setwd("/Volumes/GoogleDrive/Mi unidad/Agrosavia/Colaboraciones/Laura/BD Estadística SGR Santander/data")
datos4<-read.table("parcel18.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 483 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 501 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 8823.839 8944.026 -4391.919
## fit.ar1.diam 2 20 8683.043 8803.231 -4321.522
## fit.ar1het.diam 3 32 7770.285 7962.585 -3853.143 2 vs 3 936.758 <.0001
anova(fit.ar1.diam)
## Denom. DF: 3009
## numDF F-value p-value
## (Intercept) 1 24076.340 <.0001
## semana 1 5610.753 <.0001
## forestal 2 3.448 0.0319
## gen 4 11.897 <.0001
## bloque 2 145.325 <.0001
## forestal:gen 8 12.107 <.0001
anova(fit.ar1het.diam)
## Denom. DF: 3009
## numDF F-value p-value
## (Intercept) 1 15839.518 <.0001
## semana 1 6592.155 <.0001
## forestal 2 1.877 0.1532
## gen 4 5.008 0.0005
## bloque 2 87.169 <.0001
## forestal:gen 8 7.601 <.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 29900.00 30020.06 -14930.00
## fit.ar1.alt 2 20 29720.54 29840.61 -14840.27
## fit.ar1het.alt 3 32 28689.94 28882.05 -14312.97 2 vs 3 1054.597 <.0001
anova(fit.ar1.alt)
## Denom. DF: 2991
## numDF F-value p-value
## (Intercept) 1 26435.999 <.0001
## semana 1 4326.269 <.0001
## forestal 2 1.877 0.1533
## gen 4 26.941 <.0001
## bloque 2 119.434 <.0001
## forestal:gen 8 10.118 <.0001
anova(fit.ar1het.alt)
## Denom. DF: 2991
## numDF F-value p-value
## (Intercept) 1 23331.698 <.0001
## semana 1 6236.115 <.0001
## forestal 2 0.730 0.4822
## gen 4 9.499 <.0001
## bloque 2 41.302 <.0001
## forestal:gen 8 2.070 0.0353
#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 c TCS01
## TCS06 ab 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 b Roble
## Terminalia c 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 ab Abarco:CCN51
## Abarco:TCS01 fg Abarco:TCS01
## Abarco:TCS06 acde Abarco:TCS06
## Abarco:TCS13 efg Abarco:TCS13
## Abarco:TCS19 b Abarco:TCS19
## Roble:CCN51 def Roble:CCN51
## Roble:TCS01 abc Roble:TCS01
## Roble:TCS06 abcde Roble:TCS06
## Roble:TCS13 g Roble:TCS13
## Roble:TCS19 abcd Roble:TCS19
## Terminalia:CCN51 cde Terminalia:CCN51
## Terminalia:TCS01 efg Terminalia:TCS01
## Terminalia:TCS06 ab Terminalia:TCS06
## Terminalia:TCS13 abcd Terminalia:TCS13
## Terminalia:TCS19 abcd 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 bc CCN51
## TCS01 c TCS01
## TCS06 ab TCS06
## TCS13 a TCS13
## TCS19 d 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 acdef Abarco:CCN51
## Abarco:TCS01 h Abarco:TCS01
## Abarco:TCS06 fgh Abarco:TCS06
## Abarco:TCS13 defg Abarco:TCS13
## Abarco:TCS19 b Abarco:TCS19
## Roble:CCN51 gh Roble:CCN51
## Roble:TCS01 acde Roble:TCS01
## Roble:TCS06 efg Roble:TCS06
## Roble:TCS13 gh Roble:TCS13
## Roble:TCS19 abc Roble:TCS19
## Terminalia:CCN51 gh Terminalia:CCN51
## Terminalia:TCS01 