Analisis datos_fisio
Paola Andrea Ospina Sanchez
22/5/2022
Comparaciones
Longitud parte area
datos1<-read_excel("~/Paola/Computacion/DATOS.xlsx")
datos1## # A tibble: 64 x 10
## Muestreo tratamiento LPA NH PM ABD DR AF PE CA
## <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 35 T0 17 8 158.56 133.7 23.72 183.66 69 91.26
## 2 35 T0 15 7 182.15 263.34 29.78 191.23 63 91.88
## 3 35 T0 13 6 162.84 167.94 30.72 171.45 67 91.67
## 4 35 T0 16 7 264.33 299.64 37.35 232.82 74 93.63
## 5 42 T0 16.4 7 212.71 848.10 41.02 301.33 41 94.29
## 6 42 T0 17.1 7 212.71 573.89 40.55 266.77 42 95.66
## 7 42 T0 18.9 7 212.71 540.59 42.01 257.1 50 95.62
## 8 42 T0 17.4 7 212.71 562.74 44.04 207.83 24 93.96
## 9 49 T0 18 10 246.28 854.82 49.31 400.45 76 94.89
## 10 49 T0 19 10 179.3 547.06 35.01 328.61 61 94.59
## # ... with 54 more rows##Muestreo 1
LP1<-c(17,15,13,16,14.3,13.1,16,14.5,14.1,15,12.1,12.7,10.8,11.7,8.6,11.8)
TL1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TL1<-as.factor(TL1)
aL1 <- aov(LP1~TL1)
summary(aL1)## Df Sum Sq Mean Sq F value Pr(>F)
## TL1 3 46.85 15.617 7.547 0.00425 **
## Residuals 12 24.83 2.069
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CLP1<-HSD.test(aL1,trt="TL1")
CLP1## $statistics
## MSerror Df Mean CV MSD
## 2.069375 12 13.48125 10.67061 3.019954
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TL1 4 4.19866 0.05
##
## $means
## LP1 std r Min Max Q25 Q50 Q75
## T0 15.250 1.707825 4 13.0 17.0 14.50 15.50 16.250
## T1 14.475 1.189888 4 13.1 16.0 14.00 14.40 14.875
## T2 13.475 1.317510 4 12.1 15.0 12.55 13.40 14.325
## T3 10.725 1.486327 4 8.6 11.8 10.25 11.25 11.725
##
## $comparison
## NULL
##
## $groups
## LP1 groups
## T0 15.250 a
## T1 14.475 a
## T2 13.475 ab
## T3 10.725 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
DATOSM2<-c(16.4,17.1,18.9,17.4,12.1,13.6,14.7,13,17.4,14.2,14.6,18.4,13.3,12.9,14.1,14.5)
TrataM2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TrataM2<-as.factor(TrataM2)
anovaM2 <- aov(DATOSM2~TrataM2)
CLP2<-HSD.test(anovaM2,trt="TrataM2")
CLP2## $statistics
## MSerror Df Mean CV MSD
## 1.7775 12 15.1625 8.792938 2.798888
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TrataM2 4 4.19866 0.05
##
## $means
## DATOSM2 std r Min Max Q25 Q50 Q75
## T0 17.45 1.0535654 4 16.4 18.9 16.925 17.25 17.775
## T1 13.35 1.0908712 4 12.1 14.7 12.775 13.30 13.875
## T2 16.15 2.0680103 4 14.2 18.4 14.500 16.00 17.650
## T3 13.70 0.7302967 4 12.9 14.5 13.200 13.70 14.200
##
## $comparison
## NULL
##
## $groups
## DATOSM2 groups
## T0 17.45 a
## T2 16.15 ab
## T3 13.70 bc
## T1 13.35 c
##
## attr(,"class")
## [1] "group"#Muestreo 3
DATOSM3<-c(18,19,16.5,17,17,16,15.5,12,15,15,14,15,14,13,14.5,14)
TrataM3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TrataM3<-as.factor(TrataM3)
anovaM3 <- aov(DATOSM3~TrataM3)
CLP3<-HSD.test(anovaM3,trt="TrataM3")
CLP3## $statistics
## MSerror Df Mean CV MSD
## 1.651042 12 15.34375 8.37428 2.697489
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TrataM3 4 4.19866 0.05
##
## $means
## DATOSM3 std r Min Max Q25 Q50 Q75
## T0 17.625 1.1086779 4 16.5 19.0 16.875 17.50 18.250
## T1 15.125 2.1746647 4 12.0 17.0 14.625 15.75 16.250
## T2 14.750 0.5000000 4 14.0 15.0 14.750 15.00 15.000
## T3 13.875 0.6291529 4 13.0 14.5 13.750 14.00 14.125
##
## $comparison
## NULL
##
## $groups
## DATOSM3 groups
## T0 17.625 a
## T1 15.125 ab
## T2 14.750 b
## T3 13.875 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
DATOSM4<-c(17,16.5,16,16.5,16.5,16.5,11.5,18.5,19,12.5,19.5,15.5,20,16.5,14,14.5)
TrataM4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TrataM4<-as.factor(TrataM4)
anovaM4 <- aov(DATOSM4~TrataM4)
CLP4<-HSD.test(anovaM4,trt="TrataM4")
CLP4## $statistics
## MSerror Df Mean CV MSD
## 6.807292 12 16.28125 16.02505 5.477317
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TrataM4 4 4.19866 0.05
##
## $means
## DATOSM4 std r Min Max Q25 Q50 Q75
## T0 16.500 0.4082483 4 16.0 17.0 16.375 16.50 16.625
## T1 15.750 2.9860788 4 11.5 18.5 15.250 16.50 17.000
## T2 16.625 3.2755407 4 12.5 19.5 14.750 17.25 19.125
## T3 16.250 2.7233558 4 14.0 20.0 14.375 15.50 17.375
##
## $comparison
## NULL
##
## $groups
## DATOSM4 groups
## T2 16.625 a
## T0 16.500 a
## T3 16.250 a
## T1 15.750 a
##
## attr(,"class")
## [1] "group"CLP<- c("a","a","ab","b","a","c","ab","bc","a","ab","b","b","a","a","a","a")
data<-data.frame(LA=as.numeric(datos1$LPA), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w = mean(LA,na.rm=TRUE), sd = sd(LA,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt$CLP<- CLPggplot(dt, aes(muestreo,w, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Longitud parte aerea") + coord_cartesian(ylim=c(0,25)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CLP), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = w-sd, ymax = w+sd),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 25, label = "**") +
geom_text(data = NULL, x = 2, y = 25, label = "**") +
geom_text(data = NULL, x = 3, y = 25, label = "**") +
geom_text(data = NULL, x = 4, y = 25, label = "**")
## Numero de hojas
# Muestreo 1
NHM1<-c(8,7,6,7,7,5,5,6,6,5,6,7,6,6,6,6)
TRATHM1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATHM1<-as.factor(TRATHM1)
aovNM1 <- aov(NHM1~TRATHM1)
NH1<-HSD.test(aovNM1,trt="TRATHM1")
NH1## $statistics
## MSerror Df Mean CV MSD
## 0.5625 12 6.1875 12.12121 1.574498
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATHM1 4 4.19866 0.05
##
## $means
## NHM1 std r Min Max Q25 Q50 Q75
## T0 7.00 0.8164966 4 6 8 6.75 7.0 7.25
## T1 5.75 0.9574271 4 5 7 5.00 5.5 6.25
## T2 6.00 0.8164966 4 5 7 5.75 6.0 6.25
## T3 6.00 0.0000000 4 6 6 6.00 6.0 6.00
##
## $comparison
## NULL
##
## $groups
## NHM1 groups
## T0 7.00 a
## T2 6.00 a
## T3 6.00 a
## T1 5.75 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
NHM2<-c(7,7,7,7,7,6,7,7,7,7,7,7,6,7,8,8)
TRATHM2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATHM2<-as.factor(TRATHM2)
aovNM2 <- aov(NHM2~TRATHM2)
NH2<-HSD.test(aovNM2,trt="TRATHM2")
NH2## $statistics
## MSerror Df Mean CV MSD
## 0.2916667 12 7 7.715167 1.133768
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATHM2 4 4.19866 0.05
##
## $means
## NHM2 std r Min Max Q25 Q50 Q75
## T0 7.00 0.0000000 4 7 7 7.00 7.0 7
## T1 6.75 0.5000000 4 6 7 6.75 7.0 7
## T2 7.00 0.0000000 4 7 7 7.00 7.0 7
## T3 7.25 0.9574271 4 6 8 6.75 7.5 8
##
## $comparison
## NULL
##
## $groups
## NHM2 groups
## T3 7.25 a
## T0 7.00 a
## T2 7.00 a
## T1 6.75 a
##
## attr(,"class")
## [1] "group"# Muestreo 3
NHM3<-c(10,10,7,8,7,8,7,7,8,9,8,9,7,7,7,9)
TRATHM3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATHM3<-as.factor(TRATHM3)
aovNM3 <- aov(NHM3~TRATHM3)
NH3<-HSD.test(aovNM3,trt="TRATHM3")
NH3## $statistics
## MSerror Df Mean CV MSD
## 0.9583333 12 8 12.23681 2.055129
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATHM3 4 4.19866 0.05
##
## $means
## NHM3 std r Min Max Q25 Q50 Q75
## T0 8.75 1.5000000 4 7 10 7.75 9.0 10.00
## T1 7.25 0.5000000 4 7 8 7.00 7.0 7.25
## T2 8.50 0.5773503 4 8 9 8.00 8.5 9.00
## T3 7.50 1.0000000 4 7 9 7.00 7.0 7.50
##
## $comparison
## NULL
##
## $groups
## NHM3 groups
## T0 8.75 a
## T2 8.50 a
## T3 7.50 a
## T1 7.25 a
##
## attr(,"class")
## [1] "group"# Muestreo 4
NHM4<-c(9,11,9,8,6,8,8,8,10,9,8,9,13,8,7,9)
TRATHM4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATHM4<-as.factor(TRATHM4)
aovNM4 <- aov(NHM4~TRATHM4)
NH4<-HSD.test(aovNM4,trt="TRATHM4")
NH4## $statistics
## MSerror Df Mean CV MSD
## 2.541667 12 8.75 18.22012 3.346879
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATHM4 4 4.19866 0.05
##
## $means
## NHM4 std r Min Max Q25 Q50 Q75
## T0 9.25 1.2583057 4 8 11 8.75 9.0 9.50
## T1 7.50 1.0000000 4 6 8 7.50 8.0 8.00
## T2 9.00 0.8164966 4 8 10 8.75 9.0 9.25
## T3 9.25 2.6299556 4 7 13 7.75 8.5 10.00
##
## $comparison
## NULL
##
## $groups
## NHM4 groups
## T0 9.25 a
## T3 9.25 a
## T2 9.00 a
## T1 7.50 a
##
## attr(,"class")
## [1] "group"CNH<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data_2<-data.frame(nh=as.numeric(datos1$NH), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt2 <-data_2 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w2 = mean(nh), sd2 = sd(nh),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt2$CNH<- CNHggplot(dt2, aes(muestreo,w2, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Numero de hojas") + coord_cartesian(ylim=c(0,15)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CNH), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = w2-sd2, ymax = w2+sd2),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 15, label = "**") +
geom_text(data = NULL, x = 2, y = 15, label = "**") +
geom_text(data = NULL, x = 3, y = 15, label = "**") +
geom_text(data = NULL, x = 4, y = 15, label = "**")
Peso materas
# Muestreo 1
PMMM1<-c(158.56,182.15,162.84,264.33,146.38,121.02,167.59,137.16,139.37,122.26,144.23,129.26,140.55,150.35,130.63,147.43)
TRATPMMM1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATPMMM1<-as.factor(TRATPMMM1)
aovPMMM1 <- aov(PMMM1~TRATPMMM1)
Comp<-HSD.test(aovPMMM1,trt="TRATPMMM1")
Comp## $statistics
## MSerror Df Mean CV MSD
## 746.204 12 152.7569 17.88249 57.34684
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATPMMM1 4 4.19866 0.05
##
## $means
## PMMM1 std r Min Max Q25 Q50 Q75
## T0 191.9700 49.319323 4 158.56 264.33 161.770 172.495 202.6950
## T1 143.0375 19.436331 4 121.02 167.59 133.125 141.770 151.6825
## T2 133.7800 9.892613 4 122.26 144.23 127.510 134.315 140.5850
## T3 142.2400 8.762732 4 130.63 150.35 138.070 143.990 148.1600
##
## $comparison
## NULL
##
## $groups
## PMMM1 groups
## T0 191.9700 a
## T1 143.0375 ab
## T3 142.2400 ab
## T2 133.7800 b
##
## attr(,"class")
## [1] "group"# Muestreo 2
PMM2<-c(212.71,212.71,212.71,212.71,150,152.54,134.31,143.46,147.07,152,161.42,135.26,132.52,134.55,152.38,197.84)
TRATPM2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATPM2<-as.factor(TRATPM2)
dfPMM2<-data.frame(PMM2,TRATPM2)
aovPM2 <- aov(PMM2~TRATPM2)
PM2<-HSD.test(aovPM2,trt="TRATPM2")
PM2## $statistics
