#################### Crecimiento y desarrollo ####################
library(nortest)
library(agricolae)
library(readxl)
library(readxl)
DAT <- read_excel("C:/Users/Johan Forigua/Documents/Universidad Nacional/Semestre 9/Fisiologia de la produccion/Analisis/DAT.xlsx")
Dat <- DAT
Dat$TRT <- as.factor(Dat$TRT)
Dat$B <- as.factor(Dat$B)
########## Spad ##########
#### Descriptivos }
Med <- tapply(Dat$SPAD,list(Dat$TRT,Dat$DDS),mean);Med
## 46 57 63
## Ctrl 28.4125 32.075 31.4500
## Post 38.8375 35.675 35.0125
## PrMn 39.2000 37.125 35.6750
## PrPt 34.2125 33.675 38.2375
Dvs <- tapply(Dat$SPAD,list(Dat$TRT,Dat$DDS),sd);Dvs
## 46 57 63
## Ctrl 2.082881 2.2081289 5.245134
## Post 3.867054 0.4031129 3.216226
## PrMn 2.770250 1.3647344 3.294909
## PrPt 2.948819 4.7289005 3.730928
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 7.330861 6.884268 16.677691
## Post 9.957011 1.129959 9.185935
## PrMn 7.066965 3.676052 9.235906
## PrPt 8.619129 14.042763 9.757248
#### ANOVAS
MdSPAD1 <- aov(SPAD~TRT+B,data = Dat[1:32,])
MdSPAD2 <- aov(SPAD~TRT+B,data = Dat[33:48,])
MdSPAD3 <- aov(SPAD~TRT+B,data = Dat[49:80,])
summary(MdSPAD1);summary(MdSPAD2);summary(MdSPAD3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 610.2 203.39 21.57 4.1e-07 ***
## B 3 13.9 4.62 0.49 0.693
## Residuals 25 235.8 9.43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 59.03 19.676 3.660 0.0567 .
## B 3 39.41 13.136 2.443 0.1309
## Residuals 9 48.38 5.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 188.0 62.68 3.678 0.0254 *
## B 3 12.4 4.15 0.243 0.8653
## Residuals 25 426.0 17.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(MdSPAD1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV MSD
## 9.431312 25 35.16562 8.733086 4.223674
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## SPAD std r Min Max Q25 Q50 Q75
## Ctrl 28.4125 2.082881 8 25.7 30.9 26.75 28.60 30.075
## Post 38.8375 3.867054 8 31.7 45.2 37.55 39.05 40.425
## PrMn 39.2000 2.770250 8 35.9 43.8 37.35 38.40 40.625
## PrPt 34.2125 2.948819 8 30.3 39.1 32.65 33.50 36.175
##
## $comparison
## NULL
##
## $groups
## SPAD groups
## PrMn 39.2000 a
## Post 38.8375 a
## PrPt 34.2125 b
## Ctrl 28.4125 c
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(MdSPAD2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 5.375833 9 34.6375 6.693856 5.118148
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## SPAD std r Min Max Q25 Q50 Q75
## Ctrl 32.075 2.2081289 4 29.2 34.0 30.925 32.55 33.700
## Post 35.675 0.4031129 4 35.2 36.1 35.425 35.70 35.950
## PrMn 37.125 1.3647344 4 35.2 38.4 36.775 37.45 37.800
## PrPt 33.675 4.7289005 4 28.5 38.9 30.450 33.65 36.875
##
## $comparison
## NULL
##
## $groups
## SPAD groups
## PrMn 37.125 a
## Post 35.675 a
## PrPt 33.675 a
## Ctrl 32.075 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(MdSPAD3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 17.03945 25 35.09375 11.76246 5.677172
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## SPAD std r Min Max Q25 Q50 Q75
## Ctrl 31.4500 5.245134 8 25.9 38.9 26.850 30.45 35.175
## Post 35.0125 3.216226 8 28.9 38.0 32.900 35.90 37.800
## PrMn 35.6750 3.294909 8 28.7 39.3 35.325 36.55 37.450
## PrPt 38.2375 3.730928 8 34.5 44.0 34.900 37.70 40.450
##
## $comparison
## NULL
##
## $groups
## SPAD groups
## PrPt 38.2375 a
## PrMn 35.6750 ab
## Post 35.0125 ab
## Ctrl 31.4500 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,59),
ylab = "SPAD", xlab = "dds",
main = "Contenido relativo de clorofilas",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 3.5, c("c","a","a","b",
"a","a","a","a",
"b", "ab","ab","a"))
segments(0,0,200,0)
legend("topleft", c("C","Pos","Pr-Des","Pr-Pos"),
cex=.8, bty = "n",horiz=T,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- MdSPAD1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.98927, p-value = 0.9835
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.14885, p-value = 0.9596
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.99052, p-value = 0.973
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 4, p-value = 0.5494
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- MdSPAD2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.98133, p-value = 0.9733
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.19945, p-value = 0.8597
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.98268, p-value = 0.9519
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 3.25, p-value = 0.5169
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- MdSPAD3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.96086, p-value = 0.2898
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.55647, p-value = 0.1389
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.96683, p-value = 0.3531
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 10.5, p-value = 0.06225
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Temperatura ##########
#### Descriptivos
Med <- tapply(Dat$Temp,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 16.3250 NaN 18.9875
## Post 16.1375 NaN 21.0125
## PrMn 16.8500 NaN 20.5625
## PrPt 16.6125 NaN 18.4500
Dvs <- tapply(Dat$Temp,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 1.5294724 NA 3.787362
## Post 0.7520211 NA 4.044551
## PrMn 2.0248457 NA 3.927172
## PrPt 2.3135857 NA 3.684717
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 9.368897 NA 19.94660
## Post 4.660084 NA 19.24831
## PrMn 12.016888 NA 19.09871
## PrPt 13.926776 NA 19.97137
#### ANOVAS
Md1 <- aov(Temp~TRT+B,data = Dat[1:32,])
Md3 <- aov(Temp~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 2.37 0.789 0.261 0.853
## B 3 10.87 3.624 1.198 0.331
## Residuals 25 75.63 3.025
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 36.2 12.07 0.793 0.509
## B 3 37.3 12.44 0.817 0.496
## Residuals 25 380.6 15.22
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV MSD
## 3.02525 25 16.48125 10.55335 2.392131
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Temp std r Min Max Q25 Q50 Q75
## Ctrl 16.3250 1.5294724 8 15.0 19.4 15.350 15.70 16.750
## Post 16.1375 0.7520211 8 15.3 17.8 15.775 15.95 16.250
## PrMn 16.8500 2.0248457 8 14.7 21.4 15.800 16.45 16.800
## PrPt 16.6125 2.3135857 8 11.5 19.1 16.200 17.10 17.725
##
## $comparison
## NULL
##
## $groups
## Temp groups
## PrMn 16.8500 a
## PrPt 16.6125 a
## Ctrl 16.3250 a
## Post 16.1375 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 15.22341 25 19.75313 19.75241 5.366119
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Temp std r Min Max Q25 Q50 Q75
## Ctrl 18.9875 3.787362 8 13.5 25.3 16.950 19.15 21.100
## Post 21.0125 4.044551 8 16.6 28.8 18.475 20.35 22.825
## PrMn 20.5625 3.927172 8 15.9 27.6 17.525 19.95 23.100
## PrPt 18.4500 3.684717 8 13.9 24.1 15.200 18.85 20.475
##
## $comparison
## NULL
##
## $groups
## Temp groups
## Post 21.0125 a
## PrMn 20.5625 a
## Ctrl 18.9875 a
## PrPt 18.4500 a
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med[,-2], beside = T, ylim = c(0,35),
ylab = "°C", xlab = "dds",
main = "Temperatura Foliar",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med[,-2] - Dvs[,-2], Graf, Med[,-2] + Dvs[,-2])
segments(Graf - 0.1, Med[,-2] - Dvs[,-2], Graf + 0.1, Med[,-2] - Dvs[,-2])
segments(Graf - 0.1, Med[,-2] + Dvs[,-2], Graf + 0.1, Med[,-2] + Dvs[,-2])
text(Graf, Med[,-2] + Dvs[,-2] + 3.5, c("a"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=T,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.93913, p-value = 0.07074
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.79617, p-value = 0.03488
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.92551, p-value = 0.03111
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 7, p-value = 0.2206
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.95329, p-value = 0.1787
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.45479, p-value = 0.252
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.96547, p-value = 0.326
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 6, p-value = 0.3062
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Altura ##########
#### Descriptivos
Med <- tapply(Dat$Altura,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 28.78750 36.375 35.6500
## Post 20.02500 27.300 30.3750
## PrMn 21.43750 28.875 29.4375
## PrPt 23.07143 27.375 27.4500
Dvs <- tapply(Dat$Altura,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 3.784343 7.157455 3.007609
## Post 2.261952 3.043025 4.985622
## PrMn 2.757296 2.561738 3.907845
## PrPt 3.801629 4.871259 3.586881
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 13.14578 19.676853 8.436492
## Post 11.29564 11.146611 16.413571
## PrMn 12.86202 8.871819 13.275056
## PrPt 16.47765 17.794554 13.066961
#### ANOVAS
Md1 <- aov(Altura~TRT+B,data = Dat[1:32,])
Md2 <- aov(Altura~TRT+B,data = Dat[33:48,])
Md3 <- aov(Altura~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 354.8 118.26 12.363 4.35e-05 ***