gh Terminalia:TCS01
## Terminalia:TCS06 cdef Terminalia:TCS06
## Terminalia:TCS13 abcd Terminalia:TCS13
## Terminalia:TCS19 ab 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 3.50 0.0420 3009 3.41 3.58
## TCS01 3.75 0.0398 3009 3.67 3.83
## TCS06 3.48 0.0398 3009 3.40 3.56
## TCS13 3.72 0.0432 3009 3.64 3.81
## TCS19 3.26 0.0445 3009 3.18 3.35
##
## 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 -0.2551 0.0578 3009 -4.410 0.0001
## CCN51 - TCS06 0.0183 0.0578 3009 0.316 0.9978
## CCN51 - TCS13 -0.2246 0.0601 3009 -3.739 0.0018
## CCN51 - TCS19 0.2332 0.0609 3009 3.831 0.0012
## TCS01 - TCS06 0.2734 0.0563 3009 4.856 <.0001
## TCS01 - TCS13 0.0305 0.0588 3009 0.518 0.9856
## TCS01 - TCS19 0.4883 0.0597 3009 8.181 <.0001
## TCS06 - TCS13 -0.2429 0.0588 3009 -4.134 0.0004
## TCS06 - TCS19 0.2149 0.0596 3009 3.604 0.0029
## TCS13 - TCS19 0.4578 0.0617 3009 7.417 <.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
## TCS01 3.75 0.0398 3009 3.67 3.83 A
## TCS13 3.72 0.0432 3009 3.64 3.81 A
## CCN51 3.50 0.0420 3009 3.41 3.58 B
## TCS06 3.48 0.0398 3009 3.40 3.56 B
## TCS19 3.26 0.0445 3009 3.18 3.35 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 3.55 0.0329 3009 3.48 3.61
## Roble 3.57 0.0325 3009 3.51 3.64
## Terminalia 3.50 0.0320 3009 3.44 3.57
##
## 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 -0.0243 0.0461 3009 -0.528 0.8577
## Abarco - Terminalia 0.0444 0.0458 3009 0.969 0.5967
## Roble - Terminalia 0.0687 0.0456 3009 1.507 0.2875
##
## 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 3.57 0.0325 3009 3.51 3.64 A
## Abarco 3.55 0.0329 3009 3.48 3.61 A
## Terminalia 3.50 0.0320 3009 3.44 3.57 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 3.21 0.0765 3009 3.06 3.36
## TCS01 Abarco 4.01 0.0703 3009 3.88 4.15
## TCS06 Abarco 3.58 0.0681 3009 3.45 3.72
## TCS13 Abarco 3.78 0.0745 3009 3.63 3.92
## TCS19 Abarco 3.16 0.0768 3009 3.00 3.31
## CCN51 Roble 3.66 0.0719 3009 3.51 3.80
## TCS01 Roble 3.34 0.0695 3009 3.20 3.48
## TCS06 Roble 3.53 0.0684 3009 3.40 3.67
## TCS13 Roble 4.02 0.0734 3009 3.87 4.16
## TCS19 Roble 3.32 0.0787 3009 3.16 3.47
## CCN51 Terminalia 3.62 0.0692 3009 3.49 3.76
## TCS01 Terminalia 3.90 0.0670 3009 3.77 4.03
## TCS06 Terminalia 3.32 0.0705 3009 3.18 3.45
## TCS13 Terminalia 3.37 0.0762 3009 3.22 3.52
## TCS19 Terminalia 3.32 0.0745 3009 3.17 3.46
##
## 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 -0.800945 0.1039 3009 -7.711 <.0001
## CCN51 Abarco - TCS06 Abarco -0.370876 0.1024 3009 -3.621 0.0236
## CCN51 Abarco - TCS13 Abarco -0.563247 0.1067 3009 -5.281 <.0001
## CCN51 Abarco - TCS19 Abarco 0.058319 0.1082 3009 0.539 1.0000
## CCN51 Abarco - CCN51 Roble -0.441771 0.1048 3009 -4.217 0.0024
## CCN51 Abarco - TCS01 Roble -0.125890 0.1033 3009 -1.218 0.9968
## CCN51 Abarco - TCS06 Roble -0.320383 