## MSerror Df Mean CV MSD
## 276.5063 12 165.2619 10.0619 34.90866
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATPM2 4 4.19866 0.05
##
## $means
## PMM2 std r Min Max Q25 Q50 Q75
## T0 212.7100 0.000000 4 212.71 212.71 212.7100 212.710 212.710
## T1 145.0775 8.133783 4 134.31 152.54 141.1725 146.730 150.635
## T2 148.9375 10.889648 4 135.26 161.42 144.1175 149.535 154.355
## T3 154.3225 30.352632 4 132.52 197.84 134.0425 143.465 163.745
##
## $comparison
## NULL
##
## $groups
## PMM2 groups
## T0 212.7100 a
## T3 154.3225 b
## T2 148.9375 b
## T1 145.0775 b
##
## attr(,"class")
## [1] "group"# Muestreo 3
PMM3<-c(246.28,179.3,156.33,142.88,120.05,128.16,129.37,129.49,131.81,133.15,128.54,128.49,141.44,144.37,130.21,135.34)
TRATPM3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATPM3<-as.factor(TRATPM3)
dfPMM3<-data.frame(PMM3,TRATPM3)
aovPMM3 <- aov(PMM3~TRATPM3)
PM3<-HSD.test(aovPMM3,trt="TRATPM3")
PM3## $statistics
## MSerror Df Mean CV MSD
## 543.6577 12 144.0756 16.18349 48.94897
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATPM3 4 4.19866 0.05
##
## $means
## PMM3 std r Min Max Q25 Q50 Q75
## T0 181.1975 45.920060 4 142.88 246.28 152.9675 167.815 196.0450
## T1 126.7675 4.518439 4 120.05 129.49 126.1325 128.765 129.4000
## T2 130.4975 2.353740 4 128.49 133.15 128.5275 130.175 132.1450
## T3 137.8400 6.326342 4 130.21 144.37 134.0575 138.390 142.1725
##
## $comparison
## NULL
##
## $groups
## PMM3 groups
## T0 181.1975 a
## T3 137.8400 ab
## T2 130.4975 b
## T1 126.7675 b
##
## attr(,"class")
## [1] "group"# Muestreo 4
PMM4<-c(164.51,157.17,158.85,171.14,119.01,122.99,136.07,133.63,148.07,141.86,137.3,136.05,123.07,131.23,146.05,140.97)
TRATPM4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATPM4<-as.factor(TRATPM4)
dfPMM4<-data.frame(PMM4,TRATPM4)
aovPMM4 <- aov(PMM4~TRATPM4)
PM4<-HSD.test(aovPMM4,trt="TRATPM4")
PM4## $statistics
## MSerror Df Mean CV MSD
## 60.42303 12 141.7481 5.483829 16.31857
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATPM4 4 4.19866 0.05
##
## $means
## PMM4 std r Min Max Q25 Q50 Q75
## T0 162.9175 6.317275 4 157.17 171.14 158.4300 161.68 166.1675
## T1 127.9250 8.220290 4 119.01 136.07 121.9950 128.31 134.2400
## T2 140.8200 5.440202 4 136.05 148.07 136.9875 139.58 143.4125
## T3 135.3300 10.228157 4 123.07 146.05 129.1900 136.10 142.2400
##
## $comparison
## NULL
##
## $groups
## PMM4 groups
## T0 162.9175 a
## T2 140.8200 b
## T3 135.3300 b
## T1 127.9250 b
##
## attr(,"class")
## [1] "group"CPM<- c("a","ab","b","ab","a","b","b","b","a","b","b","ab","a","b","b","b")
data3<-data.frame(pm=as.numeric(datos1$PM), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt3 <-data3 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w3 = mean(pm), sd3 = sd(pm),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt3$CPM<- CPMggplot(dt3, aes(muestreo,w3, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Peso materas") + coord_cartesian(ylim=c(0,250)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CPM), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = w3-sd3, ymax = w3+sd3),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 25, label = "**") +
geom_text(data = NULL, x = 2, y = 25, label = "**") +
geom_text(data = NULL, x = 3, y = 25, label = "**") +
geom_text(data = NULL, x = 4, y = 25, label = "**")
Area bajo dosel
#Muestreo 1
ABM1<-c(133.7,263.34,167.94,299.64,157.91,131.79,197.02,118.03,117.39,84.59,155.96,115.78,204.99,184.87,107.27,178.04)
TRATABM1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATABM1<-as.factor(TRATABM1)
dfABM1<-data.frame(ABM1,TRATABM1)
aovABM1 <- aov(ABM1~TRATABM1)
AB1<-HSD.test(aovABM1,trt="TRATABM1")
AB1## $statistics
## MSerror Df Mean CV MSD
## 2495.184 12 163.6412 30.5252 104.8653
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATABM1 4 4.19866 0.05
##
## $means
## ABM1 std r Min Max Q25 Q50 Q75
## T0 216.1550 78.14449 4 133.70 299.64 159.3800 215.640 272.4150
## T1 151.1875 34.74427 4 118.03 197.02 128.3500 144.850 167.6875
## T2 118.4300 29.22186 4 84.59 155.96 107.9825 116.585 127.0325
## T3 168.7925 42.58043 4 107.27 204.99 160.3475 181.455 189.9000
##
## $comparison
## NULL
##
## $groups
## ABM1 groups
## T0 216.1550 a
## T3 168.7925 a
## T1 151.1875 a
## T2 118.4300 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
ABM2<-c(848.109,573.894,540.599,562.741,366.392,352.864,424.466,427.871,456.589,389.149,510.148,637.14,405.347,369.01,404.653,418.293)
TRATABM2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATABM2<-as.factor(TRATABM2)
dfABM2<-data.frame(ABM2,TRATABM2)
aovABM2 <- aov(ABM2~TRATABM2)
AB2<-HSD.test(aovABM2,trt="TRATABM2")
AB2## $statistics
## MSerror Df Mean CV MSD
## 8513.969 12 480.4541 19.20499 193.7077
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATABM2 4 4.19866 0.05
##
## $means
## ABM2 std r Min Max Q25 Q50 Q75
## T0 631.3357 145.17644 4 540.599 848.109 557.2055 568.3175 642.4478
## T1 392.8982 38.83700 4 352.864 427.871 363.0100 395.4290 425.3173
## T2 498.2565 104.99311 4 389.149 637.140 439.7290 483.3685 541.8960
## T3 399.3257 21.16157 4 369.010 418.293 395.7423 405.0000 408.5835
##
## $comparison
## NULL
##
## $groups
## ABM2 groups
## T0 631.3357 a
## T2 498.2565 ab
## T3 399.3257 b
## T1 392.8982 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
ABM3<-c(854.82,547.06,419.77,430.22,225.65,264.1,225.95,398.3,383.83,333.04,324.01,345.28,409.91,412.71,342.17,407.95)
TRATABM3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATABM3<-as.factor(TRATABM3)
dfABM3<-data.frame(ABM3,TRATABM3)
aovABM3 <- aov(ABM3~TRATABM3)
AB3<-HSD.test(aovABM3,trt="TRATABM3")
AB3## $statistics
## MSerror Df Mean CV MSD
## 12436.32 12 395.2981 28.21118 234.1136
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATABM3 4 4.19866 0.05
##
## $means
## ABM3 std r Min Max Q25 Q50 Q75
## T0 562.9675 202.94363 4 419.77 854.82 427.6075 488.640 624.0000
## T1 278.5000 81.88208 4 225.65 398.30 225.8750 245.025 297.6500
## T2 346.5400 26.34377 4 324.01 383.83 330.7825 339.160 354.9175
## T3 393.1850 34.06605 4 342.17 412.71 391.5050 408.930 410.6100
##
## $comparison
## NULL
##
## $groups
## ABM3 groups
## T0 562.9675 a
## T3 393.1850 ab
## T2 346.5400 ab
## T1 278.5000 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
ABM4<-c(485.05,479.68,395.02,476.17,203.7,173.34,290.95,242.15,321.12,310.69,480.56,362.81,351.19,330.96,312.11,335.66)
TRATABM4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATABM4<-as.factor(TRATABM4)
dfABM4<-data.frame(ABM4,TRATABM4)
aovABM4 <- aov(ABM4~TRATABM4)
AB4<-HSD.test(aovABM4,trt="TRATABM4")
AB4## $statistics
## MSerror Df Mean CV MSD
## 2682.378 12 346.9475 14.92781 108.7278
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATABM4 4 4.19866 0.05
##
## $means
## ABM4 std r Min Max Q25 Q50 Q75
## T0 458.980 42.79608 4 395.02 485.05 455.8825 477.925 481.0225
## T1 227.535 50.79457 4 173.34 290.95 196.1100 222.925 254.3500
## T2 368.795 77.83815 4 310.69 480.56 318.5125 341.965 392.2475
## T3 332.480 16.09787 4 312.11 351.19 326.2475 333.310 339.5425
##
## $comparison
## NULL
##
## $groups
## ABM4 groups
## T0 458.980 a
## T2 368.795 ab
## T3 332.480 bc
## T1 227.535 c
##
## attr(,"class")
## [1] "group"CABD<- c("a","a","a","a","a","b","ab","b","a","b","ab","ab","a","c","ab","bc")
data4<-data.frame(ABD=as.numeric(datos1$ABD), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt4 <-data4 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w4 = mean(ABD), sd4 = sd(ABD),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt4$CABD<- CABDggplot(dt4, aes(muestreo,w4, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Area bajo dosel") + coord_cartesian(ylim=c(0,800)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CABD), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = w4-sd4, ymax = w4+sd4),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 2, y = 800, label = "**") +
geom_text(data = NULL, x = 3, y = 800, label = "**") +
geom_text(data = NULL, x = 4, y = 800, label = "**") 
## Diametro raiz tuberosa
#Muestreo 1
DM1<-c(133.7,263.34,167.94,299.64,157.91,131.79,197.02,118.03,117.39,84.59,155.96,115.78,204.99,184.87,107.27,178.04)
TRATDM1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATDM1<-as.factor(TRATDM1)
dfDM1<-data.frame(DM1,TRATDM1)
aovDM1 <- aov(DM1~TRATDM1)
DR1<-HSD.test(aovDM1,trt="TRATDM1")
DR1## $statistics
## MSerror Df Mean CV MSD
## 2495.184 12 163.6412 30.5252 104.8653
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATDM1 4 4.19866 0.05
##
## $means
## DM1 std r Min Max Q25 Q50 Q75
## T0 216.1550 78.14449 4 133.70 299.64 159.3800 215.640 272.4150
## T1 151.1875 34.74427 4 118.03 197.02 128.3500 144.850 167.6875
## T2 118.4300 29.22186 4 84.59 155.96 107.9825 116.585 127.0325
## T3 168.7925 42.58043 4 107.27 204.99 160.3475 181.455 189.9000
##
## $comparison
## NULL
##
## $groups
## DM1 groups
## T0 216.1550 a
## T3 168.7925 a
## T1 151.1875 a
## T2 118.4300 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
DM2<-c(41.02,40.55,42.01,44.04,31.44,35,29.67,31.51,28.6,29.53,35.59,14.64,25.08,23.72,22.35,41.24)
TRATDM2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATDM2<-as.factor(TRATDM2)
dfDM2<-data.frame(DM2,TRATDM2)
aovDM2 <- aov(DM2~TRATDM2)
DR2<-HSD.test(aovDM2,trt="TRATDM2")
DR2## $statistics
## MSerror Df Mean CV MSD
## 40.97067 12 32.24938 19.84793 13.43746
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATDM2 4 4.19866 0.05
##
## $means
## DM2 std r Min Max Q25 Q50 Q75
## T0 41.9050 1.547956 4 40.55 44.04 40.9025 41.515 42.5175
## T1 31.9050 2.232077 4 29.67 35.00 30.9975 31.475 32.3825
## T2 27.0900 8.859763 4 14.64 35.59 25.1100 29.065 31.0450
## T3 28.0975 8.832268 4 22.35 41.24 23.3775 24.400 29.1200
##
## $comparison
## NULL
##
## $groups
## DM2 groups
## T0 41.9050 a
## T1 31.9050 ab
## T3 28.0975 b
## T2 27.0900 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
DM3<-c(49.31,35.01,43.96,37.24,24.53,24.89,30.7,27.29,22.11,27.13,21.42,29.12,25.88,25.7,26.61,29.56)
TRATDM3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATDM3<-as.factor(TRATDM3)
dfDM3<-data.frame(DM3,TRATDM3)
aovDM3 <- aov(DM3~TRATDM3)
DR3<-HSD.test(aovDM3,trt="TRATDM3")
