MUESTREO 1
library(readxl)
## Warning: package 'readxl' was built under R version 4.0.3
Datos_muestreo_1 <- read_excel("C:/Users/USER/Desktop/Kahomy/Fisiologia/Datos muestreo 1.xlsx")
View(Datos_muestreo_1)
attach(Datos_muestreo_1)
names(Datos_muestreo_1)
## [1] "Tratamientos"
## [2] "Temperatura (c°)"
## [3] "Contenido relativo de clorofila (SPAD)"
## [4] "Numero de hojas"
## [5] "Estomas enves abiertos"
## [6] "Estomas enves cerrados"
## [7] "Estomas enves total"
## [8] "Longitud parte aerea (cm)"
## [9] "Area foliar (cm2)"
## [10] "Peso fresco hojas (g)"
## [11] "Peso seco hojas (g)"
## [12] "CRA Peso fresco (mg)"
## [13] "CRA Peso a saturación (mg)"
## [14] "CRA Peso seco (mg)"
## [15] "CRA (mg)"
## [16] "Diametro Raiz tuberosa (mm)"
## [17] "Peso fresco raiz tuberosa"
## [18] "Peso seco raiz tuberosa (g)"
## [19] "Perdida de electrolitos CE 60min"
## [20] "Perdida de electrolitos CE max"
## [21] "Perdida de electrolitos (%)"
summary(Datos_muestreo_1)
## Tratamientos Temperatura (c°) Contenido relativo de clorofila (SPAD)
## Length:16 Min. :17.10 Min. :28.60
## Class :character 1st Qu.:18.05 1st Qu.:34.62
## Mode :character Median :18.35 Median :36.70
## Mean :18.52 Mean :35.49
## 3rd Qu.:19.10 3rd Qu.:38.10
## Max. :20.10 Max. :39.20
## Numero de hojas Estomas enves abiertos Estomas enves cerrados
## Min. :2.0 Min. : 9.00 Min. : 6.0
## 1st Qu.:3.0 1st Qu.:14.50 1st Qu.:14.0
## Median :4.0 Median :19.50 Median :19.0
## Mean :3.5 Mean :20.19 Mean :19.0
## 3rd Qu.:4.0 3rd Qu.:25.50 3rd Qu.:25.5
## Max. :5.0 Max. :35.00 Max. :31.0
## Estomas enves total Longitud parte aerea (cm) Area foliar (cm2)
## Min. :33.00 Min. :4.800 Min. :50.37
## 1st Qu.:37.75 1st Qu.:6.650 1st Qu.:59.40
## Median :39.00 Median :7.050 Median :62.95
## Mean :39.19 Mean :7.119 Mean :62.79
## 3rd Qu.:41.25 3rd Qu.:8.250 3rd Qu.:68.22
## Max. :45.00 Max. :9.200 Max. :73.42
## Peso fresco hojas (g) Peso seco hojas (g) CRA Peso fresco (mg)
## Min. :3.194 Min. :0.3540 Min. :0.01210
## 1st Qu.:5.027 1st Qu.:0.4647 1st Qu.:0.01440
## Median :5.487 Median :0.5265 Median :0.01570
## Mean :5.547 Mean :0.5647 Mean :0.01537
## 3rd Qu.:6.494 3rd Qu.:0.6727 3rd Qu.:0.01690
## Max. :7.251 Max. :0.7910 Max. :0.01810
## CRA Peso a saturación (mg) CRA Peso seco (mg) CRA (mg)
## Min. :0.01830 Min. :0.001700 Min. :0.5896
## 1st Qu.:0.01900 1st Qu.:0.001875 1st Qu.:0.7208
## Median :0.01915 Median :0.002000 Median :0.7949
## Mean :0.01930 Mean :0.002025 Mean :0.7729
## 3rd Qu.:0.01957 3rd Qu.:0.002200 3rd Qu.:0.8645
## Max. :0.02050 Max. :0.002500 Max. :0.8933
## Diametro Raiz tuberosa (mm) Peso fresco raiz tuberosa
## Min. :12.10 Min. :2.817
## 1st Qu.:14.10 1st Qu.:3.443
## Median :15.10 Median :3.684
## Mean :15.15 Mean :3.566
## 3rd Qu.:16.62 3rd Qu.:3.838
## Max. :18.10 Max. :3.971
## Peso seco raiz tuberosa (g) Perdida de electrolitos CE 