0.1026 3009 -3.123 0.1112
## CCN51 Abarco - TCS13 Roble -0.804796 0.1058 3009 -7.610 <.0001
## CCN51 Abarco - TCS19 Roble -0.105475 0.1094 3009 -0.964 0.9998
## CCN51 Abarco - CCN51 Terminalia -0.407322 0.1032 3009 -3.949 0.0070
## CCN51 Abarco - TCS01 Terminalia -0.687460 0.1018 3009 -6.755 <.0001
## CCN51 Abarco - TCS06 Terminalia -0.102959 0.1039 3009 -0.991 0.9997
## CCN51 Abarco - TCS13 Terminalia -0.154860 0.1078 3009 -1.437 0.9840
## CCN51 Abarco - TCS19 Terminalia -0.102364 0.1066 3009 -0.960 0.9998
## TCS01 Abarco - TCS06 Abarco 0.430069 0.0978 3009 4.396 0.0011
## TCS01 Abarco - TCS13 Abarco 0.237699 0.1024 3009 2.321 0.5749
## TCS01 Abarco - TCS19 Abarco 0.859265 0.1041 3009 8.254 <.0001
## TCS01 Abarco - CCN51 Roble 0.359175 0.1005 3009 3.572 0.0279
## TCS01 Abarco - TCS01 Roble 0.675056 0.0988 3009 6.832 <.0001
## TCS01 Abarco - TCS06 Roble 0.480562 0.0980 3009 4.902 0.0001
## TCS01 Abarco - TCS13 Roble -0.003850 0.1016 3009 -0.038 1.0000
## TCS01 Abarco - TCS19 Roble 0.695470 0.1055 3009 6.591 <.0001
## TCS01 Abarco - CCN51 Terminalia 0.393623 0.0986 3009 3.993 0.0059
## TCS01 Abarco - TCS01 Terminalia 0.113486 0.0971 3009 1.169 0.9980
## TCS01 Abarco - TCS06 Terminalia 0.697986 0.0995 3009 7.015 <.0001
## TCS01 Abarco - TCS13 Terminalia 0.646086 0.1036 3009 6.235 <.0001
## TCS01 Abarco - TCS19 Terminalia 0.698582 0.1024 3009 6.822 <.0001
## TCS06 Abarco - TCS13 Abarco -0.192371 0.1009 3009 -1.906 0.8506
## TCS06 Abarco - TCS19 Abarco 0.429195 0.1027 3009 4.180 0.0028
## TCS06 Abarco - CCN51 Roble -0.070894 0.0990 3009 -0.716 1.0000
## TCS06 Abarco - TCS01 Roble 0.244986 0.0972 3009 2.519 0.4266
## TCS06 Abarco - TCS06 Roble 0.050493 0.0965 3009 0.523 1.0000
## TCS06 Abarco - TCS13 Roble -0.433919 0.1001 3009 -4.333 0.0014
## TCS06 Abarco - TCS19 Roble 0.265401 0.1041 3009 2.550 0.4048
## TCS06 Abarco - CCN51 Terminalia -0.036446 0.0970 3009 -0.376 1.0000
## TCS06 Abarco - TCS01 Terminalia -0.316584 0.0955 3009 -3.314 0.0640
## TCS06 Abarco - TCS06 Terminalia 0.267917 0.0980 3009 2.735 0.2840
## TCS06 Abarco - TCS13 Terminalia 0.216016 0.1022 3009 2.114 0.7253
## TCS06 Abarco - TCS19 Terminalia 0.268513 0.1009 3009 2.661 0.3299
## TCS13 Abarco - TCS19 Abarco 0.621566 0.1069 3009 5.814 <.0001
## TCS13 Abarco - CCN51 Roble 0.121476 0.1035 3009 1.174 0.9979
## TCS13 Abarco - TCS01 Roble 0.437357 0.1019 3009 4.291 0.0017
## TCS13 Abarco - TCS06 Roble 0.242864 0.1011 3009 2.401 0.5138
## TCS13 Abarco - TCS13 Roble -0.241549 0.1045 3009 -2.311 0.5824
## TCS13 Abarco - TCS19 Roble 0.457771 0.1083 3009 4.229 0.0023
## TCS13 Abarco - CCN51 Terminalia 0.155924 0.1017 3009 1.534 0.9713
## TCS13 Abarco - TCS01 Terminalia -0.124213 0.1003 3009 -1.239 0.9963
## TCS13 Abarco - TCS06 Terminalia 0.460288 0.1025 3009 4.490 0.0007
## TCS13 Abarco - TCS13 Terminalia 0.408387 0.1064 