DR3## $statistics
## MSerror Df Mean CV MSD
## 16.98334 12 30.02875 13.7238 8.651517
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATDM3 4 4.19866 0.05
##
## $means
## DM3 std r Min Max Q25 Q50 Q75
## T0 41.3800 6.513007 4 35.01 49.31 36.6825 40.600 45.2975
## T1 26.8525 2.842538 4 24.53 30.70 24.8000 26.090 28.1425
## T2 24.9450 3.771282 4 21.42 29.12 21.9375 24.620 27.6275
## T3 26.9375 1.792064 4 25.70 29.56 25.8350 26.245 27.3475
##
## $comparison
## NULL
##
## $groups
## DM3 groups
## T0 41.3800 a
## T3 26.9375 b
## T1 26.8525 b
## T2 24.9450 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
DM4<-c(39.74,40.75,45.61,48.54,21.98,26,26.87,32.12,38.58,35.23,33.68,34.01,32.43,33.03,38.06,36.76)
TRATDM4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATDM4<-as.factor(TRATDM4)
dfDM4<-data.frame(DM4,TRATDM4)
aovDM4 <- aov(DM4~TRATDM4)
DR4<-HSD.test(aovDM4,trt="TRATDM4")
DR4## $statistics
## MSerror Df Mean CV MSD
## 11.79788 12 35.21187 9.754676 7.210788
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATDM4 4 4.19866 0.05
##
## $means
## DM4 std r Min Max Q25 Q50 Q75
## T0 43.6600 4.141312 4 39.74 48.54 40.4975 43.180 46.3425
## T1 26.7425 4.169991 4 21.98 32.12 24.9950 26.435 28.1825
## T2 35.3750 2.238251 4 33.68 38.58 33.9275 34.620 36.0675
## T3 35.0700 2.764501 4 32.43 38.06 32.8800 34.895 37.0850
##
## $comparison
## NULL
##
## $groups
## DM4 groups
## T0 43.6600 a
## T2 35.3750 b
## T3 35.0700 b
## T1 26.7425 c
##
## attr(,"class")
## [1] "group"ggplot(dt5, aes(muestreo,w5, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Diametro Raiz tuberosa") + coord_cartesian(ylim=c(0,50)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CDR), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = w5-sd5, ymax = w5+sd5),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 2, y = 50, label = "**") +
geom_text(data = NULL, x = 3, y = 50, label = "**") +
geom_text(data = NULL, x = 4, y = 50, label = "**")
Area foliar
#Muestreo 1
AF1<-c(183.66,191.23,171.45,232.82,186.446,135.21,162.62,171.92,151.56,140.15,151.99,180.45,159.23,129.83,131.78,157.78)
TRATAF1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATAF1<-as.factor(TRATAF1)
aovAF1 <- aov(AF1~TRATAF1)
CAF1<-HSD.test(aovAF1,trt="TRATAF1")
CAF1## $statistics
## MSerror Df Mean CV MSD
## 431.6626 12 164.8829 12.60076 43.61671
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATAF1 4 4.19866 0.05
##
## $means
## AF1 std r Min Max Q25 Q50 Q75
## T0 194.7900 26.63073 4 171.45 232.820 180.6075 187.445 201.6275
## T1 164.0490 21.58169 4 135.21 186.446 155.7675 167.270 175.5515
## T2 156.0375 17.17375 4 140.15 180.450 148.7075 151.775 159.1050
## T3 144.6550 16.02334 4 129.83 159.230 131.2925 144.780 158.1425
##
## $comparison
## NULL
##
## $groups
## AF1 groups
## T0 194.7900 a
## T1 164.0490 ab
## T2 156.0375 ab
## T3 144.6550 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
AF2<-c(301.33,266.77,257.1,207.83,180.62,200.68,214.88,187.39,248.23,198.72,228.79,249.3,167.37,205.37,192.4,215.67)
TRATAF2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATAF2<-as.factor(TRATAF2)
aovAF2 <- aov(AF2~TRATAF2)
CAF2<-HSD.test(aovAF2,trt="TRATAF2")
CAF2## $statistics
## MSerror Df Mean CV MSD
## 678.678 12 220.1531 11.83333 54.69059
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATAF2 4 4.19866 0.05
##
## $means
## AF2 std r Min Max Q25 Q50 Q75
## T0 258.2575 38.60905 4 207.83 301.33 244.7825 261.935 275.4100
## T1 195.8925 15.15462 4 180.62 214.88 185.6975 194.035 204.2300
## T2 231.2600 23.65287 4 198.72 249.30 221.2725 238.510 248.4975
## T3 195.2025 20.85504 4 167.37 215.67 186.1425 198.885 207.9450
##
## $comparison
## NULL
##
## $groups
## AF2 groups
## T0 258.2575 a
## T2 231.2600 ab
## T1 195.8925 b
## T3 195.2025 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
AF3<-c(400.45,328.61,289.6,265.78,214.87,253.43,176.25,225.16,291.91,258.12,212.78,199.65,206.1,190.5,252.62,205.74)
TRATAF3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATAF3<-as.factor(TRATAF3)
aovAF3 <- aov(AF3~TRATAF3)
CAF3<-HSD.test(aovAF3,trt="TRATAF3")
CAF3## $statistics
## MSerror Df Mean CV MSD
## 1752.343 12 248.2231 16.86426 87.88005
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATAF3 4 4.19866 0.05
##
## $means
## AF3 std r Min Max Q25 Q50 Q75
## T0 321.1100 58.89362 4 265.78 400.45 283.6450 309.105 346.5700
## T1 217.4275 31.92752 4 176.25 253.43 205.2150 220.015 232.2275
## T2 240.6150 42.38915 4 199.65 291.91 209.4975 235.450 266.5675
## T3 213.7400 26.92039 4 190.50 252.62 201.9300 205.920 217.7300
##
## $comparison
## NULL
##
## $groups
## AF3 groups
## T0 321.1100 a
## T2 240.6150 ab
## T1 217.4275 b
## T3 213.7400 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
AF4<-c(310.2,250,201.21,284.29,135.41,157.07,202.7,197.47,220.69,207.43,260.78,200.82,197.64,225.34,226.57,221.43)
TRATAF4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TRATAF4<-as.factor(TRATAF4)
aovAF4 <- aov(AF4~TRATAF4)
CAF4<-HSD.test(aovAF4,trt="TRATAF4")
CAF4## $statistics
## MSerror Df Mean CV MSD
## 1043.735 12 218.6906 14.77287 67.82283
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRATAF4 4 4.19866 0.05
##
## $means
## AF4 std r Min Max Q25 Q50 Q75
## T0 261.4250 47.11046 4 201.21 310.20 237.8025 267.145 290.7675
## T1 173.1625 32.39104 4 135.41 202.70 151.6550 177.270 198.7775
## T2 222.4300 26.86846 4 200.82 260.78 205.7775 214.060 230.7125
## T3 217.7450 13.58130 4 197.64 226.57 215.4825 223.385 225.6475
##
## $comparison
## NULL
##
## $groups
## AF4 groups
## T0 261.4250 a
## T2 222.4300 ab
## T3 217.7450 ab
## T1 173.1625 b
##
## attr(,"class")
## [1] "group"CAF<- c("a","ab","ab","b","a","b","ab","b","a","b","ab","b","a","b","ab","ab")
data6<-data.frame(AF=as.numeric(datos1$AF), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt6 <-data6 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w6 = mean(AF), sd6 = sd(AF),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt6$CDAF<- CAFggplot(dt6, aes(muestreo,w6, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Area foliar") + coord_cartesian(ylim=c(0,400)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CAF), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = w6-sd6, ymax = w6+sd6),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 400, label = "**") +
geom_text(data = NULL, x = 2, y = 400, label = "**") +
geom_text(data = NULL, x = 3, y = 400, label = "**") +
geom_text(data = NULL, x = 4, y = 400, label = "**")
Temperatura
#Muestreo 1
T1<-c(21.3,21.1,21.5,22.8,21.8,21.9,21.9,21.6,21.9,21.9,23.9,24.2,22.6,24.1,22.4,23.9,23.5,23.2,24.1,22.3,21.8,21.1,21.3,23.5,22.8,21.3,23.5,21.2,21.2,22.7,20.4,20.6,22.2,20.3,20.3,20.7,21,21.3,21.5,23.2,22.4,23,23,21.6,23.2,20.3,22.8,21.1,22,21.3,19.5,19.9,19.9,21.1,22.5,20.3,21.2,20.2,20.1,20.8,19.8,20.3,19.7,22.2,19.8,19.9,20,23.4,22,20.3,20.6,21.1,23.3,20.7,23.3,20.7,22.2,20.6,20.6,22.2,21.2,20.7,20.9,22.6,21.2,21.1,21.1,23.1,21.2,20.8,23.8,23.6,22,21.3,23.2,23.8,22.3,21.2,23.7,22,21.2,20.7,20.7,20.8,20.8,20.9,20.8,20.8,21.1,21.1,22,23.2,23.2,21.9,21.4,22.5,21.9,21.8,23.5,22.8,21,23.4,22.9,20.9,20.8,22.1,21.3,20.9,22.1,20.8,21.3,20.9,20.9,20.9,21.2,21.1,21.1,21.6,21.1,21.8,23.9,24.1,22.9,21.8,24.1,24.1,24.1,23.8,23.1,23.7,21.1,23.7,23.7,22.8,21.1,22.7,23,23.7,23.7,23.4)
TT1<-rep(c("T0","T1","T2","T3"),c(40,40,40,40))
TT1<-as.factor(TT1)
dfT1<-data.frame(T1,TT1)
aovT1 <- aov(T1~TT1)
CT1<-HSD.test(aovT1,trt="TT1")
CT1## $statistics
## MSerror Df Mean CV MSD
## 1.34692 156 21.87 5.30667 0.6739344
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT1 4 3.672626 0.05
##
## $means
## T1 std r Min Max Q25 Q50 Q75
## T0 22.0950 1.145773 40 20.3 24.2 21.275 21.90 22.90
## T1 21.2225 1.209469 40 19.5 23.4 20.275 20.95 22.20
## T2 21.8475 1.039721 40 20.7 23.8 21.050 21.35 22.65
## T3 22.3150 1.237357 40 20.8 24.1 21.100 22.10 23.70
##
## $comparison
## NULL
##
## $groups
## T1 groups
## T3 22.3150 a
## T0 22.0950 a
## T2 21.8475 ab
## T1 21.2225 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
T2<-c(16,16,19,16,20,19,16,20,19,16,18,17,19,16,17,18,18,19,19,19,16,16,15,16,16,17,16,18,15,16,15,15,15,15,15,16,15,16,15,15,16,17,16,16,16,17,15,17,15,15,15,16,15,15,17,18,16,18,17,16,16,15,15,15,17,16,15,16,15,16,15,17,15,15,18,15,16,15,17,17,15,16,15,16,16,15,15,16,15,17,15,15,17,15,15,16,15,16,15,16,16,17,15,15,15,16,16,17,15,15,15,14,15,15,15,15,14,15,14,15,16,19,15,17,16,17,16,18,16,18,15,15,17,17,18,18,16,15,15,17,17,14,17,14,16,18,15,16,16,17,15,15,15,15,15,16,15,15,15,16)
TT2<-rep(c("T0","T1","T2","T3"),c(40,40,40,40))
TT2<-as.factor(TT2)
dfT2<-data.frame(T2,TT2)
aovT2 <- aov(T2~TT2)
CT2<-HSD.test(aovT2,trt="TT2")
CT2## $statistics
## MSerror Df Mean CV MSD
## 1.414263 156 16.04375 7.412406 0.6905765
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT2 4 3.672626 0.05
##
## $means
## T2 std r Min Max Q25 Q50 Q75
## T0 16.750 1.6132464 40 15 20 15.75 16 18
## T1 15.975 0.9736924 40 15 18 15.00 16 17
## T2 15.375 0.7741828 40 14 17 15.00 15 16
## T3 16.075 1.2276202 40 14 19 15.00 16 17
##
## $comparison
## NULL
##
## $groups
## T2 groups
## T0 16.750 a
## T3 16.075 ab
## T1 15.975 bc
## T2 15.375 c
##
## attr(,"class")
## [1] "group"#Muestreo 3
T3<-c(16,19,19,19,19,16,19,16,16,17,19,15,17,19,19,18,16,16,19,18,16,17,17,17,16,17,18,16,16,18,19,19,19,19,20,19,19,18,19,18,18,19,17,17,17,17,17,17,17,19,17,19,17,18,19,19,18,17,18,17,18,18,18,17,18,17,17,17,18,18,17,17,17,17,16,17,19,17,16,17,18,18,19,18,18,18,18,18,18,18,18,19,18,18,18,18,19,18,18,18,18,18,18,18,18,18,18,18,18,19,18,18,20,18,18,19,18,20,19,20,18,16,17,18,15,17,15,16,16,15,16,19,16,16,16,16,17,15,19,18,16,16,16,17,19,17,16,16,16,17,17,17,19,18,20,18,19,19,17,19)
TT3<-rep(c("T0","T1","T2","T3"),c(40,40,40,40))
TT3<-as.factor(TT3)
dfT3<-data.frame(T3,TT3)
aovT3 <- aov(T3~TT3)
CT3<-HSD.test(aovT3,trt="TT3")
CT3## $statistics
## MSerror Df Mean CV MSD
## 1.181891 156 17.63125 6.16603 0.6312995
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT3 4 3.672626 0.05
