Analisis grupo 10
Paola Andrea Ospina Sanchez
2022-06-19
M1<-read_excel("~/Paola/Computacion/M1.xlsx")
M2<-read_excel("~/Paola/Computacion/M2.xlsx")
M3<-read_excel("~/Paola/Computacion/M3.xlsx")
M4<-read_excel("~/Paola/Computacion/M4.xlsx")
datos<-read_excel("~/Paola/Computacion/DATOSCAMILO.xlsx")
PED<-read_excel("~/Paola/Computacion/PE.xlsx")
CM1<-read_excel("~/Paola/Computacion/CM1.xlsx")
CM2<-read_excel("~/Paola/Computacion/CM2.xlsx")
CM3<-read_excel("~/Paola/Computacion/CM3.xlsx")
CM4<-read_excel("~/Paola/Computacion/CM4.xlsx")
CLA<-read_excel("~/Paola/Computacion/CCLA.xlsx")
indice<-read_excel("~/Paola/Computacion/IndiceC.xlsx")
TAC1<-read_excel("~/Paola/Computacion/TAC.xlsx")Altura de la planta
# Muestreo 1
tratamiento1<-as.factor(M1$tratamiento)
altura1<-as.numeric(M1$altura)
Al1<-aov(altura1~tratamiento1)
summary(Al1)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento1 3 7.442 2.481 1.299 0.32
## Residuals 12 22.915 1.910CAL<-HSD.test(Al1,trt="tratamiento1")
CAL## $statistics
## MSerror Df Mean CV MSD
## 1.909583 12 13.5625 10.18895 2.901015
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1 4 4.19866 0.05
##
## $means
## altura1 std r Min Max Q25 Q50 Q75
## T1 13.625 1.108678 4 12.0 14.5 13.500 14.00 14.125
## T2 13.500 1.000000 4 12.0 14.0 13.500 14.00 14.000
## T3 12.600 1.529706 4 10.5 14.0 12.000 12.95 13.550
## T4 14.525 1.751904 4 13.0 16.5 13.075 14.30 15.750
##
## $comparison
## NULL
##
## $groups
## altura1 groups
## T4 14.525 a
## T1 13.625 a
## T2 13.500 a
## T3 12.600 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
tratamiento<-as.factor(M2$tratamiento)
altura<-as.numeric(M2$altura)
Al2<-aov(altura~tratamiento)
summary(Al2)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 10.54 3.514 1.991 0.169
## Residuals 12 21.18 1.765CAL2<-HSD.test(Al2,trt="tratamiento")
CAL2## $statistics
## MSerror Df Mean CV MSD
## 1.764583 12 15.2375 8.717809 2.7887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento 4 4.19866 0.05
##
## $means
## altura std r Min Max Q25 Q50 Q75
## T1 15.950 0.9882645 4 15.0 17.3 15.375 15.75 16.325
## T2 16.025 1.0404326 4 15.2 17.5 15.350 15.70 16.375
## T3 14.925 1.6398679 4 13.0 17.0 14.275 14.85 15.500
## T4 14.050 1.5198684 4 12.5 15.5 12.875 14.10 15.275
##
## $comparison
## NULL
##
## $groups
## altura groups
## T2 16.025 a
## T1 15.950 a
## T3 14.925 a
## T4 14.050 a
##
## attr(,"class")
## [1] "group"# Muestreo 3
tratamiento3<-as.factor(M3$tratamiento)
altura3<-as.numeric(M3$altura)
Al3<-aov(altura3~tratamiento3)
summary(Al3)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento3 3 7.588 2.529 1.751 0.21
## Residuals 12 17.330 1.444CAL3<-HSD.test(Al3,trt="tratamiento3")
CAL3## $statistics
## MSerror Df Mean CV MSD
## 1.444167 12 14.6625 8.195975 2.522838
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3 4 4.19866 0.05
##
## $means
## altura3 std r Min Max Q25 Q50 Q75
## T1 14.925 1.9032866 4 13.0 17.5 13.900 14.60 15.625
## T2 15.525 0.8578073 4 14.5 16.6 15.250 15.50 15.775
## T3 14.575 0.4924429 4 14.0 15.2 14.375 14.55 14.750
## T4 13.625 1.0843585 4 12.0 14.2 13.575 14.15 14.200
##
## $comparison
## NULL
##
## $groups
## altura3 groups
## T2 15.525 a
## T1 14.925 a
## T3 14.575 a
## T4 13.625 a
##
## attr(,"class")
## [1] "group"# Muestreo 4
tratamiento4<-as.factor(M4$tratamiento)
altura4<-as.numeric(M4$altura)
Al4<-aov(altura4~tratamiento4)
summary(Al4)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento4 3 6.577 2.192 1.386 0.295
## Residuals 12 18.982 1.582CAL4<-HSD.test(Al4,trt="tratamiento4")
CAL4## $statistics
## MSerror Df Mean CV MSD
## 1.581875 12 14.95625 8.409368 2.640382
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4 4 4.19866 0.05
##
## $means
## altura4 std r Min Max Q25 Q50 Q75
## T1 14.825 1.2971122 4 13.4 16.5 14.150 14.7 15.375
## T2 15.975 1.3301002 4 14.4 17.5 15.225 16.0 16.750
## T3 14.825 0.9464847 4 13.5 15.6 14.475 15.1 15.450
## T4 14.200 1.4071247 4 12.5 15.5 13.325 14.4 15.275
##
## $comparison
## NULL
##
## $groups
## altura4 groups
## T2 15.975 a
## T1 14.825 a
## T3 14.825 a
## T4 14.200 a
##
## attr(,"class")
## [1] "group"ALT<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data<-data.frame(alt=as.numeric(datos$altura), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dalt <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(wa = mean(alt,na.rm=TRUE), sda = sd(alt,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dalt$ALT<- ALTggplot(dalt, aes(muestreo,wa, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() +
xlab("Muestreo") + ylab("Altura de la planta") + 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 = ALT), position = position_dodge(width=1), vjust=-4)+
geom_errorbar(aes(ymin = wa-sda, ymax = wa+sda),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
NJ<-aov(HOJAS~tratamiento, data = M1)
summary(NJ)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 4.5 1.500 4 0.0346 *
## Residuals 12 4.5 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CNH1<-HSD.test(NJ,trt="tratamiento")
CNH1## $statistics
## MSerror Df Mean CV MSD
## 0.375 12 4.75 12.89205 1.285572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento 4 4.19866 0.05
##
## $means
## HOJAS std r Min Max Q25 Q50 Q75
## T1 5.50 0.5773503 4 5 6 5.00 5.5 6.00
## T2 5.00 0.8164966 4 4 6 4.75 5.0 5.25
## T3 4.25 0.5000000 4 4 5 4.00 4.0 4.25
## T4 4.25 0.5000000 4 4 5 4.00 4.0 4.25
##
## $comparison
## NULL
##
## $groups
## HOJAS groups
## T1 5.50 a
## T2 5.00 a
## T3 4.25 a
## T4 4.25 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
NJ2<-aov(HOJAS~tratamiento, data = M2)
summary(NJ2)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 4.5 1.500 4 0.0346 *
## Residuals 12 4.5 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CNH2<-HSD.test(NJ2,trt="tratamiento")
CNH2## $statistics
## MSerror Df Mean CV MSD
## 0.375 12 7.25 8.446516 1.285572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento 4 4.19866 0.05
##
## $means
## HOJAS std r Min Max Q25 Q50 Q75
## T1 8.00 0.8164966 4 7 9 7.75 8.0 8.25
## T2 7.50 0.5773503 4 7 8 7.00 7.5 8.00
## T3 6.75 0.5000000 4 6 7 6.75 7.0 7.00
## T4 6.75 0.5000000 4 6 7 6.75 7.0 7.00
##
## $comparison
## NULL
##
## $groups
## HOJAS groups
## T1 8.00 a
## T2 7.50 a
## T3 6.75 a
## T4 6.75 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
NJ3<-aov(HOJAS~tratamiento, data = M3)
summary(NJ3)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 6.5 2.1667 2.261 0.134
## Residuals 12 11.5 0.9583CNH3<-HSD.test(NJ3,trt="tratamiento")
CNH3## $statistics
## MSerror Df Mean CV MSD
## 0.9583333 12 5 19.5789 2.055129
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento 4 4.19866 0.05
##
## $means
## HOJAS std r Min Max Q25 Q50 Q75
## T1 5.75 0.9574271 4 5 7 5.00 5.5 6.25
## T2 4.25 0.9574271 4 3 5 3.75 4.5 5.00
## T3 4.50 0.5773503 4 4 5 4.00 4.5 5.00
## T4 5.50 1.2909944 4 4 7 4.75 5.5 6.25
##
## $comparison
## NULL
##
## $groups
## HOJAS groups
## T1 5.75 a
## T4 5.50 a
## T3 4.50 a
## T2 4.25 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
NJ4<-aov(HOJAS~tratamiento, data = M4)
summary(NJ4)## Df Sum Sq Mean Sq F value Pr(>F)
