INDICES_ANEGAMIENTO
Laura Sofia Hernandez Sanchez
14/07/2021
library(readxl)
Temp1 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM1")
Temp2 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM2")
Temp3 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM3")
Temp4 <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TempM4")
TRC<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TRC")
TRC1<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TRC1")
AFE<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "AFE")
PFE<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "PFE")
TAN <- read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "TAN")
IAF<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "IAF")
RAF<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "RAF")
RAF1<-read_excel("C:/Users/Sofia Hernandez/Downloads/Anegamiento/Muestreos.xlsx",
sheet = "RAF1")####TASA RELATIVA DE CRECIMIENTO
library(agricolae)
M2TRC <- aov(TRC~Trat, data = Temp2)
anova(M2TRC)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.19423 0.064744 136.3 1.556e-09 ***
## Residuals 12 0.00570 0.000475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3TRC <- aov(TRC~Trat, data = Temp3)
anova(M3TRC)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.279250 0.093083 1764.4 3.924e-16 ***
## Residuals 12 0.000633 0.000053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4TRC <- aov(TRC~Trat, data = Temp4)
anova(M4TRC)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.120543 0.040181 1429.1 1.385e-15 ***
## Residuals 12 0.000337 0.000028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#####PRUEBAS
#Pruebas de normalidad
shapiro.test(resid(M2TRC))##
## Shapiro-Wilk normality test
##
## data: resid(M2TRC)
## W = 0.87151, p-value = 0.02868shapiro.test(resid(M3TRC))##
## Shapiro-Wilk normality test
##
## data: resid(M3TRC)
## W = 0.97967, p-value = 0.9609shapiro.test(resid(M4TRC))##
## Shapiro-Wilk normality test
##
## data: resid(M4TRC)
## W = 0.94014, p-value = 0.3507#**Homogeneidad de varianzas**
library(carData)
bartlett.test(TRC~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: TRC by Trat
## Bartlett's K-squared = 12.076, df = 3, p-value = 0.007128bartlett.test(TRC~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: TRC by Trat
## Bartlett's K-squared = 8.3487, df = 3, p-value = 0.03933bartlett.test(TRC~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: TRC by Trat
## Bartlett's K-squared = 7.2926, df = 3, p-value = 0.06313#Pueba de tukey
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.3library(agricolae)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionM2TRC_tukey <-HSD.test(Temp2$TRC,Temp2$Trat, 12, 0.000475, alpha = 0.05);M2TRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.000475 12 0.4314961 5.050914 0.04575384
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$TRC std r Min Max Q25 Q50 Q75
## MT0W0 0.3300501 0.007841572 4 0.3240441 0.3415767 0.3261892 0.3272898 0.3311508
## MT0W1 0.5956166 0.038910992 4 0.5516937 0.6463289 0.5798277 0.5922219 0.6080108
## MT1W0 0.3317217 0.004269788 4 0.3276111 0.3358635 0.3282729 0.3317061 0.3351548
## MT1W1 0.4685958 0.017501439 4 0.4498021 0.4909662 0.4584872 0.4668075 0.4769161
##
## $comparison
## NULL
##
## $groups
## Temp2$TRC groups
## MT0W1 0.5956166 a
## MT1W1 0.4685958 b
## MT1W0 0.3317217 c
## MT0W0 0.3300501 c
##
## attr(,"class")
## [1] "group"M3TRC_tukey <-HSD.test(Temp3$TRC,Temp3$Trat, 12, 0.000053, alpha = 0.05);M3TRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 5.3e-05 12 0.3497591 2.081464 0.01528335
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$TRC std r Min Max Q25 Q50 Q75
## MT0W0 0.2304926 0.004420437 4 0.2241219 0.2336449 0.2292006 0.2321018 0.2333937
## MT0W1 0.5515230 0.011828910 4 0.5391666 0.5649262 0.5430978 0.5509997 0.5594249
## MT1W0 0.2327592 0.001556660 4 0.2314356 0.2349466 0.2317863 0.2323274 0.2333002
## MT1W1 0.3842614 0.007010022 4 0.3786035 0.3943218 0.3800700 0.3820603 0.3862517
##
## $comparison
## NULL
##
## $groups
## Temp3$TRC groups
## MT0W1 0.5515230 a
## MT1W1 0.3842614 b
## MT1W0 0.2327592 c
## MT0W0 0.2304926 c
##
## attr(,"class")
## [1] "group"M4TRC_tukey <-HSD.test(Temp4$TRC,Temp4$Trat, 12, 0.000028, alpha = 0.05);M4TRC_tukey## $statistics
## MSerror Df Mean CV MSD
## 2.8e-05 12 0.2536845 2.08586 0.01110861
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$TRC std r Min Max Q25 Q50 Q75
