setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura/BD Estadística SGR Santander/data")
datos4<-read.table("cabana.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 725 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 730 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 6456.799 6572.522 -3208.399
## fit.ar1.diam 2 20 6269.361 6385.084 -3114.681
## fit.ar1het.diam 3 32 5958.889 6144.045 -2947.444 2 vs 3 334.4723 <.0001
anova(fit.ar1.diam)
## Denom. DF: 2407
## numDF F-value p-value
## (Intercept) 1 36620.94 <.0001
## semana 1 5262.01 <.0001
## forestal 2 128.69 <.0001
## gen 4 8.26 <.0001
## bloque 2 10.67 <.0001
## forestal:gen 8 27.99 <.0001
anova(fit.ar1het.diam)
## Denom. DF: 2407
## numDF F-value p-value
## (Intercept) 1 28265.228 <.0001
## semana 1 6125.856 <.0001
## forestal 2 58.474 <.0001
## gen 4 4.900 6e-04
## bloque 2 17.403 <.0001
## forestal:gen 8 18.752 <.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 24492.00 24607.68 -12226.00
## fit.ar1.alt 2 20 24143.68 24259.36 -12051.84
## fit.ar1het.alt 3 32 23990.07 24175.16 -11963.03 2 vs 3 177.6081 <.0001
anova(fit.ar1.alt)
## Denom. DF: 2402
## numDF F-value p-value
## (Intercept) 1 24308.787 <.0001
## semana 1 2693.342 <.0001
## forestal 2 92.694 <.0001
## gen 4 8.998 <.0001
## bloque 2 21.771 <.0001
## forestal:gen 8 21.394 <.0001
anova(fit.ar1het.alt)
## Denom. DF: 2402
## numDF F-value p-value
## (Intercept) 1 20404.953 <.0001
## semana 1 3848.266 <.0001
## forestal 2 50.059 <.0001
## gen 4 6.166 1e-04
## bloque 2 25.555 <.0001
## forestal:gen 8 16.951 <.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 ab CCN51
## TCS01 ab TCS01
## TCS06 a TCS06
## TCS13 c TCS13
## TCS19 b TCS19
# Forestal
generate_label_df_forestal_diam <- function(fores.tuk.diam, variable){
Tukey.levels <- fores.tuk.diam[[variable]][,2]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.forestal.diam <- generate_label_df_forestal_diam(fores.tuk.diam, "forestal")
labels.forestal.diam
## Letters treatment
## Abarco a Abarco
## Roble a Roble
## Terminalia a Terminalia
# Interacción Forestal:Genotipo
generate_label_df_interac_diam <- function(interac.tuk.diam, variable){
Tukey.levels <- interac.tuk.diam[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
labels.interac.diam <- generate_label_df_interac_diam(interac.tuk.diam, "forestal:gen")
labels.interac.diam
## Letters treatment
## Abarco:CCN51 a Abarco:CCN51
## Abarco:TCS01 bc Abarco:TCS01
## Abarco:TCS06 def Abarco:TCS06
## Abarco:TCS13 cde Abarco:TCS13
## Abarco:TCS19 a Abarco:TCS19
## Roble:CCN51 fg Roble:CCN51
## Roble:TCS01 ef Roble:TCS01
## Roble:TCS06 ef Roble:TCS06
## Roble:TCS13 f Roble:TCS13
## Roble:TCS19 g Roble:TCS19
## Terminalia:CCN51 cde Terminalia:CCN51
## Terminalia:TCS01 bcd Terminalia:TCS01
## Terminalia:TCS06 b Terminalia:TCS06
## Terminalia:TCS13 ef Terminalia:TCS13
## Terminalia:TCS19 bcd 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 