCS19 Roble -0.474758 0.0918 3026 -5.173 <.0001
## TCS13 Abarco - CCN51 Terminalia -0.177761 0.0924 3026 -1.924 0.8411
## TCS13 Abarco - TCS01 Terminalia -0.373045 0.0973 3026 -3.832 0.0110
## TCS13 Abarco - TCS06 Terminalia -0.106902 0.0926 3026 -1.154 0.9982
## TCS13 Abarco - TCS13 Terminalia -0.456273 0.0936 3026 -4.874 0.0001
## TCS13 Abarco - TCS19 Terminalia -0.384606 0.0926 3026 -4.153 0.0031
## TCS19 Abarco - CCN51 Roble -0.191086 0.0911 3026 -2.098 0.7363
## TCS19 Abarco - TCS01 Roble 0.076426 0.0913 3026 0.837 1.0000
## TCS19 Abarco - TCS06 Roble -0.062402 0.0910 3026 -0.686 1.0000
## TCS19 Abarco - TCS13 Roble -0.051792 0.0922 3026 -0.561 1.0000
## TCS19 Abarco - TCS19 Roble -0.285295 0.0903 3026 -3.158 0.1009
## TCS19 Abarco - CCN51 Terminalia 0.011702 0.0910 3026 0.129 1.0000
## TCS19 Abarco - TCS01 Terminalia -0.183582 0.0959 3026 -1.915 0.8461
## TCS19 Abarco - TCS06 Terminalia 0.082561 0.0912 3026 0.905 0.9999
## TCS19 Abarco - TCS13 Terminalia -0.266810 0.0922 3026 -2.893 0.1998
## TCS19 Abarco - TCS19 Terminalia -0.195143 0.0912 3026 -2.140 0.7080
## CCN51 Roble - TCS01 Roble 0.267512 0.0910 3026 2.941 0.1784
## CCN51 Roble - TCS06 Roble 0.128684 0.0906 3026 1.420 0.9857
## CCN51 Roble - TCS13 Roble 0.139294 0.0919 3026 1.515 0.9742
## CCN51 Roble - TCS19 Roble -0.094209 0.0900 3026 -1.047 0.9994
## CCN51 Roble - CCN51 Terminalia 0.202787 0.0906 3026 2.237 0.6376
## CCN51 Roble - TCS01 Terminalia 0.007504 0.0956 3026 0.079 1.0000
## CCN51 Roble - TCS06 Terminalia 0.273647 0.0909 3026 3.012 0.1493
## CCN51 Roble - TCS13 Terminalia -0.075725 0.0919 3026 -0.824 1.0000
## CCN51 Roble - TCS19 Terminalia -0.004057 0.0909 3026 -0.045 1.0000
## TCS01 Roble - TCS06 Roble -0.138828 0.0909 3026 -1.528 0.9722
## TCS01 Roble - TCS13 Roble -0.128218 0.0921 3026 -1.392 0.9881
## TCS01 Roble - TCS19 Roble -0.361721 0.0902 3026 -4.009 0.0055
## TCS01 Roble - CCN51 Terminalia -0.064724 0.0909 3026 -0.712 1.0000
## TCS01 Roble - TCS01 Terminalia -0.260008 0.0958 3026 -2.713 0.2971
## TCS01 Roble - TCS06 Terminalia 0.006136 0.0911 3026 0.067 1.0000
## TCS01 Roble - TCS13 Terminalia -0.343236 0.0921 3026 -3.727 0.0162
## TCS01 Roble - TCS19 Terminalia -0.271569 0.0911 3026 -2.981 0.1612
## TCS06 Roble - TCS13 Roble 0.010610 0.0918 3026 0.116 1.0000
## TCS06 Roble - TCS19 Roble -0.222893 0.0899 3026 -2.480 0.4555
## TCS06 Roble - CCN51 Terminalia 0.074104 0.0905 3026 0.819 1.0000
## TCS06 Roble - TCS01 Terminalia -0.121180 0.0955 3026 -1.269 0.9952
## TCS06 Roble - TCS06 Terminalia 0.144963 0.0907 3026 1.598 0.9595
## TCS06 Roble - TCS13 Terminalia -0.204408 0.0918 3026 -2.227 0.6449
## TCS06 Roble - TCS19 Terminalia -0.132741 0.0907 3026 -1.463 0.9812
## TCS13 Roble - TCS19 Roble -0.233503 0.0912 3026 -2.561 0.3967
## TCS13 Roble - CCN51 Terminalia 0.063494 0.0918 3026 0.692 1.0000
## TCS13 Roble - TCS01 Terminalia -0.131790 0.0968 3026 -1.362 0.9903
## TCS13 Roble - TCS06 Terminalia 0.134353 0.0920 3026 1.460 0.9814
## TCS13 