3009 3.838 0.0107
## TCS13 Abarco - TCS19 Terminalia 0.460883 0.1053 3009 4.379 0.0012
## TCS19 Abarco - CCN51 Roble -0.500089 0.1051 3009 -4.760 0.0002
## TCS19 Abarco - TCS01 Roble -0.184209 0.1036 3009 -1.778 0.9069
## TCS19 Abarco - TCS06 Roble -0.378702 0.1028 3009 -3.683 0.0190
## TCS19 Abarco - TCS13 Roble -0.863115 0.1061 3009 -8.137 <.0001
## TCS19 Abarco - TCS19 Roble -0.163794 0.1098 3009 -1.492 0.9774
## TCS19 Abarco - CCN51 Terminalia -0.465641 0.1034 3009 -4.504 0.0007
## TCS19 Abarco - TCS01 Terminalia -0.745779 0.1020 3009 -7.311 <.0001
## TCS19 Abarco - TCS06 Terminalia -0.161278 0.1042 3009 -1.548 0.9689
## TCS19 Abarco - TCS13 Terminalia -0.213179 0.1081 3009 -1.973 0.8145
## TCS19 Abarco - TCS19 Terminalia -0.160683 0.1069 3009 -1.503 0.9759
## CCN51 Roble - TCS01 Roble 0.315881 0.1000 3009 3.159 0.1005
## CCN51 Roble - TCS06 Roble 0.121387 0.0992 3009 1.223 0.9967
## CCN51 Roble - TCS13 Roble -0.363025 0.1026 3009 -3.539 0.0312
## CCN51 Roble - TCS19 Roble 0.336295 0.1064 3009 3.161 0.1002
## CCN51 Roble - CCN51 Terminalia 0.034448 0.0998 3009 0.345 1.0000
## CCN51 Roble - TCS01 Terminalia -0.245689 0.0984 3009 -2.498 0.4420
## CCN51 Roble - TCS06 Terminalia 0.338811 0.1006 3009 3.367 0.0544
## CCN51 Roble - TCS13 Terminalia 0.286911 0.1047 3009 2.741 0.2804
## CCN51 Roble - TCS19 Terminalia 0.339407 0.1034 3009 3.281 0.0707
## TCS01 Roble - TCS06 Roble -0.194493 0.0975 3009 -1.996 0.8011
## TCS01 Roble - TCS13 Roble -0.678906 0.1011 3009 -6.717 <.0001
## TCS01 Roble - TCS19 Roble 0.020415 0.1050 3009 0.194 1.0000
## TCS01 Roble - CCN51 Terminalia -0.281432 0.0980 3009 -2.871 0.2103
## TCS01 Roble - TCS01 Terminalia -0.561570 0.0965 3009 -5.818 <.0001
## TCS01 Roble - TCS06 Terminalia 0.022931 0.0989 3009 0.232 1.0000
## TCS01 Roble - TCS13 Terminalia -0.028970 0.1031 3009 -0.281 1.0000
## TCS01 Roble - TCS19 Terminalia 0.023526 0.1019 3009 0.231 1.0000
## TCS06 Roble - TCS13 Roble -0.484412 0.1003 3009 -4.829 0.0001
## TCS06 Roble - TCS19 Roble 0.214908 0.1043 3009 2.061 0.7605
## TCS06 Roble - CCN51 Terminalia -0.086939 0.0972 3009 -0.894 0.9999
## TCS06 Roble - TCS01 Terminalia -0.367077 0.0957 3009 -3.834 0.0109
## TCS06 Roble - TCS06 Terminalia 0.217424 0.0982 3009 2.215 0.6540
## TCS06 Roble - TCS13 Terminalia 0.165523 0.1023 3009 1.617 0.9552
## TCS06 Roble - TCS19 Terminalia 0.218020 0.1011 3009 2.156 0.6963
## TCS13 Roble - TCS19 Roble 0.699320 0.1074 3009 6.513 <.0001
## TCS13 Roble - CCN51 Terminalia 0.397473 0.1009 3009 3.940 0.0073
## TCS13 Roble - TCS01 Terminalia 0.117336 0.0995 3009 1.180 0.9978
## TCS13 Roble - TCS06 Terminalia 0.701836 0.1017 3009 6.902 <.0001
## TCS13 Roble - TCS13 Terminalia 0.649936 0.1057 3009 6.149 <.0001
## TCS13 Roble - TCS19 Terminalia 0.702432 0.1045 3009 6.723 <.0001
## TCS19 Roble - CCN51 Terminalia -0.301847 