AnegamientoRab
Laura Sofia Hernandez Sanchez
24/05/2021
Temp1 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM1")
Temp2 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM2")
Temp3 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM3")
Temp4 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM4")
TEMP<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TEMP")
EA<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "EA")
CRC<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "CRC")
CRA<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "CRA")
PE<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "PE")
PSRT<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "PSRT")
diametro<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "diametro")
IAF<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "IAF")
RAF<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "RAF")
# *Temperatura*
# ANOVA
ggplot(data = TEMP, aes( x = factor( Dia ), y = TEMP, fill = Trat ) ) +
geom_bar( stat = 'identity', position = 'dodge' )+ labs(title = "Temperatura")+ylab("Temp") + xlab("Tiempo (dds)") + theme(plot.title = element_text(size = rel(2), colour = "black"))+ scale_fill_grey() + theme_classic() + geom_errorbar(aes(ymin=TEMP-sd, ymax=TEMP+sd), width=.2, position=position_dodge(.9))
ggplot(data = CRC, aes( x = factor( Dia ), y = CRC, fill = Trat ) ) +
geom_bar( stat = 'identity', position = 'dodge' )+labs(title = "CRC")+ylab("CRC") + xlab("Tiempo (dds)") + theme(plot.title = element_text(size = rel(2), color = "black"))+ scale_fill_grey() + theme_classic() + geom_errorbar(aes(ymin=CRC-sd, ymax=CRC+sd), width=.2,
position=position_dodge(.9))
ggplot(data = PE, aes( x = factor( Dia ), y = PE, fill = Trat ) ) +
geom_bar( stat = 'identity', position = 'dodge' )+ labs(title = "Perdida de electrolitos")+ylab("PE (%)") + xlab("Tiempo (dds)") + theme(plot.title = element_text(size = rel(2), colour = "black"))+ scale_fill_grey() + theme_classic() + geom_errorbar(aes(ymin=PE-sd, ymax=PE+sd), width=.2, position=position_dodge(.9))
ggplot(data = PSRT, aes( x = factor( Dia ), y = PSRT, fill = Trat ) ) +
geom_bar( stat = 'identity', position = 'dodge' )+ labs(title = "Peso fresco de la raĆz tuberosa")+ylab("PSRT") + xlab("Tiempo (dds)") + theme(plot.title = element_text(size = rel(2), colour = "black"))+ scale_fill_grey() + theme_classic() + geom_errorbar(aes(ymin=PSRT-sd, ymax=PSRT+sd), width=.2, position=position_dodge(.9))
ggplot(data = CRA, aes( x = factor( Dia ), y = CRA, fill = Trat ) ) +
geom_bar( stat = 'identity', position = 'dodge' )+ labs(title = "Contenido Relativa de agua")+ylab("CRA") + xlab("Tiempo (dds)") + theme(plot.title = element_text(size = rel(2), colour = "black"))+ scale_fill_grey() + theme_classic() + geom_errorbar(aes(ymin=CRA-sd, ymax=CRA+sd), width=.2, position=position_dodge(.9))
M1T <- aov(Temp~Trat, data = Temp1)
anova(M1T)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 27.305 9.1017 20.396 5.282e-05 ***
## Residuals 12 5.355 0.4462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2T <- aov(Temp~Trat, data = Temp2)
anova(M2T)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 15.592 5.1975 41.442 1.312e-06 ***
## Residuals 12 1.505 0.1254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3T <- aov(Temp~Trat, data = Temp3)
anova(M3T)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 10.152 3.3842 35.467 3.037e-06 ***
## Residuals 12 1.145 0.0954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4T <- aov(Temp~Trat, data = Temp4)
anova(M4T)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 37.565 12.5217 30.572 6.677e-06 ***
## Residuals 12 4.915 0.4096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1T))##
## Shapiro-Wilk normality test
##
## data: resid(M1T)
## W = 0.94527, p-value = 0.4186shapiro.test(resid(M2T))##
## Shapiro-Wilk normality test
##
## data: resid(M2T)
## W = 0.98351, p-value = 0.9853shapiro.test(resid(M3T))##
## Shapiro-Wilk normality test
##
## data: resid(M3T)
## W = 0.94101, p-value = 0.3616shapiro.test(resid(M4T))##
## Shapiro-Wilk normality test
##
## data: resid(M4T)
## W = 0.94208, p-value = 0.3753#En todos los muestreos se puede observar normalidad en los datos de temperatura.
#**Homogeneidad de varianzas**
library(carData)
bartlett.test(Temp~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: Temp by Trat
## Bartlett's K-squared = 2.6518, df = 3, p-value = 0.4485bartlett.test(Temp~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: Temp by Trat
## Bartlett's K-squared = 1.6543, df = 3, p-value = 0.6471bartlett.test(Temp~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: Temp by Trat
## Bartlett's K-squared = 0.58553, df = 3, p-value = 0.8997bartlett.test(Temp~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: Temp by Trat
## Bartlett's K-squared = 0.64407, df = 3, p-value = 0.8863En temperatura, todos los datos representan varianzas homogeneas
Se cumplen todos los supuestos
#Pueba de tukey
library(agricolae)
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, unionM1T_tukey <-HSD.test(Temp1$Temp,Temp1$Trat, 12, 0.4462, alpha = 0.05);M1T_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.4462 12 19.15 3.488157 1.402315
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$Temp std r Min Max Q25 Q50 Q75
## MT0W0 17.900 0.4082483 4 17.5 18.3 17.575 17.90 18.225
## MT0W1 20.625 0.5852350 4 19.9 21.3 20.350 20.65 20.925
## MT1W0 17.800 0.4966555 4 17.2 18.3 17.500 17.85 18.150
## MT1W1 20.275 1.0144785 4 19.5 21.7 19.575 19.95 20.650
##
## $comparison
## NULL
##
## $groups
## Temp1$Temp groups
## MT0W1 20.625 a
## MT1W1 20.275 a
## MT0W0 17.900 b
## MT1W0 17.800 b
##
## attr(,"class")
## [1] "group"M2T_tukey <-HSD.test(Temp2$Temp,Temp2$Trat, 12, 0.1254, alpha = 0.05);M2T_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.1254 12 18.7125 1.892417 0.7434119
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$Temp std r Min Max Q25 Q50 Q75
## MT0W0 17.875 0.2500000 4 17.6 18.2 17.750 17.85 17.975
## MT0W1 20.350 0.4654747 4 19.7 20.8 20.225 20.45 20.575
## MT1W0 18.000 0.2449490 4 17.8 18.3 17.800 17.95 18.150
## MT1W1 18.625 0.4031129 4 18.3 19.2 18.375 18.50 18.750
##
## $comparison
## NULL
##
## $groups
## Temp2$Temp groups
## MT0W1 20.350 a
## MT1W1 18.625 b
## MT1W0 18.000 bc
## MT0W0 17.875 c
##
## attr(,"class")
## [1] "group"M3T_tukey <-HSD.test(Temp3$Temp,Temp3$Trat, 12, 0.0954, alpha = 0.05);M3T_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0954 12 18.6875 1.65281 0.6484178
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$Temp std r Min Max Q25 Q50 Q75