## MT0W0 0.1772236 0.002760727 4 0.1749563 0.1809768 0.1752554 0.1764807 0.1784490
## MT0W1 0.3906776 0.009129056 4 0.3788289 0.3979308 0.3858240 0.3929754 0.3978290
## MT1W0 0.1804639 0.001847421 4 0.1777372 0.1817946 0.1801774 0.1811619 0.1814484
## MT1W1 0.2663728 0.004253403 4 0.2613305 0.2700096 0.2636105 0.2670756 0.2698379
##
## $comparison
## NULL
##
## $groups
## Temp4$TRC groups
## MT0W1 0.3906776 a
## MT1W1 0.2663728 b
## MT1W0 0.1804639 c
## MT0W0 0.1772236 c
##
## attr(,"class")
## [1] "group"TRCT<-bind_rows(M2TRC_tukey$groups, M3TRC_tukey$groups, M4TRC_tukey$groups, id=NULL)
TRC12 <- data.frame(TRC, TRCT$groups)
TRC12$TRCT.groups## [1] "a" "b" "c" "c" "a" "b" "c" "c" "a" "b" "c" "c"gTRC<-c("c" ,"c", "a" ,"b", "c", "c", "a", "b", "c", "c", "a", "b")
levels(TRC12$Trat)## NULLTRC12$Trat=factor(TRC12$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
TRCTT<- data.frame(gTRC,TRC1$Trat)
TRCg<-ggplot(data = TRC12, aes(x = factor(Dias), y = TRC, group = Trat, colour=Trat)) + geom_line(size=1.2)+geom_point(size=3, aes(shape=Trat))+scale_shape_manual(values=c(15, 16, 17, 18))+ylab("TRC(g/g*dÃas)") + xlab("DDT")+ylim(0,0.9)
TRCg0<-TRCg+scale_colour_grey() + theme_bw(base_family= "serif") + geom_errorbar(aes(ymin=TRC-sd, ymax=TRC+sd), width=.5, position=position_dodge(0))+scale_x_discrete(limit=c("30-35","35-42","42-49"), labels=c("1-5","5-13","13-20"))+theme(legend.position = "bottom")+theme(legend.text=element_text(size=7));TRCg0
TRCg1<-TRCg0+theme(legend.position = "right",legend.spacing.y = unit(0, "mm"),panel.border = element_rect(colour = "black", fill=NA), aspect.ratio = 1, axis.text = element_text(colour = 1, size = 12), legend.background = element_blank(), legend.box.background = element_rect(colour = "black")) + theme(legend.text=element_text(size=7))+theme(legend.title =element_text(size=0));TRCg1 
TASA DE ASIMILACIÓN NETA
library(agricolae)
M2TAN <- aov(TAN~Trat, data = Temp2)
anova(M2TAN)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.00211293 0.00070431 23.293 2.718e-05 ***
## Residuals 12 0.00036285 0.00003024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3TAN <- aov(TAN~Trat, data = Temp3)
anova(M3TAN)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0060102 0.00200340 495.08 7.746e-13 ***
## Residuals 12 0.0000486 0.00000405
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4TAN <- aov(TAN~Trat, data = Temp4)
anova(M4TAN)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.00281968 0.00093989 705.61 9.386e-14 ***
## Residuals 12 0.00001598 0.00000133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#####PRUEBAS
#Pruebas de normalidad
shapiro.test(resid(M2TAN))##
## Shapiro-Wilk normality test
##
## data: resid(M2TAN)
## W = 0.82201, p-value = 0.005421shapiro.test(resid(M3TAN))##
## Shapiro-Wilk normality test
##
## data: resid(M3TAN)
## W = 0.77384, p-value = 0.001249shapiro.test(resid(M4TAN))##
## Shapiro-Wilk normality test
##
## data: resid(M4TAN)
## W = 0.96083, p-value = 0.6769#**Homogeneidad de varianzas**
library(carData)
bartlett.test(TAN~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: TAN by Trat
## Bartlett's K-squared = 23.178, df = 3, p-value = 3.708e-05bartlett.test(TAN~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: TAN by Trat
## Bartlett's K-squared = 20.155, df = 3, p-value = 0.0001577bartlett.test(TAN~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: TAN by Trat
## Bartlett's K-squared = 11.672, df = 3, p-value = 0.008595#Pueba de tukey
library(agricolae)
library(dplyr)
library(ggplot2)
M2TAN_tukey <-HSD.test(Temp2$TAN,Temp2$Trat, 12, 0.00003024, alpha = 0.05);M2TAN_tukey## $statistics
## MSerror Df Mean CV MSD
## 3.024e-05 12 0.05888381 9.338883 0.01154441
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$TAN std r Min Max Q25 Q50