## tratamiento 3 8.187 2.729 1.955 0.175
## Residuals 12 16.750 1.396CNH4<-HSD.test(NJ4,trt="tratamiento")
CNH4## $statistics
## MSerror Df Mean CV MSD
## 1.395833 12 5.0625 23.33736 2.480262
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento 4 4.19866 0.05
##
## $means
## HOJAS std r Min Max Q25 Q50 Q75
## T1 6.00 1.414214 4 5 8 5.00 5.5 6.50
## T2 4.50 1.290994 4 3 6 3.75 4.5 5.25
## T3 4.25 0.500000 4 4 5 4.00 4.0 4.25
## T4 5.50 1.290994 4 4 7 4.75 5.5 6.25
##
## $comparison
## NULL
##
## $groups
## HOJAS groups
## T1 6.00 a
## T4 5.50 a
## T2 4.50 a
## T3 4.25 a
##
## attr(,"class")
## [1] "group"NH<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data<-data.frame(NH=as.numeric(datos$HOJAS), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dH <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w = mean(NH,na.rm=TRUE), sd = sd(NH,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dH$NH<- NHggplot(dH, aes(muestreo,w, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Numero de hojas") + coord_cartesian(ylim=c(0,15)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = NH), position = position_dodge(width=1), vjust=-4)+ geom_errorbar(aes(ymin = w-sd, ymax = w+sd),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 25, label = "**") +
geom_text(data = NULL, x = 2, y = 25, label = "**") +
geom_text(data = NULL, x = 3, y = 25, label = "**") +
geom_text(data = NULL, x = 4, y = 25, label = "**")
Peso fresco parte aerea
#Muestreo 1
TPFA<-as.factor(M1$tratamiento)
PFA<-as.numeric(M1$PFA)
PFA<-aov(PFA~TPFA)
summary(PFA)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFA 3 12.95 4.317 4.585 0.0232 *
## Residuals 12 11.30 0.942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CPFA<-HSD.test(PFA,trt="TPFA")
CPFA## $statistics
## MSerror Df Mean CV MSD
## 0.9415458 12 7.32125 13.25365 2.037049
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFA 4 4.19866 0.05
##
## $means
## PFA std r Min Max Q25 Q50 Q75
## T1 8.1100 0.7861722 4 6.97 8.72 7.9150 8.375 8.5700
## T2 8.2875 0.7058033 4 7.31 8.81 8.0000 8.515 8.8025
## T3 6.1775 1.5097986 4 5.00 8.32 5.1875 5.695 6.6850
## T4 6.7100 0.6086597 4 5.87 7.30 6.5075 6.835 7.0375
##
## $comparison
## NULL
##
## $groups
## PFA groups
## T2 8.2875 a
## T1 8.1100 ab
## T4 6.7100 ab
## T3 6.1775 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
TPFA2<-as.factor(M2$tratamiento)
PFA2<-as.numeric(M2$PFA)
PFA2<-aov(PFA2~TPFA2)
summary(PFA2)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFA2 3 36.16 12.052 13.44 0.000382 ***
## Residuals 12 10.76 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CPFA2<-HSD.test(PFA2,trt="TPFA2")
CPFA2## $statistics
## MSerror Df Mean CV MSD
## 0.8964917 12 7.81375 12.11752 1.987714
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFA2 4 4.19866 0.05
##
## $means
## PFA2 std r Min Max Q25 Q50 Q75
## T1 9.880 1.3253679 4 8.36 11.51 9.1700 9.825 10.5350
## T2 8.470 0.8786732 4 7.71 9.52 7.7625 8.325 9.0325
## T3 7.015 0.7673548 4 6.05 7.92 6.7325 7.045 7.3275
## T4 5.890 0.6844462 4 4.87 6.29 5.8075 6.200 6.2825
##
## $comparison
## NULL
##
## $groups
## PFA2 groups
## T1 9.880 a
## T2 8.470 ab
## T3 7.015 bc
## T4 5.890 c
##
## attr(,"class")
## [1] "group"#Muestreo 3
TPFA3<-as.factor(M3$tratamiento)
PFA3<-as.numeric(M3$PFA)
PFA3<-aov(PFA3~TPFA3)
summary(PFA3)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFA3 3 2.483 0.8276 0.342 0.796
## Residuals 12 29.078 2.4231CPFA3<-HSD.test(PFA3,trt="TPFA3")
CPFA3## $statistics
## MSerror Df Mean CV MSD
## 2.42314 12 8.596875 18.10709 3.267909
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFA3 4 4.19866 0.05
##
## $means
## PFA3 std r Min Max Q25 Q50 Q75
## T1 9.1200 1.558568 4 7.37 11.08 8.2850 9.015 9.8500
## T2 8.0300 2.062313 4 6.16 9.82 6.2875 8.070 9.8125
## T3 8.5075 1.096308 4 6.89 9.22 8.3000 8.960 9.1675
## T4 8.7300 1.344768 4 7.54 10.54 7.8100 8.420 9.3400
##
## $comparison
## NULL
##
## $groups
## PFA3 groups
## T1 9.1200 a
## T4 8.7300 a
## T3 8.5075 a
## T2 8.0300 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
TPFA4<-as.factor(M4$tratamiento)
PFA4<-as.numeric(M4$PFA)
PFA4<-aov(PFA4~TPFA4)
summary(PFA4)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFA4 3 16.13 5.377 1.274 0.327
## Residuals 12 50.63 4.219CPFA4<-HSD.test(PFA4,trt="TPFA4")
CPFA4## $statistics
## MSerror Df Mean CV MSD
## 4.21944 12 9.476875 21.67516 4.312292
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFA4 4 4.19866 0.05
##
## $means
## PFA4 std r Min Max Q25 Q50 Q75
## T1 11.1200 1.558568 4 9.37 13.08 10.285 11.015 11.8500
## T2 8.9475 1.478995 4 7.18 10.72 8.215 8.945 9.6775
## T3 9.3850 2.078020 4 7.49 12.22 8.060 8.915 10.2400
## T4 8.4550 2.818339 4 4.58 11.34 7.850 8.950 9.5550
##
## $comparison
## NULL
##
## $groups
## PFA4 groups
## T1 11.1200 a
## T3 9.3850 a
## T2 8.9475 a
## T4 8.4550 a
##
## attr(,"class")
## [1] "group"PFA<- c("ab","a","b","ab","a","ab","bc","c","a","a","a","a","a","a","a","a")
data<-data.frame(PFA=as.numeric(datos$PFA), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dPFA <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w2 = mean(PFA,na.rm=TRUE), sd2 = sd(PFA,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dPFA$PFA<- PFAplot_1<-ggplot(dPFA, aes(muestreo,w2, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Fresco parte aerea") + coord_cartesian(ylim=c(0,15)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = PFA), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w2-sd2, ymax = w2+sd2),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 15, label = "**") +
geom_text(data = NULL, x = 2, y = 15, label = "**") +
geom_text(data = NULL, x = 3, y = 25, label = "**") +
geom_text(data = NULL, x = 4, y = 25, label = "**")Peso seco parte aerea
#Muestreo 1
TPSA1<-as.factor(M1$tratamiento)
PSA1<-as.numeric(M1$PSA)
PSA1<-aov(PSA1~TPSA1)
summary(PSA1)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSA1 3 0.1237 0.04124 2.345 0.124
## Residuals 12 0.2110 0.01759CPSA1<-HSD.test(PSA1,trt="TPSA1")
CPSA1## $statistics
## MSerror Df Mean CV MSD
## 0.0175875 12 0.69625 19.04745 0.2784087
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSA1 4 4.19866 0.05
##
## $means
## PSA1 std r Min Max Q25 Q50 Q75
## T1 0.7600 0.15513435 4 0.60 0.97 0.6825 0.735 0.8125
## T2 0.8025 0.08500000 4 0.72 0.92 0.7575 0.785 0.8300
## T3 0.5875 0.18714967 4 0.46 0.86 0.4675 0.515 0.6350
## T4 0.6350 0.06350853 4 0.55 0.70 0.6100 0.645 0.6700
##
## $comparison
## NULL
##
## $groups
## PSA1 groups
## T2 0.8025 a
## T1 0.7600 a
## T4 0.6350 a
## T3 0.5875 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
TPSA2<-as.factor(M2$tratamiento)
PSA2<-as.numeric(M2$PSA)
PSA2<-aov(PSA2~TPSA2)
summary(PSA2)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSA2 3 0.4043 0.1348 4.292 0.0283 *
## Residuals 12 0.3768 0.0314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CPSA2<-HSD.test(PSA2,trt="TPSA2")
CPSA2## $statistics
## MSerror Df Mean CV MSD
## 0.03139792 12 0.921875 19.22111 0.3719899
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSA2 4 4.19866 0.05