## MT0W0 18.000 0.3651484 4 17.6 18.4 17.750 18.00 18.250
## MT0W1 19.975 0.2753785 4 19.7 20.3 19.775 19.95 20.150
## MT1W0 18.050 0.2380476 4 17.8 18.3 17.875 18.05 18.225
## MT1W1 18.725 0.3403430 4 18.4 19.2 18.550 18.65 18.825
##
## $comparison
## NULL
##
## $groups
## Temp3$Temp groups
## MT0W1 19.975 a
## MT1W1 18.725 b
## MT1W0 18.050 c
## MT0W0 18.000 c
##
## attr(,"class")
## [1] "group"M4T_tukey <-HSD.test(Temp4$Temp,Temp4$Trat, 12, 0.4096, alpha = 0.05);M4T_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.4096 12 19.55 3.273657 1.343571
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$Temp std r Min Max Q25 Q50 Q75
## MT0W0 18.200 0.5773503 4 17.5 18.9 17.950 18.20 18.450
## MT0W1 21.925 0.5560276 4 21.3 22.6 21.600 21.90 22.225
## MT1W0 18.200 0.5597619 4 17.7 18.9 17.775 18.10 18.525
## MT1W1 19.875 0.8261356 4 18.9 20.7 19.350 19.95 20.475
##
## $comparison
## NULL
##
## $groups
## Temp4$Temp groups
## MT0W1 21.925 a
## MT1W1 19.875 b
## MT0W0 18.200 c
## MT1W0 18.200 c
##
## attr(,"class")
## [1] "group"library(dplyr)
library(extrafont)## Warning: package 'extrafont' was built under R version 4.0.3## Registering fonts with Rloadfonts(device = "win")GrƔfica con grupos
M<-bind_rows(M1T_tukey$groups, M2T_tukey$groups, M3T_tukey$groups, M4T_tukey$groups, id=NULL)
M$groups## [1] "a" "a" "b" "b" "a" "b" "bc" "c" "a" "b" "c" "c" "a" "b" "c"
## [16] "c"TEMP1 <- data.frame(TEMP, M$groups)
ggT<-c("b" ,"b", "a" ,"a", "c", "bc", "a", "b", "c", "c", "a", "b", "c", "c", "a", "b")
levels(TEMP1$Trat)## NULLTEMP1$Trat=factor(TEMP1$Trat, levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
TG<- data.frame(ggT,TEMP1$Trat)
a1<-ggplot(data = TEMP1, aes(x = factor(Dia), y = TEMP, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylab("Temperatura (Ā°C)") + xlab("DDT")+ylim(0,25)+guides(fill=guide_legend("Tratamientos"))
a3<-a1+scale_fill_grey() + theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=TEMP-sd, ymax=TEMP+sd), width=.5, position=position_dodge(.9)) + geom_text(aes(label=TG$ggT), position=position_dodge(width=1), vjust=-1.6);a3
###### Estomas abiertos
M1EA <- aov(EA~Trat, data = Temp1)
anova(M1EA)## Analysis of Variance Table
##
## Response: EA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1496.19 498.73 55.286 2.685e-07 ***
## Residuals 12 108.25 9.02
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2EA <- aov(EA~Trat, data = Temp2)
anova(M2EA)## Analysis of Variance Table
##
## Response: EA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 517.69 172.562 51.447 4.004e-07 ***
## Residuals 12 40.25 3.354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3EA <- aov(EA~Trat, data = Temp3)
anova(M3EA)## Analysis of Variance Table
##
## Response: EA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 130.25 43.417 13.19 0.0004165 ***
## Residuals 12 39.50 3.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4EA <- aov(EA~Trat, data = Temp4)
anova(M4EA)## Analysis of Variance Table
##
## Response: EA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 467.19 155.729 115 4.169e-09 ***
## Residuals 12 16.25 1.354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1EA))##
## Shapiro-Wilk normality test
##
## data: resid(M1EA)
## W = 0.98378, p-value = 0.9865shapiro.test(resid(M2EA))##
## Shapiro-Wilk normality test
##
## data: resid(M2EA)
## W = 0.93603, p-value = 0.3032shapiro.test(resid(M3EA))##
## Shapiro-Wilk normality test
##
## data: resid(M3EA)
## W = 0.91394, p-value = 0.1347shapiro.test(resid(M4EA))##
## Shapiro-Wilk normality test
##
## data: resid(M4EA)
## W = 0.91651, p-value = 0.1482#**Homogeneidad de varianzas**
library(carData)
bartlett.test(EA~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: EA by Trat
## Bartlett's K-squared = 3.8666, df = 3, p-value = 0.2762bartlett.test(EA~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: EA by Trat
## Bartlett's K-squared = 1.9079, df = 3, p-value = 0.5917bartlett.test(EA~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: EA by Trat
## Bartlett's K-squared = 9.0715, df = 3, p-value = 0.02835bartlett.test(EA~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: EA by Trat
## Bartlett's K-squared = 1.1452, df = 3, p-value = 0.7662En temperatura, todos los datos representan varianzas homogeneas
Se cumplen todos los supuestos
#Pueba de tukey
library(agricolae)
library(dplyr)
M1EA_tukey <-HSD.test(Temp1$EA,Temp1$Trat, 12, 9.02, alpha = 0.05);M1EA_tukey## $statistics
## MSerror Df Mean CV MSD
## 9.02 12 22.1875 13.53614 6.304984
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$EA std r Min Max Q25 Q50 Q75
## MT0W0 31.00 4.242641 4 28 37 28.00 29.5 32.50
## MT0W1 8.75 1.500000 4 7 10 7.75 9.0 10.00
## MT1W0 31.75 3.593976 4 27 35 30.00 32.5 34.25
## MT1W1 17.25 1.707825 4 15 19 16.50 17.5 18.25
##
## $comparison
## NULL
##
## $groups
## Temp1$EA groups
## MT1W0 31.75 a
## MT0W0 31.00 a
## MT1W1 17.25 b
## MT0W1 8.75 c
##
## attr(,"class")
## [1] "group"M2EA_tukey <-HSD.test(Temp2$EA,Temp2$Trat, 12, 3.354, alpha = 0.05);M2EA_tukey## $statistics
## MSerror Df Mean CV MSD
## 3.354 12 25.4375 7.199579 3.844698
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$EA std r Min Max Q25 Q50 Q75
## MT0W0 31.75 1.7078251 4 30 34 30.75 31.5 32.50
## MT0W1 16.75 0.9574271 4 16 18 16.00 16.5 17.25
## MT1W0 29.00 2.1602469 4 27 32 27.75 28.5 29.75
## MT1W1 24.25 2.2173558 4 22 27 22.75 24.0 25.50
##
## $comparison
## NULL
##
## $groups
## Temp2$EA groups
## MT0W0 31.75 a
## MT1W0 29.00 a
## MT1W1 24.25 b
## MT0W1 16.75 c
##
## attr(,"class")
## [1] "group"M3EA_tukey <-HSD.test(Temp3$EA,Temp3$Trat, 12, 3.292, alpha = 0.05);M3EA_tukey## $statistics
## MSerror Df Mean CV MSD
## 3.292 12 27.625 6.567917 3.808997
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$EA std r Min Max Q25 Q50 Q75
## MT0W0 30.00 0.8164966 4 29 31 29.75 30.0 30.25
## MT0W1 22.75 0.5000000 4 22 23 22.75 23.0 23.00
## MT1W0 29.00 3.1622777 4 25 32 27.25 29.5 31.25
## MT1W1 28.75 1.5000000 4 28 31 28.00 28.0 28.75
##
## $comparison
## NULL
##
## $groups
## Temp3$EA groups
## MT0W0 30.00 a
## MT1W0 29.00 a
## MT1W1 28.75 a
## MT0W1 22.75 b
##
## attr(,"class")
## [1] "group"M4EA_tukey <-HSD.test(Temp4$EA,Temp4$Trat, 12, 1.354, alpha = 0.05);M4EA_tukey## $statistics
## MSerror Df Mean CV MSD
## 1.354 12 23.6875 4.912359 2.442812
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$EA std r Min Max Q25 Q50 Q75