##
## $means
## T3 std r Min Max Q25 Q50 Q75
## T0 17.725 1.3584965 40 15 20 16 18 19
## T1 17.500 0.8164966 40 16 19 17 17 18
## T2 18.300 0.6076436 40 18 20 18 18 18
## T3 17.000 1.3587324 40 15 20 16 17 18
##
## $comparison
## NULL
##
## $groups
## T3 groups
## T2 18.300 a
## T0 17.725 ab
## T1 17.500 bc
## T3 17.000 c
##
## attr(,"class")
## [1] "group"#Muestreo 4
T4<-c(16,17,16,15,17,17,16,15,17,17,16,15,17,17,16,15,17,16,16,16,17,16,16,15,17,16,16,15,16,17,16,25,16,17,16,16,17,16,15,15,16,16,16,18,17,15,16,17,17,16,17,17,17,16,16,17,18,17,16,17,17,16,16,17,16,17,16,16,17,17,16,16,17,16,17,17,17,16,16,17,14,16,15,14,15,16,15,14,15,15,15,15,14,16,15,15,15,15,15,14,15,15,15,14,15,16,15,14,15,16,15,14,15,16,15,15,15,16,15,14,15,16,16,15,15,16,16,14,16,16,16,14,15,16,14,14,16,16,16,14,15,16,15,14,16,16,16,14,16,16,15,14,16,16,15,14,16,16,14,14)
TT4<-rep(c("T0","T1","T2","T3"),c(40,40,40,40))
TT4<-as.factor(TT4)
dfT4<-data.frame(T4,TT4)
aovT4 <- aov(T4~TT4)
CT4<-HSD.test(aovT4,trt="TT4")
CT4## $statistics
## MSerror Df Mean CV MSD
## 1.015385 156 15.775 6.387721 0.585143
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT4 4 3.672626 0.05
##
## $means
## T4 std r Min Max Q25 Q50 Q75
## T0 16.35 1.5778921 40 15 25 16 16 17
## T1 16.55 0.6385079 40 15 18 16 17 17
## T2 14.95 0.6385079 40 14 16 15 15 15
## T3 15.25 0.8697185 40 14 16 14 16 16
##
## $comparison
## NULL
##
## $groups
## T4 groups
## T1 16.55 a
## T0 16.35 a
## T3 15.25 b
## T2 14.95 b
##
## attr(,"class")
## [1] "group"TEMP <- read_excel("~/Paola/Computacion/TEMP.xlsx")CT<- c("b","a","ab","a","a","bc","c","ab","ab","bc","a","c","a","a","b","b")
data8<-data.frame(ct=as.numeric(TEMP$temp), muestreo=as.factor(TEMP$Muestreo),Tratamiento=TEMP$tratamiento)
dt8 <-data8 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w8 = mean(ct), sd8 = sd(ct),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt8$CT<- CTplott<-ggplot(dt8, aes(muestreo,w8, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Temperatura (°C)") + coord_cartesian(ylim=c(0,30)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CT), position = position_dodge(width=1), vjust=-4, cex=3)+
geom_errorbar(aes(ymin = w8-sd8, ymax = w8+sd8),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 30, label = "**") +
geom_text(data = NULL, x = 2, y = 30, label = "**") +
geom_text(data = NULL, x = 3, y = 30, label = "**") +
geom_text(data = NULL, x = 4, y = 30, label = "**")Clorofila
#Muestreo 1
C1<-c(60.9,59.9,60.1,37.7,38.6,39,42.8,42.7,42.4,38.4,38.2,37.5,54.4,60.5,60.1,47.1,47.3,47.3,38.7,39.3,38,50.4,49.1,53,45.3,45.2,41.5,49.8,48,48.6,62.3,48.8,55.3,48.1,43.5,47,33.6,31.8,30.6,37.5,38.4,34.1,52.7,55.1,52.8,42,43.6,43.4)
TC1<-rep(c("T0","T1","T2","T3"),c(12,12,12,12))
TC1<-as.factor(TC1)
aovC1 <- aov(C1~TC1)
CC1<-HSD.test(aovC1,trt="TC1")
CC1## $statistics
## MSerror Df Mean CV MSD
## 62.86167 44 45.88333 17.27977 8.642303
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TC1 4 3.775958 0.05
##
## $means
## C1 std r Min Max Q25 Q50 Q75
## T0 44.85000 9.514343 12 37.5 60.9 38.350 40.70 47.075
## T1 48.76667 7.582915 12 38.0 60.5 45.150 48.20 53.350
## T2 48.61667 5.527589 12 41.5 62.3 45.275 48.05 49.050
## T3 41.30000 8.536339 12 30.6 55.1 33.975 40.20 45.875
##
## $comparison
## NULL
##
## $groups
## C1 groups
## T1 48.76667 a
## T2 48.61667 a
## T0 44.85000 a
## T3 41.30000 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
C2<-c(42.9,44.5,42.2,43.8,44.4,44.7,46,47.7,45.5,55,52.4,52.4,38.5,38.6,39.1,37.7,34.8,37,52.7,50.8,50.6,41.5,43.2,41,49.6,58.2,54.2,39.3,42.6,40.7,54.3,39.5,39.8,44.7,43.7,49,27.1,28.5,24.9,28.3,26.5,27.6,39.7,37,40.6,47.6,46.8,48.6)
TC2<-rep(c("T0","T1","T2","T3"),c(12,12,12,12))
TC2<-as.factor(TC2)
aovC2 <- aov(C2~TC2)
CC2<-HSD.test(aovC2,trt="TC2")
CC2## $statistics
## MSerror Df Mean CV MSD
## 45.13769 44 42.62083 15.76332 7.32329
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TC2 4 3.775958 0.05
##
## $means
## C2 std r Min Max Q25 Q50 Q75
## T0 46.79167 4.198800 12 42.2 55.0 44.250 45.10 48.875
## T1 42.12500 5.999564 12 34.8 52.7 38.300 40.05 45.050
## T2 46.30000 6.592420 12 39.3 58.2 40.475 44.20 50.750
## T3 35.26667 9.135976 12 24.9 48.6 27.475 32.75 42.150
##
## $comparison
## NULL
##
## $groups
## C2 groups
## T0 46.79167 a
## T2 46.30000 a
## T1 42.12500 ab
## T3 35.26667 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
C3<-c(51.3,50.8,49.3,44,45.3,37.7,51.1,50.6,47.8,43.6,43.1,47.3,54.8,53.6,53.5,56.3,53.4,53.3,64.8,65.8,59.6,39,38.2,38.8,54.8,53.6,53.5,56.3,53.4,53.3,64.8,65.8,59.6,39,38.2,38.8,28.4,28.2,30,36.9,37.8,40.2,39.3,38.2,40.1,35.9,34.9,31.7)
TC3<-rep(c("T0","T1","T2","T3"),c(12,12,12,12))
TC3<-as.factor(TC3)
aovC3 <- aov(C3~TC3)
CC3<-HSD.test(aovC3,trt="TC3")
CC3## $statistics
## MSerror Df Mean CV MSD
## 53.76472 44 46.78542 15.6725 7.992548
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TC3 4 3.775958 0.05
##
## $means
## C3 std r Min Max Q25 Q50 Q75
## T0 46.82500 4.194179 12 37.7 51.3 43.900 47.55 50.650
## T1 52.59167 9.421007 12 38.2 65.8 49.725 53.55 57.125
## T2 52.59167 9.421007 12 38.2 65.8 49.725 53.55 57.125
## T3 35.13333 4.467322 12 28.2 40.2 31.275 36.40 38.475
##
## $comparison
## NULL
##
## $groups
## C3 groups
## T1 52.59167 a
## T2 52.59167 a
## T0 46.82500 a
## T3 35.13333 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
C4<-c(50.7,50.5,51.7,58.3,55.2,53.4,58.6,61.2,53.2,51.3,51,49.3,46.5,49.3,46,50.6,47.8,54.7,55.8,57.8,52.8,53.8,63.9,62.5,64.6,61.1,61.5,55.6,61.3,56.2,61.4,63.2,64.1,57.7,63.2,59.2,61.4,60.9,57.9,52.7,60.3,58.3,44.3,43.5,41.6,59.2,59.7,56.6)
TC4<-rep(c("T0","T1","T2","T3"),c(12,12,12,12))
TC4<-as.factor(TC4)
aovC4 <- aov(C4~TC4)
CC4<-HSD.test(aovC4,trt="TC4")
CC4## $statistics
## MSerror Df Mean CV MSD
## 28.01678 44 55.65417 9.510677 5.7696
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TC4 4 3.775958 0.05
##
## $means
## C4 std r Min Max Q25 Q50 Q75
## T0 53.70000 3.809796 12 49.3 61.2 50.925 52.45 55.975
## T1 53.45833 5.867702 12 46.0 63.9 48.925 53.30 56.300
## T2 60.75833 2.990731 12 55.6 64.6 58.825 61.35 63.200
## T3 54.70000 7.360583 12 41.6 61.4 50.600 58.10 59.850
##
## $comparison
## NULL
##
## $groups
## C4 groups
## T2 60.75833 a
## T3 54.70000 b
## T0 53.70000 b
## T1 53.45833 b
##
## attr(,"class")
## [1] "group"cla <- read_excel("~/Paola/Computacion/cla.xlsx")CLA<- c("a","a","a","a","a","ab","a","b","a","a","a","b","b","b","a","b")
data9<-data.frame(cla=as.numeric(cla$cla), muestreo=as.factor(cla$Muestreo),Tratamiento=cla$tratamiento)
dt9 <-data9 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w9 = mean(cla), sd9 = sd(cla),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt9$CLA<- CLAplotc<-ggplot(dt9, aes(muestreo,w9, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("C relativo de clorofila") + coord_cartesian(ylim=c(0,80)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CLA), position = position_dodge(width=1), vjust=-4, cex=3)+
geom_errorbar(aes(ymin = w9-sd9, ymax = w9+sd9),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 2, y = 80, label = "**") +
geom_text(data = NULL, x = 3, y = 80, label = "**") +
geom_text(data = NULL, x = 4, y = 80, label = "**")Estomas abiertos
#Muestreo 1
E1<-c(69,63,67,74,54,33,33,27,38,16,36,41,16,30,33,42)
TE1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TE1<-as.factor(TE1)
aovE1 <- aov(E1~TE1)
CE1<-HSD.test(aovE1,trt="TE1")
CE1## $statistics
## MSerror Df Mean CV MSD
## 101.5833 12 42 23.99728 21.15885
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TE1 4 4.19866 0.05
##
## $means
## E1 std r Min Max Q25 Q50 Q75
## T0 68.25 4.573474 4 63 74 66.0 68.0 70.25
## T1 36.75 11.842719 4 27 54 31.5 33.0 38.25
## T2 32.75 11.354148 4 16 41 31.0 37.0 38.75
## T3 30.25 10.781929 4 16 42 26.5 31.5 35.25
##
## $comparison
## NULL
##
## $groups
## E1 groups
## T0 68.25 a
## T1 36.75 b
## T2 32.75 b
## T3 30.25 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
E2<-c(41,42,50,24,31,15,13,10,32,13,28,18,32,9,34,17)
TE2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TE2<-as.factor(TE2)
aovE2 <- aov(E2~TE2)
CE2<-HSD.test(aovE2,trt="TE2")
CE2## $statistics
## MSerror Df Mean CV MSD
## 107.3542 12 25.5625 40.53276 21.75155
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TE2 4 4.19866 0.05
##
## $means
## E2 std r Min Max Q25 Q50 Q75
## T0 39.25 10.935416 4 24 50 36.75 41.5 44.0
## T1 17.25 9.394147 4 10 31 12.25 14.0 19.0
## T2 22.75 8.770215 4 13 32 16.75 23.0 29.0
## T3 23.00 12.027746 4 9 34 15.00 24.5 32.5
##
## $comparison
## NULL
##
## $groups
## E2 groups
## T0 39.25 a
## T3 23.00 ab
## T2 22.75 ab
## T1 17.25 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
E3<-c(76,61,64,52,11,25,18,18,22,6,8,10,8,8,14,20)
TE3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TE3<-as.factor(TE3)
aovE3 <- aov(E3~TE3)
CE3<-HSD.test(aovE3,trt="TE3")
CE3## $statistics
## MSerror Df Mean CV MSD
## 53.89583 12 26.3125 27.90072 15.41198
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TE3 4 4.19866 0.05
##
## $means
## E3 std r Min Max Q25 Q50 Q75
## T0 63.25 9.912114 4 52 76 58.75 62.5 67.00
## T1 18.00 5.715476 4 11 25 16.25 18.0 19.75
## T2 11.50 7.187953 4 6 22 7.50 9.0 13.00
## T3 12.50 5.744563 4 8 20 8.00 11.0 15.50
##
## $comparison
## NULL
##
## $groups
## E3 groups
## T0 63.25 a
## T1 18.00 b
## T3 12.50 b
## T2 11.50 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
E4<-c(47,42,67,63,17,18,10,33,18,12,8,11,12,31,28,16)
TE4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TE4<-as.factor(TE4)
aovE4 <- aov(E4~TE4)
CE4<-HSD.test(aovE4,trt="TE4")
CE4## $statistics
## MSerror Df Mean CV MSD
## 85.60417 12 27.0625 34.18846 19.42353
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TE4 4 4.19866 0.05
##
## $means
## E4 std r Min Max Q25 Q50 Q75
## T0 54.75 12.120919 4 42 67 45.75 55.0 64.00
## T1 19.50 9.678154 4 10 33 15.25 17.5 21.75
## T2 12.25 4.193249 4 8 18 10.25 11.5 13.50
## T3 21.75 9.178780 4 12 31 15.00 22.0 28.75
##
## $comparison
## NULL
##
## $groups
## E4 groups
## T0 54.75 a
## T3 21.75 b
## T1 19.50 b
## T2 12.25 b
##
## attr(,"class")
## [1] "group"E<- c("a","b","b","b","a","b","ab","ab","a","b","b","b","a","b","b","b")
data12<-data.frame(E=as.numeric(datos1$PE), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt12 <-data12 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w12 = mean(E), sd12 = sd(E),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt12$E<- Eplotp<-ggplot(dt12, aes(muestreo,w12, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Estomas abiertos (%)") + coord_cartesian(ylim=c(0,80)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = E), position = position_dodge(width=1), vjust=-2.5, cex=3)+