Roble - TCS13 Terminalia -0.215018 0.0930 3026 -2.312 0.5818
## TCS13 Roble - TCS19 Terminalia -0.143351 0.0920 3026 -1.558 0.9671
## TCS19 Roble - CCN51 Terminalia 0.296996 0.0899 3026 3.304 0.0660
## TCS19 Roble - TCS01 Terminalia 0.101713 0.0949 3026 1.072 0.9992
## TCS19 Roble - TCS06 Terminalia 0.367856 0.0901 3026 4.082 0.0041
## TCS19 Roble - TCS13 Terminalia 0.018484 0.0911 3026 0.203 1.0000
## TCS19 Roble - TCS19 Terminalia 0.090152 0.0901 3026 1.000 0.9996
## CCN51 Terminalia - TCS01 Terminalia -0.195283 0.0955 3026 -2.044 0.7715
## CCN51 Terminalia - TCS06 Terminalia 0.070860 0.0907 3026 0.781 1.0000
## CCN51 Terminalia - TCS13 Terminalia -0.278512 0.0918 3026 -3.035 0.1407
## CCN51 Terminalia - TCS19 Terminalia -0.206844 0.0907 3026 -2.279 0.6061
## TCS01 Terminalia - TCS06 Terminalia 0.266143 0.0957 3026 2.780 0.2579
## TCS01 Terminalia - TCS13 Terminalia -0.083229 0.0967 3026 -0.861 0.9999
## TCS01 Terminalia - TCS19 Terminalia -0.011561 0.0958 3026 -0.121 1.0000
## TCS06 Terminalia - TCS13 Terminalia -0.349372 0.0920 3026 -3.798 0.0125
## TCS06 Terminalia - TCS19 Terminalia -0.277704 0.0910 3026 -3.053 0.1343
## TCS13 Terminalia - TCS19 Terminalia 0.071667 0.0920 3026 0.779 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
## TCS06 Abarco 3.25 0.0634 3026 3.12 3.37 A
## TCS19 Roble 3.24 0.0631 3026 3.11 3.36 AB
## TCS13 Terminalia 3.22 0.0658 3026 3.09 3.35 AB
## TCS19 Terminalia 3.15 0.0643 3026 3.02 3.27 ABC
## CCN51 Roble 3.14 0.0642 3026 3.02 3.27 ABC
## TCS01 Terminalia 3.13 0.0709 3026 3.00 3.27 ABC
## TCS01 Abarco 3.05 0.0651 3026 2.92 3.18 ABCD
## CCN51 Abarco 3.05 0.0650 3026 2.92 3.18 ABCD
## TCS06 Roble 3.01 0.0640 3026 2.89 3.14 ABCD
## TCS13 Roble 3.00 0.0658 3026 2.87 3.13 ABCD
## TCS19 Abarco 2.95 0.0646 3026 2.82 3.08 ABCD
## CCN51 Terminalia 2.94 0.0640 3026 2.81 3.06 BCD
## TCS01 Roble 2.87 0.0645 3026 2.75 3.00 CD
## TCS06 Terminalia 2.87 0.0643 3026 2.74 2.99 CD
## TCS13 Abarco 2.76 0.0666 3026 2.63 2.89 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.
## Gráficas contrastes de medias altura
#Gen
#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 127 1.39 3026 125 130
## TCS01 118 1.45 3026 115 121
## TCS06 126 1.38 3026 123 128
## TCS13 125 1.43 3026 123 128
## TCS19 121 1.39 3026 118 123
##
## 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 9.5496 2.01 3026 4.750 <.0001
## CCN51 - TCS06 1.9186 1.97 3026 0.976 0.8660
## CCN51 - TCS13 1.9910 2.00 3026 0.996 0.8572
## CCN51 - TCS19 6.9702 1.97 3026 3.544 0.0037
## TCS01 - TCS06 -7.6309 2.00 3026 -3.808 0.0013
## TCS01 - TCS13 -7.5586 2.04 3026 -3.709 0.0020
## TCS01 - TCS19 -2.5793 2.01 3026 -1.286 0.6999
## TCS06 - TCS13 0.0723 1.99 3026 0.036 1.0000
## TCS06 - TCS19 5.0516 1.96 3026 2.578 0.0747
## TCS13 - TCS19 4.9793 1.99 3026 2.498 0.0913
##
## 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 127 1.39 3026 125 130 A
## TCS06 126 1.38 3026 123 128 AB
## TCS13 125 1.43 3026 123 128 AB
## TCS19 121 1.39 3026 