## MT0W0 29.00 0.8164966 4 28 30 28.75 29.0 29.25
## MT0W1 15.25 0.9574271 4 14 16 14.75 15.5 16.00
## MT1W0 27.75 1.5000000 4 26 29 26.75 28.0 29.00
## MT1W1 22.75 1.2583057 4 21 24 22.50 23.0 23.25
##
## $comparison
## NULL
##
## $groups
## Temp4$EA groups
## MT0W0 29.00 a
## MT1W0 27.75 a
## MT1W1 22.75 b
## MT0W1 15.25 c
##
## attr(,"class")
## [1] "group"loadfonts(device = "win")
## GrƔfica con grupos
EE<-bind_rows(M1EA_tukey$groups, M2EA_tukey$groups, M3EA_tukey$groups, M4EA_tukey$groups, id=NULL)
EE1 <- data.frame(EA, EE$groups)
EE1$EE.groups## [1] "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "a" "b" "a" "a" "b" "c"gg<-c("a" ,"a", "c" ,"b", "a", "a", "c", "b", "a", "a", "b", "a", "a", "a", "c", "b")
levels(EE1$Trat)## NULLEE1$Trat=factor(EE1$Trat, levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
TT<- data.frame(gg,EE1$Trat)
EE_1<-ggplot(data = EE1, aes(x = factor( Dia ), y = EA, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylab("NĆŗmero de Estomas Abiertos") + ylim(0,37)+xlab("DDT")
EE_2<-EE_1+scale_fill_grey() + theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=EA-sd, ymax=EA+sd), width=.5, position=position_dodge(0.95)) + geom_text(aes(label=TT$gg), position=position_dodge(width=1), vjust=-3)
EE2<-EE_2;EE2
Numero de hojas
# ANOVA
M1nh <- aov(nh~Trat, data = Temp1)
anova(M1nh)## Analysis of Variance Table
##
## Response: nh
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 7.5 2.50000 8.5714 0.002594 **
## Residuals 12 3.5 0.29167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2nh <- aov(nh~Trat, data = Temp2)
anova(M2nh)## Analysis of Variance Table
##
## Response: nh
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 9.5 3.1667 8.4444 0.002752 **
## Residuals 12 4.5 0.3750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3nh <- aov(nh~Trat, data = Temp3)
anova(M3nh)## Analysis of Variance Table
##
## Response: nh
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 5.6875 1.89583 4.7895 0.02033 *
## Residuals 12 4.7500 0.39583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4nh <- aov(nh~Trat, data = Temp4)
anova(M4nh)## Analysis of Variance Table
##
## Response: nh
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 9.25 3.0833 8.2222 0.003055 **
## Residuals 12 4.50 0.3750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1nh))##
## Shapiro-Wilk normality test
##
## data: resid(M1nh)
## W = 0.92616, p-value = 0.2118shapiro.test(resid(M2nh))##
## Shapiro-Wilk normality test
##
## data: resid(M2nh)
## W = 0.91698, p-value = 0.1508shapiro.test(resid(M3nh))##
## Shapiro-Wilk normality test
##
## data: resid(M3nh)
## W = 0.93588, p-value = 0.3016shapiro.test(resid(M4nh))##
## Shapiro-Wilk normality test
##
## data: resid(M4nh)
## W = 0.98293, p-value = 0.9825#En todos los muestreos se puede observar normalidad en los datos de Numero de hojas.
#**Homogeneidad de varianzas**
bartlett.test(nh~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: nh by Trat
## Bartlett's K-squared = Inf, df = 3, p-value < 2.2e-16bartlett.test(nh~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: nh by Trat
## Bartlett's K-squared = 0.93077, df = 3, p-value = 0.818bartlett.test(nh~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: nh by Trat
## Bartlett's K-squared = 0.74266, df = 3, p-value = 0.8631bartlett.test(nh~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: nh by Trat
## Bartlett's K-squared = 0.93077, df = 3, p-value = 0.818M1nh_tukey <-HSD.test(Temp1$nh,Temp1$Trat, 12, 0.29167, alpha = 0.05);M1nh_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.29167 12 3.25 16.61738 1.133774
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$nh std r Min Max Q25 Q50 Q75
## MT0W0 3.75 0.5000000 4 3 4 3.75 4 4.00
## MT0W1 2.25 0.5000000 4 2 3 2.00 2 2.25
## MT1W0 4.00 0.0000000 4 4 4 4.00 4 4.00
## MT1W1 3.00 0.8164966 4 2 4 2.75 3 3.25
##
## $comparison
## NULL
##
## $groups
## Temp1$nh groups
## MT1W0 4.00 a
## MT0W0 3.75 a
## MT1W1 3.00 ab
## MT0W1 2.25 b
##
## attr(,"class")
## [1] "group"M2nh_tukey <-HSD.test(Temp2$nh,Temp2$Trat, 12, 0.3750, alpha = 0.05);M2nh_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.375 12 4 15.30931 1.285572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$nh std r Min Max Q25 Q50 Q75
## MT0W0 4.75 0.5000000 4 4 5 4.75 5.0 5.00
## MT0W1 3.00 0.8164966 4 2 4 2.75 3.0 3.25
## MT1W0 4.75 0.5000000 4 4 5 4.75 5.0 5.00
## MT1W1 3.50 0.5773503 4 3 4 3.00 3.5 4.00
##
## $comparison
## NULL
##
## $groups
## Temp2$nh groups
## MT0W0 4.75 a
## MT1W0 4.75 a
## MT1W1 3.50 ab
## MT0W1 3.00 b
##
## attr(,"class")
## [1] "group"M3nh_tukey <-HSD.test(Temp3$nh,Temp3$Trat, 12, 0.39583, alpha = 0.05);M3nh_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.39583 12 4.8125 13.07325 1.320794
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$nh std r Min Max Q25 Q50 Q75
## MT0W0 5.50 0.5773503 4 5 6 5.00 5.5 6.00
## MT0W1 4.00 0.8164966 4 3 5 3.75 4.0 4.25
## MT1W0 5.25 0.5000000 4 5 6 5.00 5.0 5.25
## MT1W1 4.50 0.5773503 4 4 5 4.00 4.5 5.00
##
## $comparison
## NULL
##
## $groups
## Temp3$nh groups
## MT0W0 5.50 a
## MT1W0 5.25 ab
## MT1W1 4.50 ab
## MT0W1 4.00 b
##
## attr(,"class")
## [1] "group"M4nh_tukey <-HSD.test(Temp4$nh,Temp4$Trat, 12, 0.3750, alpha = 0.05);M4nh_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.375 12 5.625 10.88662 1.285572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$nh std r Min Max Q25 Q50 Q75
## MT0W0 6.50 0.5773503 4 6 7 6.00 6.5 7.00
## MT0W1 4.75 0.5000000 4 4 5 4.75 5.0 5.00
## MT1W0 6.25 0.5000000 4 6 7 6.00 6.0 6.25
## MT1W1 5.00 0.8164966 4 4 6 4.75 5.0 5.25
##
## $comparison
## NULL
##
## $groups
## Temp4$nh groups
## MT0W0 6.50 a
## MT1W0 6.25 ab
## MT1W1 5.00 bc
## MT0W1 4.75 c
##
## attr(,"class")
## [1] "group"Contenido relativo de Clorofila (CRC)
# ANOVA
M1CRC <- aov(CRC~Trat, data = Temp1)
anova(M1CRC)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 688.69 229.565 75.236 4.747e-08 ***
## Residuals 12 36.62 3.051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2CRC <- aov(CRC~Trat, data = Temp2)
anova(M2CRC)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 923.73 307.911 258.07 3.683e-11 ***