## B 3 46.4 15.47 1.618 0.212
## Residuals 24 229.6 9.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 224.34 74.78 3.513 0.0623 .
## B 3 80.74 26.91 1.264 0.3438
## Residuals 9 191.60 21.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 294.1 98.03 7.036 0.00138 **
## B 3 86.0 28.66 2.057 0.13156
## Residuals 25 348.3 13.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 9.5657 24 23.33871 13.252
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.901262 0.05
##
## $means
## Altura std r Min Max Q25 Q50 Q75
## Ctrl 28.78750 3.784343 8 22.4 33.8 26.500 28.8 30.875
## Post 20.02500 2.261952 8 17.0 23.0 18.375 19.6 21.875
## PrMn 21.43750 2.757296 8 16.0 24.0 19.875 22.5 23.500
## PrPt 23.07143 3.801629 7 16.0 28.0 22.250 24.0 24.500
##
## $comparison
## NULL
##
## $groups
## Altura groups
## Ctrl 28.78750 a
## PrPt 23.07143 b
## PrMn 21.43750 b
## Post 20.02500 b
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 21.28896 9 29.98125 15.3896 10.18514
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## Altura std r Min Max Q25 Q50 Q75
## Ctrl 36.375 7.157455 4 31.5 47.0 32.625 33.50 37.250
## Post 27.300 3.043025 4 23.7 31.0 25.800 27.25 28.750
## PrMn 28.875 2.561738 4 26.0 31.5 27.125 29.00 30.750
## PrPt 27.375 4.871259 4 20.5 32.0 26.500 28.50 29.375
##
## $comparison
## NULL
##
## $groups
## Altura groups
## Ctrl 36.375 a
## PrMn 28.875 a
## PrPt 27.375 a
## Post 27.300 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 13.93221 25 30.72812 12.14714 5.133509
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Altura std r Min Max Q25 Q50 Q75
## Ctrl 35.6500 3.007609 8 30.8 39.4 34.225 35.90 37.225
## Post 30.3750 4.985622 8 20.9 36.4 28.275 30.55 34.325
## PrMn 29.4375 3.907845 8 23.7 33.4 27.200 29.75 33.225
## PrPt 27.4500 3.586881 8 19.9 30.8 26.100 28.00 30.275
##
## $comparison
## NULL
##
## $groups
## Altura groups
## Ctrl 35.6500 a
## Post 30.3750 b
## PrMn 29.4375 b
## PrPt 27.4500 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,59),
ylab = "cm", xlab = "dds",
main = "Altura de la planta",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 3.5, c("a","b","b","b",
"a","a","a","a",
"a","b","b","b"))
segments(0,0,200,0)
legend("topleft", c("C","Pos","Pr-Des","Pr-Pos"),
cex=.8, bty = "n",horiz=T,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.96707, p-value = 0.4422
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.48228, p-value = 0.2146
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.96627, p-value = 0.3581
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 13.129, p-value = 0.0222
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.986, p-value = 0.9938
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.12881, p-value = 0.9781
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.98067, p-value = 0.9289
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 2.375, p-value = 0.6671
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.97171, p-value = 0.5478
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.38203, p-value = 0.3788
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.97252, p-value = 0.4878
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 4.5, p-value = 0.4799
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Ancho de tallo ##########
#### Descriptivos
Med <- tapply(Dat$Ancho,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 4.485000 5.5625 5.700
## Post 4.378750 7.0250 7.225
## PrMn 6.400000 6.9500 7.600
## PrPt 4.811429 7.1000 6.775
Dvs <- tapply(Dat$Ancho,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 0.8590859 0.7888547 0.8585702
## Post 0.8273786 1.0688779 0.5775564
## PrMn 0.3515273 0.3439961 2.7422618
## PrPt 0.9254626 0.4778424 1.3677406
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 19.154646 14.181657 15.062636
## Post 18.895315 15.215344 7.993861
## PrMn 5.492614 4.949585 36.082393
## PrPt 19.234673 6.730174 20.188053
#### ANOVAS
Md1 <- aov(Ancho~TRT+B,data = Dat[1:32,])
Md2 <- aov(Ancho~TRT+B,data = Dat[33:48,])
Md3 <- aov(Ancho~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 21.118 7.039 13.144 2.8e-05 ***