d CCN51
## TCS01 ac TCS01
## TCS06 ab TCS06
## TCS13 cd TCS13
## TCS19 b 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 a Abarco:CCN51
## Abarco:TCS01 bcde Abarco:TCS01
## Abarco:TCS06 bcd Abarco:TCS06
## Abarco:TCS13 bcde Abarco:TCS13
## Abarco:TCS19 j Abarco:TCS19
## Roble:CCN51 i Roble:CCN51
## Roble:TCS01 fgh Roble:TCS01
## Roble:TCS06 cdef Roble:TCS06
## Roble:TCS13 efgh Roble:TCS13
## Roble:TCS19 hi Roble:TCS19
## Terminalia:CCN51 ghi Terminalia:CCN51
## Terminalia:TCS01 bc Terminalia:TCS01
## Terminalia:TCS06 ab Terminalia:TCS06
## Terminalia:TCS13 defg Terminalia:TCS13
## Terminalia:TCS19 bcd 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 4.45 0.0450 2407 4.36 4.54
## TCS01 4.54 0.0463 2407 4.45 4.63
## TCS06 4.59 0.0453 2407 4.50 4.67
## TCS13 4.80 0.0444 2407 4.72 4.89
## TCS19 4.38 0.0464 2407 4.29 4.47
##
## 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 -0.0896 0.0577 2407 -1.553 0.5278
## CCN51 - TCS06 -0.1388 0.0574 2407 -2.421 0.1101
## CCN51 - TCS13 -0.3562 0.0569 2407 -6.264 <.0001
## CCN51 - TCS19 0.0678 0.0581 2407 1.167 0.7700
## TCS01 - TCS06 -0.0493 0.0580 2407 -0.850 0.9148
## TCS01 - TCS13 -0.2666 0.0575 2407 -4.637 <.0001
## TCS01 - TCS19 0.1574 0.0587 2407 2.683 0.0568
## TCS06 - TCS13 -0.2174 0.0572 2407 -3.803 0.0014
## TCS06 - TCS19 0.2066 0.0584 2407 3.541 0.0037
## TCS13 - TCS19 0.4240 0.0579 2407 7.326 <.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 4.80 0.0444 2407 4.72 4.89 A
## TCS06 4.59 0.0453 2407 4.50 4.67 B
## TCS01 4.54 0.0463 2407 4.45 4.63 BC
## CCN51 4.45 0.0450 2407 4.36 4.54 BC
## TCS19 4.38 0.0464 2407 4.29 4.47 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 4.13 0.0383 2407 4.06 4.21
## Roble 5.00 0.0378 2407 4.93 5.08
## Terminalia 4.52 0.0362 2407 4.44 4.59
##
## 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 -0.868 0.0451 2407 -19.231 <.0001
## Abarco - Terminalia -0.381 0.0447 2407 -8.527 <.0001
## Roble - Terminalia 0.486 0.0444 2407 10.945 <.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
## Roble 5.00 0.0378 2407 4.93 5.08 A
## Terminalia 4.52 0.0362 2407 4.44 4.59 B
## Abarco 4.13 0.0383 2407 4.06 4.21 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 3.71 0.0734 2407 3.56 3.85
## TCS01 Abarco 4.31 0.0759 2407 4.16 4.46
## TCS06 Abarco 4.71 0.0743 2407 4.57 4.86
## TCS13 Abarco 4.57 0.0721 2407 4.43 4.72
## TCS19 Abarco 3.36 0.0778 2407 3.21 3.52
## CCN51 Roble 5.01 0.0733 2407 4.86 5.15
## TCS01 Roble 4.86 0.0741 2407 4.72 5.01
## TCS06 Roble 4.80 0.0739 2407 4.66 4.95
## TCS13 Roble 5.00 0.0732 2407 4.85 5.14
## TCS19 Roble 5.33 0.0745 2407 5.19 5.48
## CCN51 Terminalia 4.63 0.0712 2407 4.49 4.77
## TCS01 Terminalia 4.44 0.0731 2407 4.29 4.58
## TCS06 Terminalia 4.24 0.0716 2407 4.10 4.38
## TCS13 Terminalia 4.84 0.0706 2407 4.70 4.98
## TCS19 Terminalia 4.44 0.0729 2407 4.30 4.58
##
## 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 -0.60190 0.1013 