## MT0W0 0.04852684 0.0004278957 4 0.04813339 0.04907108 0.04821559 0.04845145
## MT0W1 0.07638635 0.0102567383 4 0.06787760 0.09130085 0.07159511 0.07318346
## MT1W0 0.04863656 0.0006578466 4 0.04822856 0.04961597 0.04826908 0.04835085
## MT1W1 0.06198551 0.0038901797 4 0.05711153 0.06554454 0.05974951 0.06264298
## Q75
## MT0W0 0.04876270
## MT0W1 0.07797471
## MT1W0 0.04871833
## MT1W1 0.06487898
##
## $comparison
## NULL
##
## $groups
## Temp2$TAN groups
## MT0W1 0.07638635 a
## MT1W1 0.06198551 b
## MT1W0 0.04863656 c
## MT0W0 0.04852684 c
##
## attr(,"class")
## [1] "group"M3TAN_tukey <-HSD.test(Temp3$TAN,Temp3$Trat, 12, 0.00000405, alpha = 0.05);M3TAN_tukey## $statistics
## MSerror Df Mean CV MSD
## 4.05e-06 12 0.06170916 3.261203 0.00422482
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$TAN std r Min Max Q25 Q50
## MT0W0 0.04364437 0.0002743925 4 0.04328496 0.04395218 0.04356069 0.04367017
## MT0W1 0.08978478 0.0038556828 4 0.08411642 0.09253996 0.08903781 0.09124138
## MT1W0 0.04370629 0.0003153233 4 0.04338422 0.04409060 0.04349003 0.04367518
## MT1W1 0.06970121 0.0010702720 4 0.06872717 0.07122797 0.06921712 0.06942484
## Q75
## MT0W0 0.04375385
## MT0W1 0.09198835
## MT1W0 0.04389144
## MT1W1 0.06990893
##
## $comparison
## NULL
##
## $groups
## Temp3$TAN groups
## MT0W1 0.08978478 a
## MT1W1 0.06970121 b
## MT1W0 0.04370629 c
## MT0W0 0.04364437 c
##
## attr(,"class")
## [1] "group"M4TAN_tukey <-HSD.test(Temp4$TAN,Temp4$Trat, 12, 0.00000133, alpha = 0.05);M4TAN_tukey## $statistics
## MSerror Df Mean CV MSD
## 1.33e-06 12 0.04790083 2.407591 0.002421066
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$TAN std r Min Max Q25 Q50
## MT0W0 0.03674414 0.0001597832 4 0.03654360 0.03691380 0.03666086 0.03675959
## MT0W1 0.06938017 0.0012312322 4 0.06818425 0.07107104 0.06869997 0.06913269
## MT1W0 0.03700332 0.0005251171 4 0.03656422 0.03775987 0.03671719 0.03684459
## MT1W1 0.04847569 0.0018737478 4 0.04645545 0.05060070 0.04719454 0.04842331
## Q75
## MT0W0 0.03684288
## MT0W1 0.06981289
## MT1W0 0.03713072
## MT1W1 0.04970447
##
## $comparison
## NULL
##
## $groups
## Temp4$TAN groups
## MT0W1 0.06938017 a
## MT1W1 0.04847569 b
## MT1W0 0.03700332 c
## MT0W0 0.03674414 c
##
## attr(,"class")
## [1] "group"TANT<-bind_rows(M2TAN_tukey$groups, M3TAN_tukey$groups, M4TAN_tukey$groups, id=NULL)
TAN1 <- data.frame(TAN, TANT$groups)
TAN1$TANT.groups## [1] "a" "b" "c" "c" "a" "b" "c" "c" "a" "b" "c" "c"levels(TAN1$Trat)## NULLTAN1$Trat=factor(TAN1$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
TANg<-ggplot(data = TAN1, aes(x = factor(Dias), y = TAN, group = Trat, colour=Trat)) + geom_line(size=1.2)+geom_point(size=3, aes(shape=Trat))+scale_shape_manual(values=c(15, 16, 17, 18))+ylab("TAN (g/cm²*dÃas)") + xlab("DDT")+ylim(0.02,0.1);TANg
TANg0<-TANg+scale_colour_grey() + theme_bw(base_family= "serif") + geom_errorbar(aes(ymin=TAN-sd, ymax=TAN+sd), width=.5, position=position_dodge(0))+guides(guide_legend("Tratamientos"))+scale_x_discrete(limit=c("30-35","35-42","42-49"), labels=c("1-5","5-13","13-20"));TANg0
TANg1<-TANg0+theme(legend.position = "right",legend.spacing.y = unit(0, "mm"),panel.border = element_rect(colour = "black", fill=NA), aspect.ratio = 1, axis.text = element_text(colour = 1, size = 12), legend.background = element_blank(), legend.box.background = element_rect(colour = "black")) + theme(legend.text=element_text(size=7))+theme(legend.title =element_text(size=0));TANg1
AREA FOLIAR
library(agricolae)
M1RAF <- aov(RAF~Trat, data = Temp1)
anova(M1RAF)## Analysis of Variance Table
##
## Response: RAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0085473 0.00284910 30.004 7.368e-06 ***
## Residuals 12 0.0011395 0.00009496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2RAF <- aov(RAF~Trat, data = Temp2)
anova(M2RAF)## Analysis of Variance Table
##
## Response: RAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0062684 0.00208948 27.482 1.165e-05 ***
## Residuals 12 0.0009124 0.00007603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3RAF <- aov(RAF~Trat, data = Temp3)
anova(M3RAF)## Analysis of Variance Table
##
## Response: RAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0050075 0.0016692 130.4 2.012e-09 ***
## Residuals 12 0.0001536 0.0000128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4RAF <- aov(RAF~Trat, data = Temp4)
anova(M4RAF)## Analysis of Variance Table
##
## Response: RAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0070592 0.00235308 77.857 3.908e-08 ***
## Residuals 12 0.0003627 0.00003022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#####PRUEBAS
#Pruebas de normalidad