## B 3 3.109 1.036 1.935 0.151
## Residuals 24 12.853 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 6.462 2.1539 3.088 0.0826 .
## B 3 0.057 0.0189 0.027 0.9935
## Residuals 9 6.278 0.6975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 16.23 5.410 2.071 0.130
## B 3 7.93 2.644 1.012 0.404
## Residuals 25 65.30 2.612
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 0.5355459 24 5.025484 14.56198
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.901262 0.05
##
## $means
## Ancho std r Min Max Q25 Q50 Q75
## Ctrl 4.485000 0.8590859 8 3.35 5.49 3.9450 4.385 5.3025
## Post 4.378750 0.8273786 8 3.34 5.37 3.9250 4.185 5.2425
## PrMn 6.400000 0.3515273 8 6.00 7.00 6.0875 6.400 6.5875
## PrPt 4.811429 0.9254626 7 3.42 6.16 4.1850 5.120 5.3050
##
## $comparison
## NULL
##
## $groups
## Ancho groups
## PrMn 6.400000 a
## PrPt 4.811429 b
## Ctrl 4.485000 b
## Post 4.378750 b
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 0.6975174 9 6.659375 12.54134 1.843603
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## Ancho std r Min Max Q25 Q50 Q75
## Ctrl 5.5625 0.7888547 4 4.70 6.35 5.0000 5.600 6.1625
## Post 7.0250 1.0688779 4 6.10 8.50 6.3250 6.750 7.4500
## PrMn 6.9500 0.3439961 4 6.45 7.20 6.8625 7.075 7.1625
## PrPt 7.1000 0.4778424 4 6.60 7.75 6.9000 7.025 7.2250
##
## $comparison
## NULL
##
## $groups
## Ancho groups
## PrPt 7.1000 a
## Post 7.0250 a
## PrMn 6.9500 a
## Ctrl 5.5625 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 2.6119 25 6.825 23.67967 2.222709
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Ancho std r Min Max Q25 Q50 Q75
## Ctrl 5.700 0.8585702 8 4.4 6.7 5.025 5.85 6.425
## Post 7.225 0.5775564 8 6.4 8.1 6.875 7.30 7.600
## PrMn 7.600 2.7422618 8 5.1 14.0 6.225 7.25 7.425
## PrPt 6.775 1.3677406 8 5.0 9.8 6.250 6.60 6.850
##
## $comparison
## NULL
##
## $groups
## Ancho groups
## PrMn 7.600 a
## Post 7.225 a
## PrPt 6.775 a
## Ctrl 5.700 a
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,13),
ylab = "cm", xlab = "dds",
main = "Ancho del tallo",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + .9, c("b","b","a","b",
"a","a","a","a",
"a","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=T,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.9493, p-value = 0.1493
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.50653, p-value = 0.1863
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.96289, p-value = 0.295
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 4.3548, p-value = 0.4995
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.96644, p-value = 0.7781
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.2217, p-value = 0.7951
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.97024, p-value = 0.7594
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 5, p-value = 0.2873
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.86206, p-value = 0.0007658
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 1.153, p-value = 0.004377
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.83974, p-value = 0.000531
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 10, p-value = 0.07524
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Numero de hojas ##########
#### Descriptivos
Med <- tapply(Dat$NoHojas,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 3.495833 6.50 6.000
## Post 7.833333 20.75 14.250
## PrMn 9.958333 10.25 16.125
## PrPt 7.190476 13.25 18.500
Dvs <- tapply(Dat$NoHojas,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 1.569090 2.5166115 4.070802
## Post 2.648749 14.1745076 3.150964
## PrMn 1.855387 6.5510813 4.580627
## PrPt 2.713868 0.9574271 9.561829
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 44.88458 38.717100 67.84670
## Post 33.81382 68.310880 22.11203
## PrMn 18.63150 63.912989 28.40699
## PrPt 37.74253 7.225865 51.68556
#### ANOVAS
Md1 <- aov(NoHojas~TRT+B,data = Dat[1:32,])
Md2 <- aov(NoHojas~TRT+B,data = Dat[33:48,])
Md3 <- aov(NoHojas~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 173.63 57.88 13.143 2.8e-05 ***
## B 3 28.95 9.65 2.191 0.115
## Residuals 24 105.69 4.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 438.2 146.06 1.978 0.188
## B 3 88.7 29.56 0.400 0.756
## Residuals 9 664.6 73.84
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 708.1 236.03 6.645 0.00188 **
## B 3 84.3 28.11 0.791 0.51010
## Residuals 25 888.0 35.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 4.403587 24 7.117204 29.4845
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.901262 0.05
##
## $means
## NoHojas std r Min Max Q25 Q50 Q75
## Ctrl 3.495833 1.569090 8 1.633333 6.666667 2.583333 3.166667 3.916667
## Post 7.833333 2.648749 8 4.333333 11.666667 6.083333 7.333333 9.750000
## PrMn 9.958333 1.855387 8 7.000000 12.333333 8.833333 10.000000 11.250000
## PrPt 7.190476 2.713868 7 4.333333 11.666667 5.000000 7.333333 8.500000
##
## $comparison
## NULL
##
## $groups
## NoHojas groups
## PrMn 9.958333 a
## Post 7.833333 a
## PrPt 7.190476 a
## Ctrl 3.495833 b
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 73.84028 9 12.6875 67.72837 18.96866
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## NoHojas std r Min Max Q25 Q50 Q75
## Ctrl 6.50 2.5166115 4 4 10 5.50 6.0 7.00
## Post 20.75 14.1745076 4 6 38 11.25 19.5 29.00
## PrMn 10.25 6.5510813 4 2 18 8.00 10.5 12.75
## PrPt 13.25 0.9574271 4 12 14 12.75 13.5 14.00
##
## $comparison
## NULL
##
## $groups
## NoHojas groups
## Post 20.75 a
## PrPt 13.25 a
## PrMn 10.25 a
## Ctrl 6.50 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 35.52125 25 13.71875 43.44398 8.196877
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## NoHojas std r Min Max Q25 Q50 Q75
## Ctrl 6.000 4.070802 8 3 15 4.0 4.0 6.00
## Post 14.250 3.150964 8 11 21 12.5 14.0 15.00
## PrMn 16.125 4.580627 8 8 20 14.0 17.5 20.00
## PrPt 18.500 9.561829 8 7 33 11.0 15.5 27.25
##
## $comparison
## NULL
##
## $groups
## NoHojas groups
## PrPt 18.500 a
## PrMn 16.125 a
## Post 14.250 a
## Ctrl 6.000 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,39), xlab = "dds",
main = "Número de hojas",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 2.5, c("b","a","a","a",
"a","a","a","a",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.9795, p-value = 0.7987
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.216, p-value = 0.8306
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.98456, p-value = 0.8578
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 4.871, p-value = 0.4318
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.98189, p-value = 0.9767
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.21634, p-value = 0.8118
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.97554, p-value = 0.8536
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 5.875, p-value = 0.2087
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.96817, p-value = 0.4505
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.42363, p-value = 0.3006
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.96274, p-value = 0.2773
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 10, p-value = 0.07524
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso fresco total ##########
#### Descriptivos
Med <- tapply(Dat$Pfto,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 17.24375 27.2550 22.02125
## Post 24.59375 54.6350 65.05250
## PrMn 36.66875 68.2525 65.47000
## PrPt 28.74000 54.2400 53.95000
Dvs <- tapply(Dat$Pfto,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 1.998628 10.93097 10.06189
## Post 4.649178 20.55726 24.39197
## PrMn 6.207609 25.04196 19.44004
## PrPt 6.827662 12.44932 24.11544
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 11.59045 40.10628 45.69173
## Post 18.90390 37.62654 37.49582
## PrMn 16.92888 36.69018 29.69305
## PrPt 23.75665 22.95229 44.69961
#### ANOVAS
Md1 <- aov(Pfto~TRT+B,data = Dat[1:32,])
Md2 <- aov(Pfto~TRT+B,data = Dat[33:48,])
Md3 <- aov(Pfto~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 1574.9 525.0 25.588 1.19e-07 ***