##
## $means
## PSA2 std r Min Max Q25 Q50 Q75
## T1 1.1525 0.2236627 4 0.83 1.33 1.0925 1.225 1.2850
## T2 0.9500 0.1669331 4 0.80 1.14 0.8150 0.930 1.0650
## T3 0.8750 0.1862794 4 0.74 1.15 0.7775 0.805 0.9025
## T4 0.7100 0.1140175 4 0.58 0.85 0.6475 0.705 0.7675
##
## $comparison
## NULL
##
## $groups
## PSA2 groups
## T1 1.1525 a
## T2 0.9500 ab
## T3 0.8750 ab
## T4 0.7100 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
TPSA3<-as.factor(M3$tratamiento)
PSA3<-as.numeric(M3$PSA)
PSA3<-aov(PSA3~TPSA3)
summary(PSA3)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSA3 3 0.0262 0.00873 0.17 0.914
## Residuals 12 0.6152 0.05127CPSA3<-HSD.test(PSA3,trt="TPSA3")
CPSA3## $statistics
## MSerror Df Mean CV MSD
## 0.05126667 12 1.09 20.77261 0.4753333
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSA3 4 4.19866 0.05
##
## $means
## PSA3 std r Min Max Q25 Q50 Q75
## T1 1.140 0.14764823 4 1.0 1.33 1.0375 1.115 1.2175
## T2 1.115 0.36792209 4 0.8 1.59 0.8375 1.035 1.3125
## T3 1.035 0.09110434 4 0.9 1.10 1.0275 1.070 1.0775
## T4 1.070 0.19899749 4 0.9 1.28 0.9000 1.050 1.2200
##
## $comparison
## NULL
##
## $groups
## PSA3 groups
## T1 1.140 a
## T2 1.115 a
## T4 1.070 a
## T3 1.035 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
TPSA4<-as.factor(M4$tratamiento)
PSA4<-as.numeric(M4$PSA)
PSA4<-aov(PSA4~TPSA4)
summary(PSA4)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSA4 3 0.3377 0.1126 1.241 0.338
## Residuals 12 1.0885 0.0907CPSA4<-HSD.test(PSA4,trt="TPSA4")
CPSA4## $statistics
## MSerror Df Mean CV MSD
## 0.09070417 12 1.22875 24.51038 0.632258
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSA4 4 4.19866 0.05
##
## $means
## PSA4 std r Min Max Q25 Q50 Q75
## T1 1.4500 0.2540341 4 1.23 1.67 1.2300 1.450 1.670
## T2 1.0775 0.4324639 4 0.65 1.45 0.7325 1.105 1.450
## T3 1.2650 0.2932007 4 0.97 1.67 1.1350 1.210 1.340
## T4 1.1225 0.1590335 4 0.98 1.27 0.9875 1.120 1.255
##
## $comparison
## NULL
##
## $groups
## PSA4 groups
## T1 1.4500 a
## T3 1.2650 a
## T4 1.1225 a
## T2 1.0775 a
##
## attr(,"class")
## [1] "group"PSA<- c("a","a","a","a","a","ab","ab","b","a","a","a","a","a","a","a","a")
data<-data.frame(PSA=as.numeric(datos$PSA), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dPSA <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w3 = mean(PSA,na.rm=TRUE), sd3 = sd(PSA,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dPSA$PSA<- PSAplot_2<-ggplot(dPSA, aes(muestreo,w3, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Seco parte aerea") + coord_cartesian(ylim=c(0,3)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = PSA), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w3-sd3, ymax = w3+sd3),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 25, label = "**") +
geom_text(data = NULL, x = 2, y = 3, label = "**") +
geom_text(data = NULL, x = 3, y = 25, label = "**") +
geom_text(data = NULL, x = 4, y = 25, label = "**")Peso fresco raiz tuberosa
#Muestreo 1
TPFR1<-as.factor(M1$tratamiento)
PFR1<-as.numeric(M1$PFR)
PFR1<-aov(PFR1~TPFR1)
summary(PFR1)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFR1 3 209.4 69.80 2.468 0.112
## Residuals 12 339.4 28.28CPFR1<-HSD.test(PFR1,trt="TPFR1")
CPFR1## $statistics
## MSerror Df Mean CV MSD
## 28.28487 12 9.11875 58.32326 11.16498
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFR1 4 4.19866 0.05
##
## $means
## PFR1 std r Min Max Q25 Q50 Q75
## T1 15.2000 8.404451 4 6.07 24.95 9.6250 14.890 20.4650
## T2 7.9950 4.079775 4 2.52 12.36 6.7575 8.550 9.7875
## T3 5.6875 4.656081 4 2.02 12.15 2.4325 4.290 7.5450
## T4 7.5925 2.044755 4 5.88 10.42 6.2175 7.035 8.4100
##
## $comparison
## NULL
##
## $groups
## PFR1 groups
## T1 15.2000 a
## T2 7.9950 a
## T4 7.5925 a
## T3 5.6875 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
TPFR2<-as.factor(M2$tratamiento)
PFR2<-as.numeric(M2$PFR)
PFR2<-aov(PFR2~TPFR2)
summary(PFR2)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFR2 3 346.8 115.59 1.976 0.171
## Residuals 12 701.8 58.49CPFR2<-HSD.test(PFR2,trt="TPFR2")
CPFR2## $statistics
## MSerror Df Mean CV MSD
## 58.48556 12 17.89125 42.74483 16.05481
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFR2 4 4.19866 0.05
##
## $means
## PFR2 std r Min Max Q25 Q50 Q75
## T1 23.3075 6.723141 4 17.15 30.18 17.7425 22.95 28.5150
## T2 21.6575 10.527916 4 8.87 34.12 16.6250 21.82 26.8525
## T3 14.0125 6.564548 4 7.71 22.54 9.5925 12.90 17.3200
## T4 12.5875 5.900109 4 8.72 21.19 8.7500 10.22 14.0575
##
## $comparison
## NULL
##
## $groups
## PFR2 groups
## T1 23.3075 a
## T2 21.6575 a
## T3 14.0125 a
## T4 12.5875 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
TPFR3<-as.factor(M3$tratamiento)
PFR3<-as.numeric(M3$PFR)
PFR3<-aov(PFR3~TPFR3)
summary(PFR3)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFR3 3 92.9 30.98 0.441 0.728
## Residuals 12 842.7 70.23CPFR3<-HSD.test(PFR3,trt="TPFR3")
CPFR3## $statistics
## MSerror Df Mean CV MSD
## 70.2252 12 14.02187 59.7641 17.59249
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFR3 4 4.19866 0.05
##
## $means
## PFR3 std r Min Max Q25 Q50 Q75
## T1 18.1650 10.694361 4 11.81 34.17 12.9425 13.340 18.5625
## T2 12.9725 10.289896 4 2.85 26.62 6.5925 11.210 17.5900
## T3 12.1750 5.694983 4 4.83 18.24 9.5475 12.815 15.4425
## T4 12.7750 5.311933 4 8.01 20.09 9.4200 11.500 14.8550
##
## $comparison
## NULL
##
## $groups
## PFR3 groups
## T1 18.1650 a
## T2 12.9725 a
## T4 12.7750 a
## T3 12.1750 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
TPFR4<-as.factor(M4$tratamiento)
PFR4<-as.numeric(M4$PFR)
PFR4<-aov(PFR4~TPFR4)
summary(PFR4)## Df Sum Sq Mean Sq F value Pr(>F)
## TPFR4 3 58.1 19.35 0.199 0.895
## Residuals 12 1165.3 97.11CPFR4<-HSD.test(PFR4,trt="TPFR4")
CPFR4## $statistics
## MSerror Df Mean CV MSD
## 97.10624 12 15.45187 63.77381 20.68732
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPFR4 4 4.19866 0.05
##
## $means
## PFR4 std r Min Max Q25 Q50 Q75
## T1 18.4700 12.549932 4 11.35 37.27 12.1000 12.630 19.00
## T2 14.5775 10.883763 4 3.86 28.82 7.6025 12.815 19.79
## T3 15.4625 8.365303 4 8.93 27.24 9.8225 12.840 18.48
## T4 13.2975 6.518402 4 7.88 22.19 8.7275 11.560 16.13
##
## $comparison
## NULL
##
## $groups
## PFR4 groups
## T1 18.4700 a
## T3 15.4625 a
## T2 14.5775 a
## T4 13.2975 a
##
## attr(,"class")
## [1] "group"PFR<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data<-data.frame(PFR=as.numeric(datos$PFR), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dPFR <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w4 = mean(PFR,na.rm=TRUE), sd4 = sd(PFR,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dPFR$PFR<- PFRplot_3<-ggplot(dPFR, aes(muestreo,w4, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Fresco raiz tuberosa") + coord_cartesian(ylim=c(0,40)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = PFR), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w4-sd4, ymax = w4+sd4),width = .5, position = position_dodge(0.9))Peso seco raiz tuberosa
#Muestreo 1
TPSR2<-as.factor(M1$tratamiento)
PSR2<-as.numeric(M1$PSR)
PSR2<-aov(PSR2~TPSR2)
summary(PSR2)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSR2 3 0.8373 0.27909 2.981 0.0738 .
