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library(TukeyC)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(readxl)
E_H_A <- read_excel("C:/Users/LENOVO/Desktop/Datos_Repro.xlsx",
sheet = "R(E_H_A)");E_H_A=data.frame(E_H_A)
E_H_A$L_P_R= round(E_H_A$L_P_R, 2);E_H_A
## Cultivo Tratamiento Repeticion L_P_R N_R Peso.callo..mg.
## 1 Romero Apice - Auxinas 1 71.50 2 0.0
## 2 Romero Apice - Auxinas 2 77.00 8 0.0
## 3 Romero Apice - Auxinas 3 0.00 0 0.0
## 4 Romero Apice - Auxinas 4 0.00 0 0.0
## 5 Romero Apice - Auxinas 5 77.00 12 0.0
## 6 Romero Base - Auxinas 1 84.80 5 0.0
## 7 Romero Base - Auxinas 2 101.83 6 0.0
## 8 Romero Base - Auxinas 3 105.00 5 0.0
## 9 Romero Base - Auxinas 4 73.67 6 0.0
## 10 Romero Base - Auxinas 5 89.67 6 0.0
## 11 Romero Apice + Auxinas 1 0.00 0 0.0
## 12 Romero Apice + Auxinas 2 0.00 0 37.4
## 13 Romero Apice + Auxinas 3 165.00 1 0.0
## 14 Romero Apice + Auxinas 4 67.00 2 0.0
## 15 Romero Apice + Auxinas 5 0.00 0 0.0
## 16 Romero Base + Auxinas 1 81.00 13 0.0
## 17 Romero Base + Auxinas 2 40.00 9 0.0
## 18 Romero Base + Auxinas 3 0.00 0 43.0
## 19 Romero Base + Auxinas 4 68.60 21 0.0
## 20 Romero Base + Auxinas 5 0.00 0 29.7
## 21 Lavanda Apice - Auxinas 1 31.00 3 0.0
## 22 Lavanda Apice - Auxinas 2 98.50 6 0.0
## 23 Lavanda Apice - Auxinas 3 85.86 7 0.0
## 24 Lavanda Apice - Auxinas 4 104.50 6 0.0
## 25 Lavanda Apice - Auxinas 5 88.33 6 0.0
## 26 Lavanda Base - Auxinas 1 40.00 7 0.0
## 27 Lavanda Base - Auxinas 2 74.00 12 0.0
## 28 Lavanda Base - Auxinas 3 74.90 10 0.0
## 29 Lavanda Base - Auxinas 4 87.00 6 0.0
## 30 Lavanda Base - Auxinas 5 0.00 0 260.0
## 31 Lavanda Apice + Auxinas 1 85.00 8 0.0
## 32 Lavanda Apice + Auxinas 2 89.30 8 0.0
## 33 Lavanda Apice + Auxinas 3 82.50 9 0.0
## 34 Lavanda Apice + Auxinas 4 78.10 13 0.0
## 35 Lavanda Apice + Auxinas 5 76.20 10 0.0
## 36 Lavanda Base + Auxinas 1 12.00 1 0.0
## 37 Lavanda Base + Auxinas 2 79.60 9 0.0
## 38 Lavanda Base + Auxinas 3 78.90 8 0.0
## 39 Lavanda Base + Auxinas 4 39.50 4 0.0
## 40 Lavanda Base + Auxinas 5 72.00 5 0.0
## Peso.raices..mg.
## 1 240.0
## 2 310.0
## 3 0.0
## 4 0.0
## 5 700.0
## 6 500.0
## 7 880.0
## 8 550.0
## 9 210.0
## 10 460.0
## 11 0.0
## 12 0.0
## 13 220.0
## 14 90.0
## 15 0.0
## 16 583.0
## 17 202.0
## 18 0.0
## 19 362.0
## 20 0.0
## 21 110.0
## 22 1090.0
## 23 1100.0
## 24 1260.0
## 25 570.0
## 26 220.0
## 27 1180.0
## 28 500.0
## 29 1440.0
## 30 0.0
## 31 920.0
## 32 1120.0
## 33 1040.0
## 34 1420.0
## 35 1100.0
## 36 2.7
## 37 880.0
## 38 890.0
## 39 400.0
## 40 260.0
E_H_S<- read_excel("C:/Users/LENOVO/Desktop/Datos_Repro.xlsx",
sheet = "R (E_H_S)");E_H_S=data.frame(E_H_S);E_H_S
## Tipo.de.esqueje Tratamiento Repeticion L_P_R N_R Peso.callo..mg.
## 1 De hoja (- Auxinas) 1 0.0 0 0.0
## 2 De hoja (- Auxinas) 2 0.0 0 23.8
## 3 De hoja (- Auxinas) 3 2.0 6 0.0
## 4 De hoja (- Auxinas) 4 5.0 1 0.0
## 5 De hoja (+ Auxinas) 1 2.0 9 0.0
## 6 De hoja (+ Auxinas) 2 6.0 1 0.0
## 7 De hoja (+ Auxinas) 3 0.0 0 0.0
## 8 De hoja (+ Auxinas) 4 1.0 7 0.0
## 9 De tallo (- Auxinas) 1 74.0 6 0.0
## 10 De tallo (- Auxinas) 2 94.2 6 0.0
## 11 De tallo (- Auxinas) 3 89.0 9 0.0
## 12 De tallo (- Auxinas) 4 119.3 8 0.0
## 13 De tallo (+ Auxinas) 1 31.5 8 0.0
## 14 De tallo (+ Auxinas) 2 25.0 12 0.0
## 15 De tallo (+ Auxinas) 3 64.6 11 0.0
## 16 De tallo (+ Auxinas) 4 27.5 13 0.0
## Peso.raices..mg.
## 1 0.0
## 2 0.0
## 3 16.9
## 4 9.4
## 5 49.7
## 6 21.7
## 7 0.0
## 8 9.1
## 9 738.5
## 10 1314.7
## 11 768.7
## 12 1220.0
## 13 788.6
## 14 245.5
## 15 616.0
## 16 304.0
E_L <- read_excel("C:/Users/LENOVO/Desktop/Datos_Repro.xlsx",