geom_errorbar(aes(ymin = w12-sd12, ymax = w12+sd12),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 80, label = "**") +
geom_text(data = NULL, x = 2, y = 80, label = "**") +
geom_text(data = NULL, x = 3, y = 80, label = "**") +
geom_text(data = NULL, x = 4, y = 80, label = "**")plot_grid(plott,plotp,plotc, labels = "AUTO")
CRA
#Muestreo 1
CRA1<-c(0.696,0.684,0.889,0.607,0.85,1,0.762,0.938,0.875,1.056,1,1,0.179,0.15,-0.188,0.308,0.133,0.233,0.385,0.579,0.6,0.636,0.565,0.529,0.625,0.625,0.688,0.722,0.929,0.929,0.818,0.739,0.65,0.714,0.929,0.481,0.84,0.84,0.8,0.852,0.808,0.87,0.792,0.818,0.731,0.8,0.81,0.762)
TCRA1<-rep(c("T0","T1","T2","T3"),c(12,12,12,12))
TCRA1<-as.factor(TCRA1)
aovCRA1 <- aov(CRA1~TCRA1)
CCRA1<-HSD.test(aovCRA1,trt="TCRA1")
CCRA1## $statistics
## MSerror Df Mean CV MSD
## 0.02653193 44 0.6882917 23.6653 0.1775501
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA1 4 3.775958 0.05
##
## $means
## CRA1 std r Min Max Q25 Q50 Q75
## T0 0.8630833 0.14642308 12 0.607 1.056 0.74550 0.8820 1.00000
## T1 0.3424167 0.25167889 12 -0.188 0.636 0.17175 0.3465 0.56850
## T2 0.7374167 0.14093935 12 0.481 0.929 0.64375 0.7180 0.84575
## T3 0.8102500 0.03849469 12 0.731 0.870 0.79800 0.8090 0.84000
##
## $comparison
## NULL
##
## $groups
## CRA1 groups
## T0 0.8630833 a
## T3 0.8102500 a
## T2 0.7374167 a
## T1 0.3424167 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
CRA2<-c(0.882,0.933,0.824,0.884,0.97,0.871,0.814,0.972,0.833,0.971,0.875,0.775,1,1.032,0.829,0.842)
TCRA2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TCRA2<-as.factor(TCRA2)
aovCRA2 <- aov(CRA2~TCRA2)
CCRA2<-HSD.test(aovCRA2,trt="TCRA2")
CCRA2## $statistics
## MSerror Df Mean CV MSD
## 0.006477438 12 0.8941875 9.000632 0.1689594
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA2 4 4.19866 0.05
##
## $means
## CRA2 std r Min Max Q25 Q50 Q75
## T0 0.88075 0.04458232 4 0.824 0.933 0.86750 0.8830 0.89625
## T1 0.90675 0.07775764 4 0.814 0.972 0.85675 0.9205 0.97050
## T2 0.86350 0.08256513 4 0.775 0.971 0.81850 0.8540 0.89900
## T3 0.92575 0.10516138 4 0.829 1.032 0.83875 0.9210 1.00800
##
## $comparison
## NULL
##
## $groups
## CRA2 groups
## T3 0.92575 a
## T1 0.90675 a
## T0 0.88075 a
## T2 0.86350 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
CRA3<-c(0.808,0.712,0.61,0.6,0.451,0.929,0.372,0.796,0.515,0.594,0.776,0.647,0.556,0.653,0.623,0.561)
TCRA3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TCRA3<-as.factor(TCRA3)
aovCRA3 <- aov(CRA3~TCRA3)
CCRA3<-HSD.test(aovCRA3,trt="TCRA3")
CCRA3## $statistics
## MSerror Df Mean CV MSD
## 0.02391015 12 0.6376875 24.24841 0.3246174
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA3 4 4.19866 0.05
##
## $means
## CRA3 std r Min Max Q25 Q50 Q75
## T0 0.68250 0.09778037 4 0.600 0.808 0.60750 0.6610 0.73600
## T1 0.63700 0.26793407 4 0.372 0.929 0.43125 0.6235 0.82925
## T2 0.63300 0.10968136 4 0.515 0.776 0.57425 0.6205 0.67925
## T3 0.59825 0.04754910 4 0.556 0.653 0.55975 0.5920 0.63050
##
## $comparison
## NULL
##
## $groups
## CRA3 groups
## T0 0.68250 a
## T1 0.63700 a
## T2 0.63300 a
## T3 0.59825 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
CRA4<-c(0.618,0.413,0.791,0.603,0.51,0.42,0.688,0.529,0.51,0.42,0.688,0.529,0.493,0.329,0.486,0.5)
TCRA4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TCRA4<-as.factor(TCRA4)
aovCRA4<- aov(CRA4~TCRA4)
CCRA4<-HSD.test(aovCRA4,trt="TCRA4")
CCRA4## $statistics
## MSerror Df Mean CV MSD
## 0.01387185 12 0.5329375 22.09993 0.2472567
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA4 4 4.19866 0.05
##
## $means
## CRA4 std r Min Max Q25 Q50 Q75
## T0 0.60625 0.15451726 4 0.413 0.791 0.55550 0.6105 0.66125
## T1 0.53675 0.11147907 4 0.420 0.688 0.48750 0.5195 0.56875
## T2 0.53675 0.11147907 4 0.420 0.688 0.48750 0.5195 0.56875
## T3 0.45200 0.08219895 4 0.329 0.500 0.44675 0.4895 0.49475
##
## $comparison
## NULL
##
## $groups
## CRA4 groups
## T0 0.60625 a
## T1 0.53675 a
## T2 0.53675 a
## T3 0.45200 a
##
## attr(,"class")
## [1] "group"Perdida de electrolitos
PE<-c(8.01,17.23,8.01,17.23,6.69,20.4,6.38,33.36,6.08,21.76,7.17,28.81,7.1,17.94,8.55,27.49)
TPE<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovPE <- aov(PE~TPE)
CPE<-HSD.test(aovPE,trt="TPE")
CPE## $statistics
## MSerror Df Mean CV MSD
## 102.0801 12 15.13813 66.7419 21.21052
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPE 4 4.19866 0.05
##
## $means
## PE std r Min Max Q25 Q50 Q75
## T0 12.6200 5.323169 4 8.01 17.23 8.0100 12.620 17.2300
## T1 16.7075 12.883424 4 6.38 33.36 6.6125 13.545 23.6400
## T2 15.9550 11.160061 4 6.08 28.81 6.8975 14.465 23.5225
## T3 15.2700 9.458058 4 7.10 27.49 8.1875 13.245 20.3275
##
## $comparison
## NULL
##
## $groups
## PE groups
## T1 16.7075 a
## T2 15.9550 a
## T3 15.2700 a
## T0 12.6200 a
##
## attr(,"class")
## [1] "group"PERD<-read_excel("~/Paola/Computacion/PER.xlsx")PER<- c("a","a","a","a")
data14<-data.frame(PER=as.numeric(PERD$PER), muestreo=as.factor(PERD$Muestreo),Tratamiento=PERD$tratamiento)
dt14 <-data14 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w14 = mean(PER), sd14 = sd(PER),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt14$PER<- PERplotPER<-ggplot(dt14, aes(muestreo,w14, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Perdida de electrolitos (%)") + coord_cartesian(ylim=c(0,30)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = PER), position = position_dodge(width=1), vjust=-1, cex=3)+
geom_errorbar(aes(ymin = w14-sd14, ymax = w14+sd14),width = .5, position = position_dodge(0.9))Contenido de agua
#Muestreo 1
CA1<-c(91.26,91.88,91.67,93.63,84.74,86.34,89.99,85.98,97.04,89.52,89.61,84.39,91.99,95.47,82.2,90.36)
TCA1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovCA1 <- aov(CA1~TCA1)
CCA1<-HSD.test(aovCA1,trt="TCA1")
CCA1## $statistics
## MSerror Df Mean CV MSD
## 16.23048 12 89.75437 4.488591 8.457586
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCA1 4 4.19866 0.05
##
## $means
## CA1 std r Min Max Q25 Q50 Q75
## T0 92.1100 1.045530 4 91.26 93.63 91.5675 91.775 92.3175
## T1 86.7625 2.258176 4 84.74 89.99 85.6700 86.160 87.2525
## T2 90.1400 5.206976 4 84.39 97.04 88.2375 89.565 91.4675
## T3 90.0050 5.622885 4 82.20 95.47 88.3200 91.175 92.8600
##
## $comparison
## NULL
##
## $groups
## CA1 groups
## T0 92.1100 a
## T2 90.1400 a
## T3 90.0050 a
## T1 86.7625 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
CA2<-c(94.29,95.66,95.62,93.96,93.1,94.63,92.11,92.61,89.69,92.06,93.49,88.96,90.93,90.96,89.21,93.5)
TCA2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovCA2 <- aov(CA2~TCA2)
CCA2<-HSD.test(aovCA2,trt="TCA2")
CCA2## $statistics
## MSerror Df Mean CV MSD
## 2.372746 12 92.54875 1.66439 3.233749
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCA2 4 4.19866 0.05
##
## $means
## CA2 std r Min Max Q25 Q50 Q75
## T0 94.8825 0.8851506 4 93.96 95.66 94.2075 94.955 95.6300
## T1 93.1125 1.0894150 4 92.11 94.63 92.4850 92.855 93.4825
## T2 91.0500 2.0969343 4 88.96 93.49 89.5075 90.875 92.4175
## T3 91.1500 1.7673521 4 89.21 93.50 90.5000 90.945 91.5950
##
## $comparison
## NULL
##
## $groups
## CA2 groups
## T0 94.8825 a
## T1 93.1125 ab
## T3 91.1500 b
## T2 91.0500 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
CA3<-c(94.89,94.59,92.73,90.54,85.6,88.45,88.37,89.4,87.9,91.64,88.54,87.71,87.8,90.59,89.28,88.79)
TCA3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovCA3 <- aov(CA3~TCA3)
CCA3<-HSD.test(aovCA3,trt="TCA3")
CCA3## $statistics
## MSerror Df Mean CV MSD
## 2.851446 12 89.80125 1.8804 3.544976
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCA3 4 4.19866 0.05
##
## $means
## CA3 std r Min Max Q25 Q50 Q75
## T0 93.1875 2.006994 4 90.54 94.89 92.1825 93.660 94.6650
## T1 87.9550 1.638221 4 85.60 89.40 87.6775 88.410 88.6875
## T2 88.9475 1.829779 4 87.71 91.64 87.8525 88.220 89.3150
## T3 89.1150 1.160129 4 87.80 90.59 88.5425 89.035 89.6075
##
## $comparison
## NULL
##
## $groups
## CA3 groups
## T0 93.1875 a
## T3 89.1150 b
## T2 88.9475 b
## T1 87.9550 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
CA4<-c(91.64,87.13,90.59,90.8,78.93,78.8,86.39,84.82,89.97,89.96,85.92,86.9,86.98,88.74,91.18,92.1)
TCA4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovCA4 <- aov(CA4~TCA4)
CCA4<-HSD.test(aovCA4,trt="TCA4")
CCA4## $statistics
## MSerror Df Mean CV MSD
## 7.329615 12 87.55312 3.09221 5.683571
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCA4 4 4.19866 0.05
##
## $means
## CA4 std r Min Max Q25 Q50 Q75
## T0 90.0400 1.992335 4 87.13 91.64 89.7250 90.695 91.0100
## T1 82.2350 3.944131 4 78.80 86.39 78.8975 81.875 85.2125
## T2 88.1875 2.091114 4 85.92 89.97 86.6550 88.430 89.9625
## T3 89.7500 2.328118 4 86.98 92.10 88.3000 89.960 91.4100
##
## $comparison
## NULL
##
## $groups
## CA4 groups
## T0 90.0400 a
## T3 89.7500 a
## T2 88.1875 a
## T1 82.2350 b
##
## attr(,"class")
## [1] "group"CA<- c("a","a","a","a","a","ab","b","b","a","b","b","b","a","b","a","a")
data13<-data.frame(CA=as.numeric(datos1$CA), muestreo=as.factor(datos1$Muestreo),Tratamiento=datos1$tratamiento)
dt13 <-data13 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w13 = mean(CA), sd13 = sd(CA),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt13$CA<- CAplotCA<-ggplot(dt13, aes(muestreo,w13, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Contenido de agua (%)") + coord_cartesian(ylim=c(0,110)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CA), position = position_dodge(width=1), vjust=-2.5, cex=3)+
geom_errorbar(aes(ymin = w13-sd13, ymax = w13+sd13),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 2, y = 110, label = "**") +