## Residuals 12 1.1233 0.09361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CPSR2<-HSD.test(PSR2,trt="TPSR2")
CPSR2## $statistics
## MSerror Df Mean CV MSD
## 0.09360833 12 0.76125 40.19111 0.6423001
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSR2 4 4.19866 0.05
##
## $means
## PSR2 std r Min Max Q25 Q50 Q75
## T1 1.100 0.4080033 4 0.61 1.47 0.8425 1.16 1.4175
## T2 0.810 0.3040833 4 0.37 1.07 0.7675 0.90 0.9425
## T3 0.475 0.2886174 4 0.24 0.88 0.2850 0.39 0.5800
## T4 0.660 0.1794436 4 0.47 0.87 0.5375 0.65 0.7725
##
## $comparison
## NULL
##
## $groups
## PSR2 groups
## T1 1.100 a
## T2 0.810 a
## T4 0.660 a
## T3 0.475 a
##
## attr(,"class")
## [1] "group"#Muestreo 2
TPSR2<-as.factor(M2$tratamiento)
PSR2<-as.numeric(M2$PSR)
PSR2<-aov(PSR2~TPSR2)
summary(PSR2)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSR2 3 27.29 9.097 0.788 0.523
## Residuals 12 138.50 11.542CPSR2<-HSD.test(PSR2,trt="TPSR2")
CPSR2## $statistics
## MSerror Df Mean CV MSD
## 11.54185 12 4.2825 79.33054 7.132118
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSR2 4 4.19866 0.05
##
## $means
## PSR2 std r Min Max Q25 Q50 Q75
## T1 6.2025 4.828356 4 2.05 11.05 2.0500 5.855 10.0075
## T2 4.7600 3.155482 4 1.26 7.82 2.5350 4.980 7.2050
## T3 3.2150 2.168156 4 1.15 6.13 1.8550 2.790 4.1500
## T4 2.9525 2.862940 4 1.23 7.23 1.4325 1.675 3.1950
##
## $comparison
## NULL
##
## $groups
## PSR2 groups
## T1 6.2025 a
## T2 4.7600 a
## T3 3.2150 a
## T4 2.9525 a
##
## attr(,"class")
## [1] "group"#Muestreo 3
TPSR3<-as.factor(M3$tratamiento)
PSR3<-as.numeric(M3$PSR)
PSR3<-aov(PSR3~TPSR3)
summary(PSR3)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSR3 3 0.9214 0.3071 1.329 0.311
## Residuals 12 2.7734 0.2311CPSR3<-HSD.test(PSR3,trt="TPSR3")
CPSR3## $statistics
## MSerror Df Mean CV MSD
## 0.2311208 12 1.36 35.34929 1.009254
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSR3 4 4.19866 0.05
##
## $means
## PSR3 std r Min Max Q25 Q50 Q75
## T1 1.7700 0.5270674 4 1.40 2.55 1.490 1.565 1.8450
## T2 1.2875 0.6030685 4 0.60 2.00 0.930 1.275 1.6325
## T3 1.1950 0.4163732 4 0.60 1.57 1.110 1.305 1.3900
## T4 1.1875 0.3310967 4 0.89 1.66 1.025 1.100 1.2625
##
## $comparison
## NULL
##
## $groups
## PSR3 groups
## T1 1.7700 a
## T2 1.2875 a
## T3 1.1950 a
## T4 1.1875 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
TPSR4<-as.factor(M4$tratamiento)
PSR4<-as.numeric(M4$PSR)
PSR4<-aov(PSR4~TPSR4)
summary(PSR4)## Df Sum Sq Mean Sq F value Pr(>F)
## TPSR4 3 0.7031 0.2344 0.954 0.446
## Residuals 12 2.9483 0.2457CPSR4<-HSD.test(PSR4,trt="TPSR4")
CPSR4## $statistics
## MSerror Df Mean CV MSD
## 0.2456875 12 1.44125 34.39159 1.040572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPSR4 4 4.19866 0.05
##
## $means
## PSR4 std r Min Max Q25 Q50 Q75
## T1 1.7875 0.5864228 4 1.37 2.65 1.4525 1.565 1.9000
## T2 1.4200 0.5525094 4 0.67 2.00 1.2700 1.505 1.6550
## T3 1.2425 0.3997812 4 0.67 1.57 1.1275 1.365 1.4800
## T4 1.3150 0.4168533 4 0.84 1.67 1.0275 1.375 1.6625
##
## $comparison
## NULL
##
## $groups
## PSR4 groups
## T1 1.7875 a
## T2 1.4200 a
## T4 1.3150 a
## T3 1.2425 a
##
## attr(,"class")
## [1] "group"PSR<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data<-data.frame(PSR=as.numeric(datos$PSR), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dPSR <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w5 = mean(PSR,na.rm=TRUE), sd5 = sd(PSR,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dPSR$PSR<- PSRplot_4<-ggplot(dPSR, aes(muestreo,w5, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Seco raiz tuberosa") + coord_cartesian(ylim=c(0,10)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = PSR), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w5-sd5, ymax = w5+sd5),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 25, label = "**") +
geom_text(data = NULL, x = 2, y = 24, label = "**") +
geom_text(data = NULL, x = 3, y = 25, label = "**") +
geom_text(data = NULL, x = 4, y = 25, label = "**")plot_grid(plot_1,plot_2,plot_3,plot_4,labels = "AUTO")
## CRA
#Muestreo 1
TCRA1<-as.factor(M1$tratamiento)
CRA1<-as.numeric(M1$CRA)
CRA1<-aov(CRA1~TCRA1)
summary(CRA1)## Df Sum Sq Mean Sq F value Pr(>F)
## TCRA1 3 1039.6 346.5 6.553 0.00714 **
## Residuals 12 634.5 52.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CCRA1<-HSD.test(CRA1,trt="TCRA1")
CCRA1## $statistics
## MSerror Df Mean CV MSD
## 52.87812 12 78.58875 9.252895 15.26577
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA1 4 4.19866 0.05
##
## $means
## CRA1 std r Min Max Q25 Q50 Q75
## T1 66.850 6.836524 4 58.49 75.19 64.3025 66.860 69.4075
## T2 76.665 5.517152 4 71.07 82.84 72.5775 76.375 80.4625
## T3 88.960 9.611892 4 76.62 100.00 85.5825 89.610 92.9875
## T4 81.880 6.476650 4 73.91 87.70 77.9825 82.955 86.8525
##
## $comparison
## NULL
##
## $groups
## CRA1 groups
## T3 88.960 a
## T4 81.880 ab
## T2 76.665 ab
## T1 66.850 b
##
## attr(,"class")
## [1] "group"#Muestreo 2
TCRA2<-as.factor(M2$tratamiento)
CRA2<-as.numeric(M2$CRA)
CRA2<-aov(CRA2~TCRA2)
summary(CRA2)## Df Sum Sq Mean Sq F value Pr(>F)
## TCRA2 3 1952 650.5 3.76 0.041 *
## Residuals 12 2076 173.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CCRA2<-HSD.test(CRA2,trt="TCRA2")
CCRA2## $statistics
## MSerror Df Mean CV MSD
## 173.0082 12 67.155 19.58642 27.61303
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA2 4 4.19866 0.05
##
## $means
## CRA2 std r Min Max Q25 Q50 Q75
## T1 84.8125 13.495099 4 70.89 100.00 75.0225 84.180 93.9700
## T2 68.0500 9.216001 4 54.23 72.97 67.9250 72.500 72.6250
## T3 59.1425 18.986814 4 37.24 80.22 47.3275 59.555 71.3700
## T4 56.6150 8.030037 4 45.21 63.03 53.9775 59.110 61.7475
##
## $comparison
## NULL
##
## $groups
## CRA2 groups
## T1 84.8125 a
## T2 68.0500 ab
## T3 59.1425 ab
## T4 56.6150 b
##
## attr(,"class")
## [1] "group"#Muestreo 3
TCRA3<-as.factor(M3$tratamiento)
CRA3<-as.numeric(M3$CRA)
CRA3<-aov(CRA3~TCRA3)
summary(CRA3)## Df Sum Sq Mean Sq F value Pr(>F)
## TCRA3 3 114.3 38.11 0.389 0.763
## Residuals 12 1174.4 97.87CCRA3<-HSD.test(CRA3,trt="TCRA3")
CCRA3## $statistics
## MSerror Df Mean CV MSD
## 97.8672 12 77.66563 12.73766 20.76822
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA3 4 4.19866 0.05