## Residuals 12 14.32 1.193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3CRC <- aov(CRC~Trat, data = Temp3)
anova(M3CRC)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 242.59 80.863 125.86 2.472e-09 ***
## Residuals 12 7.71 0.642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4CRC <- aov(CRC~Trat, data = Temp4)
anova(M4CRC)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 346.77 115.589 287.92 1.929e-11 ***
## Residuals 12 4.82 0.401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1CRC))##
## Shapiro-Wilk normality test
##
## data: resid(M1CRC)
## W = 0.93027, p-value = 0.2462shapiro.test(resid(M2CRC))##
## Shapiro-Wilk normality test
##
## data: resid(M2CRC)
## W = 0.96432, p-value = 0.7404shapiro.test(resid(M3CRC))##
## Shapiro-Wilk normality test
##
## data: resid(M3CRC)
## W = 0.93619, p-value = 0.3049shapiro.test(resid(M4CRC))##
## Shapiro-Wilk normality test
##
## data: resid(M4CRC)
## W = 0.89691, p-value = 0.07173#En todos los muestreos se puede observar normalidad en los datos de Contenido relativo de Clorofila
#**Homogeneidad de varianzas**
bartlett.test(CRC~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: CRC by Trat
## Bartlett's K-squared = 6.5517, df = 3, p-value = 0.08764bartlett.test(CRC~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: CRC by Trat
## Bartlett's K-squared = 1.3954, df = 3, p-value = 0.7066bartlett.test(CRC~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: CRC by Trat
## Bartlett's K-squared = 7.6802, df = 3, p-value = 0.0531bartlett.test(CRC~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: CRC by Trat
## Bartlett's K-squared = 0.10113, df = 3, p-value = 0.9917M1CRC_tukey <-HSD.test(Temp1$CRC,Temp1$Trat, 12, 3.051, alpha = 0.05);M1CRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 3.051 12 30.625 5.703547 3.666923
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$CRC std r Min Max Q25 Q50 Q75
## MT0W0 36.750 0.7047458 4 36.1 37.5 36.175 36.7 37.275
## MT0W1 21.175 2.0966243 4 18.6 23.7 20.325 21.2 22.050
## MT1W0 36.750 0.6952218 4 35.8 37.4 36.475 36.9 37.175
## MT1W1 27.825 2.6132674 4 25.4 31.5 26.450 27.2 28.575
##
## $comparison
## NULL
##
## $groups
## Temp1$CRC groups
## MT0W0 36.750 a
## MT1W0 36.750 a
## MT1W1 27.825 b
## MT0W1 21.175 c
##
## attr(,"class")
## [1] "group"M2CRC_tukey <-HSD.test(Temp2$CRC,Temp2$Trat, 12, 1.193, alpha = 0.05);M2CRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 1.193 12 33.00625 3.309208 2.292984
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$CRC std r Min Max Q25 Q50 Q75
## MT0W0 40.275 0.9464847 4 39.5 41.6 39.650 40.00 40.625
## MT0W1 21.850 0.6855655 4 20.9 22.4 21.575 22.05 22.325
## MT1W0 39.750 1.4011900 4 38.2 41.6 39.175 39.60 40.175
## MT1W1 30.150 1.2013881 4 28.9 31.7 29.425 30.00 30.725
##
## $comparison
## NULL
##
## $groups
## Temp2$CRC groups
## MT0W0 40.275 a
## MT1W0 39.750 a
## MT1W1 30.150 b
## MT0W1 21.850 c
##
## attr(,"class")
## [1] "group"M3CRC_tukey <-HSD.test(Temp3$CRC,Temp3$Trat, 12, 0.642, alpha = 0.05);M3CRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.642 12 34.2 2.342833 1.682086
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$CRC std r Min Max Q25 Q50 Q75
## MT0W0 37.975 0.2986079 4 37.6 38.3 37.825 38.00 38.150
## MT0W1 29.075 1.3647344 4 27.6 30.8 28.275 28.95 29.750
## MT1W0 37.975 0.3304038 4 37.6 38.3 37.750 38.00 38.225
## MT1W1 31.775 0.7135592 4 31.2 32.8 31.350 31.55 31.975
##
## $comparison
## NULL
##
## $groups
## Temp3$CRC groups
## MT0W0 37.975 a
## MT1W0 37.975 a
## MT1W1 31.775 b
## MT0W1 29.075 c
##
## attr(,"class")
## [1] "group"M4CRC_tukey <-HSD.test(Temp4$CRC,Temp4$Trat, 12, 0.401, alpha = 0.05);M4CRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.401 12 33.51875 1.889228 1.329392
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$CRC std r Min Max Q25 Q50 Q75
## MT0W0 36.750 0.6658328 4 35.8 37.3 36.550 36.95 37.150
## MT0W1 26.800 0.5597619 4 26.1 27.3 26.475 26.90 27.225
## MT1W0 38.800 0.6377042 4 37.9 39.3 38.575 39.00 39.225
## MT1W1 31.725 0.6652067 4 30.9 32.5 31.425 31.75 32.050
##
## $comparison
## NULL
##
## $groups
## Temp4$CRC groups
## MT1W0 38.800 a
## MT0W0 36.750 b
## MT1W1 31.725 c
## MT0W1 26.800 d
##
## attr(,"class")
## [1] "group"CRCg<-bind_rows(M1CRC_tukey$groups, M2CRC_tukey$groups, M3CRC_tukey$groups, M4CRC_tukey$groups, id=NULL)
CRCg$groups## [1] "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "b" "c" "a" "b" "c" "d"CRC_g<-data.frame(CRC,CRCg$groups)
CRCT<-c("a" ,"a", "b" ,"c", "a", "a", "b", "c", "a", "a", "b", "c", "a", "b", "c", "d")
levels(CRC$Trat)## NULLCRC_g$Trat=factor(CRC_g$Trat, levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
CRCGT<- data.frame(CRCT,CRC_g$Trat)
CRCg1<-ggplot(data = CRC_g, aes(x = factor( Dia ), y = CRC, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylab("Contenido Relativo de Clorofila (SPAD)") +ylim(0,48)+ xlab("DDT")+ guides(fill=guide_legend("Tratamientos"))
CRCg3<-CRCg1+scale_fill_grey()+theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=CRC-sd, ymax=CRC+sd), width=.5, position=position_dodge(.9)) + geom_text(aes(label=CRCGT$CRCT), position=position_dodge(width=1), vjust=-1);CRCg3
Contenido relativo de Agua (CRA)
# ANOVA
M1CRA <- aov(CRA~Trat, data = Temp1)
anova(M1CRA)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3660.3 1220.08 51.572 3.95e-07 ***
## Residuals 12 283.9 23.66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2CRA <- aov(CRA~Trat, data = Temp2)
anova(M2CRA)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2869.62 956.54 86.347 2.166e-08 ***
## Residuals 12 132.93 11.08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3CRA <- aov(CRA~Trat, data = Temp3)
anova(M3CRA)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 920.30 306.765 64.228 1.161e-07 ***
## Residuals 12 57.31 4.776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4CRA <- aov(CRA~Trat, data = Temp4)
anova(M4CRA)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1003.0 334.35 61.071 1.54e-07 ***