0.1048 3009 -2.880 0.2060
## TCS19 Roble - TCS01 Terminalia -0.581985 0.1034 3009 -5.626 <.0001
## TCS19 Roble - TCS06 Terminalia 0.002516 0.1055 3009 0.024 1.0000
## TCS19 Roble - TCS13 Terminalia -0.049384 0.1094 3009 -0.452 1.0000
## TCS19 Roble - TCS19 Terminalia 0.003112 0.1082 3009 0.029 1.0000
## CCN51 Terminalia - TCS01 Terminalia -0.280137 0.0963 3009 -2.909 0.1923
## CCN51 Terminalia - TCS06 Terminalia 0.304363 0.0987 3009 3.083 0.1238
## CCN51 Terminalia - TCS13 Terminalia 0.252463 0.1029 3009 2.454 0.4743
## CCN51 Terminalia - TCS19 Terminalia 0.304959 0.1016 3009 3.000 0.1538
## TCS01 Terminalia - TCS06 Terminalia 0.584501 0.0973 3009 6.010 <.0001
## TCS01 Terminalia - TCS13 Terminalia 0.532600 0.1015 3009 5.248 <.0001
## TCS01 Terminalia - TCS19 Terminalia 0.585096 0.1002 3009 5.837 <.0001
## TCS06 Terminalia - TCS13 Terminalia -0.051901 0.1037 3009 -0.500 1.0000
## TCS06 Terminalia - TCS19 Terminalia 0.000596 0.1025 3009 0.006 1.0000
## TCS13 Terminalia - TCS19 Terminalia 0.052496 0.1064 3009 0.493 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
## TCS13 Roble 4.02 0.0734 3009 3.87 4.16 A
## TCS01 Abarco 4.01 0.0703 3009 3.88 4.15 A
## TCS01 Terminalia 3.90 0.0670 3009 3.77 4.03 AB
## TCS13 Abarco 3.78 0.0745 3009 3.63 3.92 ABC
## CCN51 Roble 3.66 0.0719 3009 3.51 3.80 BCD
## CCN51 Terminalia 3.62 0.0692 3009 3.49 3.76 BCD
## TCS06 Abarco 3.58 0.0681 3009 3.45 3.72 BCD
## TCS06 Roble 3.53 0.0684 3009 3.40 3.67 CDE
## TCS13 Terminalia 3.37 0.0762 3009 3.22 3.52 DEF
## TCS01 Roble 3.34 0.0695 3009 3.20 3.48 DEF
## TCS19 Roble 3.32 0.0787 3009 3.16 3.47 DEF
## TCS06 Terminalia 3.32 0.0705 3009 3.18 3.45 DEF
## TCS19 Terminalia 3.32 0.0745 3009 3.17 3.46 DEF
## CCN51 Abarco 3.21 0.0765 3009 3.06 3.36 EF
## TCS19 Abarco 3.16 0.0768 3009 3.00 3.31 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
#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 1.44 2991 133 139
## TCS01 140 1.37 2991 137 143
## TCS06 134 1.37 2991 131 137
## TCS13 131 1.49 2991 128 134
## TCS19 113 1.52 2991 110 116
##
## 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 -3.86 1.98 2991 -1.949 0.2918
## CCN51 - TCS06 2.43 1.98 2991 1.229 0.7345
## CCN51 - TCS13 5.62 2.06 2991 2.731 0.0497
## CCN51 - TCS19 23.36 2.08 2991 11.224 <.0001
## TCS01 - TCS06 6.30 1.93 2991 3.256 0.0101
## TCS01 - TCS13 9.49 2.02 2991 4.695 <.0001
## TCS01 - TCS19 27.22 2.04 2991 13.315 <.0001
## TCS06 - TCS13 3.19 2.02 2991 1.580 0.5102
## TCS06 - TCS19 20.93 2.04 2991 10.244 <.0001
## TCS13 - TCS19 17.74 2.12 2991 8.383 <.