## B 3 236.3 78.8 3.839 0.0224 *
## Residuals 24 492.4 20.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 3541 1180.2 2.726 0.106
## B 3 76 25.3 0.059 0.980
## Residuals 9 3896 432.9
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 10030 3343 7.540 0.000935 ***
## B 3 504 168 0.379 0.768909
## Residuals 25 11086 443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 20.51658 24 26.74935 16.9332
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.901262 0.05
##
## $means
## Pfto std r Min Max Q25 Q50 Q75
## Ctrl 17.24375 1.998628 8 14.18 20.67 16.3075 17.025 17.9150
## Post 24.59375 4.649178 8 19.31 34.45 22.0700 24.300 25.7125
## PrMn 36.66875 6.207609 8 25.38 43.95 33.2800 38.470 40.6800
## PrPt 28.74000 6.827662 7 15.54 35.76 27.2550 29.300 33.0350
##
## $comparison
## NULL
##
## $groups
## Pfto groups
## PrMn 36.66875 a
## PrPt 28.74000 b
## Post 24.59375 b
## Ctrl 17.24375 c
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 432.9442 9 51.09562 40.72229 45.931
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## Pfto std r Min Max Q25 Q50 Q75
## Ctrl 27.2550 10.93097 4 14.71 39.65 20.5225 27.330 34.0625
## Post 54.6350 20.55726 4 31.99 76.34 40.3975 55.105 69.3425
## PrMn 68.2525 25.04196 4 43.29 89.84 48.4050 69.940 89.7875
## PrPt 54.2400 12.44932 4 43.38 72.10 47.9025 50.740 57.0775
##
## $comparison
## NULL
##
## $groups
## Pfto groups
## PrMn 68.2525 a
## Post 54.6350 a
## PrPt 54.2400 a
## Ctrl 27.2550 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 443.4206 25 51.62344 40.79068 28.96091
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Pfto std r Min Max Q25 Q50 Q75
## Ctrl 22.02125 10.06189 8 12.23 42.29 15.2125 19.76 24.5700
## Post 65.05250 24.39197 8 34.02 115.38 51.9550 62.30 72.3600
## PrMn 65.47000 19.44004 8 37.57 97.14 54.2100 64.92 77.6475
## PrPt 53.95000 24.11544 8 19.00 103.35 42.6800 50.50 61.5875
##
## $comparison
## NULL
##
## $groups
## Pfto groups
## PrMn 65.47000 a
## Post 65.05250 a
## PrPt 53.95000 a
## Ctrl 22.02125 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,100),
ylab = "g", xlab = "dds",
main = "Peso fresco total",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 5, c("c","b","a","b",
"a","a","a","a",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.96945, p-value = 0.5042
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.3965, p-value = 0.3491
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.95617, p-value = 0.1992
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 4.871, p-value = 0.4318
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.93357, p-value = 0.2775
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.37192, p-value = 0.3772
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.95406, p-value = 0.4745
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 5, p-value = 0.2873
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.94381, p-value = 0.09609
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.60812, p-value = 0.1044
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.94119, p-value = 0.07635
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 5.5, p-value = 0.3579
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso seco total ##########
#### Descriptivos
Med <- tapply(Dat$Psto,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 2.264375 3.8450 3.86500
## Post 4.033000 7.1850 11.09625
## PrMn 4.848625 11.4425 10.99125
## PrPt 3.920286 7.5775 9.16250
Dvs <- tapply(Dat$Psto,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 0.2325117 1.6176835 1.916612
## Post 1.4307421 3.1070404 3.772888
## PrMn 0.9099835 2.6877422 3.842869
## PrPt 0.7474164 0.8520319 3.465329
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 10.26825 42.07239 49.58892
## Post 35.47588 43.24343 34.00147
## PrMn 18.76787 23.48912 34.96298
## PrPt 19.06535 11.24423 37.82078
#### ANOVAS
Md1 <- aov(Psto~TRT+B,data = Dat[1:32,])
Md2 <- aov(Psto~TRT+B,data = Dat[33:48,])
Md3 <- aov(Psto~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 28.076 9.359 11.858 6.77e-05 ***
## B 3 3.657 1.219 1.544 0.23
## Residuals 23 18.152 0.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 116.03 38.68 5.941 0.0162 *
## B 3 2.07 0.69 0.106 0.9545
## Residuals 9 58.59 6.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 276.47 92.16 8.132 0.000601 ***
## B 3 29.48 9.83 0.867 0.471117
## Residuals 25 283.31 11.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 0.7892253 23 3.752567 23.67402
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.91356 0.05
##
## $means
## Psto std r Min Max Q25 Q50 Q75
## Ctrl 2.264375 0.2325117 8 1.922 2.602 2.138 2.2550 2.43525
## Post 4.033000 1.4307421 7 3.009 6.197 3.091 3.3650 4.73900
## PrMn 4.848625 0.9099835 8 3.483 5.934 4.134 4.9785 5.57825
## PrPt 3.920286 0.7474164 7 2.528 4.616 3.713 4.1140 4.37900
##
## $comparison
## NULL
##
## $groups
## Psto groups
## PrMn 4.848625 a
## Post 4.033000 a
## PrPt 3.920286 a
## Ctrl 2.264375 b
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 6.5103 9 7.5125 33.96378 5.63236
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## Psto std r Min Max Q25 Q50 Q75
## Ctrl 3.8450 1.6176835 4 1.71 5.63 3.3300 4.020 4.535
## Post 7.1850 3.1070404 4 3.01 10.23 5.8900 7.750 9.045
## PrMn 11.4425 2.6877422 4 7.98 14.53 10.5675 11.630 12.505
## PrPt 7.5775 0.8520319 4 6.41 8.33 7.2275 7.785 8.135
##
## $comparison
## NULL
##
## $groups
## Psto groups
## PrMn 11.4425 a
## PrPt 7.5775 ab
## Post 7.1850 ab
## Ctrl 3.8450 b
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 11.33232 25 8.77875 38.3466 4.629817
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Psto std r Min Max Q25 Q50 Q75
## Ctrl 3.86500 1.916612 8 2.27 7.71 2.5525 3.035 4.7050
## Post 11.09625 3.772888 8 6.09 18.65 9.0925 10.465 12.5175
## PrMn 10.99125 3.842869 8 6.28 15.60 7.7325 10.610 14.7550
## PrPt 9.16250 3.465329 8 3.80 15.35 7.7450 8.890 10.4675
##
## $comparison
## NULL
##
## $groups
## Psto groups
## Post 11.09625 a
## PrMn 10.99125 a
## PrPt 9.16250 a
## Ctrl 3.86500 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,20),
ylab = "g", xlab = "dds",
main = "Peso seco total",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 1.3, c("b","a","a","b",
"b","ab","a","ab",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("C","Pos","Pr-Des","Pr-Pos"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.91074, p-value = 0.01554
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.89095, p-value = 0.0199
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.91209, p-value = 0.0191
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 10, p-value = 0.07524
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.96895, p-value = 0.8213
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.25793, p-value = 0.6701
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.96709, p-value = 0.7007
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 0.625, p-value = 0.9602
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.95287, p-value = 0.1738
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.59807, p-value = 0.1107
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.95921, p-value = 0.2244
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 10, p-value = 0.07524
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso Fresco hojas ##########
#### Descriptivos
Med <- tapply(Dat$PfH,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 8.92125 16.13704 11.43625
## Post 14.88500 37.91651 35.46000
## PrMn 22.41125 45.66323 35.73625
## PrPt 17.60714 36.09923 30.36375
Dvs <- tapply(Dat$PfH,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 1.740086 7.265478 7.031374
## Post 2.745214 9.170334 13.627576
## PrMn 4.165472 16.181102 10.114127
## PrPt 3.363994 6.315352 13.631865
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 19.50495 45.02362 61.48322
## Post 18.44282 24.18559 38.43084
## PrMn 18.58652 35.43574 28.30215
## PrPt 19.10585 17.49442 44.89520
#### ANOVAS
Md1 <- aov(PfH~TRT+B,data = Dat[1:32,])
Md2 <- aov(PfH~TRT+B,data = Dat[33:48,])
Md3 <- aov(PfH~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 757.4 252.48 29.499 3.21e-08 ***