2407 -5.941 <.0001
## CCN51 Abarco - TCS06 Abarco -1.00690 0.1006 2407 -10.009 <.0001
## CCN51 Abarco - TCS13 Abarco -0.86664 0.0991 2407 -8.748 <.0001
## CCN51 Abarco - TCS19 Abarco 0.34324 0.1031 2407 3.330 0.0612
## CCN51 Abarco - CCN51 Roble -1.29864 0.0997 2407 -13.029 <.0001
## CCN51 Abarco - TCS01 Roble -1.15713 0.1003 2407 -11.538 <.0001
## CCN51 Abarco - TCS06 Roble -1.09731 0.1003 2407 -10.942 <.0001
## CCN51 Abarco - TCS13 Roble -1.29043 0.1000 2407 -12.909 <.0001
## CCN51 Abarco - TCS19 Roble -1.62710 0.1008 2407 -16.149 <.0001
## CCN51 Abarco - CCN51 Terminalia -0.91967 0.0986 2407 -9.323 <.0001
## CCN51 Abarco - TCS01 Terminalia -0.72801 0.1000 2407 -7.283 <.0001
## CCN51 Abarco - TCS06 Terminalia -0.53064 0.0991 2407 -5.356 <.0001
## CCN51 Abarco - TCS13 Terminalia -1.12986 0.0984 2407 -11.484 <.0001
## CCN51 Abarco - TCS19 Terminalia -0.73109 0.0998 2407 -7.325 <.0001
## TCS01 Abarco - TCS06 Abarco -0.40500 0.1017 2407 -3.980 0.0062
## TCS01 Abarco - TCS13 Abarco -0.26474 0.1003 2407 -2.640 0.3431
## TCS01 Abarco - TCS19 Abarco 0.94514 0.1042 2407 9.074 <.0001
## TCS01 Abarco - CCN51 Roble -0.69675 0.1008 2407 -6.911 <.0001
## TCS01 Abarco - TCS01 Roble -0.55523 0.1014 2407 -5.475 <.0001
## TCS01 Abarco - TCS06 Roble -0.49542 0.1014 2407 -4.884 0.0001
## TCS01 Abarco - TCS13 Roble -0.68853 0.1011 2407 -6.807 <.0001
## TCS01 Abarco - TCS19 Roble -1.02520 0.1019 2407 -10.060 <.0001
## TCS01 Abarco - CCN51 Terminalia -0.31777 0.0999 2407 -3.181 0.0947
## TCS01 Abarco - TCS01 Terminalia -0.12611 0.1012 2407 -1.247 0.9960
## TCS01 Abarco - TCS06 Terminalia 0.07126 0.1003 2407 0.710 1.0000
## TCS01 Abarco - TCS13 Terminalia -0.52796 0.0997 2407 -5.297 <.0001
## TCS01 Abarco - TCS19 Terminalia -0.12919 0.1010 2407 -1.279 0.9948
## TCS06 Abarco - TCS13 Abarco 0.14026 0.0995 2407 1.409 0.9866
## TCS06 Abarco - TCS19 Abarco 1.35014 0.1035 2407 13.045 <.0001
## TCS06 Abarco - CCN51 Roble -0.29175 0.1001 2407 -2.914 0.1906
## TCS06 Abarco - TCS01 Roble -0.15023 0.1008 2407 -1.491 0.9776
## TCS06 Abarco - TCS06 Roble -0.09042 0.1007 2407 -0.898 0.9999
## TCS06 Abarco - TCS13 Roble -0.28353 0.1004 2407 -2.823 0.2350
## TCS06 Abarco - TCS19 Roble -0.62020 0.1012 2407 -6.127 <.0001
## TCS06 Abarco - CCN51 Terminalia 0.08723 0.0991 2407 0.880 0.9999
## TCS06 Abarco - TCS01 Terminalia 0.27889 0.1004 2407 2.777 0.2602
## TCS06 Abarco - TCS06 Terminalia 0.47626 0.0996 2407 4.783 0.0002
## TCS06 Abarco - TCS13 Terminalia -0.12296 0.0989 2407 -1.243 0.9961
## TCS06 Abarco - TCS19 Terminalia 0.27581 0.1003 2407 2.750 0.2756
## TCS13 Abarco - TCS19 Abarco 1.20988 0.1020 2407 11.858 <.0001
## TCS13 Abarco - CCN51 Roble -0.43201 0.0986 2407 -4.381 0.0012
## TCS13 Abarco - TCS01 Roble -0.29049 0.0992 2407 -2.927 0.1844
## TCS13 Abarco - TCS06 Roble -0.23068 0.0992 2407 -2.325 0.5720
## TCS13 Abarco - TCS13 Roble -0.42379 0.0989 2407 -4.284 0.0018
## TCS13 Abarco - TCS19 Roble -0.76046 