## Residuals 12 65.7 5.47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1CRA))##
## Shapiro-Wilk normality test
##
## data: resid(M1CRA)
## W = 0.94608, p-value = 0.4303shapiro.test(resid(M2CRA))##
## Shapiro-Wilk normality test
##
## data: resid(M2CRA)
## W = 0.96394, p-value = 0.7335shapiro.test(resid(M3CRA))##
## Shapiro-Wilk normality test
##
## data: resid(M3CRA)
## W = 0.88901, p-value = 0.05373shapiro.test(resid(M4CRA))##
## Shapiro-Wilk normality test
##
## data: resid(M4CRA)
## W = 0.94878, p-value = 0.4706#En todos los muestreos se puede observar normalidad en los datos de Contenido relativo de Agua
#**Homogeneidad de varianzas**
bartlett.test(CRA~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: CRA by Trat
## Bartlett's K-squared = 4.6317, df = 3, p-value = 0.2008bartlett.test(CRA~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: CRA by Trat
## Bartlett's K-squared = 5.9575, df = 3, p-value = 0.1137bartlett.test(CRA~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: CRA by Trat
## Bartlett's K-squared = 5.9197, df = 3, p-value = 0.1156bartlett.test(CRA~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: CRA by Trat
## Bartlett's K-squared = 6.0597, df = 3, p-value = 0.1087M1CRA_tukey <-HSD.test(Temp1$CRA,Temp1$Trat, 12, 23.66, alpha = 0.05);M1CRA_tukey## $statistics
## MSerror Df Mean CV MSD
## 23.66 12 73.34523 6.631863 10.21147
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$CRA std r Min Max Q25 Q50 Q75
## MT0W0 88.83759 1.773784 4 86.33880 90.24390 88.20481 89.38382 90.01660
## MT0W1 53.06593 3.307962 4 48.57143 56.00000 51.63990 53.84615 55.27219
## MT1W0 86.97131 5.739036 4 81.76101 92.39130 82.14238 86.86646 91.69539
## MT1W1 64.50608 6.899712 4 57.05521 70.92199 59.45014 65.02355 70.07949
##
## $comparison
## NULL
##
## $groups
## Temp1$CRA groups
## MT0W0 88.83759 a
## MT1W0 86.97131 a
## MT1W1 64.50608 b
## MT0W1 53.06593 c
##
## attr(,"class")
## [1] "group"M2CRA_tukey <-HSD.test(Temp2$CRA,Temp2$Trat, 12, 11.08, alpha = 0.05);M2CRA_tukey## $statistics
## MSerror Df Mean CV MSD
## 11.08 12 78.03175 4.265781 6.987963
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$CRA std r Min Max Q25 Q50 Q75
## MT0W0 91.05101 1.768157 4 88.42105 92.17877 90.82447 91.80211 92.02865
## MT0W1 59.41063 1.582033 4 57.60870 61.46341 58.79873 59.28520 59.89710
## MT1W0 90.46251 2.483222 4 86.91099 92.69663 89.98172 91.12120 91.60199
## MT1W1 71.20286 5.702258 4 64.11765 76.83616 67.95249 71.92882 75.17919
##
## $comparison
## NULL
##
## $groups
## Temp2$CRA groups
## MT0W0 91.05101 a
## MT1W0 90.46251 a
## MT1W1 71.20286 b
## MT0W1 59.41063 c
##
## attr(,"class")
## [1] "group"M3CRA_tukey <-HSD.test(Temp3$CRA,Temp3$Trat, 12, 4.776, alpha = 0.05);M3CRA_tukey## $statistics
## MSerror Df Mean CV MSD
## 4.776 12 82.15703 2.660035 4.587889
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$CRA std r Min Max Q25 Q50 Q75
## MT0W0 89.21130 1.164334 4 87.57062 90.19608 88.81573 89.53924 89.93481
## MT0W1 70.69442 1.116503 4 69.47368 72.02073 69.98036 70.64164 71.35570
## MT1W0 88.78857 1.513220 4 87.83069 91.01124 87.83069 88.15618 89.11407
## MT1W1 79.93385 3.769986 4 77.15736 85.48387 77.91878 78.54708 80.56214
##
## $comparison
## NULL
##
## $groups
## Temp3$CRA groups
## MT0W0 89.21130 a
## MT1W0 88.78857 a
## MT1W1 79.93385 b
## MT0W1 70.69442 c
##
## attr(,"class")
## [1] "group"M4CRA_tukey <-HSD.test(Temp4$CRA,Temp4$Trat, 12, 5.47, alpha = 0.05);M4CRA_tukey## $statistics
## MSerror Df Mean CV MSD
## 5.47 12 83.51618 2.800419 4.90992
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$CRA std r Min Max Q25 Q50 Q75
## MT0W0 90.16407 2.3263429 4 87.39130 92.09486 88.66601 90.58506 92.08312
## MT0W1 70.40700 2.7815961 4 67.71930 73.89831 68.44498 70.00520 71.96722
## MT1W0 89.37775 0.5030428 4 88.67187 89.74359 89.18925 89.54777 89.73628
## MT1W1 84.11589 2.9149807 4 81.28655 87.95812 82.24024 83.60945 85.48510
##
## $comparison
## NULL
##
## $groups
## Temp4$CRA groups
## MT0W0 90.16407 a
## MT1W0 89.37775 a
## MT1W1 84.11589 b
## MT0W1 70.40700 c
##
## attr(,"class")
## [1] "group"CRAg<-bind_rows(M1CRA_tukey$groups, M2CRA_tukey$groups, M3CRA_tukey$groups, M4CRA_tukey$groups, id=NULL)
CRAg$groups## [1] "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "b" "c"CRA_g<-data.frame(CRA,CRAg$groups)
CRAT<-c("a" ,"a", "c" ,"b", "a", "a", "c", "b", "a", "a", "c", "b", "a", "a", "c", "b")
levels(CRA$Trat)## NULLCRA_g$Trat=factor(CRA_g$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
CRAT1<- data.frame(CRAT,CRA_g$Trat)
CRAg1<-ggplot(data = CRA_g, aes(x = factor( Dia ), y = CRA, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylim(0,100)+ylab("Contenido Relativo de Agua (%)")+ xlab("DDT")+guides(fill=guide_legend("Tratamientos"))
CRAg2<-CRAg1+scale_fill_grey()+theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=CRA-sd, ymax=CRA+sd), width=.5, position=position_dodge(.9)) + geom_text(aes(label=CRAT1$CRAT),position=position_dodge(width=1), vjust=-2.2)
CRAg3<-CRAg2+ theme(legend.position = "none");CRAg3
Diametro de la raĆz tuberosa
# ANOVA
M1D <- aov(diametro~Trat, data = Temp1)
anova(M1D)## Analysis of Variance Table
##
## Response: diametro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 257.505 85.835 247.9 4.67e-11 ***
## Residuals 12 4.155 0.346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2D <- aov(diametro~Trat, data = Temp2)
anova(M2D)## Analysis of Variance Table
##
## Response: diametro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 542.77 180.922 757.57 6.143e-14 ***
## Residuals 12 2.87 0.239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3D <- aov(diametro~Trat, data = Temp3)
anova(M3D)## Analysis of Variance Table
##
## Response: diametro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1620.60 540.20 3003.4 < 2.2e-16 ***
## Residuals 12 2.16 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4D <- aov(diametro~Trat, data = Temp4)
anova(M4D)## Analysis of Variance Table
##
## Response: diametro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2609.81 869.94 1488.7 1.085e-15 ***
## Residuals 12 7.01 0.58
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1D))##
## Shapiro-Wilk normality test
##
## data: resid(M1D)
## W = 0.96575, p-value = 0.766shapiro.test(resid(M2D))##
## Shapiro-Wilk normality test
##
## data: resid(M2D)
## W = 0.95869, p-value = 0.6382shapiro.test(resid(M3D))##
## Shapiro-Wilk normality test
##
## data: resid(M3D)
## W = 0.87852, p-value = 0.0368shapiro.test(resid(M4D))##
## Shapiro-Wilk normality test
##
## data: resid(M4D)
## W = 0.93175, p-value = 0.2598#En todos los muestreos se puede observar normalidad en los datos de Diametro de la raĆz tuberosa.