## B 3 57.9 19.30 2.255 0.108
## Residuals 24 205.4 8.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 1899.4 633.1 4.537 0.0336 *
## B 3 59.7 19.9 0.143 0.9318
## Residuals 9 1256.1 139.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 3162 1053.9 7.383 0.00105 **
## B 3 94 31.5 0.220 0.88134
## Residuals 25 3569 142.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 8.558918 24 15.9029 18.39641
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.901262 0.05
##
## $means
## PfH std r Min Max Q25 Q50 Q75
## Ctrl 8.92125 1.740086 8 7.06 11.88 7.5125 8.660 10.1650
## Post 14.88500 2.745214 8 10.99 20.17 13.6075 14.625 15.8150
## PrMn 22.41125 4.165472 8 15.02 28.67 20.3475 23.280 24.5925
## PrPt 17.60714 3.363994 7 10.50 20.93 17.8450 18.040 19.0450
##
## $comparison
## NULL
##
## $groups
## PfH groups
## PrMn 22.41125 a
## PrPt 17.60714 b
## Post 14.88500 b
## Ctrl 8.92125 c
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 139.5626 9 33.954 34.79313 26.07801
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## PfH std r Min Max Q25 Q50 Q75
## Ctrl 16.13704 7.265478 4 7.139942 24.57877 12.79735 16.41472 19.75441
## Post 37.91651 9.170334 4 28.947153 46.77480 30.59161 37.97205 45.29695
## PrMn 45.66323 16.181102 4 29.806850 61.15056 32.74219 45.84776 58.76880
## PrPt 36.09923 6.315352 4 29.574197 44.74874 33.63683 35.03699 37.49939
##
## $comparison
## NULL
##
## $groups
## PfH groups
## PrMn 45.66323 a
## Post 37.91651 ab
## PrPt 36.09923 ab
## Ctrl 16.13704 b
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 142.7424 25 28.24906 42.29339 16.43164
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## PfH std r Min Max Q25 Q50 Q75
## Ctrl 11.43625 7.031374 8 4.27 25.72 7.1450 9.385 13.3625
## Post 35.46000 13.627576 8 16.14 62.49 28.1175 34.010 39.3600
## PrMn 35.73625 10.114127 8 19.58 50.62 28.3600 37.790 41.8725
## PrPt 30.36375 13.631865 8 11.11 58.24 24.9450 27.265 34.3225
##
## $comparison
## NULL
##
## $groups
## PfH groups
## PrMn 35.73625 a
## Post 35.46000 a
## PrPt 30.36375 a
## Ctrl 11.43625 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,70),
ylab = "g", xlab = "dds",
main = "Peso fresco hojas",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 4, c("c","b","a","b",
"b","ab","a","ab",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.9383, p-value = 0.07408
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.66212, p-value = 0.07592
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.9231, p-value = 0.03033
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 4.871, p-value = 0.4318
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.9108, p-value = 0.1199
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.5355, p-value = 0.1434
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.93233, p-value = 0.2267
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 5, p-value = 0.2873
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.94702, p-value = 0.1186
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.57181, p-value = 0.1266
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.94572, p-value = 0.09978
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 10, p-value = 0.07524
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso seco hojas ##########
#### Descriptivos
Med <- tapply(Dat$PsH,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 1.359625 2.3000 2.07625
## Post 2.694429 4.9150 6.89500
## PrMn 3.403750 7.6925 6.64125
## PrPt 2.691143 5.0775 5.69500
Dvs <- tapply(Dat$PsH,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 0.1623585 1.1003636 1.265520
## Post 0.7565628 1.6227240 2.526935
## PrMn 0.6851074 1.8490786 2.252532
## PrPt 0.5613500 0.5586517 2.234860
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 11.94141 47.84189 60.95220
## Post 28.07879 33.01575 36.64880
## PrMn 20.12802 24.03742 33.91728
## PrPt 20.85917 11.00249 39.24250
#### ANOVAS
Md1 <- aov(PsH~TRT+B,data = Dat[1:32,])
Md2 <- aov(PsH~TRT+B,data = Dat[33:48,])
Md3 <- aov(PsH~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 17.436 5.812 17.008 4.85e-06 ***
## B 3 0.935 0.312 0.912 0.45
## Residuals 23 7.860 0.342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 58.21 19.404 8.160 0.00618 **
## B 3 1.33 0.442 0.186 0.90343
## Residuals 9 21.40 2.378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 119.11 39.70 8.664 0.00041 ***
## B 3 11.82 3.94 0.860 0.47487
## Residuals 25 114.57 4.58
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 0.3417329 23 2.526867 23.13455
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.91356 0.05
##
## $means
## PsH std r Min Max Q25 Q50 Q75
## Ctrl 1.359625 0.1623585 8 1.192 1.628 1.228 1.3345 1.45850
## Post 2.694429 0.7565628 7 2.104 4.128 2.215 2.3790 2.91000
## PrMn 3.403750 0.6851074 8 2.363 4.424 3.037 3.4130 3.89175
## PrPt 2.691143 0.5613500 7 1.445 3.061 2.773 2.8450 2.97050
##
## $comparison
## NULL
##
## $groups
## PsH groups
## PrMn 3.403750 a
## Post 2.694429 a
## PrPt 2.691143 a
## Ctrl 1.359625 b
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 2.377842 9 4.99625 30.86365 3.403936
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## PsH std r Min Max Q25 Q50 Q75
## Ctrl 2.3000 1.1003636 4 0.83 3.49 1.9700 2.440 2.770
## Post 4.9150 1.6227240 4 2.93 6.84 4.1750 4.945 5.685
## PrMn 7.6925 1.8490786 4 5.37 9.89 7.0725 7.755 8.375
## PrPt 5.0775 0.5586517 4 4.37 5.73 4.8725 5.105 5.310
##
## $comparison
## NULL
##
## $groups
## PsH groups
## PrMn 7.6925 a
## PrPt 5.0775 ab
## Post 4.9150 ab
## Ctrl 2.3000 b
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 4.582769 25 5.326875 40.18755 2.944207
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## PsH std r Min Max Q25 Q50 Q75
## Ctrl 2.07625 1.265520 8 0.91 4.79 1.2775 1.640 2.5800
## Post 6.89500 2.526935 8 3.41 11.70 5.3675 6.575 7.9075
## PrMn 6.64125 2.252532 8 3.97 9.55 4.9750 6.190 8.7325
## PrPt 5.69500 2.234860 8 2.46 9.45 4.7450 5.465 6.5800
##
## $comparison
## NULL
##
## $groups
## PsH groups
## Post 6.89500 a
## PrMn 6.64125 a
## PrPt 5.69500 a
## Ctrl 2.07625 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,13),
ylab = "g", xlab = "dds",
main = "Peso seco total",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 1, c("c","b","a","b",
"b","ab","a","ab",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.93953, p-value = 0.08835
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.58694, p-value = 0.1174
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.93271, p-value = 0.0567
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 4.1333, p-value = 0.5304
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.93475, p-value = 0.2896
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.52594, p-value = 0.1521
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.933, p-value = 0.2321
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 7.625, p-value = 0.1063
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.95149, p-value = 0.1588
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.55376, p-value = 0.1412
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.95961, p-value = 0.2299
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 6, p-value = 0.3062
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso fresco Tallos ##########
#### Descriptivos
Med <- tapply(Dat$PfT,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 8.32625 11.11796 10.58500
## Post 9.72125 16.71849 29.59250
## PrMn 14.13125 22.58927 29.73375
## PrPt 11.10286 18.14077 23.58625
Dvs <- tapply(Dat$PfT,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 1.086974 3.999901 3.137301
## Post 2.059788 12.288586 11.014925
## PrMn 2.125468 9.005926 10.398340
## PrPt 3.564696 6.305999 10.665694
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 13.05478 35.97692 29.63912
## Post 21.18851 73.50299 37.22201
## PrMn 15.04090 39.86816 34.97151
## PrPt 32.10611 34.76147 45.21996
#### ANOVAS
Md1 <- aov(PfT~TRT+B,data = Dat[1:32,])
Md2 <- aov(PfT~TRT+B,data = Dat[33:48,])
Md3 <- aov(PfT~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 147.68 49.23 14.978 1.05e-05 ***