geom_text(data = NULL, x = 3, y = 110, label = "**") +
geom_text(data = NULL, x = 4, y = 110, label = "**")plot_grid(plotPER,plotCA, labels = "AUTO")
PESOS<-read_excel("~/Paola/Computacion/PESOS.xlsx")Peso fresco raiz
#Muestreo 1
FR1<-c(16.09,15.52,15.04,25.85,5.28,6.6,11.28,6.83,9.58,2.53,9.48,3.56,11.61,13.27,6.46,10.82)
TFR1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFR1 <- aov(FR1~TFR1)
CFR1<-HSD.test(aovFR1,trt="TFR1")
CFR1## $statistics
## MSerror Df Mean CV MSD
## 14.04242 12 10.6125 35.31045 7.866866
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFR1 4 4.19866 0.05
##
## $means
## FR1 std r Min Max Q25 Q50 Q75
## T0 18.1250 5.167853 4 15.04 25.85 15.4000 15.805 18.5300
## T1 7.4975 2.612513 4 5.28 11.28 6.2700 6.715 7.9425
## T2 6.2875 3.767876 4 2.53 9.58 3.3025 6.520 9.5050
## T3 10.5400 2.905317 4 6.46 13.27 9.7300 11.215 12.0250
##
## $comparison
## NULL
##
## $groups
## FR1 groups
## T0 18.1250 a
## T3 10.5400 ab
## T1 7.4975 b
## T2 6.2875 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
FR2<-c(34.54,37.94,30.61,35.97,16.18,22.02,13.27,21.42,11.78,15.89,23.36,4.48,9.18,6.57,6.85,31.53)
TFR2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFR2 <- aov(FR2~TFR2)
CFR2<-HSD.test(aovFR2,trt="TFR2")
CFR2## $statistics
## MSerror Df Mean CV MSD
## 58.73819 12 20.09937 38.13096 16.08944
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFR2 4 4.19866 0.05
##
## $means
## FR2 std r Min Max Q25 Q50 Q75
## T0 34.7650 3.100930 4 30.61 37.94 33.5575 35.255 36.4625
## T1 18.2225 4.216795 4 13.27 22.02 15.4525 18.800 21.5700
## T2 13.8775 7.888398 4 4.48 23.36 9.9550 13.835 17.7575
## T3 13.5325 12.055241 4 6.57 31.53 6.7800 8.015 14.7675
##
## $comparison
## NULL
##
## $groups
## FR2 groups
## T0 34.7650 a
## T1 18.2225 b
## T2 13.8775 b
## T3 13.5325 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
FR3<-c(53.14,35.09,30.09,22.96,8.48,8.04,10.96,10.11,6.13,11.31,6.5,14.73,8.98,9.21,6.75,13)
TFR3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFR3 <- aov(FR3~TFR3)
CFR3<-HSD.test(aovFR3,trt="TFR3")
CFR3## $statistics
## MSerror Df Mean CV MSD
## 47.86677 12 15.9675 43.32915 14.52439
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFR3 4 4.19866 0.05
##
## $means
## FR3 std r Min Max Q25 Q50 Q75
## T0 35.3200 12.880577 4 22.96 53.14 28.3075 32.590 39.6025
## T1 9.3975 1.370362 4 8.04 10.96 8.3700 9.295 10.3225
## T2 9.6675 4.117996 4 6.13 14.73 6.4075 8.905 12.1650
## T3 9.4850 2.592688 4 6.75 13.00 8.4225 9.095 10.1575
##
## $comparison
## NULL
##
## $groups
## FR3 groups
## T0 35.3200 a
## T2 9.6675 b
## T3 9.4850 b
## T1 9.3975 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
FR4<-c(38.1,29.65,27.41,34.46,5.08,8.46,13.36,14.53,21.05,16.21,14.64,13.46,12.18,21.27,25.64,24.52)
TFR4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFR4 <- aov(FR4~TFR4)
CFR4<-HSD.test(aovFR4,trt="TFR4")
CFR4## $statistics
## MSerror Df Mean CV MSD
## 22.68384 12 20.00125 23.81229 9.998596
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFR4 4 4.19866 0.05
##
## $means
## FR4 std r Min Max Q25 Q50 Q75
## T0 32.4050 4.802642 4 27.41 38.10 29.0900 32.055 35.3700
## T1 10.3575 4.392314 4 5.08 14.53 7.6150 10.910 13.6525
## T2 16.3400 3.335936 4 13.46 21.05 14.3450 15.425 17.4200
## T3 20.9025 6.103203 4 12.18 25.64 18.9975 22.895 24.8000
##
## $comparison
## NULL
##
## $groups
## FR4 groups
## T0 32.4050 a
## T3 20.9025 b
## T2 16.3400 bc
## T1 10.3575 c
##
## attr(,"class")
## [1] "group"CFR<- c("a","b","b","ab","a","b","b","b","a","b","b","b","a","c","bc","b")
data10<-data.frame(FR=as.numeric(PESOS$PFR), muestreo=as.factor(PESOS$Muestreo),Tratamiento=PESOS$tratamiento)
dt10 <-data10 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w10 = mean(FR), sd10 = sd(FR),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt10$CFR<- CFRplot_1<-ggplot(dt10, aes(muestreo,w10, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Peso fresco raiz") + coord_cartesian(ylim=c(0,50)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CFR), position = position_dodge(width=1), vjust=-4,cex=3)+
geom_errorbar(aes(ymin = w10-sd10, ymax = w10+sd10),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 50, label = "**") +
geom_text(data = NULL, x = 2, y = 50, label = "**") +
geom_text(data = NULL, x = 3, y = 50, label = "**") +
geom_text(data = NULL, x = 4, y = 50, label = "**")Peso seco raiz
#Muestreo 1
SR1<-c(1.34,1.24,1.14,1.3,0.74,0.84,1.21,0.85,0.43,0.07,1.12,0.6,1,0.8,0.93,1)
TSR1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSR1 <- aov(SR1~TSR1)
CSR1<-HSD.test(aovSR1,trt="TSR1")
CSR1## $statistics
## MSerror Df Mean CV MSD
## 0.06240625 12 0.913125 27.35797 0.5244388
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSR1 4 4.19866 0.05
##
## $means
## SR1 std r Min Max Q25 Q50 Q75
## T0 1.2550 0.08698659 4 1.14 1.34 1.2150 1.270 1.31
## T1 0.9100 0.20607442 4 0.74 1.21 0.8150 0.845 0.94
## T2 0.5550 0.43669211 4 0.07 1.12 0.3400 0.515 0.73
## T3 0.9325 0.09429563 4 0.80 1.00 0.8975 0.965 1.00
##
## $comparison
## NULL
##
## $groups
## SR1 groups
## T0 1.2550 a
## T3 0.9325 ab
## T1 0.9100 ab
## T2 0.5550 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
SR2<-c(1.34,1.02,0.78,1.73,0.89,0.85,0.8,1.44,0.95,1.15,1.14,0.46,0.74,0.45,0.72,1.68)
TSR2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSR2 <- aov(SR2~TSR2)
CSR2<-HSD.test(aovSR2,trt="TSR2")
CSR2## $statistics
## MSerror Df Mean CV MSD
## 0.1632292 12 1.00875 40.05118 0.8481636
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSR2 4 4.19866 0.05
##
## $means
## SR2 std r Min Max Q25 Q50 Q75
## T0 1.2175 0.4115317 4 0.78 1.73 0.9600 1.180 1.4375
## T1 0.9950 0.2989426 4 0.80 1.44 0.8375 0.870 1.0275
## T2 0.9250 0.3233677 4 0.46 1.15 0.8275 1.045 1.1425
## T3 0.8975 0.5381682 4 0.45 1.68 0.6525 0.730 0.9750
##
## $comparison
## NULL
##
## $groups
## SR2 groups
## T0 1.2175 a
## T1 0.9950 a
## T2 0.9250 a
## T3 0.8975 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
SR3<-c(1.71,1.36,1.56,1.71,1.18,0.92,1.17,0.95,0.57,0.71,0.61,1.36,1.18,0.68,0.61,1.43)
TSR3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSR3 <- aov(SR3~TSR3)
CSR3<-HSD.test(aovSR3,trt="TSR3")
CSR3## $statistics
## MSerror Df Mean CV MSD
## 0.08499792 12 1.106875 26.33938 0.6120471
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSR3 4 4.19866 0.05
##
## $means
## SR3 std r Min Max Q25 Q50 Q75
## T0 1.5850 0.1658312 4 1.36 1.71 1.5100 1.635 1.7100
## T1 1.0550 0.1391642 4 0.92 1.18 0.9425 1.060 1.1725
## T2 0.8125 0.3697184 4 0.57 1.36 0.6000 0.660 0.8725
## T3 0.9750 0.3955165 4 0.61 1.43 0.6625 0.930 1.2425
##
## $comparison
## NULL
##
## $groups
## SR3 groups
## T0 1.5850 a
## T1 1.0550 ab
## T3 0.9750 ab
## T2 0.8125 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
SR4<-c(2.57,2.03,2.16,2.71,0.98,1.75,1.7,2.04,1.64,1.32,2.04,1.77,1.62,2.12,1.99,1.51)
TSR4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSR4 <- aov(SR4~TSR4)
CSR4<-HSD.test(aovSR4,trt="TSR4")
CSR4## $statistics
## MSerror Df Mean CV MSD
## 0.1206188 12 1.871875 18.5537 0.7291018
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSR4 4 4.19866 0.05
##
## $means
## SR4 std r Min Max Q25 Q50 Q75
## T0 2.3675 0.3241784 4 2.03 2.71 2.1275 2.365 2.6050
## T1 1.6175 0.4506569 4 0.98 2.04 1.5200 1.725 1.8225
## T2 1.6925 0.2990401 4 1.32 2.04 1.5600 1.705 1.8375
## T3 1.8100 0.2913188 4 1.51 2.12 1.5925 1.805 2.0225
##
## $comparison
## NULL
##
## $groups
## SR4 groups
## T0 2.3675 a
## T3 1.8100 ab
## T2 1.6925 ab
## T1 1.6175 b
##
## attr(,"class")
## [1] "group"CSR<- c("a","ab","b","ab","a","a","a","a","a","ab","b","ab","a","b","ab","ab")
data11<-data.frame(SR=as.numeric(PESOS$PSR), muestreo=as.factor(PESOS$Muestreo),Tratamiento=PESOS$tratamiento)
dt11 <-data11 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w11 = mean(SR), sd11 = sd(SR),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt11$CSR<- CSRplot_2<-ggplot(dt11, aes(muestreo,w11, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Muestreo") + ylab("Peso seco raiz") + coord_cartesian(ylim=c(0,5)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CSR), position = position_dodge(width=1), vjust=-4,cex=3)+
geom_errorbar(aes(ymin = w11-sd11, ymax = w11+sd11),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 5, label = "**") +
geom_text(data = NULL, x = 3, y = 5, label = "**") +
geom_text(data = NULL, x = 4, y = 5, label = "**")Peso fresco parte aerea
#Muestreo 1
FA1<-c(8.85,9.8,7.83,10.7,7.09,4.12,6.22,6.44,6.14,5.02,6.74,6.98,6.29,5.96,5.03,5.7)
TFA1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFA1 <- aov(FA1~TFA1)
CFA1<-HSD.test(aovFA1,trt="TFA1")
CFA1## $statistics
## MSerror Df Mean CV MSD
## 1.05704 12 6.806875 15.1042 2.158372
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFA1 4 4.19866 0.05
##
## $means
## FA1 std r Min Max Q25 Q50 Q75
## T0 9.2950 1.2346794 4 7.83 10.70 8.5950 9.325 10.0250
## T1 5.9675 1.2858557 4 4.12 7.09 5.6950 6.330 6.6025
## T2 6.2200 0.8745284 4 5.02 6.98 5.8600 6.440 6.8000
## T3 5.7450 0.5343220 4 5.03 6.29 5.5325 5.830 6.0425
##
## $comparison
## NULL
##
## $groups
## FA1 groups
## T0 9.2950 a
## T2 6.2200 b
## T1 5.9675 b
## T3 5.7450 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
FA2<-c(11.55,10.25,9.37,8.4,7.01,7.36,9.71,8.67,9.3,8.4,9.52,10.86,6.13,9.18,9.35,8.28)
TFA2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFA2 <- aov(FA2~TFA2)
CFA2<-HSD.test(aovFA2,trt="TFA2")
CFA2## $statistics
## MSerror Df Mean CV MSD
## 1.638788 12 8.95875 14.2894 2.68746
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFA2 4 4.19866 0.05
##
## $means
## FA2 std r Min Max Q25 Q50 Q75
## T0 9.8925 1.338616 4 8.40 11.55 9.1275 9.810 10.5750
## T1 8.1875 1.241246 4 7.01 9.71 7.2725 8.015 8.9300
## T2 9.5200 1.016268 4 8.40 10.86 9.0750 9.410 9.8550
## T3 8.2350 1.479786 4 6.13 9.35 7.7425 8.730 9.2225
##
## $comparison
## NULL
##
## $groups
## FA2 groups
## T0 9.8925 a
## T2 9.5200 a
## T3 8.2350 a
## T1 8.1875 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
FA3<-c(14.07,15.61,11.82,11.57,8.82,10.71,8.79,9.14,11.24,11.9,8.9,7.9,9.21,7.84,11.47,9.31)