sheet = "R(E_L)"); E_L=data.frame(E_L);E_L
## Tratamiento Repeticion L_P_R N_R Peso.callo..mg. Peso.raices..mg.
## 1 Aplical 1 0 0 0 0.00
## 2 Aplical 2 0 0 0 0.00
## 3 Aplical 3 0 0 0 0.00
## 4 Aplical 4 0 0 0 0.00
## 5 Tallo 1 17 9 0 98.80
## 6 Tallo 2 53 9 0 76.35
## 7 Tallo 3 52 10 0 1011.70
## 8 Tallo 4 24 5 0 56.60
L_P_R= aov(L_P_R~Tratamiento+Tipo.de.esqueje, E_H_S)
anova(L_P_R)
## Analysis of Variance Table
##
## Response: L_P_R
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 3189.4 3189.4 7.6329 0.01614 *
## Tipo.de.esqueje 1 16198.9 16198.9 38.7669 3.086e-05 ***
## Residuals 13 5432.1 417.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_P_R ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) -28.2375 -50.31809 -6.156914 0.0161394
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja 63.6375 41.55691 85.71809 3.09e-05
tc=TukeyC(L_P_R,'Tratamiento')
plot(tc, title ="L_P_R")
TukeyHSD(L_P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_P_R ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) -28.2375 -50.31809 -6.156914 0.0161394
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja 63.6375 41.55691 85.71809 3.09e-05
tc=TukeyC(L_P_R,'Tipo.de.esqueje')
plot(tc, title ="L_P_R")
datos_resumen= E_H_S |>
group_by(Tratamiento, Tipo.de.esqueje) |>
summarise(media= mean(L_P_R),
desviacion=sd(L_P_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Longitud promedio de raÃces (mm).")+
facet_grid(~Tipo.de.esqueje)+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Numero raices
N_R= aov(N_R~Tratamiento+Tipo.de.esqueje, E_H_S)
anova(N_R)
## Analysis of Variance Table
##
## Response: N_R
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 39.062 39.062 4.7992 0.0472980 *
## Tipo.de.esqueje 1 150.062 150.062 18.4365 0.0008733 ***
## Residuals 13 105.812 8.139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(N_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_R ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) 3.125 0.04326934 6.206731 0.047298
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja 6.125 3.043269 9.206731 0.0008733
tc=TukeyC(N_R,'Tratamiento')
plot(tc, title ="N_R")
TukeyHSD(N_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_R ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) 3.125 0.04326934 6.206731 0.047298
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja 6.125 3.043269 9.206731 0.0008733
tc=TukeyC(N_R,'Tipo.de.esqueje')
plot(tc, title ="N_R")
datos_resumen= E_H_S |>
group_by(Tratamiento, Tipo.de.esqueje) |>
summarise(media= mean(N_R),
desviacion=sd(N_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Numero de raices")+
facet_grid(~Tipo.de.esqueje)+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Peso.raices..mg.