##
## $means
## CRA3 std r Min Max Q25 Q50 Q75
## T1 80.2300 9.207377 4 67.08 87.99 77.5950 82.925 85.5600
## T2 80.2850 10.774880 4 64.42 88.21 78.4975 84.255 86.0425
## T3 75.9975 8.564992 4 65.40 86.22 72.5400 76.185 79.6425
## T4 74.1500 10.827551 4 59.22 83.32 69.7650 77.030 81.4150
##
## $comparison
## NULL
##
## $groups
## CRA3 groups
## T2 80.2850 a
## T1 80.2300 a
## T3 75.9975 a
## T4 74.1500 a
##
## attr(,"class")
## [1] "group"#Muestreo 4
TCRA4<-as.factor(M4$tratamiento)
CRA4<-as.numeric(M4$CRA)
CRA4<-aov(CRA4~TCRA4)
summary(CRA4)## Df Sum Sq Mean Sq F value Pr(>F)
## TCRA4 3 783.9 261.3 1.394 0.292
## Residuals 12 2249.8 187.5CCRA4<-HSD.test(CRA4,trt="TCRA4")
CCRA4## $statistics
## MSerror Df Mean CV MSD
## 187.4793 12 74.34437 18.41741 28.74467
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCRA4 4 4.19866 0.05
##
## $means
## CRA4 std r Min Max Q25 Q50 Q75
## T1 84.8650 11.136428 4 73.86 99.11 77.4300 83.245 90.6800
## T2 76.5175 21.122104 4 44.89 88.21 75.2125 86.485 87.7900
## T3 67.9500 11.291486 4 53.98 79.61 61.6225 69.105 75.4325
## T4 68.0450 7.228833 4 57.72 74.55 66.5025 69.955 71.4975
##
## $comparison
## NULL
##
## $groups
## CRA4 groups
## T1 84.8650 a
## T2 76.5175 a
## T4 68.0450 a
## T3 67.9500 a
##
## attr(,"class")
## [1] "group"CRA<- c("b","ab","a","ab","a","ab","ab","b","a","a","a","a","a","a","a","a")
data<-data.frame(CRA=as.numeric(datos$CRA), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dCRA <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w6 = mean(CRA,na.rm=TRUE), sd6 = sd(CRA,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dCRA$CRA<- CRAplot_5<-ggplot(dCRA, aes(muestreo,w6, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Contenido relativo de agua") + coord_cartesian(ylim=c(0,110)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = CRA), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w6-sd6, ymax = w6+sd6),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 1, y = 110, label = "**") +
geom_text(data = NULL, x = 2, y = 110, label = "**") Perdida de electrolitos
TPE<-as.factor(PED$tratamiento)
PE<-as.numeric(PED$PE)
APE<-aov(PE~TPE)
summary(APE)## Df Sum Sq Mean Sq F value Pr(>F)
## TPE 3 66.8 22.27 0.514 0.684
## Residuals 8 346.7 43.33CPE<-HSD.test(APE,trt="TPE")
CPE## $statistics
## MSerror Df Mean CV MSD
## 43.33478 8 11.98917 54.9072 17.21241
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPE 4 4.52881 0.05
##
## $means
## PE std r Min Max Q25 Q50 Q75
## T1 11.88000 7.133085 3 7.48 20.11 7.765 8.05 14.080
## T2 15.86667 8.261709 3 6.35 21.20 13.200 20.05 20.625
## T3 10.35000 6.332875 3 5.84 17.59 6.730 7.62 12.605
## T4 9.86000 3.754610 3 6.61 13.97 7.805 9.00 11.485
##
## $comparison
## NULL
##
## $groups
## PE groups
## T2 15.86667 a
## T1 11.88000 a
## T3 10.35000 a
## T4 9.86000 a
##
## attr(,"class")
## [1] "group"PE1<- c("a","a","a","a")
data<-data.frame(PE1=as.numeric(PED$PE), muestreo=as.factor(PED$Muestreo),Tratamiento=PED$tratamiento)
dPE <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w7 = mean(PE1,na.rm=TRUE), sd7 = sd(PE1,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dPE$PE1<- PE1plot_6<-ggplot(dPE, aes(muestreo,w7, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Perdida de electrolitos") + coord_cartesian(ylim=c(0,30)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = PE1), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w7-sd7, ymax = w7+sd7),width = .5, position = position_dodge(0.9))plot_grid(plot_5,plot_6,labels = "AUTO")
## Area foliar
# Muestreo 1
TAF1<-as.factor(M1$tratamiento)
AF1<-as.numeric(M1$AF)
AAF1<-aov(AF1~TAF1)
summary(AAF1)## Df Sum Sq Mean Sq F value Pr(>F)
## TAF1 3 5010 1670.1 3.49 0.05 .
## Residuals 12 5742 478.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CAF1<-HSD.test(AAF1,trt="TAF1")
CAF1## $statistics
## MSerror Df Mean CV MSD
## 478.5173 12 167.2562 13.07876 45.92293
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF1 4 4.19866 0.05
##
## $means
## AF1 std r Min Max Q25 Q50 Q75
## T1 177.625 23.21226 4 154.1 209.2 165.650 173.60 185.575
## T2 189.800 18.33594 4 167.6 208.7 178.925 191.45 202.325
## T3 143.900 25.60456 4 124.2 179.7 126.075 135.85 153.675
## T4 157.700 19.58213 4 136.7 182.5 146.075 155.80 167.425
##
## $comparison
## NULL
##
## $groups
## AF1 groups
## T2 189.800 a
## T1 177.625 a
## T4 157.700 a
## T3 143.900 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
TAF2<-as.factor(M2$tratamiento)
AF2<-as.numeric(M2$AF)
AAF2<-aov(AF2~TAF2)
summary(AAF2)## Df Sum Sq Mean Sq F value Pr(>F)
## TAF2 3 20077 6692 13.78 0.000341 ***
## Residuals 12 5826 486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CAF2<-HSD.test(AAF2,trt="TAF2")
CAF2## $statistics
## MSerror Df Mean CV MSD
## 485.5354 12 183.2694 12.02321 46.25846
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF2 4 4.19866 0.05
##
## $means
## AF2 std r Min Max Q25 Q50 Q75
## T1 231.5300 30.628526 4 210.09 276.96 216.255 219.535 234.8100
## T2 199.1250 5.217851 4 191.74 203.60 197.470 200.580 202.2350
## T3 165.0550 23.385633 4 138.42 195.20 154.935 163.300 173.4200
## T4 137.3675 20.734537 4 111.04 161.38 129.445 138.525 146.4475
##
## $comparison
## NULL
##
## $groups
## AF2 groups
## T1 231.5300 a
## T2 199.1250 ab
## T3 165.0550 bc
## T4 137.3675 c
##
## attr(,"class")
## [1] "group"# Muestreo 3
TAF3<-as.factor(M3$tratamiento)
AF3<-as.numeric(M3$AF)
AAF3<-aov(AF3~TAF3)
summary(AAF3)## Df Sum Sq Mean Sq F value Pr(>F)
## TAF3 3 1506 502 0.446 0.725
## Residuals 12 13509 1126CAF3<-HSD.test(AAF3,trt="TAF3")
CAF3## $statistics
## MSerror Df Mean CV MSD
## 1125.737 12 205.2562 16.3464 70.43674
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF3 4 4.19866 0.05
##
## $means
## AF3 std r Min Max Q25 Q50 Q75
## T1 214.700 42.47799 4 174.3 266.4 183.225 209.05 240.525
## T2 189.025 38.09596 4 143.2 227.3 165.775 192.80 216.050
## T3 209.300 17.87867 4 188.0 231.5 201.800 208.85 216.350
## T4 208.000 30.45685 4 181.2 240.9 182.550 204.95 230.400
##
## $comparison
## NULL
##
## $groups
## AF3 groups
## T1 214.700 a
## T3 209.300 a
## T4 208.000 a
## T2 189.025 a
##
## attr(,"class")
## [1] "group"# Muestreo 4
TAF4<-as.factor(M4$tratamiento)
AF4<-as.numeric(M4$AF)
AAF4<-aov(AF4~TAF4)
summary(AAF4)## Df Sum Sq Mean Sq F value Pr(>F)
## TAF4 3 558 186.0 0.639 0.604
## Residuals 12 3494 291.2CAF4<-HSD.test(AAF4,trt="TAF4")
CAF4## $statistics
## MSerror Df Mean CV MSD
## 291.2081 12 213.6062 7.988915 35.82469
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TAF4 4 4.19866 0.05