0.0997 2407 -7.626 <.0001
## TCS13 Abarco - CCN51 Terminalia -0.05303 0.0976 2407 -0.543 1.0000
## TCS13 Abarco - TCS01 Terminalia 0.13863 0.0989 2407 1.402 0.9873
## TCS13 Abarco - TCS06 Terminalia 0.33600 0.0980 2407 3.428 0.0450
## TCS13 Abarco - TCS13 Terminalia -0.26322 0.0973 2407 -2.705 0.3024
## TCS13 Abarco - TCS19 Terminalia 0.13555 0.0988 2407 1.373 0.9896
## TCS19 Abarco - CCN51 Roble -1.64189 0.1026 2407 -16.003 <.0001
## TCS19 Abarco - TCS01 Roble -1.50037 0.1032 2407 -14.537 <.0001
## TCS19 Abarco - TCS06 Roble -1.44055 0.1032 2407 -13.960 <.0001
## TCS19 Abarco - TCS13 Roble -1.63367 0.1029 2407 -15.876 <.0001
## TCS19 Abarco - TCS19 Roble -1.97034 0.1037 2407 -19.006 <.0001
## TCS19 Abarco - CCN51 Terminalia -1.26291 0.1016 2407 -12.425 <.0001
## TCS19 Abarco - TCS01 Terminalia -1.07125 0.1029 2407 -10.409 <.0001
## TCS19 Abarco - TCS06 Terminalia -0.87388 0.1021 2407 -8.562 <.0001
## TCS19 Abarco - TCS13 Terminalia -1.47310 0.1014 2407 -14.527 <.0001
## TCS19 Abarco - TCS19 Terminalia -1.07433 0.1028 2407 -10.451 <.0001
## CCN51 Roble - TCS01 Roble 0.14152 0.0998 2407 1.418 0.9858
## CCN51 Roble - TCS06 Roble 0.20133 0.0998 2407 2.017 0.7884
## CCN51 Roble - TCS13 Roble 0.00822 0.0995 2407 0.083 1.0000
## CCN51 Roble - TCS19 Roble -0.32845 0.1003 2407 -3.275 0.0722
## CCN51 Roble - CCN51 Terminalia 0.37898 0.0982 2407 3.859 0.0100
## CCN51 Roble - TCS01 Terminalia 0.57064 0.0995 2407 5.734 <.0001
## CCN51 Roble - TCS06 Terminalia 0.76801 0.0986 2407 7.786 <.0001
## CCN51 Roble - TCS13 Terminalia 0.16878 0.0980 2407 1.723 0.9262
## CCN51 Roble - TCS19 Terminalia 0.56756 0.0994 2407 5.711 <.0001
## TCS01 Roble - TCS06 Roble 0.05981 0.1004 2407 0.596 1.0000
## TCS01 Roble - TCS13 Roble -0.13330 0.1001 2407 -1.331 0.9923
## TCS01 Roble - TCS19 Roble -0.46997 0.1009 2407 -4.657 0.0003
## TCS01 Roble - CCN51 Terminalia 0.23746 0.0988 2407 2.403 0.5130
## TCS01 Roble - TCS01 Terminalia 0.42912 0.1001 2407 4.285 0.0018
## TCS01 Roble - TCS06 Terminalia 0.62649 0.0993 2407 6.311 <.0001
## TCS01 Roble - TCS13 Terminalia 0.02727 0.0986 2407 0.277 1.0000
## TCS01 Roble - TCS19 Terminalia 0.42604 0.1000 2407 4.261 0.0020
## TCS06 Roble - TCS13 Roble -0.19311 0.1001 2407 -1.929 0.8386
## TCS06 Roble - TCS19 Roble -0.52978 0.1009 2407 -5.250 <.0001
## TCS06 Roble - CCN51 Terminalia 0.17765 0.0988 2407 1.798 0.8993
## TCS06 Roble - TCS01 Terminalia 0.36931 0.1001 2407 3.689 0.0187
## TCS06 Roble - TCS06 Terminalia 0.56668 0.0992 2407 5.710 <.0001
## TCS06 Roble - TCS13 Terminalia -0.03255 0.0986 2407 -0.330 1.0000
## TCS06 Roble - TCS19 Terminalia 0.36623 0.1000 2407 3.663 0.0204
## TCS13 Roble - TCS19 Roble -0.33667 0.1006 2407 -3.347 0.0581
## TCS13 Roble - CCN51 Terminalia 0.37076 0.0985 2407 3.764 0.0142
## TCS13 Roble - TCS01 Terminalia 0.56242 0.0998 2407 5.635 <.0001
## TCS13 Roble - TCS06 Terminalia 0.75979 0.0989 2407 7.681 <.0001
## TCS13 Roble - TCS13 