## B 3 66.96 22.32 6.791 0.00179 **
## Residuals 24 78.88 3.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 268.6 89.52 0.972 0.448
## B 3 34.7 11.58 0.126 0.943
## Residuals 9 828.9 92.10
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 1941.8 647.3 7.084 0.00133 **
## B 3 187.3 62.4 0.683 0.57064
## Residuals 25 2284.1 91.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 3.286561 24 10.81129 16.76847
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.901262 0.05
##
## $means
## PfT std r Min Max Q25 Q50 Q75
## Ctrl 8.32625 1.086974 8 6.97 10.28 7.4125 8.345 8.8800
## Post 9.72125 2.059788 8 7.60 14.31 8.6675 9.140 10.1175
## PrMn 14.13125 2.125468 8 10.36 16.74 12.9425 14.765 15.5525
## PrPt 11.10286 3.564696 7 5.06 15.37 9.4050 11.230 13.6250
##
## $comparison
## NULL
##
## $groups
## PfT groups
## PrMn 14.13125 a
## PrPt 11.10286 b
## Post 9.72125 bc
## Ctrl 8.32625 c
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 92.10179 9 17.14162 55.98635 21.18478
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## PfT std r Min Max Q25 Q50 Q75
## Ctrl 11.11796 3.999901 4 7.5700585 15.07123 7.72515 10.91528 14.30809
## Post 16.71849 12.288586 4 0.8502326 29.56520 10.90219 18.22925 24.04555
## PrMn 22.58927 9.005926 4 13.4831496 31.79512 15.66281 22.53940 29.46586
## PrPt 18.14077 6.305999 4 13.8058034 27.35126 14.19675 15.70301 19.64704
##
## $comparison
## NULL
##
## $groups
## PfT groups
## PrMn 22.58927 a
## PrPt 18.14077 a
## Post 16.71849 a
## Ctrl 11.11796 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 91.36426 25 23.37438 40.89292 13.14596
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## PfT std r Min Max Q25 Q50 Q75
## Ctrl 10.58500 3.137301 8 6.48 16.57 8.6525 10.195 11.4775
## Post 29.59250 11.014925 8 17.88 52.89 23.4775 28.035 31.9175
## PrMn 29.73375 10.398340 8 17.12 46.52 20.7725 30.660 35.4900
## PrPt 23.58625 10.665694 8 7.89 45.11 18.7050 23.185 25.4550
##
## $comparison
## NULL
##
## $groups
## PfT groups
## PrMn 29.73375 a
## Post 29.59250 a
## PrPt 23.58625 ab
## Ctrl 10.58500 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,48),
ylab = "g", xlab = "dds",
main = "Peso fresco Tallo",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + 3, c("c","bc","a","b",
"b","ab","a","ab",
"a","a","a","ab"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.98718, p-value = 0.9648
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.14503, p-value = 0.9645
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.98387, p-value = 0.8392
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 3.3226, p-value = 0.6504
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.97673, p-value = 0.9323
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.21707, p-value = 0.8096
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.97902, p-value = 0.9071
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 2.375, p-value = 0.6671
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.94296, p-value = 0.09087
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.61658, p-value = 0.09935
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.94166, p-value = 0.07848
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 6, p-value = 0.3062
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso seco Tallos ##########
#### Descriptivos
Med <- tapply(Dat$PsT,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 0.904750 1.545 1.78875
## Post 1.338571 2.270 4.20125
## PrMn 1.444875 3.750 4.35000
## PrPt 1.229143 2.500 3.46750
Dvs <- tapply(Dat$PsT,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 0.1553629 0.5519360 0.715191
## Post 0.7744890 1.5510212 1.279837
## PrMn 0.2842471 0.8796590 1.622960
## PrPt 0.3960764 0.4741308 1.293475
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 17.17192 35.72401 39.98273
## Post 57.85937 68.32692 30.46325
## PrMn 19.67278 23.45757 37.30943
## PrPt 32.22378 18.96523 37.30282
#### ANOVAS
Md1 <- aov(PsT~TRT+B,data = Dat[1:32,])
Md2 <- aov(PsT~TRT+B,data = Dat[33:48,])
Md3 <- aov(PsT~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 1.298 0.4325 2.386 0.0952 .
## B 3 1.106 0.3687 2.034 0.1371
## Residuals 23 4.169 0.1812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 10.105 3.368 2.835 0.0985 .
## B 3 0.432 0.144 0.121 0.9452
## Residuals 9 10.694 1.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 33.08 11.025 6.771 0.0017 **
## B 3 4.49 1.496 0.918 0.4463
## Residuals 25 40.71 1.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 0.1812474 23 1.2257 34.73375
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.91356 0.05
##
## $means
## PsT std r Min Max Q25 Q50 Q75
## Ctrl 0.904750 0.1553629 8 0.730 1.161 0.79350 0.8810 0.99525
## Post 1.338571 0.7744890 7 0.782 2.852 0.84800 0.9050 1.56750
## PrMn 1.444875 0.2842471 8 1.049 1.889 1.21075 1.5275 1.59425
## PrPt 1.229143 0.3960764 7 0.473 1.641 1.10500 1.2940 1.49300
##
## $comparison
## NULL
##
## $groups
## PsT groups
## PrMn 1.444875 a
## Post 1.338571 a
## PrPt 1.229143 a
## Ctrl 0.904750 a
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 1.188253 9 2.51625 43.32122 2.40627
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## PsT std r Min Max Q25 Q50 Q75
## Ctrl 1.545 0.5519360 4 0.88 2.14 1.2250 1.580 1.9000
## Post 2.270 1.5510212 4 0.08 3.39 1.7150 2.805 3.3600
## PrMn 3.750 0.8796590 4 2.61 4.64 3.3225 3.875 4.3025
## PrPt 2.500 0.4741308 4 2.04 3.16 2.2650 2.400 2.6350
##
## $comparison
## NULL
##
## $groups
## PsT groups
## PrMn 3.750 a
## PrPt 2.500 a
## Post 2.270 a
## Ctrl 1.545 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 1.628372 25 3.451875 36.96765 1.755016
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## PsT std r Min Max Q25 Q50 Q75
## Ctrl 1.78875 0.715191 8 1.12 2.92 1.3000 1.475 2.1250
## Post 4.20125 1.279837 8 2.68 6.95 3.5300 4.020 4.4500
## PrMn 4.35000 1.622960 8 2.19 6.18 2.7875 4.420 5.9900
## PrPt 3.46750 1.293475 8 1.34 5.90 2.8125 3.560 3.8825
##
## $comparison
## NULL
##
## $groups
## PsT groups
## PrMn 4.35000 a
## Post 4.20125 a
## PrPt 3.46750 ab
## Ctrl 1.78875 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,10),
ylab = "g", xlab = "dds",
main = "Peso seco tallo",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + .5, c("a","a","a","a",
"a","a","a","a",
"b","a","a","ab"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.92861, p-value = 0.04511
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.62233, p-value = 0.09544
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.90971, p-value = 0.01693
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 11.6, p-value = 0.0407
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.9571, p-value = 0.6097
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.2341, p-value = 0.754
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.95925, p-value = 0.5588
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 1.5, p-value = 0.8266
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.96579, p-value = 0.3919
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.42361, p-value = 0.3006
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.97148, p-value = 0.4605
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 7, p-value = 0.2206
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Peso seco Tallos ##########
#### Descriptivos
Med <- tapply(Dat$PsT,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl 0.904750 1.545 1.78875
## Post 1.338571 2.270 4.20125
## PrMn 1.444875 3.750 4.35000
## PrPt 1.229143 2.500 3.46750
Dvs <- tapply(Dat$PsT,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl 0.1553629 0.5519360 0.715191
## Post 0.7744890 1.5510212 1.279837
## PrMn 0.2842471 0.8796590 1.622960
## PrPt 0.3960764 0.4741308 1.293475
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl 17.17192 35.72401 39.98273
## Post 57.85937 68.32692 30.46325
## PrMn 19.67278 23.45757 37.30943
## PrPt 32.22378 18.96523 37.30282
#### ANOVAS
Md1 <- aov(PsT~TRT+B,data = Dat[1:32,])
Md2 <- aov(PsT~TRT+B,data = Dat[33:48,])
Md3 <- aov(PsT~TRT+B,data = Dat[49:80,])
summary(Md1);summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 1.298 0.4325 2.386 0.0952 .