#**Homogeneidad de varianzas**
bartlett.test(diametro~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: diametro by Trat
## Bartlett's K-squared = 4.3232, df = 3, p-value = 0.2286bartlett.test(diametro~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: diametro by Trat
## Bartlett's K-squared = 1.93, df = 3, p-value = 0.5871bartlett.test(diametro~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: diametro by Trat
## Bartlett's K-squared = 5.3191, df = 3, p-value = 0.1499bartlett.test(diametro~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: diametro by Trat
## Bartlett's K-squared = 2.2095, df = 3, p-value = 0.5301M1D_tukey <-HSD.test(Temp1$diametro,Temp1$Trat, 12, 0.346, alpha = 0.05);M1D_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.346 12 10.7 5.497361 1.234863
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$diametro std r Min Max Q25 Q50 Q75
## MT0W0 14.800 0.4966555 4 14.2 15.3 14.500 14.85 15.150
## MT0W1 6.100 0.2160247 4 5.8 6.3 6.025 6.15 6.225
## MT1W0 14.575 0.8845903 4 13.7 15.8 14.150 14.40 14.825
## MT1W1 7.325 0.5560276 4 6.8 8.1 7.025 7.20 7.500
##
## $comparison
## NULL
##
## $groups
## Temp1$diametro groups
## MT0W0 14.800 a
## MT1W0 14.575 a
## MT1W1 7.325 b
## MT0W1 6.100 b
##
## attr(,"class")
## [1] "group"M2D_tukey <-HSD.test(Temp2$diametro,Temp2$Trat, 12, 0.239, alpha = 0.05);M2D_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.239 12 19.23313 2.541845 1.026313
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$diametro std r Min Max Q25 Q50 Q75
## MT0W0 24.8725 0.3374784 4 24.39 25.16 24.7800 24.970 25.0625
## MT0W1 11.9275 0.6529102 4 11.35 12.86 11.5900 11.750 12.0875
## MT1W0 25.0225 0.5584129 4 24.19 25.37 24.9625 25.265 25.3250
## MT1W1 15.1100 0.3213513 4 14.71 15.38 14.9200 15.175 15.3650
##
## $comparison
## NULL
##
## $groups
## Temp2$diametro groups
## MT1W0 25.0225 a
## MT0W0 24.8725 a
## MT1W1 15.1100 b
## MT0W1 11.9275 c
##
## attr(,"class")
## [1] "group"M3D_tukey <-HSD.test(Temp3$diametro,Temp3$Trat, 12, 0.18, alpha = 0.05);M3D_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.18 12 26.77938 1.584294 0.8906703
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$diametro std r Min Max Q25 Q50 Q75
## MT0W0 36.2225 0.3073950 4 35.89 36.62 36.0550 36.190 36.3575
## MT0W1 12.9075 0.1537043 4 12.75 13.11 12.8175 12.885 12.9750
## MT1W0 36.5350 0.3644631 4 35.99 36.75 36.5075 36.700 36.7275
## MT1W1 21.4525 0.6844645 4 20.43 21.88 21.4125 21.750 21.7900
##
## $comparison
## NULL
##
## $groups
## Temp3$diametro groups
## MT1W0 36.5350 a
## MT0W0 36.2225 a
## MT1W1 21.4525 b
## MT0W1 12.9075 c
##
## attr(,"class")
## [1] "group"M4D_tukey <-HSD.test(Temp4$diametro,Temp4$Trat, 12, 0.58, alpha = 0.05);M4D_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.58 12 35.98938 2.116117 1.598802
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$diametro std r Min Max Q25 Q50 Q75
## MT0W0 48.3875 0.7630804 4 47.32 49.13 48.2125 48.550 48.7250
## MT0W1 18.7950 0.7409228 4 17.82 19.59 18.5025 18.885 19.1775
## MT1W0 48.1750 0.3862210 4 47.73 48.67 48.0150 48.150 48.3100
## MT1W1 28.6000 1.0281051 4 27.18 29.64 28.3725 28.790 29.0175
##
## $comparison
## NULL
##
## $groups
## Temp4$diametro groups
## MT0W0 48.3875 a
## MT1W0 48.1750 a
## MT1W1 28.6000 b
## MT0W1 18.7950 c
##
## attr(,"class")
## [1] "group"Dg<-bind_rows(M1D_tukey$groups, M2D_tukey$groups, M3D_tukey$groups, M4D_tukey$groups, id=NULL)
Dg$groups## [1] "a" "a" "b" "b" "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "b" "c"D_g<-data.frame(diametro,Dg$groups)
DT<-c("a" ,"a", "b" ,"c", "a", "a", "b", "c", "a", "a", "b", "c", "a", "a", "b", "c")
levels(diametro$Trat)## NULLD_g$Trat=factor(D_g$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
DT1<- data.frame(DT,D_g$Trat)
Dg1<-ggplot(data = D_g, aes(x = factor( Dia ), y = diametro, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylim(0,52)+ylab("DiĆ”metro de la raĆz tuberosa (mm)")+ xlab("DDT")+guides(fill=guide_legend("Tratamientos"))
Dg1+scale_fill_grey()+theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=diametro-sd, ymax=diametro+sd), width=.2, position=position_dodge(.9)) +geom_text(aes(label=DT1$DT),position=position_dodge(width=1), vjust=-.7)
Peso fresco de la raĆz tuberosa
# ANOVA
M1pfrt <- aov(pfrt~Trat, data = Temp1)
anova(M1pfrt)## Analysis of Variance Table
##
## Response: pfrt
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 14.283 4.7609 380.8 3.683e-12 ***
## Residuals 12 0.150 0.0125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2pfrt <- aov(pfrt~Trat, data = Temp2)
anova(M2pfrt)## Analysis of Variance Table
##
## Response: pfrt
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 57.128 19.0427 869.81 2.693e-14 ***
## Residuals 12 0.263 0.0219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3pfrt <- aov(pfrt~Trat, data = Temp3)
anova(M3pfrt)## Analysis of Variance Table
##
## Response: pfrt
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 256.993 85.664 1164.2 4.72e-15 ***
## Residuals 12 0.883 0.074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4pfrt <- aov(pfrt~Trat, data = Temp4)
anova(M4pfrt)## Analysis of Variance Table