60min
## Min. :0.2180 Min. :0.04500
## 1st Qu.:0.2913 1st Qu.:0.05375
## Median :0.3515 Median :0.08710
## Mean :0.3367 Mean :0.13395
## 3rd Qu.:0.3880 3rd Qu.:0.17625
## Max. :0.4190 Max. :0.32700
## Perdida de electrolitos CE max Perdida de electrolitos (%)
## Min. :0.9340 Min. : 4.601
## 1st Qu.:0.9780 1st Qu.: 5.226
## Median :0.9935 Median : 8.440
## Mean :1.0179 Mean :13.407
## 3rd Qu.:1.0337 3rd Qu.:17.965
## Max. :1.1690 Max. :33.436
Temperatura
boxplot(`Temperatura (c°)`~ Tratamientos)
aov1 = aov(Datos_muestreo_1$`Temperatura (c°)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov1)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 8.795 2.9317 20.75 4.84e-05 ***
## Residuals 12 1.695 0.1412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov1)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Temperatura (c°)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.250 -0.5389963 1.0389963 0.7840959
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 1.925 1.1360037 2.7139963 0.0000526
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.825 0.0360037 1.6139963 0.0395370
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 1.675 0.8860037 2.4639963 0.0001994
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.575 -0.2139963 1.3639963 0.1886778
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -1.100 -1.8889963 -0.3110037 0.0064920
shapiro.test(Datos_muestreo_1$`Temperatura (c°)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Temperatura (c°)`
## W = 0.96371, p-value = 0.7292
Contenido relativo de clorofila
boxplot(`Contenido relativo de clorofila (SPAD)`~ Tratamientos)
aov2 = aov(Datos_muestreo_1$`Contenido relativo de clorofila (SPAD)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 182.25 60.75 108.1 5.95e-09 ***
## Residuals 12 6.74 0.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Contenido relativo de clorofila (SPAD)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 1.025 -0.5486226 2.5986226 0.2653743
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -7.650 -9.2236226 -6.0763774 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -1.300 -2.8736226 0.2736226 0.1193435
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -8.675 -10.2486226 -7.1013774 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -2.325 -3.8986226 -0.7513774 0.0042457
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 6.350 4.7763774 7.9236226 0.0000003
shapiro.test(Datos_muestreo_1$`Contenido relativo de clorofila (SPAD)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Contenido relativo de clorofila (SPAD)`
## W = 0.81241, p-value = 0.003999
Numero de Hojas
boxplot(`Numero de hojas`~ Tratamientos)
aov3 = aov(Datos_muestreo_1$`Numero de hojas`~Datos_muestreo_1$Tratamientos)
summary.aov(aov3)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 4.5 1.5000 3.273 0.0589 .