##
## $means
## AF4 std r Min Max Q25 Q50 Q75
## T1 206.350 22.63706 4 189.6 239.2 192.450 198.30 212.200
## T2 209.350 19.48923 4 185.4 233.1 202.725 209.45 216.075
## T3 220.475 10.77168 4 207.5 232.4 214.400 221.00 227.075
## T4 218.250 12.51146 4 204.5 231.6 209.600 218.45 227.100
##
## $comparison
## NULL
##
## $groups
## AF4 groups
## T3 220.475 a
## T4 218.250 a
## T2 209.350 a
## T1 206.350 a
##
## attr(,"class")
## [1] "group"AF<- c("a","a","a","a","a","ab","bc","c","a","a","a","a","a","a","a","a")
data<-data.frame(AF=as.numeric(datos$AF), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dAF <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w8 = mean(AF,na.rm=TRUE), sd8 = sd(AF,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dAF$AF<- AFggplot(dAF, aes(muestreo,w8, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Area foliar") + coord_cartesian(ylim=c(0,300)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = AF), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w8-sd8, ymax = w8+sd8),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 2, y = 300, label = "**") 
Temperatura
# Muestreo 1
TT1<-as.factor(M1$tratamiento)
T1<-as.numeric(M1$T)
AT1<-aov(T1~TT1)
summary(AT1)## Df Sum Sq Mean Sq F value Pr(>F)
## TT1 3 0.02188 0.007292 0.304 0.822
## Residuals 12 0.28750 0.023958CT1<-HSD.test(AT1,trt="TT1")
CT1## $statistics
## MSerror Df Mean CV MSD
## 0.02395833 12 13.09375 1.182127 0.3249444
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT1 4 4.19866 0.05
##
## $means
## T1 std r Min Max Q25 Q50 Q75
## T1 13.150 0.19148542 4 13 13.4 13 13.10 13.250
## T2 13.050 0.10000000 4 13 13.2 13 13.00 13.050
## T3 13.100 0.20000000 4 13 13.4 13 13.00 13.100
## T4 13.075 0.09574271 4 13 13.2 13 13.05 13.125
##
## $comparison
## NULL
##
## $groups
## T1 groups
## T1 13.150 a
## T3 13.100 a
## T4 13.075 a
## T2 13.050 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
TT2<-as.factor(M2$tratamiento)
T2<-as.numeric(M2$T)
AT2<-aov(T2~TT2)
summary(AT2)## Df Sum Sq Mean Sq F value Pr(>F)
## TT2 3 7.717 2.572 1.973 0.172
## Residuals 12 15.648 1.304CT2<-HSD.test(AT2,trt="TT2")
CT2## $statistics
## MSerror Df Mean CV MSD
## 1.303958 12 22.71875 5.026289 2.397246
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT2 4 4.19866 0.05
##
## $means
## T2 std r Min Max Q25 Q50 Q75
## T1 23.375 1.0307764 4 22.5 24.5 22.500 23.25 24.125
## T2 22.050 1.7916473 4 20.0 24.0 20.900 22.10 23.250
## T3 22.000 0.4082483 4 21.5 22.5 21.875 22.00 22.125
## T4 23.450 0.8812869 4 22.5 24.5 22.875 23.40 23.975
##
## $comparison
## NULL
##
## $groups
## T2 groups
## T4 23.450 a
## T1 23.375 a
## T2 22.050 a
## T3 22.000 a
##
## attr(,"class")
## [1] "group"# Muestreo 3
TT3<-as.factor(M3$tratamiento)
T3<-as.numeric(M3$T)
AT3<-aov(T3~TT3)
summary(AT3)## Df Sum Sq Mean Sq F value Pr(>F)
## TT3 3 22.882 7.627 9.21 0.00194 **
## Residuals 12 9.938 0.828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CT3<-HSD.test(AT3,trt="TT3")
CT3## $statistics
## MSerror Df Mean CV MSD
## 0.828125 12 21.95625 4.144668 1.910419
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT3 4 4.19866 0.05
##
## $means
## T3 std r Min Max Q25 Q50 Q75
## T1 23.550 1.1561430 4 22.2 24.7 22.800 23.65 24.400
## T2 20.475 0.6701990 4 19.8 21.1 19.950 20.50 21.025
## T3 22.600 0.8640988 4 21.4 23.4 22.300 22.80 23.100
## T4 21.200 0.8831761 4 20.0 22.0 20.825 21.40 21.775
##
## $comparison
## NULL
##
## $groups
## T3 groups
## T1 23.550 a
## T3 22.600 ab
## T4 21.200 bc
## T2 20.475 c
##
## attr(,"class")
## [1] "group"# Muestreo 4
TT4<-as.factor(M4$tratamiento)
T4<-as.numeric(M4$T)
AT4<-aov(T4~TT4)
summary(AT4)## Df Sum Sq Mean Sq F value Pr(>F)
## TT4 3 13.81 4.605 4.185 0.0304 *
## Residuals 12 13.21 1.100
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CT4<-HSD.test(AT4,trt="TT4")
CT4## $statistics
## MSerror Df Mean CV MSD
## 1.100417 12 21.5 4.879104 2.202213
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TT4 4 4.19866 0.05
##
## $means
## T4 std r Min Max Q25 Q50 Q75
## T1 22.500 0.6782330 4 22.0 23.5 22.15 22.25 22.600
## T2 20.475 0.6701990 4 19.8 21.1 19.95 20.50 21.025
## T3 22.350 0.8544004 4 21.4 23.4 21.85 22.30 22.800
## T4 20.675 1.6620770 4 19.5 23.1 19.65 20.05 21.075
##
## $comparison
## NULL
##
## $groups
## T4 groups
## T1 22.500 a
## T3 22.350 a
## T4 20.675 a
## T2 20.475 a
##
## attr(,"class")
## [1] "group"T<- c("a","a","a","a","a","a","a","a","a","c","ab","bc","a","a","a","a")
data<-data.frame(T=as.numeric(datos$T), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dT <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w9 = mean(T,na.rm=TRUE), sd9 = sd(T,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dT$T<- Tplot_7<-ggplot(dT, aes(muestreo,w9, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("°C de la hoja") + coord_cartesian(ylim=c(0,30)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = T), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w9-sd9, ymax = w9+sd9),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 3, y = 30, label = "**") Estomas cerrados
# Muestreo 1
TEC1<-as.factor(M1$tratamiento)
EC1<-as.numeric(M1$EC)
AEC1<-aov(EC1~TEC1)
summary(AEC1)## Df Sum Sq Mean Sq F value Pr(>F)
## TEC1 3 510 170.0 0.644 0.601
## Residuals 12 3166 263.9CEC1<-HSD.test(AEC1,trt="TEC1")
CEC1## $statistics
## MSerror Df Mean CV MSD
## 263.8714 12 42.09764 38.58677 34.10177
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TEC1 4 4.19866 0.05
##
## $means
## EC1 std r Min Max Q25 Q50 Q75
## T1 47.12886 15.65615 4 36.363636 70.27027 38.35920 40.94077 49.71042
## T2 33.80859 17.67727 4 9.756098 50.00000 26.43902 37.73913 45.10870
## T3 39.97346 15.32995 4 20.000000 56.09756 33.12500 41.89815 48.74661
## T4 47.47964 16.21350 4 26.666667 62.50000 38.80952 50.37594 59.04605
##
## $comparison
## NULL
##
## $groups
## EC1 groups
## T4 47.47964 a
## T1 47.12886 a
## T3 39.97346 a
## T2 33.80859 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
TEC2<-as.factor(M2$tratamiento)
EC2<-as.numeric(M2$EC)
AEC2<-aov(EC2~TEC2)
summary(AEC2)## Df Sum Sq Mean Sq F value Pr(>F)
## TEC2 3 150.2 50.05 1.385 0.295
## Residuals 12 433.7 36.15CEC2<-HSD.test(AEC2,trt="TEC2")
CEC2## $statistics
## MSerror Df Mean CV MSD
## 36.14502 12 48.50412 12.39497 12.62133
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TEC2 4 4.19866 0.05