Terminalia 0.16056 0.0982 2407 1.635 0.9511
## TCS13 Roble - TCS19 Terminalia 0.55934 0.0997 2407 5.612 <.0001
## TCS19 Roble - CCN51 Terminalia 0.70743 0.0993 2407 7.124 <.0001
## TCS19 Roble - TCS01 Terminalia 0.89909 0.1006 2407 8.937 <.0001
## TCS19 Roble - TCS06 Terminalia 1.09646 0.0997 2407 10.994 <.0001
## TCS19 Roble - TCS13 Terminalia 0.49724 0.0991 2407 5.020 0.0001
## TCS19 Roble - TCS19 Terminalia 0.89601 0.1005 2407 8.919 <.0001
## CCN51 Terminalia - TCS01 Terminalia 0.19166 0.0985 2407 1.946 0.8293
## CCN51 Terminalia - TCS06 Terminalia 0.38903 0.0976 2407 3.987 0.0061
## CCN51 Terminalia - TCS13 Terminalia -0.21019 0.0969 2407 -2.170 0.6866
## CCN51 Terminalia - TCS19 Terminalia 0.18858 0.0983 2407 1.918 0.8443
## TCS01 Terminalia - TCS06 Terminalia 0.19737 0.0989 2407 1.995 0.8013
## TCS01 Terminalia - TCS13 Terminalia -0.40185 0.0982 2407 -4.091 0.0040
## TCS01 Terminalia - TCS19 Terminalia -0.00308 0.0997 2407 -0.031 1.0000
## TCS06 Terminalia - TCS13 Terminalia -0.59922 0.0973 2407 -6.159 <.0001
## TCS06 Terminalia - TCS19 Terminalia -0.20045 0.0988 2407 -2.030 0.7806
## TCS13 Terminalia - TCS19 Terminalia 0.39877 0.0981 2407 4.067 0.0044
##
## Results are averaged over the levels of: bloque
## P value adjustment: tukey method for comparing a family of 15 estimates
cld_forestal_gen <-multcomp::cld(contrast, alpha = 0.05, Letters = LETTERS, reversed=T)
cld_forestal_gen
## gen forestal emmean SE df lower.CL upper.CL .group
## TCS19 Roble 5.33 0.0745 2407 5.19 5.48 A
## CCN51 Roble 5.01 0.0733 2407 4.86 5.15 AB
## TCS13 Roble 5.00 0.0732 2407 4.85 5.14 AB
## TCS01 Roble 4.86 0.0741 2407 4.72 5.01 BC
## TCS13 Terminalia 4.84 0.0706 2407 4.70 4.98 BC
## TCS06 Roble 4.80 0.0739 2407 4.66 4.95 BC
## TCS06 Abarco 4.71 0.0743 2407 4.57 4.86 BCD
## CCN51 Terminalia 4.63 0.0712 2407 4.49 4.77 CDE
## TCS13 Abarco 4.57 0.0721 2407 4.43 4.72 CDE
## TCS19 Terminalia 4.44 0.0729 2407 4.30 4.58 DEF
## TCS01 Terminalia 4.44 0.0731 2407 4.29 4.58 DEF
## TCS01 Abarco 4.31 0.0759 2407 4.16 4.46 EF
## TCS06 Terminalia 4.24 0.0716 2407 4.10 4.38 F
## CCN51 Abarco 3.71 0.0734 2407 3.56 3.85 G
## TCS19 Abarco 3.36 0.0778 2407 3.21 3.52 G
##
## 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 183 1.92 2402 179 187
## TCS01 174 1.98 2402 170 178
## TCS06 168 1.95 2402 165 172
## TCS13 179 1.90 2402 175 183
## TCS19 163 1.98 2402 160 167
##
## 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 8.72 2.47 2402 3.531 0.0039
## CCN51 - TCS06 14.56 2.46 2402 5.910 <.0001
## CCN51 - TCS13 4.15 2.43 2402 1.704 0.4315
## CCN51 - TCS19 19.45 2.49 2402 7.822 <.0001
## TCS01 - TCS06 5.84 2.49 2402 2.345 0.1311
## TCS01 - TCS13 -4.57 2.46 2402 -1.856 0.3414
## TCS01 - TCS19 10.73 2.51 2402 4.271 0.0002
## TCS06 - TCS13 -10.41 2.45 2402 -4.240 0.0002
## TCS06 - TCS19 4.89 2.51 2402 1.952 0.2902
## TCS13 - TCS19 15.30 2.48 2402 6.173 <.