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
## TCS01 140 1.37 2991 137 143 A
## CCN51 136 1.44 2991 133 139 AB
## TCS06 134 1.37 2991 131 137 BC
## TCS13 131 1.49 2991 128 134 C
## TCS19 113 1.52 2991 110 116 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 131 1.12 2991 129 133
## Roble 131 1.12 2991 129 134
## Terminalia 130 1.10 2991 128 132
##
## 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 -0.539 1.58 2991 -0.341 0.9380
## Abarco - Terminalia 0.675 1.57 2991 0.431 0.9028
## Roble - Terminalia 1.213 1.57 2991 0.775 0.7183
##
## 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 131 1.12 2991 129 134 A
## Abarco 131 1.12 2991 129 133 A
## Terminalia 130 1.10 2991 128 132 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 126 2.62 2991 121 131
## TCS01 Abarco 149 2.40 2991 144 154
## TCS06 Abarco 139 2.33 2991 134 143
## TCS13 Abarco 132 2.55 2991 127 137
## TCS19 Abarco 108 2.63 2991 103 113
## CCN51 Roble 141 2.46 2991 136 145
## TCS01 Roble 127 2.41 2991 122 131
## TCS06 Roble 133 2.37 2991 129 138
## TCS13 Roble 139 2.54 2991 134 144
## TCS19 Roble 117 2.69 2991 112 122
## CCN51 Terminalia 143 2.36 2991 138 147
## TCS01 Terminalia 145 2.29 2991 140 149
## TCS06 Terminalia 129 2.41 2991 125 134
## TCS13 Terminalia 121 2.62 2991 115 126
## TCS19 Terminalia 114 2.55 2991 109 119
##
## 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 -23.383 3.55 2991 -6.584 <.0001
## CCN51 Abarco - TCS06 Abarco -13.120 3.50 2991 -3.746 0.0151
## CCN51 Abarco - TCS13 Abarco -6.499 3.65 2991 -1.782 0.9054
## CCN51 Abarco - TCS19 Abarco 17.541 3.70 2991 4.743 0.0002
## CCN51 Abarco - CCN51 Roble -14.859 3.58 2991 -4.148 0.0032
## CCN51 Abarco - TCS01 Roble -0.796 3.55 2991 -0.224 1.0000
## CCN51 Abarco - TCS06 Roble -7.745 3.53 2991 -2.195 0.6684
## CCN51 Abarco - TCS13 Roble -13.415 3.63 2991 -3.692 0.0184
## CCN51 Abarco - TCS19 Roble 8.663 3.74 2991 2.316 0.5789
## CCN51 Abarco - CCN51 Terminalia -16.816 3.53 2991 -4.768 0.0002
## CCN51 Abarco - TCS01 Terminalia -19.082 3.48 2991 -5.484 <.0001
## CCN51 Abarco - TCS06 Terminalia -3.507 3.55 2991 -0.987 0.9997
## CCN51 Abarco - TCS13 Terminalia 5.115 3.70 2991 1.384 0.9888
## CCN51 Abarco - TCS19 Terminalia 12.203 3.65 2991 3.348 0.0577
## TCS01 Abarco - TCS06 Abarco 10.263 3.34 2991 3.068 0.1288
## TCS01 Abarco - TCS13 Abarco 16.883 3.50 2991 4.821 0.0001
## TCS01 Abarco - TCS19 Abarco 40.924 3.56 2991 11.497 <.0001
## TCS01 Abarco - CCN51 Roble 8.523 3.44 2991 2.479 0.4557
## TCS01 Abarco - TCS01 Roble 22.586 3.40 2991 6.638 <.0001
## TCS01 Abarco - TCS06 Roble 15.637 3.37 2991 4.634 0.0004
## TCS01 Abarco - TCS13 Roble 9.967 3.49 2991 2.852 0.2199
## TCS01 Abarco - TCS19 Roble 32.045 3.61 2991 8.882 <.0001
## TCS01 Abarco - CCN51 Terminalia 6.567 3.37 2991 1.948 0.8282
## TCS01 Abarco - TCS01 Terminalia 4.300 3.32 2991 1.295 0.9941
## TCS01 Abarco - TCS06 Terminalia 19.876 3.40 2991 5.842 <.0001
## TCS01 Abarco - TCS13 Terminalia 28.497 3.55 2991 8.018 <.0001
## TCS01 Abarco - TCS19 Terminalia 35.586 3.50 2991 10.163 <.0001
## TCS06 Abarco - TCS13 Abarco 6.620 3.45 2991 1.918 0.8442
## TCS06 