shapiro.test(resid(M1RAF))##
## Shapiro-Wilk normality test
##
## data: resid(M1RAF)
## W = 0.94999, p-value = 0.4896shapiro.test(resid(M2RAF))##
## Shapiro-Wilk normality test
##
## data: resid(M2RAF)
## W = 0.97444, p-value = 0.9044shapiro.test(resid(M3RAF))##
## Shapiro-Wilk normality test
##
## data: resid(M3RAF)
## W = 0.85806, p-value = 0.01794#**Homogeneidad de varianzas**
library(carData)
bartlett.test(RAF~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: RAF by Trat
## Bartlett's K-squared = 7.3096, df = 3, p-value = 0.06266bartlett.test(RAF~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: RAF by Trat
## Bartlett's K-squared = 9.1323, df = 3, p-value = 0.02758bartlett.test(RAF~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: RAF by Trat
## Bartlett's K-squared = 5.8067, df = 3, p-value = 0.1214#Pueba de tukey
library(agricolae)
library(dplyr)
M1RAF_tukey <-HSD.test(Temp1$RAF,Temp2$Trat, 12, 0.00009496, alpha = 0.05);M1RAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 9.496e-05 12 0.8396236 1.160608 0.02045743
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp1$RAF std r Min Max Q25 Q50 Q75
## MT0W0 0.8155841 0.006085008 4 0.8087353 0.8232160 0.8123207 0.8151927 0.8184562
## MT0W1 0.8633250 0.006959266 4 0.8550925 0.8718770 0.8600433 0.8631653 0.8664470
## MT1W0 0.8174643 0.008647870 4 0.8050562 0.8243297 0.8149500 0.8202356 0.8227499
## MT1W1 0.8621211 0.014818451 4 0.8497663 0.8836593 0.8554578 0.8575295 0.8641928
##
## $comparison
## NULL
##
## $groups
## Temp1$RAF groups
## MT0W1 0.8633250 a
## MT1W1 0.8621211 a
## MT1W0 0.8174643 b
## MT0W0 0.8155841 b
##
## attr(,"class")
## [1] "group"M2RAF_tukey <-HSD.test(Temp2$RAF,Temp2$Trat, 12, 0.00007603, alpha = 0.05);M2RAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 7.603e-05 12 0.8246378 1.057376 0.01830515
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$RAF std r Min Max Q25 Q50 Q75
## MT0W0 0.8055475 0.002870041 4 0.8033053 0.8095007 0.8034915 0.8046920 0.8067481
## MT0W1 0.8464024 0.014799190 4 0.8283268 0.8616245 0.8377970 0.8478291 0.8564344
## MT1W0 0.8042560 0.004525771 4 0.7992608 0.8102075 0.8022317 0.8037779 0.8058023
## MT1W1 0.8423453 0.007509086 4 0.8341081 0.8507405 0.8372828 0.8422664 0.8473289
##
## $comparison
## NULL
##
## $groups
## Temp2$RAF groups
## MT0W1 0.8464024 a
## MT1W1 0.8423453 a
## MT0W0 0.8055475 b
## MT1W0 0.8042560 b
##
## attr(,"class")
## [1] "group"M3RAF_tukey <-HSD.test(Temp3$RAF,Temp3$Trat, 12, 0.0000128, alpha = 0.05);M3RAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 1.28e-05 12 0.8280091 0.4320857 0.007510792
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$RAF std r Min Max Q25 Q50
## MT0W0 0.8157697 0.0008322429 4 0.8151750 0.8169979 0.8153187 0.8154528
## MT0W1 0.8579633 0.0062617617 4 0.8532884 0.8671432 0.8544876 0.8557108
## MT1W0 0.8142570 0.0021494511 4 0.8123248 0.8171389 0.8127969 0.8137822
## MT1W1 0.8240464 0.0025842145 4 0.8224021 0.8278941 0.8226428 0.8229446
## Q75
## MT0W0 0.8159038
## MT0W1 0.8591864
## MT1W0 0.8152423
## MT1W1 0.8243482
##
## $comparison
## NULL
##
## $groups
## Temp3$RAF groups
## MT0W1 0.8579633 a
## MT1W1 0.8240464 b
## MT0W0 0.8157697 c
## MT1W0 0.8142570 c
##
## attr(,"class")
## [1] "group"M4RAF_tukey <-HSD.test(Temp4$RAF,Temp4$Trat, 12, 0.00003022, alpha = 0.05);M4RAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 3.022e-05 12 0.8180453 0.6720009 0.01154059
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$RAF std r Min Max Q25 Q50 Q75
## MT0W0 0.7974604 0.002390197 4 0.7951773 0.8007059 0.7960172 0.7969793 0.7984225
## MT0W1 0.8429969 0.005682941 4 0.8380029 0.8508262 0.8393196 0.8415792 0.8452565
## MT1W0 0.7970344 0.002556204 4 0.7936825 0.7991685 0.7957237 0.7976433 0.7989540
## MT1W1 0.8346896 0.008737842 4 0.8269945 0.8444475 0.8274770 0.8336582 0.8408708
##
## $comparison
## NULL
##
## $groups
## Temp4$RAF groups
## MT0W1 0.8429969 a
## MT1W1 0.8346896 a
## MT0W0 0.7974604 b
## MT1W0 0.7970344 b
##
## attr(,"class")
## [1] "group"library(dplyr)
library(extrafont)## Warning: package 'extrafont' was built under R version 4.0.3## Registering fonts with Rloadfonts(device = "win")