118 123 BC
## TCS01 118 1.45 3026 115 121 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 122 1.09 3026 120 124
## Roble 126 1.08 3026 123 128
## Terminalia 122 1.11 3026 120 125
##
## 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 -3.437 1.53 3026 -2.241 0.0646
## Abarco - Terminalia -0.345 1.55 3026 -0.223 0.9731
## Roble - Terminalia 3.092 1.54 3026 2.001 0.1121
##
## 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 126 1.08 3026 123 128 A
## Terminalia 122 1.11 3026 120 125 A
## Abarco 122 1.09 3026 120 124 A
##
## Results are averaged over the levels of: gen, bloque
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 3 estimates
## significance level used: alpha = 0.05
## NOTE: Compact letter displays can be misleading
## because they show NON-findings rather than findings.
## Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
#Forestal*Gen
contrast <- emmeans(aov.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 125 2.44 3026 120 129
## TCS01 Abarco 120 2.44 3026 115 124
## TCS06 Abarco 134 2.38 3026 129 138
## TCS13 Abarco 117 2.50 3026 112 122
## TCS19 Abarco 116 2.43 3026 111 120
## CCN51 Roble 135 2.41 3026 130 139
## TCS01 Roble 114 2.42 3026 109 119
## TCS06 Roble 125 2.40 3026 121 130
## TCS13 Roble 126 2.47 3026 122 131
## TCS19 Roble 127 2.37 3026 123 132
## CCN51 Terminalia 123 2.40 3026 118 128
## TCS01 Terminalia 120 2.66 3026 115 125
## TCS06 Terminalia 118 2.41 3026 113 122
## TCS13 Terminalia 133 2.47 3026 128 138
## TCS19 Terminalia 119 2.41 3026 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 5.102 3.45 3026 1.479 0.9792
## CCN51 Abarco - TCS06 Abarco -9.025 3.41 3026 -2.650 0.3368
## CCN51 Abarco - TCS13 Abarco 7.562 3.49 3026 2.166 0.6897
## CCN51 Abarco - TCS19 Abarco 9.192 3.44 3026 2.673 0.3219
## CCN51 Abarco - CCN51 Roble -9.939 3.43 3026 -2.901 0.1962
## CCN51 Abarco - TCS01 Roble 10.470 3.43 3026 3.049 0.1355
## CCN51 Abarco - TCS06 Roble -0.520 3.42 3026 -0.152 1.0000
## CCN51 Abarco - TCS13 Roble -1.647 3.47 3026 -0.475 1.0000
## CCN51 Abarco - TCS19 Roble -2.720 3.40 3026 -0.800 1.0000
## CCN51 Abarco - CCN51 Terminalia 1.597 3.42 3026 0.467 1.0000
## CCN51 Abarco - TCS01 Terminalia 4.734 3.61 3026 1.312 0.9933
## CCN51 Abarco - TCS06 Terminalia 6.959 3.43 3026 2.029 0.7811
## CCN51 Abarco - TCS13 Terminalia -8.284 3.47 3026 -2.389 0.5235
## CCN51 Abarco - TCS19 Terminalia 6.097 3.43 3026 1.777 0.9074
## TCS01 Abarco - TCS06 Abarco -14.128 3.41 3026 -4.143 0.0032
## TCS01 Abarco - TCS13 Abarco 2.459 3.50 3026 0.704 1.0000
## TCS01 Abarco - TCS19 Abarco 4.089 3.44 3026 1.188 0.9976
## TCS01 Abarco - CCN51 Roble -15.041 3.43 3026 -4.385 0.0011
## TCS01 Abarco - TCS01 Roble 5.368 3.44 3026 1.561 0.9666
## TCS01 Abarco - TCS06 Roble -5.623 3.43 3026 -1.641 0.9495
## TCS01 Abarco - TCS13 Roble -6.750 3.47 3026 -1.943 0.8308
## TCS01 Abarco - TCS19 Roble -7.822 3.40 3026 -2.299 0.5912
## TCS01 Abarco - CCN51 Terminalia -3.506 3.43 3026 -1.023 0.9995
## TCS01 Abarco - TCS01 Terminalia -0.368 3.61 3026 -0.102 1.0000
## TCS01 Abarco - TCS06 Terminalia 1.857 3.43 3026 0.541 1.0000
## TCS01 Abarco - TCS13 Terminalia -13.386 3.47 3026 -3.855 0.0101
## TCS01 Abarco - TCS19 Terminalia 0.994 3.43 3026 0.290 1.0000
## TCS06 Abarco - TCS13 Abarco 16.587 3.45 3026 4.807 0.0002
## TCS06 Abarco - TCS19 Abarco 18.217 3.40 3026 5.362 <.0001
## TCS06 Abarco - CCN51 Roble -0.914 3.38 3026 -0.270 1.0000
## TCS06 Abarco - TCS01 Roble 19.495 3.39 3026 5.746 <.0001
## TCS06 Abarco - TCS06 Roble 8.505 3.38 3026 2.516 0.4291
## TCS06 Abarco - TCS13 Roble 7.378 3.43 3026 2.152 0.6992
## TCS06 Abarco - TCS19 Roble 6.305 3.36 3026 1.879 0.8640
## TCS06 Abarco - CCN51 Terminalia 10.622 3.38 3026 3.142 0.1055
## TCS06 Abarco - TCS01 Terminalia 13.759 3.57 3026 3.856 0.0101
## TCS06 Abarco - TCS06 Terminalia 15.985 3.39 3026 4.717 0.0002
## TCS06 Abarco - TCS13 Terminalia 0.741 3.43 3026 0.216 1.0000
## TCS06 Abarco - TCS19 Terminalia 15.122 3.39 3026 4.462 0.0008
## TCS13 Abarco - TCS19 Abarco 1.630 3.48 3026 0.468 1.0000
## TCS13 Abarco - CCN51 Roble -17.501 3.47 3026 -5.042 <.0001
## TCS13 Abarco - TCS01 Roble 2.908 3.48 3026 0.836 1.0000
## TCS13 Abarco - TCS06 Roble -8.082 3.47 3026 -2.332 0.5666
## TCS13 Abarco - TCS13 Roble -9.209 3.51 3026 -2.622 0.3551
## TCS13 Abarco - TCS19 Roble -10.282 3.44 3026 -2.986 0.1594
## TCS13 Abarco - CCN51 Terminalia -5.965 3.47 3026 -1.721 0.9269
## TCS13 Abarco - TCS01 Terminalia -2.828 3.65 3026 -0.774 1.0000
## TCS13 Abarco - TCS06 Terminalia -0.603 3.47 3026 -0.173 1.0000
## TCS13 Abarco - TCS13 Terminalia -15.846 3.51 3026 -4.512 0.0006
## TCS13 Abarco - TCS19 Terminalia -1.465 3.47 3026 -0.422 1.0000
## TCS19 Abarco - CCN51 Roble -19.131 3.42 3026 -5.599 <.0001
## TCS19 Abarco - TCS01 Roble 1.278 3.43 3026 0.373 1.0000
## TCS19 Abarco - TCS06 Roble -9.712 3.41 3026 -2.845 0.2233
## TCS19 Abarco - TCS13 Roble -10.839 3.46 3026 -3.132 0.1086
## TCS19 Abarco - TCS19 Roble -11.911 3.39 3026 -3.514 0.0339
## TCS19 Abarco - CCN51 Terminalia -7.595 3.41 3026 -2.225 0.6466
## TCS19 Abarco - TCS01 Terminalia -4.457 3.60 3026 -1.239 0.9962
## TCS19 Abarco - TCS06 Terminalia -2.232 3.42 3026 -0.652 1.0000
## TCS19 Abarco - TCS13 Terminalia -17.476 3.46 3026 -5.051 <.0001
## TCS19 Abarco - TCS19 Terminalia -3.095 3.42 3026 -0.904 0.9999
## CCN51 Roble - TCS01 Roble 20.409 3.41 3026 5.979 <.0001
## CCN51 Roble - TCS06 Roble 9.419 3.40 3026 2.770 0.2640
## CCN51 Roble - TCS13 Roble 8.292 3.45 3026 2.404 0.5116
## CCN51 Roble - TCS19 Roble 7.219 3.38 3026 2.138 0.7092
## CCN51 Roble - CCN51 Terminalia 11.536 3.40 3026 3.392 0.0503
## CCN51 Roble - TCS01 Terminalia 14.673 3.59 3026 4.092 0.0040
## CCN51 Roble - TCS06 Terminalia 16.898 3.41 3026 4.957 0.0001
## CCN51 Roble - TCS13 Terminalia 1.655 3.45 3026 0.480 1.0000
## CCN51 Roble - TCS19 Terminalia 16.036 3.41 3026 4.703 0.0003
## TCS01 Roble - TCS06 Roble -10.990 3.41 3026 -3.224 0.0837
## TCS01 Roble - TCS13 Roble -12.117 