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
## CCN51 183 1.92 2402 179 187 A
## TCS13 179 1.90 2402 175 183 AB
## TCS01 174 1.98 2402 170 178 BC
## TCS06 168 1.95 2402 165 172 CD
## TCS19 163 1.98 2402 160 167 D
##
## 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 157 1.64 2402 153 160
## Roble 190 1.62 2402 187 193
## Terminalia 174 1.55 2402 171 177
##
## 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 -33.4 1.94 2402 -17.236 <.0001
## Abarco - Terminalia -17.8 1.92 2402 -9.254 <.0001
## Roble - Terminalia 15.6 1.90 2402 8.200 <.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
## Roble 190 1.62 2402 187 193 A
## Terminalia 174 1.55 2402 171 177 B
## Abarco 157 1.64 2402 153 160 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 151 3.14 2402 144 157
## TCS01 Abarco 169 3.25 2402 162 175
## TCS06 Abarco 169 3.23 2402 163 175
## TCS13 Abarco 170 3.09 2402 164 176
## TCS19 Abarco 124 3.33 2402 117 130
## CCN51 Roble 205 3.14 2402 199 211
## TCS01 Roble 187 3.17 2402 181 194
## TCS06 Roble 175 3.16 2402 169 181
## TCS13 Roble 184 3.14 2402 178 190
## TCS19 Roble 197 3.19 2402 191 204
## CCN51 Terminalia 193 3.05 2402 187 199
## TCS01 Terminalia 166 3.13 2402 160 173
## TCS06 Terminalia 161 3.07 2402 155 167
## TCS13 Terminalia 182 3.02 2402 176 188
## TCS19 Terminalia 169 3.12 2402 163 175
##
## 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 -18.227 4.34 2402 -4.202 0.0025
## CCN51 Abarco - TCS06 Abarco -18.362 4.34 2402 -4.228 0.0023
## CCN51 Abarco - TCS13 Abarco -19.752 4.24 2402 -4.656 0.0003
## CCN51 Abarco - TCS19 Abarco 26.749 4.41 2402 6.061 <.0001
## CCN51 Abarco - CCN51 Roble -54.734 4.27 2402 -12.825 <.0001
## CCN51 Abarco - TCS01 Roble -36.801 4.29 2402 -8.570 <.0001
## CCN51 Abarco - TCS06 Roble -24.638 4.29 2402 -5.738 <.0001
## CCN51 Abarco - TCS13 Roble -33.440 4.28 2402 -7.813 <.0001
## CCN51 Abarco - TCS19 Roble -46.744 4.31 2402 -10.835 <.0001
## CCN51 Abarco - CCN51 Terminalia -42.252 4.22 2402 -10.003 <.0001
## CCN51 Abarco - TCS01 Terminalia -15.798 4.28 2402 -3.691 0.0185
## CCN51 Abarco - TCS06 Terminalia -10.316 4.24 2402 -2.432 0.4912
## CCN51 Abarco - TCS13 Terminalia -31.346 4.21 2402 -7.441 <.0001
## CCN51 Abarco - TCS19 Terminalia -18.649 4.27 2402 -4.364 0.0013
## TCS01 Abarco - TCS06 Abarco -0.135 4.39 2402 -0.031 1.0000
## TCS01 Abarco - TCS13 Abarco -1.525 4.29 2402 -0.355 1.0000
## TCS01 Abarco - TCS19 Abarco 44.976 4.46 2402 10.085 <.0001
## TCS01 Abarco - CCN51 Roble -36.507 4.32 2402 -8.458 <.0001
## TCS01 Abarco - TCS01 Roble -18.574 4.34 2402 -4.277 0.0018
## TCS01 Abarco - TCS06 Roble -6.411 4.34 2402 -1.476 0.9795
## TCS01 Abarco - TCS13 Roble -15.213 4.33 2402 -3.513 0.0341
## TCS01 Abarco - TCS19 Roble -28.517 4.36 2402 -6.535 <.0001
## TCS01 Abarco - CCN51 Terminalia -24.025 4.28 2402 -5.617 <.0001
## TCS01 Abarco - TCS01 Terminalia 2.428 4.33 2402 0.561 1.0000
## TCS01 Abarco - TCS06 Terminalia 7.911 4.30 2402 1.842 0.8810
## TCS01 Abarco - TCS13 