RAFE<-bind_rows(M1RAF_tukey$groups, M2RAF_tukey$groups, M3RAF_tukey$groups, M4RAF_tukey$groups, id=NULL)
RAF12 <- data.frame(RAF, RAFE$groups)
RAF12$RAFE.groups## [1] "a" "a" "b" "b" "a" "a" "b" "b" "a" "b" "c" "c" "a" "a" "b" "b"levels(RAF12$Trat)## NULLRAF12$Trat=factor(RAF12$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
RAF22<- data.frame(RAF12$RAFE.groups,RAF12$Trat)
RAFg<-ggplot(data = RAF12, aes(x = factor(Dias), y = RAF, group = Trat, colour=Trat)) + geom_line(size=1.2)+geom_point(size=3, aes(shape=Trat))+scale_shape_manual(values=c(15, 16, 17, 18))+ylab("RAF (cm²/g)") + xlab("DDT");RAFg
RAFg0<-RAFg+scale_color_grey() + theme_bw(base_family= "serif") + geom_errorbar(aes(ymin=RAF-sd, ymax=RAF+sd), width=.5, position=position_dodge(0.1))+guides(guide_legend("Tratamientos"));RAFg0
RAFg1<-RAFg0+theme(legend.position = "right",legend.spacing.y = unit(0, "mm"),panel.border = element_rect(colour = "black", fill=NA), aspect.ratio = 1, axis.text = element_text(colour = 1, size = 12), legend.background = element_blank(), legend.box.background = element_rect(colour = "black")) + theme(legend.text=element_text(size=7))+theme(legend.title =element_text(size=0));RAFg1
IAF
M1IAF <- aov(IAF~Trat, data = Temp1)
anova(M1IAF)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.22407 0.074690 121.78 2.992e-09 ***
## Residuals 12 0.00736 0.000613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2IAF <- aov(IAF~Trat, data = Temp2)
anova(M2IAF)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.38725 0.129082 54.447 2.923e-07 ***
## Residuals 12 0.02845 0.002371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3IAF <- aov(IAF~Trat, data = Temp3)
anova(M3IAF)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2.11750 0.70583 1676.8 5.323e-16 ***
## Residuals 12 0.00505 0.00042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4IAF <- aov(IAF~Trat, data = Temp4)
anova(M4IAF)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2.18309 0.72770 692.55 1.049e-13 ***
## Residuals 12 0.01261 0.00105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#####PRUEBAS
#Pruebas de normalidad
shapiro.test(resid(M1IAF))##
## Shapiro-Wilk normality test
##
## data: resid(M1IAF)
## W = 0.92292, p-value = 0.1879shapiro.test(resid(M2IAF))##
## Shapiro-Wilk normality test
##
## data: resid(M2IAF)
## W = 0.90305, p-value = 0.08995shapiro.test(resid(M3IAF))##
## Shapiro-Wilk normality test
##
## data: resid(M3IAF)
## W = 0.89995, p-value = 0.08021shapiro.test(resid(M4IAF))##
## Shapiro-Wilk normality test
##
## data: resid(M4IAF)
## W = 0.97215, p-value = 0.8721#**Homogeneidad de varianzas**
library(carData)
bartlett.test(IAF~Trat, Temp1)##
## Bartlett test of homogeneity of variances
##
## data: IAF by Trat
## Bartlett's K-squared = 8.1514, df = 3, p-value = 0.04298bartlett.test(IAF~Trat, Temp2)##
## Bartlett test of homogeneity of variances
##
## data: IAF by Trat
## Bartlett's K-squared = 12.53, df = 3, p-value = 0.00577bartlett.test(IAF~Trat, Temp3)##
## Bartlett test of homogeneity of variances
##
## data: IAF by Trat
## Bartlett's K-squared = 5.5305, df = 3, p-value = 0.1368bartlett.test(IAF~Trat, Temp4)##
## Bartlett test of homogeneity of variances
##
## data: IAF by Trat
## Bartlett's K-squared = 8.544, df = 3, p-value = 0.03601#Pueba de tukey
library(agricolae)
library(dplyr)
M1IAF_tukey <-HSD.test(Temp1$IAF,Temp2$Trat, 12, 0.000613, alpha = 0.05);M1IAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.000613 12 0.538315 4.599321 0.05197697
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp1$IAF std r Min Max Q25 Q50 Q75
## MT0W0 0.6549425 0.027237470 4 0.61734 0.68247 0.648375 0.65998 0.6665475
## MT0W1 0.3855925 0.009848138 4 0.37176 0.39469 0.382650 0.38796 0.3909025
## MT1W0 0.6523825 0.007563515 4 0.64287 0.66138 0.650085 0.65264 0.6549375
## MT1W1 0.4603425 0.039461947 4 0.42796 0.51649 0.436060 0.44846 0.4727425
##
## $comparison
## NULL
##
## $groups
## Temp1$IAF groups
## MT0W0 0.6549425 a
## MT1W0 0.6523825 a
## MT1W1 0.4603425 b
## MT0W1 0.3855925 c
##
## attr(,"class")
## [1] "group"M2IAF_tukey <-HSD.test(Temp2$IAF,Temp2$Trat, 12, 0.002371, alpha = 0.05);M2IAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.002371 12 0.8825875 5.517064 0.1022225
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$IAF std r Min Max Q25 Q50 Q75