##
## Response: pfrt
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 401.47 133.824 646.3 1.584e-13 ***
## Residuals 12 2.48 0.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1pfrt))##
## Shapiro-Wilk normality test
##
## data: resid(M1pfrt)
## W = 0.96592, p-value = 0.7691shapiro.test(resid(M2pfrt))##
## Shapiro-Wilk normality test
##
## data: resid(M2pfrt)
## W = 0.95428, p-value = 0.5603shapiro.test(resid(M3pfrt))##
## Shapiro-Wilk normality test
##
## data: resid(M3pfrt)
## W = 0.96657, p-value = 0.7805shapiro.test(resid(M4pfrt))##
## Shapiro-Wilk normality test
##
## data: resid(M4pfrt)
## W = 0.93187, p-value = 0.261#En todos los muestreos se puede observar normalidad en los datos de Peso fresco de la raĆz tuberosa
#**Homogeneidad de varianzas**
bartlett.test(pfrt~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: pfrt by Trat
## Bartlett's K-squared = 5.5417, df = 3, p-value = 0.1362bartlett.test(pfrt~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: pfrt by Trat
## Bartlett's K-squared = 1.1095, df = 3, p-value = 0.7748bartlett.test(pfrt~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: pfrt by Trat
## Bartlett's K-squared = 4.4726, df = 3, p-value = 0.2147bartlett.test(pfrt~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: pfrt by Trat
## Bartlett's K-squared = 0.56314, df = 3, p-value = 0.9048library(agricolae)
M1pfrt_tukey <-HSD.test(Temp1$pfrt,Temp1$Trat, 12, 0.0125, alpha = 0.05);M1pfrt_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0125 12 2.5575 4.371589 0.2347122
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$pfrt std r Min Max Q25 Q50 Q75
## MT0W0 3.52575 0.04871943 4 3.496 3.598 3.49675 3.5045 3.53350
## MT0W1 1.44825 0.05010905 4 1.394 1.512 1.41950 1.4435 1.47225
## MT1W0 3.46250 0.15985931 4 3.279 3.648 3.36675 3.4615 3.55725
## MT1W1 1.79350 0.13989162 4 1.633 1.946 1.70425 1.7975 1.88675
##
## $comparison
## NULL
##
## $groups
## Temp1$pfrt groups
## MT0W0 3.52575 a
## MT1W0 3.46250 a
## MT1W1 1.79350 b
## MT0W1 1.44825 c
##
## attr(,"class")
## [1] "group"M2pfrt_tukey <-HSD.test(Temp2$pfrt,Temp2$Trat, 12, 0.0219, alpha = 0.05);M2pfrt_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0219 12 6.268562 2.360772 0.3106725
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$pfrt std r Min Max Q25 Q50 Q75
## MT0W0 8.11600 0.1087443 4 7.991 8.237 8.04800 8.1180 8.18600
## MT0W1 3.87125 0.1992007 4 3.716 4.159 3.75200 3.8050 3.92425
## MT1W0 8.12000 0.1267307 4 7.934 8.219 8.10575 8.1635 8.17775
## MT1W1 4.96700 0.1414379 4 4.768 5.069 4.91500 5.0155 5.06750
##
## $comparison
## NULL
##
## $groups
## Temp2$pfrt groups
## MT1W0 8.12000 a
## MT0W0 8.11600 a
## MT1W1 4.96700 b
## MT0W1 3.87125 c
##
## attr(,"class")
## [1] "group"M3pfrt_tukey <-HSD.test(Temp3$pfrt,Temp3$Trat, 12, 0.074, alpha = 0.05);M3pfrt_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.074 12 10.54794 2.578982 0.5710795
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$pfrt std r Min Max Q25 Q50 Q75
## MT0W0 14.38050 0.40248023 4 13.996 14.864 14.08000 14.3310 14.6315
## MT0W1 5.03425 0.09717467 4 4.962 5.174 4.97175 5.0005 5.0630
## MT1W0 14.36725 0.27894728 4 14.096 14.754 14.22425 14.3095 14.4525
## MT1W1 8.40975 0.21234779 4 8.119 8.627 8.34925 8.4465 8.5070
##
## $comparison
## NULL
##
## $groups
## Temp3$pfrt groups
## MT0W0 14.38050 a
## MT1W0 14.36725 a
## MT1W1 8.40975 b
## MT0W1 5.03425 c
##
## attr(,"class")
## [1] "group"M4pfrt_tukey <-HSD.test(Temp4$pfrt,Temp4$Trat, 12, 0.207, alpha = 0.05);M4pfrt_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.207 12 14.02137 3.24485 0.9551375
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$pfrt std r Min Max Q25 Q50 Q75
## MT0W0 19.03100 0.5205555 4 18.427 19.588 18.70150 19.0545 19.38400
## MT0W1 7.27975 0.3595677 4 6.861 7.729 7.11000 7.2645 7.43425
## MT1W0 18.65050 0.5220016 4 17.932 19.068 18.43225 18.8010 19.01925
## MT1W1 11.12425 0.3943293 4 10.575 11.438 10.97100 11.2420 11.39525
##
## $comparison
## NULL
##
## $groups
## Temp4$pfrt groups
## MT0W0 19.03100 a
## MT1W0 18.65050 a
## MT1W1 11.12425 b
## MT0W1 7.27975 c
##
## attr(,"class")
## [1] "group"PFRTg<-bind_rows(M1pfrt_tukey$groups, M2pfrt_tukey$groups, M3pfrt_tukey$groups, M4pfrt_tukey$groups, id=NULL)
PFRTg$groups## [1] "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "b" "c" "a" "a" "b" "c"PFRT_g<-data.frame(PSRT,PFRTg$groups)
PFRTT<-c("a" ,"a", "b" ,"c", "a", "a", "b", "c", "a", "a", "b", "c", "a", "a", "b", "c")
levels(PSRT$Trat)## NULLPFRT_g$Trat=factor(PFRT_g$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
PFRT1<- data.frame(PFRTT,PFRT_g$Trat)
PFRg1<-ggplot(data = PFRT_g, aes(x = factor( Dia ), y = PSRT, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylim(0,21)+ylab("Peso fresco de la raĆz tuberosa (g)")+ xlab("DDT")+guides(fill=guide_legend("Tratamientos"))
PFRg1+scale_fill_grey()+theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=PSRT-sd, ymax=PSRT+sd), width=.2, position=position_dodge(.9)) +geom_text(aes(label=PFRT1$PFRTT),position=position_dodge(width=1), vjust=-0.8)
Perdida de electrolitos
# ANOVA
M1PE <- aov(PE~Trat, data = Temp1)