Abarco - TCS19 Abarco 30.661 3.51 2991 8.735 <.0001
## TCS06 Abarco - CCN51 Roble -1.739 3.39 2991 -0.514 1.0000
## TCS06 Abarco - TCS01 Roble 12.323 3.35 2991 3.679 0.0192
## TCS06 Abarco - TCS06 Roble 5.374 3.32 2991 1.618 0.9549
## TCS06 Abarco - TCS13 Roble -0.296 3.44 2991 -0.086 1.0000
## TCS06 Abarco - TCS19 Roble 21.782 3.56 2991 6.121 <.0001
## TCS06 Abarco - CCN51 Terminalia -3.696 3.32 2991 -1.114 0.9988
## TCS06 Abarco - TCS01 Terminalia -5.962 3.27 2991 -1.826 0.8879
## TCS06 Abarco - TCS06 Terminalia 9.613 3.35 2991 2.870 0.2109
## TCS06 Abarco - TCS13 Terminalia 18.234 3.50 2991 5.203 <.0001
## TCS06 Abarco - TCS19 Terminalia 25.323 3.45 2991 7.339 <.0001
## TCS13 Abarco - TCS19 Abarco 24.040 3.66 2991 6.577 <.0001
## TCS13 Abarco - CCN51 Roble -8.360 3.54 2991 -2.363 0.5432
## TCS13 Abarco - TCS01 Roble 5.703 3.51 2991 1.626 0.9533
## TCS13 Abarco - TCS06 Roble -1.246 3.48 2991 -0.358 1.0000
## TCS13 Abarco - TCS13 Roble -6.916 3.59 2991 -1.925 0.8408
## TCS13 Abarco - TCS19 Roble 15.162 3.70 2991 4.096 0.0039
## TCS13 Abarco - CCN51 Terminalia -10.316 3.48 2991 -2.968 0.1667
## TCS13 Abarco - TCS01 Terminalia -12.583 3.43 2991 -3.670 0.0198
## TCS13 Abarco - TCS06 Terminalia 2.992 3.50 2991 0.854 0.9999
## TCS13 Abarco - TCS13 Terminalia 11.614 3.65 2991 3.183 0.0940
## TCS13 Abarco - TCS19 Terminalia 18.703 3.60 2991 5.197 <.0001
## TCS19 Abarco - CCN51 Roble -32.400 3.59 2991 -9.020 <.0001
## TCS19 Abarco - TCS01 Roble -18.337 3.56 2991 -5.145 <.0001
## TCS19 Abarco - TCS06 Roble -25.286 3.54 2991 -7.149 <.0001
## TCS19 Abarco - TCS13 Roble -30.956 3.65 2991 -8.493 <.0001
## TCS19 Abarco - TCS19 Roble -8.878 3.75 2991 -2.366 0.5408
## TCS19 Abarco - CCN51 Terminalia -34.357 3.54 2991 -9.719 <.0001
## TCS19 Abarco - TCS01 Terminalia -36.623 3.49 2991 -10.501 <.0001
## TCS19 Abarco - TCS06 Terminalia -21.048 3.56 2991 -5.910 <.0001
## TCS19 Abarco - TCS13 Terminalia -12.426 3.71 2991 -3.354 0.0567
## TCS19 Abarco - TCS19 Terminalia -5.338 3.65 2991 -1.461 0.9814
## CCN51 Roble - TCS01 Roble 14.063 3.44 2991 4.086 0.0041
## CCN51 Roble - TCS06 Roble 7.114 3.41 2991 2.084 0.7461
## CCN51 Roble - TCS13 Roble 1.444 3.53 2991 0.409 1.0000
## CCN51 Roble - TCS19 Roble 23.522 3.64 2991 6.466 <.0001
## CCN51 Roble - CCN51 Terminalia -1.957 3.41 2991 -0.573 1.0000
## CCN51 Roble - TCS01 Terminalia -4.223 3.36 2991 -1.256 0.9957
## CCN51 Roble - TCS06 Terminalia 11.352 3.44 2991 3.300 0.0668
## CCN51 Roble - TCS13 Terminalia 19.974 3.59 2991 5.564 <.0001
## CCN51 Roble - TCS19 Terminalia 27.062 3.54 2991 7.652 <.0001
## TCS01 Roble - TCS06 Roble -6.949 3.38 2991 -2.057 0.7634
## TCS01 Roble - TCS13 Roble -12.619 3.50 2991 -3.608 0.0247
## TCS01 Roble - TCS19 Roble 9.459 3.61 2991 2.620 0.3568
## TCS01 Roble - CCN51 Terminalia -16.019 3.38 2991 -4.746 0.0002
## TCS01 