## MT0W0 1.030417 0.00905914 4 1.01893 1.03878 1.0253875 1.03198 1.0370100
## MT0W1 0.662680 0.08076135 4 0.54764 0.73662 0.6468875 0.68323 0.6990225
## MT1W0 1.028173 0.01372940 4 1.00774 1.03673 1.0264225 1.03411 1.0358600
## MT1W1 0.809080 0.05186605 4 0.76284 0.87548 0.7706925 0.79900 0.8373875
##
## $comparison
## NULL
##
## $groups
## Temp2$IAF groups
## MT0W0 1.030417 a
## MT1W0 1.028173 a
## MT1W1 0.809080 b
## MT0W1 0.662680 c
##
## attr(,"class")
## [1] "group"M3IAF_tukey <-HSD.test(Temp3$IAF,Temp3$Trat, 12, 0.00042, alpha = 0.05);M3IAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00042 12 1.247702 1.642531 0.04302346
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$IAF std r Min Max Q25 Q50 Q75
## MT0W0 1.603920 0.01010187 4 1.59264 1.61719 1.599870 1.602925 1.606975
## MT0W1 0.780765 0.03486736 4 0.75643 0.83218 0.760975 0.767225 0.787015
## MT1W0 1.601662 0.01153876 4 1.58764 1.61349 1.594855 1.602760 1.609568
## MT1W1 1.004463 0.01526070 4 0.98276 1.01852 1.001420 1.008285 1.011328
##
## $comparison
## NULL
##
## $groups
## Temp3$IAF groups
## MT0W0 1.603920 a
## MT1W0 1.601662 a
## MT1W1 1.004463 b
## MT0W1 0.780765 c
##
## attr(,"class")
## [1] "group"M4IAF_tukey <-HSD.test(Temp4$IAF,Temp4$Trat, 12, 0.00105, alpha = 0.05);M4IAF_tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00105 12 1.562977 2.073204 0.06802607
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$IAF std r Min Max Q25 Q50 Q75
## MT0W0 1.905092 0.008292854 4 1.89631 1.91552 1.899962 1.90427 1.909400
## MT0W1 1.009170 0.017744466 4 0.98493 1.02663 1.002787 1.01256 1.018943
## MT1W0 1.892005 0.026561262 4 1.85382 1.91444 1.885410 1.89988 1.906475
## MT1W1 1.445640 0.055801895 4 1.38338 1.50682 1.408475 1.44618 1.483345
##
## $comparison
## NULL
##
## $groups
## Temp4$IAF groups
## MT0W0 1.905092 a
## MT1W0 1.892005 a
## MT1W1 1.445640 b
## MT0W1 1.009170 c
##
## attr(,"class")
## [1] "group"library(dplyr)
library(extrafont)
loadfonts(device = "win")
IAFE<-bind_rows(M1IAF_tukey$groups, M2IAF_tukey$groups, M3IAF_tukey$groups, M4IAF_tukey$groups, id=NULL)
IAFE1 <- data.frame(IAF, IAFE$groups)
IAFE1$IAFEE.groups## NULLgIAFE<-c("b" ,"c", "a" ,"d", "b", "a", "b", "c", "c", "ab", "b", "a","c", "b", "a", "a")
levels(IAFE1$Trat)## NULLIAFE1$Trat=factor(IAFE1$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
IAFE2<- data.frame(IAFE1$IAFE.groups,IAFE1$Trat)
IAFTT<- data.frame(gIAFE,IAFE1$Trat)
IAFg<-ggplot(data = IAFE1, aes(x = factor(Dias), y = IAF, group = Trat, colour=Trat)) + geom_line(size=1.2)+geom_point(size=3, aes(shape=Trat))+scale_shape_manual(values=c(15, 16, 17, 18))+ylab("IAF") + xlab("DDT");IAFg
IAFg0<-IAFg+scale_colour_grey() + theme_bw(base_family= "serif") + geom_errorbar(aes(ymin=IAF-sd, ymax=IAF+sd), width=.5, position=position_dodge(0))+guides(fill = guide_legend(title = "LEFT"));IAFg0
IAFg1<-IAFg0+theme(legend.position = "right",legend.spacing.y = unit(0, "mm"),panel.border = element_rect(colour = "black", fill=NA), aspect.ratio = 1, axis.text = element_text(colour = 1, size = 12), legend.background = element_blank(), legend.box.background = element_rect(colour = "black")) +theme(legend.text=element_text(size=7))+theme(legend.title =element_text(size=0));IAFg1
#########TRC1
library(agricolae)
M2TRC1 <- aov(TRC1~Trat, data = Temp2)
anova(M2TRC1)## Analysis of Variance Table
##
## Response: TRC1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 22.0787 7.3596 92.459 1.465e-08 ***
## Residuals 12 0.9552 0.0796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3TRC1 <- aov(TRC1~Trat, data = Temp3)
anova(M3TRC1)## Analysis of Variance Table
##
## Response: TRC1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 52.874 17.6246 647.98 1.56e-13 ***
## Residuals 12 0.326 0.0272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4TRC1 <- aov(TRC1~Trat, data = Temp4)
anova(M4TRC1)## Analysis of Variance Table
##
## Response: TRC1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 11.2255 3.7418 207.18 1.343e-10 ***
## Residuals 12 0.2167 0.0181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pueba de tukey
M2TRC_tukey1 <-HSD.test(Temp2$TRC1,Temp2$Trat, 12, 0.0796, alpha = 0.05);M2TRC_tukey1## $statistics