P_R= aov(Peso.raices..mg.~Tratamiento+Tipo.de.esqueje, E_H_S)
anova(P_R)
## Analysis of Variance Table
##
## Response: Peso.raices..mg.
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 258471 258471 4.4408 0.05508 .
## Tipo.de.esqueje 1 2167667 2167667 37.2427 3.764e-05 ***
## Residuals 13 756650 58204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.raices..mg. ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) -254.2 -514.7995 6.39952 0.055076
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja 736.15 475.5505 996.7495 3.76e-05
tc=TukeyC(P_R,'Tratamiento')
plot(tc, title ="P_R")
TukeyHSD(P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.raices..mg. ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) -254.2 -514.7995 6.39952 0.055076
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja 736.15 475.5505 996.7495 3.76e-05
tc=TukeyC(P_R,'Tipo.de.esqueje')
plot(tc, title ="P_R")
datos_resumen= E_H_S |>
group_by(Tratamiento, Tipo.de.esqueje) |>
summarise(media= mean(Peso.raices..mg.),
desviacion=sd(Peso.raices..mg.), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Peso callo mg")+
facet_grid(~Tipo.de.esqueje)+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Peso.callo..mg.
P_C= aov(Peso.callo..mg.~Tratamiento+Tipo.de.esqueje, E_H_S)
anova(P_C)
## Analysis of Variance Table
##
## Response: Peso.callo..mg.
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 35.40 35.402 1 0.3356
## Tipo.de.esqueje 1 35.40 35.402 1 0.3356
## Residuals 13 460.23 35.402
TukeyHSD(P_C)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.callo..mg. ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) -2.975 -9.402097 3.452097 0.3355613
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja -2.975 -9.402097 3.452097 0.3355613
tc=TukeyC(P_C,'Tratamiento')
plot(tc, title ="P_C")
TukeyHSD(P_C)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.callo..mg. ~ Tratamiento + Tipo.de.esqueje, data = E_H_S)
##
## $Tratamiento
## diff lwr upr p adj
## (+ Auxinas)-(- Auxinas) -2.975 -9.402097 3.452097 0.3355613
##
## $Tipo.de.esqueje
## diff lwr upr p adj
## De tallo-De hoja -2.975 -9.402097 3.452097 0.3355613
tc=TukeyC(P_R,'Tipo.de.esqueje')
plot(tc, title ="P_C")
datos_resumen= E_H_S |>
group_by(Tratamiento, Tipo.de.esqueje) |>
summarise(media= mean(Peso.callo..mg.),
desviacion=sd(Peso.callo..mg.), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Peso callo mg")+
facet_grid(~Tipo.de.esqueje)+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
L_P_R= aov(L_P_R~Tratamiento+Cultivo, E_H_A)
anova(L_P_R)
## Analysis of Variance Table
##
## Response: L_P_R
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 3504 1167.8 0.7443 0.5329
## Cultivo 1 1892 1892.3 1.2060 0.2796
## Residuals 35 54915 1569.0
TukeyHSD(L_P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_P_R ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 0.941 -46.83282 48.71482 0.9999451