Roble - TCS01 Terminalia -18.286 3.32 2991 -5.500 <.0001
## TCS01 Roble - TCS06 Terminalia -2.710 3.41 2991 -0.796 1.0000
## TCS01 Roble - TCS13 Terminalia 5.911 3.56 2991 1.660 0.9446
## TCS01 Roble - TCS19 Terminalia 13.000 3.51 2991 3.707 0.0174
## TCS06 Roble - TCS13 Roble -5.670 3.47 2991 -1.634 0.9513
## TCS06 Roble - TCS19 Roble 16.408 3.58 2991 4.578 0.0005
## TCS06 Roble - CCN51 Terminalia -9.070 3.35 2991 -2.710 0.2990
## TCS06 Roble - TCS01 Terminalia -11.337 3.30 2991 -3.439 0.0433
## TCS06 Roble - TCS06 Terminalia 4.239 3.38 2991 1.255 0.9957
## TCS06 Roble - TCS13 Terminalia 12.860 3.53 2991 3.641 0.0220
## TCS06 Roble - TCS19 Terminalia 19.949 3.48 2991 5.735 <.0001
## TCS13 Roble - TCS19 Roble 22.078 3.69 2991 5.985 <.0001
## TCS13 Roble - CCN51 Terminalia -3.400 3.47 2991 -0.980 0.9997
## TCS13 Roble - TCS01 Terminalia -5.667 3.42 2991 -1.656 0.9457
## TCS13 Roble - TCS06 Terminalia 9.909 3.50 2991 2.834 0.2291
## TCS13 Roble - TCS13 Terminalia 18.530 3.64 2991 5.085 <.0001
## TCS13 Roble - TCS19 Terminalia 25.619 3.59 2991 7.134 <.0001
## TCS19 Roble - CCN51 Terminalia -25.478 3.58 2991 -7.111 <.0001
## TCS19 Roble - TCS01 Terminalia -27.745 3.54 2991 -7.845 <.0001
## TCS19 Roble - TCS06 Terminalia -12.170 3.61 2991 -3.372 0.0535
## TCS19 Roble - TCS13 Terminalia -3.548 3.75 2991 -0.946 0.9998
## TCS19 Roble - TCS19 Terminalia 3.540 3.70 2991 0.957 0.9998
## CCN51 Terminalia - TCS01 Terminalia -2.266 3.29 2991 -0.688 1.0000
## CCN51 Terminalia - TCS06 Terminalia 13.309 3.38 2991 3.943 0.0072
## CCN51 Terminalia - TCS13 Terminalia 21.930 3.53 2991 6.216 <.0001
## CCN51 Terminalia - TCS19 Terminalia 29.019 3.48 2991 8.350 <.0001
## TCS01 Terminalia - TCS06 Terminalia 15.575 3.33 2991 4.684 0.0003
## TCS01 Terminalia - TCS13 Terminalia 24.197 3.48 2991 6.950 <.0001
## TCS01 Terminalia - TCS19 Terminalia 31.285 3.43 2991 9.128 <.0001
## TCS06 Terminalia - TCS13 Terminalia 8.622 3.56 2991 2.424 0.4968
## TCS06 Terminalia - TCS19 Terminalia 15.710 3.50 2991 4.484 0.0007
## TCS13 Terminalia - TCS19 Terminalia 7.088 3.65 2991 1.943 0.8312
##
## 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 149 2.40 2991 144 154 A
## TCS01 Terminalia 145 2.29 2991 140 149 A
## CCN51 Terminalia 143 2.36 2991 138 147 AB
## CCN51 Roble 141 2.46 2991 136 145 ABC
## TCS13 Roble 139 2.54 2991 134 144 ABC
## TCS06 Abarco 139 2.33 2991 134 143 ABC
## TCS06 Roble 133 2.37 2991 129 138 BCD
## TCS13 Abarco 132 2.55 2991 127 137 BCDE
## TCS06 Terminalia 129 2.41 2991 125 134 CDEF
## TCS01 Roble 127 2.41 2991 122 131 DEF
## CCN51 Abarco 126 2.62 2991 121 131 DEFG
## TCS13 Terminalia 121 2.62 2991 115 126 EFGH
## TCS19 Roble 117 2.69 2991 112 122 FGH
## TCS19 Terminalia 114 2.55 2991 109 119 GH
## TCS19 Abarco 108 2.63 2991 103 113 H
##
## 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)