## MSerror Df Mean CV MSD
## 0.0796 12 4.856743 5.809134 0.5922939
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$TRC1 std r Min Max Q25 Q50 Q75
## MT0W0 3.814834 0.09295926 4 3.731343 3.940110 3.752385 3.793941 3.856391
## MT0W1 6.634004 0.49864046 4 6.172840 7.342144 6.407023 6.510516 6.737497
## MT1W0 3.780171 0.10793816 4 3.684598 3.888025 3.688677 3.774031 3.865525
## MT1W1 5.197964 0.22238943 4 4.930966 5.411255 5.059272 5.224817 5.363509
##
## $comparison
## NULL
##
## $groups
## Temp2$TRC1 groups
## MT0W1 6.634004 a
## MT1W1 5.197964 b
## MT0W0 3.814834 c
## MT1W0 3.780171 c
##
## attr(,"class")
## [1] "group"M3TRC_tukey1 <-HSD.test(Temp3$TRC1,Temp3$Trat, 12, 0.0272, alpha = 0.05);M3TRC_tukey1## $statistics
## MSerror Df Mean CV MSD
## 0.0272 12 4.534843 3.636823 0.3462304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$TRC1 std r Min Max Q25 Q50 Q75
## MT0W0 2.898370 0.09399626 4 2.843217 3.038194 2.843217 2.856035 2.911188
## MT0W1 7.324078 0.27786982 4 7.150153 7.734807 7.155636 7.205675 7.374116
## MT1W0 2.934701 0.10431688 4 2.816901 3.067485 2.882649 2.927208 2.979261
## MT1W1 4.982225 0.10894357 4 4.861111 5.076142 4.904666 4.995824 5.073383
##
## $comparison
## NULL
##
## $groups
## Temp3$TRC1 groups
## MT0W1 7.324078 a
## MT1W1 4.982225 b
## MT1W0 2.934701 c
## MT0W0 2.898370 c
##
## attr(,"class")
## [1] "group"M4TRC_tukey1 <-HSD.test(Temp4$TRC1,Temp4$Trat, 12, 0.0181, alpha = 0.05);M4TRC_tukey1## $statistics
## MSerror Df Mean CV MSD
## 0.0181 12 2.514171 5.351118 0.282436
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$TRC1 std r Min Max Q25 Q50 Q75
## MT0W0 1.786796 0.05114944 4 1.741294 1.856272 1.752764 1.774810 1.808842
## MT0W1 3.835770 0.22287691 4 3.595275 4.120071 3.707807 3.813868 3.941831
## MT1W0 1.796274 0.09188181 4 1.712748 1.912046 1.728014 1.780151 1.848411
## MT1W1 2.637843 0.10728183 4 2.516175 2.732240 2.563458 2.651477 2.725862
##
## $comparison
## NULL
##
## $groups
## Temp4$TRC1 groups
## MT0W1 3.835770 a
## MT1W1 2.637843 b
## MT1W0 1.796274 c
## MT0W0 1.786796 c
##
## attr(,"class")
## [1] "group"TRC2<-data.frame(TRC1)
levels(TRC2$Trat)## NULLTRC2$Trat=factor(TRC2$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
TRCg1<-ggplot(data = TRC2, aes(x = factor(Dias), y = TRC1, group = Trat, colour=Trat)) + geom_line(size=1.2)+geom_point(size=3, aes(shape=Trat))+scale_shape_manual(values=c(15, 16, 17,18))+ylab("TRC(g/g*dÃas)") + xlab("DDT")
TRCg00<-TRCg1+scale_colour_grey() + theme_bw(base_family= "serif") + geom_errorbar(aes(ymin=TRC1-sd, ymax=TRC1+sd), width=.5, position=position_dodge(0))+scale_x_discrete(limit=c("30-35","35-42","42-49"), labels=c("1-5","5-13","13-20"))+theme(legend.text=element_text(size=7));TRCg00
TRCg2<-TRCg00+theme(legend.position = "right",legend.spacing.y = unit(0, "mm"),panel.border = element_rect(colour = "black", fill=NA), aspect.ratio = 1, axis.text = element_text(colour = 1, size = 12), legend.background = element_blank(), legend.box.background = element_rect(colour = "black")) + theme(legend.text=element_text(size=7))+theme(legend.title =element_text(size=0));TRCg2 
AREA FOLIAR
library(agricolae)
M1RAF1 <- aov(RAF1~Trat, data = Temp1)
anova(M1RAF1)## Analysis of Variance Table
##
## Response: RAF1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 449.67 149.89 9.2124 0.001942 **
## Residuals 12 195.24 16.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M2RAF1 <- aov(RAF1~Trat, data = Temp2)
anova(M2RAF1)## Analysis of Variance Table
##
## Response: RAF1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 247.466 82.489 10.945 0.000946 ***
## Residuals 12 90.438 7.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M3RAF1 <- aov(RAF1~Trat, data = Temp3)
anova(M3RAF1)## Analysis of Variance Table
##
## Response: RAF1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 591.53 197.177 28.036 1.05e-05 ***
## Residuals 12 84.40 7.033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1M4RAF1 <- aov(RAF1~Trat, data = Temp4)
anova(M4RAF1)## Analysis of Variance Table
##
## Response: RAF1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 158.297 52.766 13.12 0.0004265 ***
## Residuals 12 48.261 4.022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Pueba de tukey
library(agricolae)
library(dplyr)