anova(M1PE)## Analysis of Variance Table
##
## Response: PE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 4623.9 1541.31 96.031 1.178e-08 ***
## Residuals 12 192.6 16.05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2PE <- aov(PE~Trat, data = Temp2)
anova(M2PE)## Analysis of Variance Table
##
## Response: PE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 4186.0 1395.35 778.05 5.239e-14 ***
## Residuals 12 21.5 1.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3PE <- aov(PE~Trat, data = Temp3)
anova(M3PE)## Analysis of Variance Table
##
## Response: PE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1902.53 634.18 364.71 4.759e-12 ***
## Residuals 12 20.87 1.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4PE <- aov(PE~Trat, data = Temp4)
anova(M4PE)## Analysis of Variance Table
##
## Response: PE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3360.8 1120.27 237.74 5.976e-11 ***
## Residuals 12 56.5 4.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pruebas de normalidad
shapiro.test(resid(M1PE))##
## Shapiro-Wilk normality test
##
## data: resid(M1PE)
## W = 0.85499, p-value = 0.01615shapiro.test(resid(M2PE))##
## Shapiro-Wilk normality test
##
## data: resid(M2PE)
## W = 0.95073, p-value = 0.5014shapiro.test(resid(M3PE))##
## Shapiro-Wilk normality test
##
## data: resid(M3PE)
## W = 0.97827, p-value = 0.9484shapiro.test(resid(M4PE))##
## Shapiro-Wilk normality test
##
## data: resid(M4PE)
## W = 0.93334, p-value = 0.2752#En todos los muestreos se puede observar normalidad en los datos de PE.
#**Homogeneidad de varianzas**
bartlett.test(PE~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: PE by Trat
## Bartlett's K-squared = 12.123, df = 3, p-value = 0.006973bartlett.test(PE~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: PE by Trat
## Bartlett's K-squared = 2.1669, df = 3, p-value = 0.5385bartlett.test(PE~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: PE by Trat
## Bartlett's K-squared = 4.8537, df = 3, p-value = 0.1828bartlett.test(PE~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: PE by Trat
## Bartlett's K-squared = 8.5314, df = 3, p-value = 0.03622M1PE_tukey <-HSD.test(Temp1$PE,Temp1$Trat, 12, 16.05, alpha = 0.05);M1PE_tukey## $statistics
## MSerror Df Mean CV MSD
## 16.05 12 22.84372 17.53762 8.410431
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp1$Trat 4 4.19866 0.05
##
## $means
## Temp1$PE std r Min Max Q25 Q50 Q75
## MT0W0 5.611760 0.9592854 4 4.728370 6.906339 4.988063 5.406166 6.029863
## MT0W1 43.489185 6.7823337 4 36.780576 52.923077 40.508021 42.126544 45.107708
## MT1W0 6.525624 0.9815999 4 5.550582 7.886435 6.075145 6.332739 6.783218
## MT1W1 35.748317 4.0394049 4 31.727749 41.361789 34.105897 34.951865 36.594285
##
## $comparison
## NULL
##
## $groups
## Temp1$PE groups
## MT0W1 43.489185 a
## MT1W1 35.748317 a
## MT1W0 6.525624 b
## MT0W0 5.611760 b
##
## attr(,"class")
## [1] "group"M2PE_tukey <-HSD.test(Temp2$PE,Temp2$Trat, 12, 1.79, alpha = 0.05);M2PE_tukey## $statistics
## MSerror Df Mean CV MSD
## 1.79 12 23.13266 5.783635 2.808712
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$PE std r Min Max Q25 Q50 Q75
## MT0W0 7.278877 1.2132281 4 5.610860 8.409321 6.819805 7.547664 8.006735
## MT0W1 44.752361 1.9812773 4 42.078708 46.311111 43.836107 45.309813 46.226067
## MT1W0 7.777074 0.9168115 4 6.962664 8.847737 7.040308 7.648948 8.385715
## MT1W1 32.722346 0.9672954 4 31.659836 33.998006 32.288000 32.615770 33.050116
##
## $comparison
## NULL
##
## $groups
## Temp2$PE groups
## MT0W1 44.752361 a
## MT1W1 32.722346 b
## MT1W0 7.777074 c
## MT0W0 7.278877 c
##
## attr(,"class")
## [1] "group"M3PE_tukey <-HSD.test(Temp3$PE,Temp3$Trat, 12, 1.74, alpha = 0.05);M3PE_tukey## $statistics
## MSerror Df Mean CV MSD
## 1.74 12 16.49284 7.99796 2.769207
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$PE std r Min Max Q25 Q50 Q75
## MT0W0 6.549404 0.5592162 4 5.968586 7.312049 6.308660 6.458490 6.699233
## MT0W1 32.873860 1.1652557 4 31.841302 34.378265 32.016929 32.637935 33.494865
## MT1W0 6.652035 0.8674533 4 5.866426 7.754280 6.012061 6.493718 7.133692
## MT1W1 19.896054 2.1289465 4 17.652330 22.492401 18.499377 19.719743 21.116420
##
## $comparison
## NULL
##
## $groups
## Temp3$PE groups
## MT0W1 32.873860 a
## MT1W1 19.896054 b
## MT1W0 6.652035 c
## MT0W0 6.549404 c
##
## attr(,"class")
## [1] "group"M4PE_tukey <-HSD.test(Temp4$PE,Temp4$Trat, 12, 4.71, alpha = 0.05);M4PE_tukey## $statistics
## MSerror Df Mean CV MSD
## 4.71 12 19.79408 10.96416 4.556078
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$PE std r Min Max Q25 Q50 Q75
## MT0W0 6.091213 0.6490043 4 5.166375 6.619860 5.889584 6.289309 6.490938
## MT0W1 40.243388 3.4012024 4 37.106918 43.658810 37.430576 40.103912 42.916725
## MT1W0 6.137384 0.7985746 4 4.954955 6.712173 6.066708 6.441204 6.511880
## MT1W1 26.704318 2.4942713 4 24.099485 30.074389 25.512449 26.321698 27.513567
##
## $comparison
## NULL
##
## $groups
## Temp4$PE groups
## MT0W1 40.243388 a
## MT1W1 26.704318 b
## MT1W0 6.137384 c
## MT0W0 6.091213 c
##
## attr(,"class")
## [1] "group"PEg<-bind_rows(M1PE_tukey$groups, M2PE_tukey$groups, M3PE_tukey$groups, M4PE_tukey$groups, id=NULL)
PEg$groups## [1] "a" "a" "b" "b" "a" "b" "c" "c" "a" "b" "c" "c" "a" "b" "c" "c"PE_g<-data.frame(PE,PEg$groups)
PET<-c("b" ,"b", "a" ,"a", "c", "c", "a", "b", "c", "c", "a", "b", "c", "c", "a", "b")
levels(PE$Trat)## NULLPE_g$Trat=factor(PE_g$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
PET1<- data.frame(PET,PE_g$Trat)
PEg1<-ggplot(data = PE_g, aes(x = factor( Dia ), y = PE, fill = Trat) ) + geom_bar( stat = 'identity', position = 'dodge', color = "black")+ ylim(0,52)+ylab("Perdida de Electrolitos (%)")+ xlab("DDT")+guides(fill=guide_legend("Tratamientos"))
PEg1+scale_fill_grey()+theme_classic(base_family= "serif") + geom_errorbar(aes(ymin=PE-sd, ymax=PE+sd), width=.2, position=position_dodge(.9)) +geom_text(aes(label=PET1$PET),position=position_dodge(width=1), vjust=-2.8)