TFA3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFA3 <- aov(FA3~TFA3)
CFA3<-HSD.test(aovFA3,trt="TFA3")
CFA3## $statistics
## MSerror Df Mean CV MSD
## 2.592629 12 10.51875 15.30756 3.380266
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFA3 4 4.19866 0.05
##
## $means
## FA3 std r Min Max Q25 Q50 Q75
## T0 13.2675 1.9242379 4 11.57 15.61 11.7575 12.945 14.4550
## T1 9.3650 0.9105493 4 8.79 10.71 8.8125 8.980 9.5325
## T2 9.9850 1.8944392 4 7.90 11.90 8.6500 10.070 11.4050
## T3 9.4575 1.4999417 4 7.84 11.47 8.8675 9.260 9.8500
##
## $comparison
## NULL
##
## $groups
## FA3 groups
## T0 13.2675 a
## T2 9.9850 ab
## T3 9.4575 b
## T1 9.3650 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
FA4<-c(11.55,12.42,11.38,13.17,6.45,5.57,9.91,7.84,9.51,8.66,11.2,10.24,9.85,10.35,11.04,9.73)
TFA4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovFA4 <- aov(FA4~TFA4)
CFA4<-HSD.test(aovFA4,trt="TFA4")
CFA4## $statistics
## MSerror Df Mean CV MSD
## 1.446835 12 9.929375 12.114 2.525168
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TFA4 4 4.19866 0.05
##
## $means
## FA4 std r Min Max Q25 Q50 Q75
## T0 12.1300 0.8295782 4 11.38 13.17 11.5075 11.985 12.6075
## T1 7.4425 1.8919016 4 5.57 9.91 6.2300 7.145 8.3575
## T2 9.9025 1.0793941 4 8.66 11.20 9.2975 9.875 10.4800
## T3 10.2425 0.5956159 4 9.73 11.04 9.8200 10.100 10.5225
##
## $comparison
## NULL
##
## $groups
## FA4 groups
## T0 12.1300 a
## T3 10.2425 a
## T2 9.9025 ab
## T1 7.4425 b
##
## attr(,"class")
## [1] "group"CFA<- c("a","b","b","b","a","a","a","a","a","b","ab","b","a","b","ab","a")
data12<-data.frame(FA=as.numeric(PESOS$PFA), muestreo=as.factor(PESOS$Muestreo),Tratamiento=PESOS$tratamiento)
dt12<-data12 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w12 = mean(FA), sd12 = sd(FA),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt12$CFA<- CFAplot_3<-ggplot(dt12, aes(muestreo,w12, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Peso Fresco hoja") + coord_cartesian(ylim=c(0,20)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CFA), position = position_dodge(width=1), vjust=-4, cex=3)+
geom_errorbar(aes(ymin = w12-sd12, ymax = w12+sd12),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 20, label = "**") +
geom_text(data = NULL, x = 3, y = 20, label = "**") +
geom_text(data = NULL, x = 4, y = 20, label = "**")Peso seco parte aerea
#Muestreo 1
SA1<-c(0.84,0.82,0.76,1.03,1.14,0.62,0.55,1.02,0.04,0.72,0.57,1.05,0.43,0.07,1.12,0.6)
TSA1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSA1 <- aov(SA1~TSA1)
CSA1<-HSD.test(aovSA1,trt="TSA1")
CSA1## $statistics
## MSerror Df Mean CV MSD
## 0.1165792 12 0.71125 48.00519 0.7167888
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSA1 4 4.19866 0.05
##
## $means
## SA1 std r Min Max Q25 Q50 Q75
## T0 0.8625 0.1167262 4 0.76 1.03 0.8050 0.830 0.8875
## T1 0.8325 0.2913617 4 0.55 1.14 0.6025 0.820 1.0500
## T2 0.5950 0.4208325 4 0.04 1.05 0.4375 0.645 0.8025
## T3 0.5550 0.4366921 4 0.07 1.12 0.3400 0.515 0.7300
##
## $comparison
## NULL
##
## $groups
## SA1 groups
## T0 0.8625 a
## T1 0.8325 a
## T2 0.5950 a
## T3 0.5550 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
SA2<-c(1.29,1.07,0.97,0.95,0.71,0.73,1.02,0.79,1.22,0.78,1,1.23,0.65,0.97,1.03,0.91)
TSA2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSA2 <- aov(SA2~TSA2)
CSA2<-HSD.test(aovSA2,trt="TSA2")
CSA2## $statistics
## MSerror Df Mean CV MSD
## 0.0295125 12 0.9575 17.94173 0.3606482
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSA2 4 4.19866 0.05
##
## $means
## SA2 std r Min Max Q25 Q50 Q75
## T0 1.0700 0.1557776 4 0.95 1.29 0.965 1.02 1.1250
## T1 0.8125 0.1424488 4 0.71 1.02 0.725 0.76 0.8475
## T2 1.0575 0.2132878 4 0.78 1.23 0.945 1.11 1.2225
## T3 0.8900 0.1673320 4 0.65 1.03 0.845 0.94 0.9850
##
## $comparison
## NULL
##
## $groups
## SA2 groups
## T0 1.0700 a
## T2 1.0575 a
## T3 0.8900 a
## T1 0.8125 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
SA3<-c(1.73,1.39,1.49,1.55,1.31,1.25,1.13,1.09,1.54,1.23,1.15,1.42,1.03,0.93,1.34,1.07)
TSA3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSA3 <- aov(SA3~TSA3)
CSA3<-HSD.test(aovSA3,trt="TSA3")
CSA3## $statistics
## MSerror Df Mean CV MSD
## 0.02327292 12 1.290625 11.82021 0.3202625
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSA3 4 4.19866 0.05
##
## $means
## SA3 std r Min Max Q25 Q50 Q75
## T0 1.5400 0.1428286 4 1.39 1.73 1.465 1.520 1.5950
## T1 1.1950 0.1024695 4 1.09 1.31 1.120 1.190 1.2650
## T2 1.3350 0.1774824 4 1.15 1.54 1.210 1.325 1.4500
## T3 1.0925 0.1751904 4 0.93 1.34 1.005 1.050 1.1375
##
## $comparison
## NULL
##
## $groups
## SA3 groups
## T0 1.5400 a
## T2 1.3350 ab
## T1 1.1950 b
## T3 1.0925 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
SA4<-c(1.59,3.38,1.49,1.67,1.46,1.23,1.47,1.36,1.42,1.17,1.6,1.34,1.25,1.45,1.25,1.2)
TSA4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
aovSA4 <- aov(SA4~TSA4)
CSA4<-HSD.test(aovSA4,trt="TSA4")
CSA4## $statistics
## MSerror Df Mean CV MSD
## 0.2172688 12 1.520625 30.65325 0.9785418
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TSA4 4 4.19866 0.05
##
## $means
## SA4 std r Min Max Q25 Q50 Q75
## T0 2.0325 0.9013462 4 1.49 3.38 1.5650 1.63 2.0975
## T1 1.3800 0.1116542 4 1.23 1.47 1.3275 1.41 1.4625
## T2 1.3825 0.1785824 4 1.17 1.60 1.2975 1.38 1.4650
## T3 1.2875 0.1108678 4 1.20 1.45 1.2375 1.25 1.3000
##
## $comparison
## NULL
##
## $groups
## SA4 groups
## T0 2.0325 a
## T2 1.3825 a
## T1 1.3800 a
## T3 1.2875 a
##
## attr(,"class")
## [1] "group"CSA<- c("a","a","a","a","a","a","a","a","a","b","ab","b","a","a","a","a")
data13<-data.frame(SA=as.numeric(PESOS$PSA), muestreo=as.factor(PESOS$Muestreo),Tratamiento=PESOS$tratamiento)
dt13<-data13 %>%
group_by(muestreo, Tratamiento) %>%
summarise(w13 = mean(SA), sd13 = sd(SA),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt13$CSA<- CSAplot_4<-ggplot(dt13, aes(muestreo,w13, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Peso seco hoja (g)") +coord_cartesian(ylim=c(0,5)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = CSA), position = position_dodge(width=1), vjust=-4, cex=3)+
geom_errorbar(aes(ymin = w13-sd13, ymax = w13+sd13),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 3, y = 5, label = "**") plot_grid(plot_1,plot_2,plot_3, plot_4, labels = "AUTO")
Indices
Tasa relativa de crecimiento
indice<-read_excel("~/Paola/Computacion/Indices.xlsx")#Muestreo 1
TRC1<-c(0.091896,0.092326,0.089429,0.102819,0.07187,0.067769,0.081771,0.073872,0.078711,0.057774,0.079612,0.067299,0.08241,0.084469,0.069747,0.08012)
TTRC1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TTRC1<-as.factor(TTRC1)
aovTRC1 <- aov(TRC1~TTRC1)
CTRC1<-HSD.test(aovTRC1,trt="TTRC1")
CTRC1## $statistics
## MSerror Df Mean CV MSD
## 5.499263e-05 12 0.07949338 9.328704 0.01556801
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TTRC1 4 4.19866 0.05
##
## $means
## TRC1 std r Min Max Q25 Q50 Q75
## T0 0.0941175 0.005939772 4 0.089429 0.102819 0.09127925 0.092111 0.09494925
## T1 0.0738205 0.005877590 4 0.067769 0.081771 0.07084475 0.072871 0.07584675
## T2 0.0708490 0.010362743 4 0.057774 0.079612 0.06491775 0.073005 0.07893625
## T3 0.0791865 0.006538892 4 0.069747 0.084469 0.07752675 0.081265 0.08292475
##
## $comparison
## NULL
##
## $groups
## TRC1 groups
## T0 0.0941175 a
## T3 0.0791865 ab
## T1 0.0738205 b
## T2 0.0708490 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
TRC2<-c(0.091204,0.092265,0.087819,0.090299,0.074851,0.080484,0.074635,0.081055,0.07258,0.075961,0.083163,0.065009,0.064958,0.065631,0.066302,0.087718)
TTRC2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TTRC2<-as.factor(TTRC2)
aovTRC2 <- aov(TRC2~TTRC2)
CTRC2<-HSD.test(aovTRC2,trt="TTRC2")
CTRC2## $statistics
## MSerror Df Mean CV MSD
## 4.871974e-05 12 0.07837088 8.906309 0.01465322
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TTRC2 4 4.19866 0.05
##
## $means
## TRC2 std r Min Max Q25 Q50 Q75
## T0 0.09039675 0.001897047 4 0.087819 0.092265 0.08967900 0.0907515 0.09146925
## T1 0.07775625 0.003488316 4 0.074635 0.081055 0.07479700 0.0776675 0.08062675
## T2 0.07417825 0.007539529 4 0.065009 0.083163 0.07068725 0.0742705 0.07776150
## T3 0.07115225 0.011057455 4 0.064958 0.087718 0.06546275 0.0659665 0.07165600
##
## $comparison
## NULL
##
## $groups
## TRC2 groups
## T0 0.09039675 a
## T1 0.07775625 ab
## T2 0.07417825 b
## T3 0.07115225 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
TRC3<-c(0.085875,0.080123,0.076233,0.072283,0.058177,0.059817,0.060876,0.060357,0.058265,0.064172,0.055798,0.063662,0.059198,0.057884,0.059229,0.06336)
TTRC3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TTRC3<-as.factor(TTRC3)
aovTRC3 <- aov(TRC3~TTRC3)
CTRC3<-HSD.test(aovTRC3,trt="TTRC3")
CTRC3## $statistics
## MSerror Df Mean CV MSD
## 1.436768e-05 12 0.06470681 5.857919 0.007957453
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TTRC3 4 4.19866 0.05
##
## $means
## TRC3 std r Min Max Q25 Q50 Q75
## T0 0.07862850 0.005795087 4 0.072283 0.085875 0.07524550 0.0781780 0.08156100
## T1 0.05980675 0.001169367 4 0.058177 0.060876 0.05940700 0.0600870 0.06048675
## T2 0.06047425 0.004106223 4 0.055798 0.064172 0.05764825 0.0609635 0.06378950
## T3 0.05991775 0.002378910 4 0.057884 0.063360 0.05886950 0.0592135 0.06026175
##
## $comparison
## NULL
##
## $groups
## TRC3 groups
## T0 0.07862850 a
## T2 0.06047425 b
## T3 0.05991775 b
## T1 0.05980675 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
TRC4<-c(0.06973,0.066776,0.065326,0.068992,0.043668,0.047155,0.056202,0.05549,0.061068,0.057385,0.058069,0.056526,0.05522,0.061677,0.064326,0.0631)
TTRC4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TTRC4<-as.factor(TTRC4)
aovTRC4 <- aov(TRC4~TTRC4)
CTRC4<-HSD.test(aovTRC4,trt="TTRC4")
CTRC4## $statistics
## MSerror Df Mean CV MSD
## 1.570818e-05 12 0.05941937 6.670138 0.00832039
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TTRC4 4 4.19866 0.05