M1RAF_tukey1 <-HSD.test(Temp1$RAF,Temp2$Trat, 12, 16.27, alpha = 0.05);M1RAF_tukey1## $statistics
## MSerror Df Mean CV MSD
## 16.27 12 0.8396236 480.4068 8.467876
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp1$RAF std r Min Max Q25 Q50 Q75
## MT0W0 0.8155841 0.006085008 4 0.8087353 0.8232160 0.8123207 0.8151927 0.8184562
## MT0W1 0.8633250 0.006959266 4 0.8550925 0.8718770 0.8600433 0.8631653 0.8664470
## MT1W0 0.8174643 0.008647870 4 0.8050562 0.8243297 0.8149500 0.8202356 0.8227499
## MT1W1 0.8621211 0.014818451 4 0.8497663 0.8836593 0.8554578 0.8575295 0.8641928
##
## $comparison
## NULL
##
## $groups
## Temp1$RAF groups
## MT0W1 0.8633250 a
## MT1W1 0.8621211 a
## MT1W0 0.8174643 a
## MT0W0 0.8155841 a
##
## attr(,"class")
## [1] "group"M2RAF_tukey1 <-HSD.test(Temp2$RAF,Temp2$Trat, 12, 7.536, alpha = 0.05);M2RAF_tukey1## $statistics
## MSerror Df Mean CV MSD
## 7.536 12 0.8246378 332.8949 5.763034
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp2$Trat 4 4.19866 0.05
##
## $means
## Temp2$RAF std r Min Max Q25 Q50 Q75
## MT0W0 0.8055475 0.002870041 4 0.8033053 0.8095007 0.8034915 0.8046920 0.8067481
## MT0W1 0.8464024 0.014799190 4 0.8283268 0.8616245 0.8377970 0.8478291 0.8564344
## MT1W0 0.8042560 0.004525771 4 0.7992608 0.8102075 0.8022317 0.8037779 0.8058023
## MT1W1 0.8423453 0.007509086 4 0.8341081 0.8507405 0.8372828 0.8422664 0.8473289
##
## $comparison
## NULL
##
## $groups
## Temp2$RAF groups
## MT0W1 0.8464024 a
## MT1W1 0.8423453 a
## MT0W0 0.8055475 a
## MT1W0 0.8042560 a
##
## attr(,"class")
## [1] "group"M3RAF_tukey1 <-HSD.test(Temp3$RAF,Temp3$Trat, 12, 7.033 , alpha = 0.05);M3RAF_tukey1## $statistics
## MSerror Df Mean CV MSD
## 7.033 12 0.8280091 320.284 5.567382
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp3$Trat 4 4.19866 0.05
##
## $means
## Temp3$RAF std r Min Max Q25 Q50
## MT0W0 0.8157697 0.0008322429 4 0.8151750 0.8169979 0.8153187 0.8154528
## MT0W1 0.8579633 0.0062617617 4 0.8532884 0.8671432 0.8544876 0.8557108
## MT1W0 0.8142570 0.0021494511 4 0.8123248 0.8171389 0.8127969 0.8137822
## MT1W1 0.8240464 0.0025842145 4 0.8224021 0.8278941 0.8226428 0.8229446
## Q75
## MT0W0 0.8159038
## MT0W1 0.8591864
## MT1W0 0.8152423
## MT1W1 0.8243482
##
## $comparison
## NULL
##
## $groups
## Temp3$RAF groups
## MT0W1 0.8579633 a
## MT1W1 0.8240464 a
## MT0W0 0.8157697 a
## MT1W0 0.8142570 a
##
## attr(,"class")
## [1] "group"M4RAF_tukey1 <-HSD.test(Temp4$RAF,Temp4$Trat, 12, 4.022, alpha = 0.05);M4RAF_tukey1## $statistics
## MSerror Df Mean CV MSD
## 4.022 12 0.8180453 245.1566 4.210191
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Temp4$Trat 4 4.19866 0.05
##
## $means
## Temp4$RAF std r Min Max Q25 Q50 Q75
## MT0W0 0.7974604 0.002390197 4 0.7951773 0.8007059 0.7960172 0.7969793 0.7984225
## MT0W1 0.8429969 0.005682941 4 0.8380029 0.8508262 0.8393196 0.8415792 0.8452565
## MT1W0 0.7970344 0.002556204 4 0.7936825 0.7991685 0.7957237 0.7976433 0.7989540
## MT1W1 0.8346896 0.008737842 4 0.8269945 0.8444475 0.8274770 0.8336582 0.8408708
##
## $comparison
## NULL
##
## $groups
## Temp4$RAF groups
## MT0W1 0.8429969 a
## MT1W1 0.8346896 a
## MT0W0 0.7974604 a
## MT1W0 0.7970344 a
##
## attr(,"class")
## [1] "group"library(dplyr)
library(extrafont)
loadfonts(device = "win")
RAF5 <- data.frame(RAF1)
levels(RAF5$Trat)## NULLRAF5$Trat=factor(RAF5$Trat,levels=c("MT0W0","MT1W0","MT0W1","MT1W1"))
RAFg5<-ggplot(data = RAF5, aes(x = factor(Dias), y = RAF1, group = Trat, colour=Trat)) + geom_line(size=1.2)+geom_point(size=3, aes(shape=Trat))+scale_shape_manual(values=c(15, 16, 17, 18))+ylab("RAF (cm²/g)") + xlab("DDT");RAFg5
RAFg05<-RAFg5+scale_color_grey() + theme_bw(base_family= "serif") + geom_errorbar(aes(ymin=RAF1-sd, ymax=RAF1+sd), width=.5, position=position_dodge(0.1))+guides(guide_legend("Tratamientos"));RAFg05
RAFg15<-RAFg05+theme(legend.position = "right",legend.spacing.y = unit(0, "mm"),panel.border = element_rect(colour = "black", fill=NA), aspect.ratio = 1, axis.text = element_text(colour = 1, size = 12), legend.background = element_blank(), legend.box.background = element_rect(colour = "black")) + theme(legend.text=element_text(size=7))+theme(legend.title =element_text(size=0));RAFg15
library(ggpubr)
ggarrange(RAFg15, IAFg1, TRCg2, TANg1, labels=c("A","B","C","D"))