## B 3 1.106 0.3687 2.034 0.1371
## Residuals 23 4.169 0.1812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 10.105 3.368 2.835 0.0985 .
## B 3 0.432 0.144 0.121 0.9452
## Residuals 9 10.694 1.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 33.08 11.025 6.771 0.0017 **
## B 3 4.49 1.496 0.918 0.4463
## Residuals 25 40.71 1.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp1 <- HSD.test(Md1,"TRT");Sp1
## $statistics
## MSerror Df Mean CV
## 0.1812474 23 1.2257 34.73375
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.91356 0.05
##
## $means
## PsT std r Min Max Q25 Q50 Q75
## Ctrl 0.904750 0.1553629 8 0.730 1.161 0.79350 0.8810 0.99525
## Post 1.338571 0.7744890 7 0.782 2.852 0.84800 0.9050 1.56750
## PrMn 1.444875 0.2842471 8 1.049 1.889 1.21075 1.5275 1.59425
## PrPt 1.229143 0.3960764 7 0.473 1.641 1.10500 1.2940 1.49300
##
## $comparison
## NULL
##
## $groups
## PsT groups
## PrMn 1.444875 a
## Post 1.338571 a
## PrPt 1.229143 a
## Ctrl 0.904750 a
##
## attr(,"class")
## [1] "group"
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 1.188253 9 2.51625 43.32122 2.40627
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## PsT std r Min Max Q25 Q50 Q75
## Ctrl 1.545 0.5519360 4 0.88 2.14 1.2250 1.580 1.9000
## Post 2.270 1.5510212 4 0.08 3.39 1.7150 2.805 3.3600
## PrMn 3.750 0.8796590 4 2.61 4.64 3.3225 3.875 4.3025
## PrPt 2.500 0.4741308 4 2.04 3.16 2.2650 2.400 2.6350
##
## $comparison
## NULL
##
## $groups
## PsT groups
## PrMn 3.750 a
## PrPt 2.500 a
## Post 2.270 a
## Ctrl 1.545 a
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 1.628372 25 3.451875 36.96765 1.755016
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## PsT std r Min Max Q25 Q50 Q75
## Ctrl 1.78875 0.715191 8 1.12 2.92 1.3000 1.475 2.1250
## Post 4.20125 1.279837 8 2.68 6.95 3.5300 4.020 4.4500
## PrMn 4.35000 1.622960 8 2.19 6.18 2.7875 4.420 5.9900
## PrPt 3.46750 1.293475 8 1.34 5.90 2.8125 3.560 3.8825
##
## $comparison
## NULL
##
## $groups
## PsT groups
## PrMn 4.35000 a
## Post 4.20125 a
## PrPt 3.46750 ab
## Ctrl 1.78875 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{Graf <- barplot(Med, beside = T, ylim = c(0,10),
ylab = "g", xlab = "dds",
main = "Peso seco tallo",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med - Dvs, Graf, Med + Dvs)
segments(Graf - 0.1, Med - Dvs, Graf + 0.1, Med - Dvs)
segments(Graf - 0.1, Med + Dvs, Graf + 0.1, Med + Dvs)
text(Graf, Med + Dvs + .5, c("a","a","a","a",
"a","a","a","a",
"b","a","a","ab"))
segments(0,0,200,0)
legend("topleft", c("Ctrl","Post","PrMn","PrPt"),
cex=.8, bty = "n",horiz=F,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.92861, p-value = 0.04511
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.62233, p-value = 0.09544
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.90971, p-value = 0.01693
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 11.6, p-value = 0.0407
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.9571, p-value = 0.6097
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.2341, p-value = 0.754
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.95925, p-value = 0.5588
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 1.5, p-value = 0.8266
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.96579, p-value = 0.3919
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.42361, p-value = 0.3006
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.97148, p-value = 0.4605
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 7, p-value = 0.2206
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Relacion de area foliar ##########
#### Descriptivos
Med <- tapply(Dat$RAF,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl NaN 0.009062815 0.008716131
## Post NaN 0.006311254 0.009038144
## PrMn NaN 0.006930461 0.008613272
## PrPt NaN 0.007896439 0.008390358
Dvs <- tapply(Dat$RAF,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl NA 0.004164912 0.0017094735
## Post NA 0.001085955 0.0007603613
## PrMn NA 0.001145386 0.0013778514
## PrPt NA 0.002865114 0.0010928986
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl NA 45.95606 19.612757
## Post NA 17.20664 8.412804
## PrMn NA 16.52683 15.996841
## PrPt NA 36.28362 13.025649
#### ANOVAS
Md2 <- aov(Afol~TRT+B,data = Dat[33:48,])
Md3 <- aov(Afol~TRT+B,data = Dat[49:80,])
summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 2745119 915040 4.074 0.044 *
## B 3 109489 36496 0.163 0.919
## Residuals 9 2021221 224580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 3552570 1184190 6.674 0.00183 **
## B 3 392140 130713 0.737 0.54007
## Residuals 25 4436160 177446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 224580.1 9 1103.975 42.92659 1046.106
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## Afol std r Min Max Q25 Q50 Q75
## Ctrl 531.5602 335.7513 4 113.256 908.630 367.1168 552.1775 716.621
## Post 1108.1030 392.1495 4 601.679 1463.580 900.7047 1183.5765 1390.975
## PrMn 1702.0683 503.0449 4 954.406 1999.769 1630.0518 1927.0490 1999.065
## PrPt 1074.1678 436.6604 4 539.573 1605.770 909.7332 1075.6640 1240.099
##
## $comparison
## NULL
##
## $groups
## Afol groups
## PrMn 1702.0683 a
## Post 1108.1030 ab
## PrPt 1074.1678 ab
## Ctrl 531.5602 b
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 177446.4 25 1025.379 41.08177 579.3458
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Afol std r Min Max Q25 Q50 Q75
## Ctrl 456.6281 240.8003 8 210.838 956.570 316.3972 403.6715 484.5158
## Post 1245.4782 464.6873 8 592.064 2202.295 992.2043 1236.7985 1352.8727
## PrMn 1275.1069 384.5220 8 678.059 1816.356 1045.4070 1249.4685 1602.0757
## PrPt 1124.3034 517.6687 8 460.188 2151.347 828.8767 1068.4970 1269.7368
##
## $comparison
## NULL
##
## $groups
## Afol groups
## PrMn 1275.1069 a
## Post 1245.4782 a
## PrPt 1124.3034 a
## Ctrl 456.6281 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{ par(mar=c(4,5,4,4))
Graf <- barplot(Med[,-1], beside = T, ylim = c(0,.017),
ylab = expression(g/cm^2), xlab = "dds",
main = "Relacion de área foliar",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med[,-1] - Dvs[,-1], Graf, Med[,-1] + Dvs[,-1])
segments(Graf - 0.1, Med[,-1] - Dvs[,-1], Graf + 0.1, Med[,-1] - Dvs[,-1])
segments(Graf - 0.1, Med[,-1] + Dvs[,-1], Graf + 0.1, Med[,-1] + Dvs[,-1])