3.46 3026 -3.506 0.0349
## TCS01 Roble - TCS19 Roble -13.190 3.39 3026 -3.896 0.0086
## TCS01 Roble - CCN51 Terminalia -8.873 3.41 3026 -2.603 0.3681
## TCS01 Roble - TCS01 Terminalia -5.736 3.60 3026 -1.595 0.9600
## TCS01 Roble - TCS06 Terminalia -3.511 3.42 3026 -1.027 0.9995
## TCS01 Roble - TCS13 Terminalia -18.754 3.46 3026 -5.427 <.0001
## TCS01 Roble - TCS19 Terminalia -4.373 3.42 3026 -1.280 0.9948
## TCS06 Roble - TCS13 Roble -1.127 3.44 3026 -0.327 1.0000
## TCS06 Roble - TCS19 Roble -2.199 3.37 3026 -0.652 1.0000
## TCS06 Roble - CCN51 Terminalia 2.117 3.40 3026 0.623 1.0000
## TCS06 Roble - TCS01 Terminalia 5.254 3.58 3026 1.466 0.9808
## TCS06 Roble - TCS06 Terminalia 7.480 3.40 3026 2.197 0.6671
## TCS06 Roble - TCS13 Terminalia -7.764 3.44 3026 -2.255 0.6246
## TCS06 Roble - TCS19 Terminalia 6.617 3.40 3026 1.944 0.8308
## TCS13 Roble - TCS19 Roble -1.073 3.42 3026 -0.314 1.0000
## TCS13 Roble - CCN51 Terminalia 3.244 3.44 3026 0.942 0.9998
## TCS13 Roble - TCS01 Terminalia 6.381 3.63 3026 1.758 0.9145
## TCS13 Roble - TCS06 Terminalia 8.607 3.45 3026 2.493 0.4454
## TCS13 Roble - TCS13 Terminalia -6.637 3.49 3026 -1.902 0.8526
## TCS13 Roble - TCS19 Terminalia 7.744 3.45 3026 2.244 0.6328
## TCS19 Roble - CCN51 Terminalia 4.317 3.37 3026 1.280 0.9948
## TCS19 Roble - TCS01 Terminalia 7.454 3.56 3026 2.093 0.7396
## TCS19 Roble - TCS06 Terminalia 9.679 3.38 3026 2.863 0.2145
## TCS19 Roble - TCS13 Terminalia -5.564 3.42 3026 -1.627 0.9529
## TCS19 Roble - TCS19 Terminalia 8.817 3.38 3026 2.608 0.3649
## CCN51 Terminalia - TCS01 Terminalia 3.137 3.58 3026 0.875 0.9999
## CCN51 Terminalia - TCS06 Terminalia 5.362 3.40 3026 1.575 0.9640
## CCN51 Terminalia - TCS13 Terminalia -9.881 3.44 3026 -2.870 0.2111
## CCN51 Terminalia - TCS19 Terminalia 4.500 3.40 3026 1.322 0.9928
## TCS01 Terminalia - TCS06 Terminalia 2.225 3.59 3026 0.620 1.0000
## TCS01 Terminalia - TCS13 Terminalia -13.018 3.63 3026 -3.588 0.0265
## TCS01 Terminalia - TCS19 Terminalia 1.363 3.59 3026 0.379 1.0000
## TCS06 Terminalia - TCS13 Terminalia -15.243 3.45 3026 -4.417 0.0010
## TCS06 Terminalia - TCS19 Terminalia -0.863 3.41 3026 -0.253 1.0000
## TCS13 Terminalia - TCS19 Terminalia 14.381 3.45 3026 4.166 0.0029
##
## 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 135 2.41 3026 130 139 A
## TCS06 Abarco 134 2.38 3026 129 138 A
## TCS13 Terminalia 133 2.47 3026 128 138 A
## TCS19 Roble 127 2.37 3026 123 132 AB
## TCS13 Roble 126 2.47 3026 122 131 ABC
## TCS06 Roble 125 2.40 3026 121 130 ABCD
## CCN51 Abarco 125 2.44 3026 120 129 ABCD
## CCN51 Terminalia 123 2.40 3026 118 128 ABCD
## TCS01 Terminalia 120 2.66 3026 115 125 BCD
## TCS01 Abarco 120 2.44 3026 115 124 BCD
## TCS19 Terminalia 119 2.41 3026 114 123 BCD
## TCS06 Terminalia 118 2.41 3026 113 122 BCD
## TCS13 Abarco 117 2.50 3026 112 122 BCD
## TCS19 Abarco 116 2.43 3026 111 120 CD
## TCS01 Roble 114 2.42 3026 109 119 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(datos4)