## Base - Auxinas-Apice - Auxinas 9.718 -38.05582 57.49182 0.9463118
## Base + Auxinas-Apice - Auxinas -16.209 -63.98282 31.56482 0.7969930
## Base - Auxinas-Apice + Auxinas 8.777 -38.99682 56.55082 0.9595710
## Base + Auxinas-Apice + Auxinas -17.150 -64.92382 30.62382 0.7682374
## Base + Auxinas-Base - Auxinas -25.927 -73.70082 21.84682 0.4696722
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -13.756 -39.18497 11.67297 0.2796137
tc=TukeyC(L_P_R,'Tratamiento')
plot(tc, title ="L_P_R")
TukeyHSD(L_P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_P_R ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 0.941 -46.83282 48.71482 0.9999451
## Base - Auxinas-Apice - Auxinas 9.718 -38.05582 57.49182 0.9463118
## Base + Auxinas-Apice - Auxinas -16.209 -63.98282 31.56482 0.7969930
## Base - Auxinas-Apice + Auxinas 8.777 -38.99682 56.55082 0.9595710
## Base + Auxinas-Apice + Auxinas -17.150 -64.92382 30.62382 0.7682374
## Base + Auxinas-Base - Auxinas -25.927 -73.70082 21.84682 0.4696722
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -13.756 -39.18497 11.67297 0.2796137
tc=TukeyC(L_P_R,'Cultivo')
plot(tc, title ="L_P_R")
datos_resumen= E_H_A |>
group_by(Tratamiento, Cultivo) |>
summarise(media= mean(L_P_R),
desviacion=sd(L_P_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Longitud promedio de raÃces (mm).")+
facet_grid(~Cultivo)+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Numero raices
N_R= aov(N_R~Tratamiento+Cultivo, E_H_A)
anova(N_R)
## Analysis of Variance Table
##
## Response: N_R
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 28.1 9.367 0.4177 0.7414
## Cultivo 1 44.1 44.100 1.9665 0.1696
## Residuals 35 784.9 22.426
TukeyHSD(N_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_R ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 0.1 -5.611543 5.811543 0.9999614
## Base - Auxinas-Apice - Auxinas 1.3 -4.411543 7.011543 0.9269899
## Base + Auxinas-Apice - Auxinas 2.0 -3.711543 7.711543 0.7812761
## Base - Auxinas-Apice + Auxinas 1.2 -4.511543 6.911543 0.9413092
## Base + Auxinas-Apice + Auxinas 1.9 -3.811543 7.611543 0.8063450
## Base + Auxinas-Base - Auxinas 0.7 -5.011543 6.411543 0.9873273
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -2.1 -5.140131 0.9401308 0.1696284
tc=TukeyC(N_R,'Tratamiento')
plot(tc, title ="N_R")
TukeyHSD(N_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_R ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 0.1 -5.611543 5.811543 0.9999614
## Base - Auxinas-Apice - Auxinas 1.3 -4.411543 7.011543 0.9269899
## Base + Auxinas-Apice - Auxinas 2.0 -3.711543 7.711543 0.7812761
## Base - Auxinas-Apice + Auxinas 1.2 -4.511543 6.911543 0.9413092
## Base + Auxinas-Apice + Auxinas 1.9 -3.811543 7.611543 0.8063450
## Base + Auxinas-Base - Auxinas 0.7 -5.011543 6.411543 0.9873273
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -2.1 -5.140131 0.9401308 0.1696284
tc=TukeyC(N_R,'Cultivo')
plot(tc, title ="N_R")
datos_resumen= E_H_A |>
group_by(Tratamiento, Cultivo) |>
summarise(media= mean(N_R),
desviacion=sd(N_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Numero de raices")+
facet_grid(~Cultivo)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
##Peso.raices..mg.