Terminalia -13.119 4.27 2402 -3.074 0.1271
## TCS01 Abarco - TCS19 Terminalia -0.423 4.33 2402 -0.098 1.0000
## TCS06 Abarco - TCS13 Abarco -1.390 4.30 2402 -0.323 1.0000
## TCS06 Abarco - TCS19 Abarco 45.111 4.47 2402 10.102 <.0001
## TCS06 Abarco - CCN51 Roble -36.372 4.32 2402 -8.413 <.0001
## TCS06 Abarco - TCS01 Roble -18.439 4.35 2402 -4.239 0.0022
## TCS06 Abarco - TCS06 Roble -6.276 4.35 2402 -1.443 0.9833
## TCS06 Abarco - TCS13 Roble -15.078 4.34 2402 -3.478 0.0383
## TCS06 Abarco - TCS19 Roble -28.382 4.37 2402 -6.496 <.0001
## TCS06 Abarco - CCN51 Terminalia -23.890 4.28 2402 -5.580 <.0001
## TCS06 Abarco - TCS01 Terminalia 2.563 4.34 2402 0.591 1.0000
## TCS06 Abarco - TCS06 Terminalia 8.046 4.30 2402 1.872 0.8672
## TCS06 Abarco - TCS13 Terminalia -12.984 4.27 2402 -3.040 0.1388
## TCS06 Abarco - TCS19 Terminalia -0.288 4.33 2402 -0.066 1.0000
## TCS13 Abarco - TCS19 Abarco 46.501 4.37 2402 10.644 <.0001
## TCS13 Abarco - CCN51 Roble -34.982 4.22 2402 -8.286 <.0001
## TCS13 Abarco - TCS01 Roble -17.049 4.25 2402 -4.013 0.0055
## TCS13 Abarco - TCS06 Roble -4.886 4.25 2402 -1.150 0.9983
## TCS13 Abarco - TCS13 Roble -13.688 4.24 2402 -3.232 0.0819
## TCS13 Abarco - TCS19 Roble -26.992 4.27 2402 -6.322 <.0001
## TCS13 Abarco - CCN51 Terminalia -22.500 4.18 2402 -5.385 <.0001
## TCS13 Abarco - TCS01 Terminalia 3.953 4.24 2402 0.933 0.9998
## TCS13 Abarco - TCS06 Terminalia 9.436 4.20 2402 2.248 0.6294
## TCS13 Abarco - TCS13 Terminalia -11.594 4.17 2402 -2.782 0.2570
## TCS13 Abarco - TCS19 Terminalia 1.102 4.23 2402 0.261 1.0000
## TCS19 Abarco - CCN51 Roble -81.483 4.39 2402 -18.548 <.0001
## TCS19 Abarco - TCS01 Roble -63.550 4.42 2402 -14.381 <.0001
## TCS19 Abarco - TCS06 Roble -51.387 4.42 2402 -11.630 <.0001
## TCS19 Abarco - TCS13 Roble -60.189 4.41 2402 -13.661 <.0001
## TCS19 Abarco - TCS19 Roble -73.493 4.44 2402 -16.556 <.0001
## TCS19 Abarco - CCN51 Terminalia -69.001 4.35 2402 -15.855 <.0001
## TCS19 Abarco - TCS01 Terminalia -42.547 4.41 2402 -9.656 <.0001
## TCS19 Abarco - TCS06 Terminalia -37.065 4.37 2402 -8.481 <.0001
## TCS19 Abarco - TCS13 Terminalia -58.095 4.34 2402 -13.380 <.0001
## TCS19 Abarco - TCS19 Terminalia -45.398 4.40 2402 -10.315 <.0001
## CCN51 Roble - TCS01 Roble 17.933 4.27 2402 4.196 0.0026
## CCN51 Roble - TCS06 Roble 30.096 4.27 2402 7.042 <.0001
## CCN51 Roble - TCS13 Roble 21.294 4.26 2402 4.998 0.0001
## CCN51 Roble - TCS19 Roble 7.990 4.29 2402 1.861 0.8724
## CCN51 Roble - CCN51 Terminalia 12.482 4.20 2402 2.969 0.1666
## CCN51 Roble - TCS01 Terminalia 38.936 4.26 2402 9.137 <.0001
## CCN51 Roble - TCS06 Terminalia 44.418 4.22 2402 10.516 <.0001
## CCN51 Roble - TCS13 Terminalia 23.388 4.19 2402 5.576 <.0001
## CCN51 Roble - TCS19 Terminalia 36.085 4.25 2402 8.481 <.0001
## TCS01 Roble - TCS06 Roble 12.163 4.30 2402 2.829 0.2321
## TCS01 Roble - TCS13 Roble 3.361 4.29 2402 0.784 1.0000
## TCS01 Roble - TCS19 Roble -9.943 4.32 2402 -2.301 0.5898
## TCS01 Roble - CCN51 Terminalia -5.451 4.23 2402 -1.288 0.9944
## TCS01 Roble - TCS01 Terminalia 21.003 4.29 2402 4.898 0.0001
## TCS01 Roble - TCS06 Terminalia 26.486 4.25 2402 6.231 <.0001
## TCS01 Roble - TCS13 Terminalia 5.455 4.22 2402 1.292 0.9942
## TCS01 Roble - TCS19 Terminalia 18.152 4.28 2402 4.240 0.0022
## TCS06 Roble - TCS13 Roble -8.802 4.29 2402 -2.053 0.7657
## TCS06 Roble - TCS19 Roble -22.106 4.32 2402 -5.117 <.0001
## TCS06 Roble - CCN51 Terminalia -17.614 4.23 2402 -4.163 0.0030
## TCS06 Roble - TCS01 Terminalia 8.839 4.29 2402 2.062 0.7602
## TCS06 Roble - TCS06 Terminalia 14.322 4.25 2402 3.370 0.0540
## TCS06 Roble - TCS13 Terminalia -6.708 4.22 2402 -1.589 0.9611
## TCS06 Roble - TCS19 Terminalia 5.988 4.28 2402 1.399 0.9875
## TCS13 Roble - TCS19 Roble -13.304 4.31 2402 -3.089 0.1222
## TCS13 Roble - CCN51 Terminalia -8.812 4.22 2402 -2.090 0.7421
## TCS13 Roble - TCS01 Terminalia 17.642 4.27 2402 4.128 0.0034
## TCS13 Roble - TCS06 Terminalia 23.124 4.24 2402 5.460 <.0001
## TCS13 Roble - TCS13 Terminalia 2.094 4.21 2402 0.498 1.0000
## TCS13 Roble - TCS19 Terminalia 14.791 4.27 2402 3.466 0.0398
## TCS19 Roble - CCN51 Terminalia 4.492 4.25 2402 1.057 0.9993
## TCS19 Roble - TCS01 Terminalia 30.945 4.31 2402 7.184 <.0001
## TCS19 Roble - TCS06 Terminalia 36.428 4.27 2402 8.531 <.0001
## TCS19 Roble - TCS13 Terminalia 15.398 4.24 2402 3.631 0.0229
## TCS19 Roble - TCS19 Terminalia 28.095 4.30 2402 6.531 <.0001
## CCN51 Terminalia - TCS01 Terminalia 26.453 4.22 2402 6.273 <.0001
## CCN51 Terminalia - TCS06 Terminalia 31.936 4.18 2402 7.644 <.0001
## CCN51 Terminalia - TCS13 Terminalia 10.906 4.15 2402 2.630 0.3504
## CCN51 Terminalia - TCS19 Terminalia 23.602 4.21 2402 5.606 <.0001
## TCS01 Terminalia - TCS06 Terminalia 5.483 4.24 2402 1.295 0.9941
## TCS01 Terminalia - TCS13 Terminalia -15.547 4.21 2402 -3.697 0.0181
## TCS01 Terminalia - TCS19 Terminalia -2.851 4.27 2402 -0.668 1.0000
## TCS06 Terminalia - TCS13 Terminalia -21.030 4.17 2402 -5.048 <.0001
## TCS06 Terminalia - TCS19 Terminalia -8.334 4.23 2402 -1.971 0.8156
## TCS13 Terminalia - TCS19 Terminalia 12.696 4.20 2402 3.024 0.1449
##
## 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 205 3.14 2402 199 211 A
## TCS19 Roble 197 3.19 2402 191 204 AB
## CCN51 Terminalia 193 3.05 2402 187 199 ABC
## TCS01 Roble 187 3.17 2402 181 194 BCD
## TCS13 Roble 184 3.14 2402 178 190 BCDE
## TCS13 Terminalia 182 3.02 2402 176 188 CDEF
## TCS06 Roble 175 3.16 2402 169 181 DEFG
## TCS13 Abarco 170 3.09 2402 164 176 EFG
## TCS19 Terminalia 169 3.12 2402 163 175 FG
## TCS06 Abarco 169 3.23 2402 163 175 FG
## TCS01 Abarco 169 3.25 2402 162 175 FG
## TCS01 Terminalia 166 3.13 2402 160 173 G
## TCS06 Terminalia 161 3.07 2402 155 167 GH
## CCN51 Abarco 151 3.14 2402 144 157 H
## TCS19 Abarco 124 3.33 2402 117 130 I
##
## 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)