text(Graf, Med[,-1] + Dvs[,-1] + .0010, c("b","ab","ab","a",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("C","Pos","Pr-Des","Pr-Pos"),
cex=.8, bty = "n",horiz=T,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.92861, p-value = 0.04511
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.62233, p-value = 0.09544
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.90971, p-value = 0.01693
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 11.6, p-value = 0.0407
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.96679, p-value = 0.7844
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.25063, p-value = 0.696
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.97476, p-value = 0.8403
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 1.5, p-value = 0.8266
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.94887, p-value = 0.1338
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.56167, p-value = 0.1346
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.94665, p-value = 0.1055
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 8, p-value = 0.1562
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296
########## Area foliar ##########
#### Descriptivos
Med <- tapply(Dat$Afol,list(Dat$TRT,Dat$DDS),mean, na.rm=T);Med
## 46 57 63
## Ctrl NaN 531.5602 456.6281
## Post NaN 1108.1030 1245.4782
## PrMn NaN 1702.0683 1275.1069
## PrPt NaN 1074.1678 1124.3034
Dvs <- tapply(Dat$Afol,list(Dat$TRT,Dat$DDS),sd, na.rm=T);Dvs
## 46 57 63
## Ctrl NA 335.7513 240.8003
## Post NA 392.1495 464.6873
## PrMn NA 503.0449 384.5220
## PrPt NA 436.6604 517.6687
CoefV <- Dvs/Med*100;CoefV
## 46 57 63
## Ctrl NA 63.16335 52.73444
## Post NA 35.38926 37.30995
## PrMn NA 29.55492 30.15606
## PrPt NA 40.65105 46.04350
#### ANOVAS
Md2 <- aov(Afol~TRT+B,data = Dat[33:48,])
Md3 <- aov(Afol~TRT+B,data = Dat[49:80,])
summary(Md2);summary(Md3)
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 2745119 915040 4.074 0.044 *
## B 3 109489 36496 0.163 0.919
## Residuals 9 2021221 224580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## TRT 3 3552570 1184190 6.674 0.00183 **
## B 3 392140 130713 0.737 0.54007
## Residuals 25 4436160 177446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### Medias Tukey
Sp2 <- HSD.test(Md2,"TRT");Sp2
## $statistics
## MSerror Df Mean CV MSD
## 224580.1 9 1103.975 42.92659 1046.106
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 4.41489 0.05
##
## $means
## Afol std r Min Max Q25 Q50 Q75
## Ctrl 531.5602 335.7513 4 113.256 908.630 367.1168 552.1775 716.621
## Post 1108.1030 392.1495 4 601.679 1463.580 900.7047 1183.5765 1390.975
## PrMn 1702.0683 503.0449 4 954.406 1999.769 1630.0518 1927.0490 1999.065
## PrPt 1074.1678 436.6604 4 539.573 1605.770 909.7332 1075.6640 1240.099
##
## $comparison
## NULL
##
## $groups
## Afol groups
## PrMn 1702.0683 a
## Post 1108.1030 ab
## PrPt 1074.1678 ab
## Ctrl 531.5602 b
##
## attr(,"class")
## [1] "group"
Sp3 <- HSD.test(Md3,"TRT");Sp3
## $statistics
## MSerror Df Mean CV MSD
## 177446.4 25 1025.379 41.08177 579.3458
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey TRT 4 3.889997 0.05
##
## $means
## Afol std r Min Max Q25 Q50 Q75
## Ctrl 456.6281 240.8003 8 210.838 956.570 316.3972 403.6715 484.5158
## Post 1245.4782 464.6873 8 592.064 2202.295 992.2043 1236.7985 1352.8727
## PrMn 1275.1069 384.5220 8 678.059 1816.356 1045.4070 1249.4685 1602.0757
## PrPt 1124.3034 517.6687 8 460.188 2151.347 828.8767 1068.4970 1269.7368
##
## $comparison
## NULL
##
## $groups
## Afol groups
## PrMn 1275.1069 a
## Post 1245.4782 a
## PrPt 1124.3034 a
## Ctrl 456.6281 b
##
## attr(,"class")
## [1] "group"
#### Graficos
{ par(mar=c(4,5,4,4))
Graf <- barplot(Med[,-1], beside = T, ylim = c(0,3000),
ylab = expression(cm^2), xlab = "dds",
main = "Ãrea foliar",
col = c("White", "Orange","Darkgreen","Darkblue"))
segments(Graf, Med[,-1] - Dvs[,-1], Graf, Med[,-1] + Dvs[,-1])
segments(Graf - 0.1, Med[,-1] - Dvs[,-1], Graf + 0.1, Med[,-1] - Dvs[,-1])
segments(Graf - 0.1, Med[,-1] + Dvs[,-1], Graf + 0.1, Med[,-1] + Dvs[,-1])
text(Graf, Med[,-1] + Dvs[,-1] + 170, c("b","ab","a","ab",
"b","a","a","a"))
segments(0,0,200,0)
legend("topleft", c("C","Pos","Pr-Des","Pr-Pos"),
cex=.8, bty = "n",horiz=T,
fill = c("White", "Orange","Darkgreen","Darkblue"))
}
#### Supuestos
Res1 <- Md1$residuals
list(shapiro.test(Res1), ad.test(Res1),sf.test(Res1),
pearson.test(Res1),bartlett.test(Dat$SPAD[1:32],Dat$TRT[1:32]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res1
## W = 0.92861, p-value = 0.04511
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res1
## A = 0.62233, p-value = 0.09544
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res1
## W = 0.90971, p-value = 0.01693
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res1
## P = 11.6, p-value = 0.0407
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[1:32] and Dat$TRT[1:32]
## Bartlett's K-squared = 2.4975, df = 3, p-value = 0.4757
Res2 <- Md2$residuals
list(shapiro.test(Res2), ad.test(Res2),sf.test(Res2),
pearson.test(Res2),bartlett.test(Dat$SPAD[33:48],Dat$TRT[33:48]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res2
## W = 0.96679, p-value = 0.7844
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res2
## A = 0.25063, p-value = 0.696
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res2
## W = 0.97476, p-value = 0.8403
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res2
## P = 1.5, p-value = 0.8266
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[33:48] and Dat$TRT[33:48]
## Bartlett's K-squared = 11.758, df = 3, p-value = 0.00826
Res3 <- Md3$residuals
list(shapiro.test(Res3), ad.test(Res3),sf.test(Res3),
pearson.test(Res3),bartlett.test(Dat$SPAD[49:80],Dat$TRT[49:80]))
## [[1]]
##
## Shapiro-Wilk normality test
##
## data: Res3
## W = 0.94887, p-value = 0.1338
##
##
## [[2]]
##
## Anderson-Darling normality test
##
## data: Res3
## A = 0.56167, p-value = 0.1346
##
##
## [[3]]
##
## Shapiro-Francia normality test
##
## data: Res3
## W = 0.94665, p-value = 0.1055
##
##
## [[4]]
##
## Pearson chi-square normality test
##
## data: Res3
## P = 8, p-value = 0.1562
##
##
## [[5]]
##
## Bartlett test of homogeneity of variances
##
## data: Dat$SPAD[49:80] and Dat$TRT[49:80]
## Bartlett's K-squared = 2.2121, df = 3, p-value = 0.5296