P_R= aov(Peso.raices..mg.~Tratamiento+Cultivo, E_H_A)
anova(P_R)
## Analysis of Variance Table
##
## Response: Peso.raices..mg.
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 370945 123648 0.8183 0.4925358
## Cultivo 1 2598807 2598807 17.1986 0.0002036 ***
## Residuals 35 5288702 151106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.raices..mg. ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 53.00 -415.8358 521.8358 0.9899896
## Base - Auxinas-Apice - Auxinas 56.00 -412.8358 524.8358 0.9882430
## Base + Auxinas-Apice - Auxinas -180.03 -648.8658 288.8058 0.7299308
## Base - Auxinas-Apice + Auxinas 3.00 -465.8358 471.8358 0.9999981
## Base + Auxinas-Apice + Auxinas -233.03 -701.8658 235.8058 0.5441306
## Base + Auxinas-Base - Auxinas -236.03 -704.8658 232.8058 0.5335417
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -509.785 -759.3362 -260.2338 0.0002036
tc=TukeyC(P_R,'Tratamiento')
plot(tc, title ="P_R")
TukeyHSD(P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.raices..mg. ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 53.00 -415.8358 521.8358 0.9899896
## Base - Auxinas-Apice - Auxinas 56.00 -412.8358 524.8358 0.9882430
## Base + Auxinas-Apice - Auxinas -180.03 -648.8658 288.8058 0.7299308
## Base - Auxinas-Apice + Auxinas 3.00 -465.8358 471.8358 0.9999981
## Base + Auxinas-Apice + Auxinas -233.03 -701.8658 235.8058 0.5441306
## Base + Auxinas-Base - Auxinas -236.03 -704.8658 232.8058 0.5335417
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -509.785 -759.3362 -260.2338 0.0002036
tc=TukeyC(P_R,'Cultivo')
plot(tc, title ="P_R")
datos_resumen= E_H_A |>
group_by(Tratamiento, Cultivo) |>
summarise(media= mean(Peso.raices..mg.),
desviacion=sd(Peso.raices..mg.), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Peso callo mg")+
facet_grid(~Cultivo)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
##Peso.callo..mg.
P_C= aov(Peso.callo..mg.~Tratamiento+Cultivo, E_H_A)
anova(P_C)
## Analysis of Variance Table
##
## Response: Peso.callo..mg.
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 4004 1334.68 0.7329 0.5394
## Cultivo 1 562 561.75 0.3085 0.5822
## Residuals 35 63740 1821.13
TukeyHSD(P_C)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.callo..mg. ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 3.74 -47.72966 55.20966 0.9972822
## Base - Auxinas-Apice - Auxinas 26.00 -25.46966 77.46966 0.5307125
## Base + Auxinas-Apice - Auxinas 7.27 -44.19966 58.73966 0.9808779
## Base - Auxinas-Apice + Auxinas 22.26 -29.20966 73.72966 0.6515985
## Base + Auxinas-Apice + Auxinas 3.53 -47.93966 54.99966 0.9977109
## Base + Auxinas-Base - Auxinas -18.73 -70.19966 32.73966 0.7608478
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -7.495 -34.89119 19.90119 0.5821585
tc=TukeyC(P_C,'Tratamiento')
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
plot(tc, title ="P_C")
TukeyHSD(P_C)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.callo..mg. ~ Tratamiento + Cultivo, data = E_H_A)
##
## $Tratamiento
## diff lwr upr p adj
## Apice + Auxinas-Apice - Auxinas 3.74 -47.72966 55.20966 0.9972822
## Base - Auxinas-Apice - Auxinas 26.00 -25.46966 77.46966 0.5307125
## Base + Auxinas-Apice - Auxinas 7.27 -44.19966 58.73966 0.9808779
## Base - Auxinas-Apice + Auxinas 22.26 -29.20966 73.72966 0.6515985
## Base + Auxinas-Apice + Auxinas 3.53 -47.93966 54.99966 0.9977109
## Base + Auxinas-Base - Auxinas -18.73 -70.19966 32.73966 0.7608478
##
## $Cultivo
## diff lwr upr p adj
## Romero-Lavanda -7.495 -34.89119 19.90119 0.5821585
tc=TukeyC(P_R,'Cultivo')
plot(tc, title ="P_C")
datos_resumen= E_H_A |>
group_by(Tratamiento, Cultivo) |>
summarise(media= mean(Peso.callo..mg.),
desviacion=sd(Peso.callo..mg.), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