##
## $means
## TRC4 std r Min Max Q25 Q50 Q75
## T0 0.06770600 0.002023172 4 0.065326 0.069730 0.06641350 0.0678840 0.06917650
## T1 0.05062875 0.006197092 4 0.043668 0.056202 0.04628325 0.0513225 0.05566800
## T2 0.05826200 0.001974311 4 0.056526 0.061068 0.05717025 0.0577270 0.05881875
## T3 0.06108075 0.004054336 4 0.055220 0.064326 0.06006275 0.0623885 0.06340650
##
## $comparison
## NULL
##
## $groups
## TRC4 groups
## T0 0.06770600 a
## T3 0.06108075 ab
## T2 0.05826200 bc
## T1 0.05062875 c
##
## attr(,"class")
## [1] "group"df_t <- data.frame(TRC = (indice$TRC),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo))TRC<- ggplot(df_t, aes(PUNT, as.numeric(TRC), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Tasa Relativa de Crecimiento",x="DDT", y = "TRC (g*g⁻¹*DDT⁻¹)")Indice de area foliar
#Muestreo 1
AF1<-c(0.92,0.96,0.86,1.16,0.93,0.68,0.81,0.86,0.76,0.7,0.76,0.9,0.8,0.65,0.66,0.79)
TAF1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TAF1<-as.factor(TAF1)
aovAF1 <- aov(AF1~TAF1)
CAF1<-HSD.test(aovAF1,trt="TAF1")
CAF1## $statistics
## MSerror Df Mean CV MSD
## 0.01045 12 0.825 12.39094 0.2146045
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF1 4 4.19866 0.05
##
## $means
## AF1 std r Min Max Q25 Q50 Q75
## T0 0.975 0.13000000 4 0.86 1.16 0.9050 0.940 1.0100
## T1 0.820 0.10551461 4 0.68 0.93 0.7775 0.835 0.8775
## T2 0.780 0.08485281 4 0.70 0.90 0.7450 0.760 0.7950
## T3 0.725 0.08103497 4 0.65 0.80 0.6575 0.725 0.7925
##
## $comparison
## NULL
##
## $groups
## AF1 groups
## T0 0.975 a
## T1 0.820 ab
## T2 0.780 ab
## T3 0.725 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
AF2<-c(1.51,1.33,1.29,1.04,0.9,1,1.07,0.94,1.24,0.99,1.14,1.25,0.84,1.03,0.96,1.08)
TAF2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TAF2<-as.factor(TAF2)
aovAF2 <- aov(AF2~TAF2)
CAF2<-HSD.test(aovAF2,trt="TAF2")
CAF2## $statistics
## MSerror Df Mean CV MSD
## 0.01709375 12 1.100625 11.87898 0.2744729
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF2 4 4.19866 0.05
##
## $means
## AF2 std r Min Max Q25 Q50 Q75
## T0 1.2925 0.19362765 4 1.04 1.51 1.2275 1.310 1.3750
## T1 0.9775 0.07410578 4 0.90 1.07 0.9300 0.970 1.0175
## T2 1.1550 0.12069245 4 0.99 1.25 1.1025 1.190 1.2425
## T3 0.9775 0.10404326 4 0.84 1.08 0.9300 0.995 1.0425
##
## $comparison
## NULL
##
## $groups
## AF2 groups
## T0 1.2925 a
## T2 1.1550 ab
## T1 0.9775 b
## T3 0.9775 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
AF3<-c(2,1.64,1.45,1.33,1.07,1.27,0.88,1.13,1.46,1.29,1.06,1,1.03,0.95,1.26,1.03)
TAF3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TAF3<-as.factor(TAF3)
aovAF3 <- aov(AF3~TAF3)
CAF3<-HSD.test(aovAF3,trt="TAF3")
CAF3## $statistics
## MSerror Df Mean CV MSD
## 0.04369375 12 1.240625 16.84881 0.438824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF3 4 4.19866 0.05
##
## $means
## AF3 std r Min Max Q25 Q50 Q75
## T0 1.6050 0.2926317 4 1.33 2.00 1.4200 1.545 1.7300
## T1 1.0875 0.1617354 4 0.88 1.27 1.0225 1.100 1.1650
## T2 1.2025 0.2123480 4 1.00 1.46 1.0450 1.175 1.3325
## T3 1.0675 0.1337597 4 0.95 1.26 1.0100 1.030 1.0875
##
## $comparison
## NULL
##
## $groups
## AF3 groups
## T0 1.6050 a
## T2 1.2025 ab
## T1 1.0875 b
## T3 1.0675 b
##
## attr(,"class")
## [1] "group"#Muestreo 4
AF4<-c(1.55,1.25,1.01,1.42,0.68,0.79,1.01,0.99,1.1,1.04,1.3,1,0.99,1.13,1.13,1.11)
TAF4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TAF4<-as.factor(TAF4)
aovAF4 <- aov(AF4~TAF4)
CAF4<-HSD.test(aovAF4,trt="TAF4")
CAF4## $statistics
## MSerror Df Mean CV MSD
## 0.02554583 12 1.09375 14.61309 0.3355373
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF4 4 4.19866 0.05
##
## $means
## AF4 std r Min Max Q25 Q50 Q75
## T0 1.3075 0.23329166 4 1.01 1.55 1.1900 1.335 1.4525
## T1 0.8675 0.15966110 4 0.68 1.01 0.7625 0.890 0.9950
## T2 1.1100 0.13316656 4 1.00 1.30 1.0300 1.070 1.1500
## T3 1.0900 0.06733003 4 0.99 1.13 1.0800 1.120 1.1300
##
## $comparison
## NULL
##
## $groups
## AF4 groups
## T0 1.3075 a
## T2 1.1100 ab
## T3 1.0900 ab
## T1 0.8675 b
##
## attr(,"class")
## [1] "group"df_IAF <- data.frame(IAF = (indice$IAF),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo))
datai<-data.frame(IAF=as.numeric(indice$IAF), muestreo=as.factor(indice$Muestreo),Tratamiento=indice$tratamiento) IAF<-ggplot(datai, aes(muestreo, as.numeric(IAF), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Indice de area foliar",x="DDT", y = "IAF")IA<- c("a","a","a","a","a","a","a","a","a","b","ab","b","a","a","a","a")
datai<-data.frame(IAF=as.numeric(indice$IAF), muestreo=as.factor(indice$Muestreo),Tratamiento=indice$tratamiento)
dti<-datai %>%
group_by(muestreo, Tratamiento) %>%
summarise(wi = mean(IAF), sdi = sd(IAF),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt13$IA<- IAggplot(dti, aes(muestreo,wi, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("IAF") +coord_cartesian(ylim=c(0,3)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = IA), position = position_dodge(width=1), vjust=-4, cex=3)+
geom_errorbar(aes(ymin = wi-sdi, ymax = wi+sdi),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 3, y = 5, label = "**") 
Peso foliar especifico
#Muestreo 1
PF1<-c(10.38,9.76,10.95,10.88,13.14,16.4,13.07,13.36,12.34,13.95,11.28,12.93,12.67,10.89,13.11,13.85)
TPF1<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TPF1<-as.factor(TPF1)
aovPF1 <- aov(PF1~TPF1)
CPF1<-HSD.test(aovPF1,trt="TPF1")
CPF1## $statistics
## MSerror Df Mean CV MSD
## 1.430787 12 12.435 9.619263 2.511125
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPF1 4 4.19866 0.05
##
## $means
## PF1 std r Min Max Q25 Q50 Q75
## T0 10.4925 0.5503559 4 9.76 10.95 10.2250 10.630 10.8975
## T1 13.9925 1.6097489 4 13.07 16.40 13.1225 13.250 14.1200
## T2 12.6250 1.1163781 4 11.28 13.95 12.0750 12.635 13.1850
## T3 12.6300 1.2580408 4 10.89 13.85 12.2250 12.890 13.2950
##
## $comparison
## NULL
##
## $groups
## PF1 groups
## T1 13.9925 a
## T3 12.6300 ab
## T2 12.6250 ab
## T0 10.4925 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
PF2<-c(23.42,22.77,24.67,23.25,26.03,30.04,24.13,24.16,25.69,25.77,23.29,24.4,24.85,23.33,21.31,24.08)
TPF2<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TPF2<-as.factor(TPF2)
aovPF2 <- aov(PF2~TPF2)
CPF2<-HSD.test(aovPF2,trt="TPF2")
CPF2## $statistics
## MSerror Df Mean CV MSD
## 3.020869 12 24.44937 7.108831 3.648771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPF2 4 4.19866 0.05
##
## $means
## PF2 std r Min Max Q25 Q50 Q75
## T0 23.5275 0.8098714 4 22.77 24.67 23.1300 23.335 23.7325
## T1 26.0900 2.7792445 4 24.13 30.04 24.1525 25.095 27.0325
## T2 24.7875 1.1793324 4 23.29 25.77 24.1225 25.045 25.7100
## T3 23.3925 1.5207098 4 21.31 24.85 22.8250 23.705 24.2725
##
## $comparison
## NULL
##
## $groups
## PF2 groups
## T1 26.0900 a
## T2 24.7875 a
## T0 23.5275 a
## T3 23.3925 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
PF3<-c(27.66,20.59,21.09,22.27,22.68,25.94,20.25,24.91,26.33,22.67,23.97,24.11,24.85,23.33,21.31,24.08)
TPF3<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TPF3<-as.factor(TPF3)
aovPF3 <- aov(PF3~TPF3)
CPF3<-HSD.test(aovPF3,trt="TPF3")
CPF3## $statistics
## MSerror Df Mean CV MSD
## 5.390671 12 23.5025 9.878872 4.874187
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPF3 4 4.19866 0.05
##
## $means
## PF3 std r Min Max Q25 Q50 Q75
## T0 22.9025 3.248932 4 20.59 27.66 20.9650 21.680 23.6175
## T1 23.4450 2.527482 4 20.25 25.94 22.0725 23.795 25.1675
## T2 24.2700 1.518684 4 22.67 26.33 23.6450 24.040 24.6650
## T3 23.3925 1.520710 4 21.31 24.85 22.8250 23.705 24.2725
##
## $comparison
## NULL
##
## $groups
## PF3 groups
## T2 24.2700 a
## T1 23.4450 a
## T3 23.3925 a
## T0 22.9025 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
PF4<-c(27.66,20.59,21.09,22.27,22.68,25.94,20.25,24.91,24.59,22.82,23.6,22.44,21.22,23.03,21.28,22.44)
TPF4<-rep(c("T0","T1","T2","T3"),c(4,4,4,4))
TPF4<-as.factor(TPF4)
aovPF4 <- aov(PF4~TPF4)
CPF4<-HSD.test(aovPF4,trt="TPF4")
CPF4## $statistics
## MSerror Df Mean CV MSD
## 4.66006 12 22.92563 9.416176 4.53186
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPF4 4 4.19866 0.05
##
## $means
## PF4 std r Min Max Q25 Q50 Q75
## T0 22.9025 3.2489319 4 20.59 27.66 20.9650 21.680 23.6175
## T1 23.4450 2.5274823 4 20.25 25.94 22.0725 23.795 25.1675
## T2 23.3625 0.9501710 4 22.44 24.59 22.7250 23.210 23.8475
## T3 21.9925 0.8908937 4 21.22 23.03 21.2650 21.860 22.5875
##
## $comparison
## NULL
##
## $groups
## PF4 groups
## T1 23.4450 a
## T2 23.3625 a
## T0 22.9025 a
## T3 21.9925 a
##
## attr(,"class")
## [1] "group"df_PF <- data.frame(PF = (indice$PF),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo)) PF<-ggplot(df_PF, aes(PUNT, as.numeric(PF), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Peso foliar específico",x="DDT", y = "PFE (g*cm⁻²))")Tasa de crecimiento de cultivo
df_tcc <- data.frame(TCC = (indice$TCC),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo)) ggplot(df_tcc, aes(PUNT, as.numeric(TCC), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Tasa de crecimiento de cultivo",x="DDT", y = "TCC")
graf2 <- plot_grid(TRC, IAF, PF,
labels = "AUTO", ncol = 2, align = 'v');graf2
df_AFE <- data.frame(AFE = (indice$AFE),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo)) ggplot(df_AFE, aes(PUNT, as.numeric(AFE), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Tasa de crecimiento de cultivo",x="DDT", y = "AFE")
afe<- c("a","a","a","a","a","a","a","a","a","b","ab","b","a","a","a","a")
dataa<-data.frame(afe=as.numeric(indice$AFE), muestreo=as.factor(indice$Muestreo),Tratamiento=indice$tratamiento)
dta<-dataa %>%
group_by(muestreo, Tratamiento) %>%
summarise(ws = mean(afe), sds = sd(afe),na.rm=TRUE)## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dt13$afe<- afeggplot(dta, aes(muestreo,ws, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Días después de tratamiento (ddt)") + ylab("Peso seco hoja (g)") +coord_cartesian(ylim=c(0,2)) +
scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),
name = "Tratamiento", labels = c("T0","T1","T2", "T3")) +
geom_text(aes(label = afe), position = position_dodge(width=1), vjust=-4, cex=3)+
geom_errorbar(aes(ymin = ws-sds, ymax = ws+sds),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 3, y = 5, label = "**") 