##
## $means
## EC2 std r Min Max Q25 Q50 Q75
## T1 44.36533 7.761744 4 39.47368 55.88235 39.86842 41.05263 45.54954
## T2 49.29436 1.689214 4 47.82609 51.35135 47.95652 49.00000 50.33784
## T3 52.84091 8.454569 4 44.44444 63.88889 47.47475 51.51515 56.88131
## T4 47.51590 3.162630 4 44.11765 51.35135 45.48887 47.29730 49.32432
##
## $comparison
## NULL
##
## $groups
## EC2 groups
## T3 52.84091 a
## T2 49.29436 a
## T4 47.51590 a
## T1 44.36533 a
##
## attr(,"class")
## [1] "group"# Muestreo 3
TEC3<-as.factor(M3$tratamiento)
EC3<-as.numeric(M3$EC)
AEC3<-aov(EC3~TEC3)
summary(AEC3)## Df Sum Sq Mean Sq F value Pr(>F)
## TEC3 3 394.6 131.55 1.975 0.172
## Residuals 12 799.3 66.61CEC3<-HSD.test(AEC3,trt="TEC3")
CEC3## $statistics
## MSerror Df Mean CV MSD
## 66.60983 12 47.69409 17.11215 17.13365
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TEC3 4 4.19866 0.05
##
## $means
## EC3 std r Min Max Q25 Q50 Q75
## T1 50.26856 6.261486 4 40.90909 54.05405 49.81061 53.05556 53.51351
## T2 41.45952 12.336334 4 23.33333 50.00000 38.92157 46.25237 48.79032
## T3 54.34411 4.181423 4 50.00000 58.62069 51.20968 54.37788 57.51232
## T4 44.70415 7.587072 4 37.03704 55.17241 41.40212 43.30357 46.60560
##
## $comparison
## NULL
##
## $groups
## EC3 groups
## T3 54.34411 a
## T1 50.26856 a
## T4 44.70415 a
## T2 41.45952 a
##
## attr(,"class")
## [1] "group"# Muestreo 4
TEC4<-as.factor(M4$tratamiento)
EC4<-as.numeric(M4$EC)
AEC4<-aov(EC4~TEC4)
summary(AEC4)## Df Sum Sq Mean Sq F value Pr(>F)
## TEC4 3 226.9 75.62 2.119 0.151
## Residuals 12 428.3 35.70CEC4<-HSD.test(AEC4,trt="TEC4")
CEC4## $statistics
## MSerror Df Mean CV MSD
## 35.69517 12 48.09029 12.4236 12.54254
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TEC4 4 4.19866 0.05
##
## $means
## EC4 std r Min Max Q25 Q50 Q75
## T1 45.97070 4.712337 4 41.02564 50.00000 42.39927 46.42857 50.00000
## T2 43.96183 7.238744 4 35.48387 51.35135 39.33972 44.50605 49.12816
## T3 54.01261 6.675676 4 45.45455 61.76471 52.07792 54.41558 56.35027
## T4 48.41604 4.859062 4 43.75000 53.57143 44.55819 48.17137 52.02922
##
## $comparison
## NULL
##
## $groups
## EC4 groups
## T3 54.01261 a
## T4 48.41604 a
## T1 45.97070 a
## T2 43.96183 a
##
## attr(,"class")
## [1] "group"EC<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data<-data.frame(EC=as.numeric(datos$EC), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dEC <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w10 = mean(EC,na.rm=TRUE), sd10 = sd(EC,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dEC$EC<- ECplot_8<-ggplot(dEC, aes(muestreo,w10, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("Estomas cerrados (%)") + coord_cartesian(ylim=c(0,100)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = EC), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w10-sd10, ymax = w10+sd10),width = .5, position = position_dodge(0.9))Estomas parcialmente abiertos
# Muestreo 1
TPA1<-as.factor(M1$tratamiento)
PA1<-as.numeric(M1$EPA)
APA1<-aov(PA1~TPA1)
summary(APA1)## Df Sum Sq Mean Sq F value Pr(>F)
## TPA1 3 437.6 145.9 0.618 0.616
## Residuals 12 2832.0 236.0CPA1<-HSD.test(APA1,trt="TPA1")
CPA1## $statistics
## MSerror Df Mean CV MSD
## 235.997 12 34.17865 44.94675 32.25032
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPA1 4 4.19866 0.05
##
## $means
## PA1 std r Min Max Q25 Q50 Q75
## T1 41.79301 18.931720 4 13.51351 53.65854 40.87838 50.00000 50.91463
## T2 35.85972 10.531133 4 26.08696 48.78049 27.95031 34.28571 42.19512
## T3 31.17342 19.565808 4 14.63415 56.25000 16.15854 26.90476 41.91964
## T4 27.88847 9.583976 4 16.66667 40.00000 23.90351 27.44361 31.42857
##
## $comparison
## NULL
##
## $groups
## PA1 groups
## T1 41.79301 a
## T2 35.85972 a
## T3 31.17342 a
## T4 27.88847 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
TPA2<-as.factor(M2$tratamiento)
PA2<-as.numeric(M2$EPA)
APA2<-aov(PA2~TPA2)
summary(APA2)## Df Sum Sq Mean Sq F value Pr(>F)
## TPA2 3 130.4 43.47 1.809 0.199
## Residuals 12 288.4 24.03CPA2<-HSD.test(APA2,trt="TPA2")
CPA2## $statistics
## MSerror Df Mean CV MSD
## 24.03416 12 27.25831 17.98522 10.29189
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPA2 4 4.19866 0.05
##
## $means
## PA2 std r Min Max Q25 Q50 Q75
## T1 28.34697 3.959835 4 26.31579 34.28571 26.31579 26.39319 28.42437
## T2 26.39272 6.873021 4 20.00000 34.78261 21.21622 25.39414 30.57065
## T3 23.23232 2.550380 4 19.44444 25.00000 23.04293 24.24242 24.43182
## T4 31.06121 5.168511 4 27.02703 37.83784 27.02703 29.68998 33.72417
##
## $comparison
## NULL
##
## $groups
## PA2 groups
## T4 31.06121 a
## T1 28.34697 a
## T2 26.39272 a
## T3 23.23232 a
##
## attr(,"class")
## [1] "group"# Muestreo 3
TPA3<-as.factor(M3$tratamiento)
PA3<-as.numeric(M3$EPA)
APA3<-aov(PA3~TPA3)
summary(APA3)## Df Sum Sq Mean Sq F value Pr(>F)
## TPA3 3 423.5 141.17 3.961 0.0355 *
## Residuals 12 427.7 35.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CPA3<-HSD.test(APA3,trt="TPA3")
CPA3## $statistics
## MSerror Df Mean CV MSD
## 35.63767 12 31.10981 19.18922 12.53243
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPA3 4 4.19866 0.05
##
## $means
## PA3 std r Min Max Q25 Q50 Q75
## T1 25.10886 1.906390 4 23.33333 27.77778 24.07658 24.66216 25.69444
## T2 34.96228 7.999106 4 28.57143 46.66667 31.33641 32.30550 35.93137
## T3 27.05013 4.350637 4 22.85714 32.25806 23.81773 26.54265 29.77504
## T4 37.31798 7.483490 4 31.03448 48.14815 33.53987 35.04464 38.82275
##
## $comparison
## NULL
##
## $groups
## PA3 groups
## T4 37.31798 a
## T2 34.96228 a
## T3 27.05013 a
## T1 25.10886 a
##
## attr(,"class")
## [1] "group"# Muestreo 4
TPA4<-as.factor(M4$tratamiento)
PA4<-as.numeric(M4$EPA)
APA4<-aov(PA4~TPA4)
summary(APA4)## Df Sum Sq Mean Sq F value Pr(>F)
## TPA4 3 225.7 75.25 1.768 0.207
## Residuals 12 510.8 42.56CPA4<-HSD.test(APA4,trt="TPA4")
CPA4## $statistics
## MSerror Df Mean CV MSD
## 42.56325 12 31.43082 20.75686 13.69614
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TPA4 4 4.19866 0.05
##
## $means
## PA4 std r Min Max Q25 Q50 Q75
## T1 25.24484 3.110099 4 21.05263 28.57143 24.49393 25.67766 26.42857
## T2 33.35399 9.648909 4 21.62162 45.16129 29.59895 33.31653 37.07157
## T3 31.92131 7.822983 4 23.52941 42.42424 28.60963 30.86580 34.17749
## T4 35.20313 2.505947 4 32.14286 37.93103 33.81696 35.36932 36.75549
##
## $comparison
## NULL
##
## $groups
## PA4 groups
## T4 35.20313 a
## T2 33.35399 a
## T3 31.92131 a
## T1 25.24484 a
##
## attr(,"class")
## [1] "group"pa<- c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a")
data<-data.frame(pa=as.numeric(datos$EPA), muestreo=as.factor(datos$Muestreo),Tratamiento=datos$tratamiento)