## `summarise()` has grouped output by 'Tratamiento'. You can override using the
## `.groups` argument.
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tipo de esqueje", y = "Peso callo mg")+
facet_grid(~Cultivo)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)
#Estacas lenosas
L_P_R= aov(L_P_R~Tratamiento, E_L)
anova(L_P_R)
## Analysis of Variance Table
##
## Response: L_P_R
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 2664.5 2664.50 15.24 0.007948 **
## Residuals 6 1049.0 174.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_P_R ~ Tratamiento, data = E_L)
##
## $Tratamiento
## diff lwr upr p adj
## Tallo-Aplical 36.5 13.62214 59.37786 0.0079483
tc=TukeyC(L_P_R,'Tratamiento')
plot(tc, title ="L_P_R")
datos_resumen= E_L |>
group_by(Tratamiento) |>
summarise(media= mean(L_P_R),
desviacion=sd(L_P_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tratamiento", y = "Longitud promedio de raÃces (mm).")+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Numero raices
N_R= aov(N_R~Tratamiento, E_L)
anova(N_R)
## Analysis of Variance Table
##
## Response: N_R
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 136.12 136.125 55.373 0.0003034 ***
## Residuals 6 14.75 2.458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(N_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_R ~ Tratamiento, data = E_L)
##
## $Tratamiento
## diff lwr upr p adj
## Tallo-Aplical 8.25 5.537163 10.96284 0.0003033
tc=TukeyC(N_R,'Tratamiento')
plot(tc, title ="N_R")
datos_resumen= E_L |>
group_by(Tratamiento) |>
summarise(media= mean(N_R),
desviacion=sd(N_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tratamiento", y = "Numero de raices")+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Peso.raices..mg.
P_R= aov(Peso.raices..mg.~Tratamiento, E_L)
anova(P_R)
## Analysis of Variance Table
##
## Response: Peso.raices..mg.
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 193271 193271 1.7683 0.2319
## Residuals 6 655789 109298
TukeyHSD(P_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.raices..mg. ~ Tratamiento, data = E_L)
##
## $Tratamiento
## diff lwr upr p adj
## Tallo-Aplical 310.8625 -261.1557 882.8807 0.2319113
tc=TukeyC(P_R,'Tratamiento')
plot(tc, title ="P_R")
datos_resumen= E_L |>
group_by(Tratamiento) |>
summarise(media= mean(Peso.raices..mg.),
desviacion=sd(Peso.raices..mg.), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tratamiento", y = "Peso callo mg")+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
##Peso.callo..mg.
P_C= aov(Peso.callo..mg.~Tratamiento, E_L)
anova(P_C)
## Analysis of Variance Table
##
## Response: Peso.callo..mg.
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 0 0 NaN NaN
## Residuals 6 0 0
TukeyHSD(P_C)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.callo..mg. ~ Tratamiento, data = E_L)
##
## $Tratamiento
## diff lwr upr p adj
## Tallo-Aplical 0 0 0 NaN
tc=TukeyC(P_C,'Tratamiento')
plot(tc, title ="P_C")
datos_resumen= E_L |>
group_by(Tratamiento) |>
summarise(media= mean(Peso.callo..mg.),
desviacion=sd(Peso.callo..mg.), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n))
ggplot(datos_resumen)+
aes(x=Tratamiento, y=media, fill=Tratamiento)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
labs(x = "Tratamiento", y = "Peso callo mg")+
#geom_text(aes(label=c("a","a"), y= media+0.2), color= "black", size=6)+
theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