dpa <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w11 = mean(pa,na.rm=TRUE), sd11 = sd(pa,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dpa$pa<- paplot_9<-ggplot(dpa, aes(muestreo,w11, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("E parcialmente abiertos (%)") + coord_cartesian(ylim=c(0,75)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = pa), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w11-sd11, ymax = w11+sd11),width = .5, position = position_dodge(0.9))Contenido relativo de clorofilas
# Muestreo 1
TCL1<-as.factor(CM1$tratamiento)
CLA1<-as.numeric(CM1$CLA)
ACL1<-aov(CLA1~TCL1)
summary(ACL1)## Df Sum Sq Mean Sq F value Pr(>F)
## TCL1 3 86.2 28.73 1.231 0.306
## Residuals 60 1400.3 23.34CCL1<-HSD.test(ACL1,trt="TCL1")
CCL1## $statistics
## MSerror Df Mean CV MSD
## 23.3376 60 48.675 9.924809 4.513377
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCL1 4 3.737089 0.05
##
## $means
## CLA1 std r Min Max Q25 Q50 Q75
## T1 49.05000 4.947188 16 42.3 58.3 44.85 48.70 52.950
## T2 49.06250 4.165873 16 41.5 56.3 47.50 48.70 51.900
## T3 49.84375 5.332038 16 40.2 57.3 47.05 52.30 53.275
## T4 46.74375 4.805271 16 36.2 57.6 44.10 47.35 48.575
##
## $comparison
## NULL
##
## $groups
## CLA1 groups
## T3 49.84375 a
## T2 49.06250 a
## T1 49.05000 a
## T4 46.74375 a
##
## attr(,"class")
## [1] "group"# Muestreo 2
TCL2<-as.factor(CM2$tratamiento)
CLA2<-as.numeric(CM2$CLA)
ACL2<-aov(CLA2~TCL2)
summary(ACL2)## Df Sum Sq Mean Sq F value Pr(>F)
## TCL2 3 80.2 26.72 0.898 0.447
## Residuals 60 1784.6 29.74CCL2<-HSD.test(ACL2,trt="TCL2")
CCL2## $statistics
## MSerror Df Mean CV MSD
## 29.74381 60 48.49688 11.24565 5.095324
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCL2 4 3.737089 0.05
##
## $means
## CLA2 std r Min Max Q25 Q50 Q75
## T1 49.61875 5.533801 16 40.6 58.8 46.525 47.70 53.60
## T2 48.15000 4.584030 16 41.0 57.2 44.525 48.10 51.25
## T3 49.40000 5.281666 16 40.0 57.2 43.450 51.15 53.20
## T4 46.81875 6.280363 16 30.1 54.8 43.825 48.80 50.20
##
## $comparison
## NULL
##
## $groups
## CLA2 groups
## T1 49.61875 a
## T3 49.40000 a
## T2 48.15000 a
## T4 46.81875 a
##
## attr(,"class")
## [1] "group"# Muestreo 3
TCL3<-as.factor(CM3$tratamiento)
CLA3<-as.numeric(CM3$CLA)
ACL3<-aov(CLA3~TCL3)
summary(ACL3)## Df Sum Sq Mean Sq F value Pr(>F)
## TCL3 3 1000 333.5 6.816 0.000498 ***
## Residuals 60 2936 48.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CCL3<-HSD.test(ACL3,trt="TCL3")
CCL3## $statistics
## MSerror Df Mean CV MSD
## 48.92865 60 36.81562 18.99982 6.535143
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCL3 4 3.737089 0.05
##
## $means
## CLA3 std r Min Max Q25 Q50 Q75
## T1 35.83750 4.764574 16 30.4 44.2 32.000 34.65 39.175
## T2 42.81875 7.673697 16 30.8 52.4 37.375 44.50 48.850
## T3 31.76250 7.196840 16 20.4 41.3 28.150 32.80 36.575
## T4 36.84375 7.895144 16 23.4 46.4 34.500 38.95 41.200
##
## $comparison
## NULL
##
## $groups
## CLA3 groups
## T2 42.81875 a
## T4 36.84375 ab
## T1 35.83750 b
## T3 31.76250 b
##
## attr(,"class")
## [1] "group"# Muestreo 4
TCL4<-as.factor(CM4$tratamiento)
CLA4<-as.numeric(CM4$CLA)
ACL4<-aov(CLA4~TCL4)
summary(ACL4)## Df Sum Sq Mean Sq F value Pr(>F)
## TCL4 3 387.6 129.20 2.825 0.0462 *
## Residuals 60 2744.5 45.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1CCL4<-HSD.test(ACL4,trt="TCL4")
CCL4## $statistics
## MSerror Df Mean CV MSD
## 45.74243 60 39.01719 17.33419 6.318778
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TCL4 4 3.737089 0.05
##
## $means
## CLA4 std r Min Max Q25 Q50 Q75
## T1 36.39375 4.335661 16 31.5 44.4 33.200 34.85 38.800
## T2 41.88125 6.167033 16 31.6 49.4 36.125 43.20 46.850
## T3 36.76250 7.196840 16 25.4 46.3 33.150 37.80 41.575
## T4 41.03125 8.622352 16 26.5 55.6 35.450 42.85 45.275
##
## $comparison
## NULL
##
## $groups
## CLA4 groups
## T2 41.88125 a
## T4 41.03125 a
## T3 36.76250 a
## T1 36.39375 a
##
## attr(,"class")
## [1] "group"cla<- c("a","a","a","a","a","a","a","a","b","a","b","ab","a","a","a","a")
data<-data.frame(cla=as.numeric(CLA$CLA), muestreo=as.factor(CLA$Muestreo),Tratamiento=CLA$tratamiento)
dcla <-data %>%
group_by(muestreo, Tratamiento) %>%
summarise(w12 = mean(cla,na.rm=TRUE), sd12 = sd(cla,na.rm=TRUE))## `summarise()` has grouped output by 'muestreo'. You can override using the
## `.groups` argument.dcla$cla<- claplot_10<-ggplot(dcla, aes(muestreo,w12, fill = Tratamiento)) +
geom_bar(position="dodge", stat="identity") +
theme_classic() + xlab("Muestreo") + ylab("C relativo de clorofila") + coord_cartesian(ylim=c(0,60)) + scale_fill_manual(values = c("grey50","grey40","grey30","grey20"),name = "Tratamiento", labels = c("T1","T2","T3", "T4")) + geom_text(aes(label = cla), position = position_dodge(width=1), vjust=-4, cex=3)+ geom_errorbar(aes(ymin = w12-sd12, ymax = w12+sd12),width = .5, position = position_dodge(0.9))+
geom_text(data = NULL, x = 3, y = 60, label = "**") plot_grid(plot_7,plot_8,plot_9,plot_10,labels = "AUTO")
Indices
Fraccion foliar
df_FF <- data.frame(FF = (indice$FF),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo))
datai<-data.frame(FF=as.numeric(indice$FF), muestreo=as.factor(indice$Muestreo),Tratamiento=indice$tratamiento) FF<-ggplot(datai, aes(muestreo, as.numeric(FF), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Fraccion foliar (%)",x="Muestreo", y = "FF")Relacion area foliar
df_RAF <- data.frame(RAF = (indice$RAF),
Tratamiento = as.factor(indice$tratamiento),
PUNT = as.factor(indice$Muestreo))
dataF<-data.frame(RAF=as.numeric(indice$RAF), muestreo=as.factor(indice$Muestreo),Tratamiento=indice$tratamiento) RAF<-ggplot(dataF, aes(muestreo, as.numeric(RAF), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Relacion de area foliar ",x="Muestreo", y = "RAF (cm⁻²/g))")Tasa absoluta de crecimiento
df_t <- data.frame(TAC = (TAC1$TAC),
Tratamiento = as.factor(TAC1$tratamiento),
PUNT = as.factor(TAC1$Tiempo))
datat<-data.frame(TAC=as.numeric(TAC1$TAC), muestreo=as.factor(TAC1$Tiempo),Tratamiento=TAC1$tratamiento) TAC<-ggplot(datat, aes(muestreo, as.numeric(TAC), group = Tratamiento)) +
geom_line(aes(linetype = Tratamiento))+
geom_point(aes(shape = Tratamiento))+
theme_classic()+
labs(title="Tasa absoluta de crecimiento",x="DDS", y = "TAC (g/g*dias)")graf<-plot_grid(FF,RAF,TAC,labels = "AUTO", ncol = 2, align = 'v');graf