## Residuals 12 5.5 0.4583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov3)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Numero de hojas` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.50 -0.9212532 1.9212532 0.7276987
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.75 -2.1712532 0.6712532 0.4316865
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.75 -2.1712532 0.6712532 0.4316865
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -1.25 -2.6712532 0.1712532 0.0918771
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.25 -2.6712532 0.1712532 0.0918771
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.00 -1.4212532 1.4212532 1.0000000
shapiro.test(Datos_muestreo_1$`Numero de hojas`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Numero de hojas`
## W = 0.8561, p-value = 0.01678
Estomas enves abiertos
boxplot(`Estomas enves abiertos`~ Tratamientos)
aov4 = aov(Datos_muestreo_1$`Estomas enves abiertos`~Datos_muestreo_1$Tratamientos)
summary.aov(aov4)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 870.7 290.23 58.29 2e-07 ***
## Residuals 12 59.7 4.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov4)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Estomas enves abiertos` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -7.25 -11.934455 -2.565545 0.0029815
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -19.50 -24.184455 -14.815545 0.0000002
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -14.50 -19.184455 -9.815545 0.0000046
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -12.25 -16.934455 -7.565545 0.0000264
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -7.25 -11.934455 -2.565545 0.0029815
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 5.00 0.315545 9.684455 0.0353363
shapiro.test(Datos_muestreo_1$`Estomas enves abiertos`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Estomas enves abiertos`
## W = 0.95969, p-value = 0.6562
Estomas enves cerrados
boxplot(`Estomas enves cerrados`~ Tratamientos)
aov5 = aov(Datos_muestreo_1$`Estomas enves cerrados`~Datos_muestreo_1$Tratamientos)
summary.aov(aov5)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 911.5 303.83 47.66 6.11e-07 ***
## Residuals 12 76.5 6.38
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov5)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Estomas enves cerrados` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 7.00 1.699451 12.300549 0.0094818
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 20.25 14.949451 25.550549 0.0000005
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 13.75 8.449451 19.050549 0.0000286
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 13.25 7.949451 18.550549 0.0000414
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 6.75 1.449451 12.050549 0.0121057
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -6.50 -11.800549 -1.199451 0.0154675
shapiro.test(Datos_muestreo_1$`Estomas enves cerrados`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Estomas enves cerrados`
## W = 0.94559, p-value = 0.4232
Estomas enves total
boxplot(`Estomas enves total`~ Tratamientos)
aov6 = aov(Datos_muestreo_1$`Estomas enves total`~Datos_muestreo_1$Tratamientos)
summary.aov(aov6)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 4.69 1.563 0.149 0.928
## Residuals 12 125.75 10.479
TukeyHSD(aov6)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Estomas enves total` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.25 -7.045855 6.545855 0.9995063
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.75 -6.045855 7.545855 0.9872468
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.75 -7.545855 6.045855 0.9872468
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 1.00 -5.795855 7.795855 0.9709012
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.50 -7.295855 6.295855 0.9961156
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -1.50 -8.295855 5.295855 0.9116666
shapiro.test(Datos_muestreo_1$`Estomas enves total`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Estomas enves total`
## W = 0.97808, p-value = 0.9466
Longitud de la parte aerea
boxplot(`Longitud parte aerea (cm)`~ Tratamientos)
aov7 = aov(Datos_muestreo_1$`Longitud parte aerea (cm)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov7)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 23.292 7.764 48.72 5.41e-07 ***
## Residuals 12 1.912 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov7)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Longitud parte aerea (cm)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 1.300 0.4619097 2.1380903 0.0029301
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -2.075 -2.9130903 -1.2369097 0.0000455
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.450 -1.2880903 0.3880903 0.4175163
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -3.375 -4.2130903 -2.5369097 0.0000003
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.750 -2.5880903 -0.9119097 0.0002325
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 1.625 0.7869097 2.4630903 0.0004551
shapiro.test(Datos_muestreo_1$`Longitud parte aerea (cm)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Longitud parte aerea (cm)`
## W = 0.94809, p-value = 0.4601
Area foliar (cm2)
boxplot(`Area foliar (cm2)`~ Tratamientos)
aov8 = aov(Datos_muestreo_1$`Area foliar (cm2)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov8)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 846.3 282.11 126.4 2.42e-09 ***
## Residuals 12 26.8 2.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov8)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Area foliar (cm2)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 8.55875 5.421987 11.695513 0.0000171
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -11.91240 -15.049163 -8.775637 0.0000005
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.64125 -3.778013 2.495513 0.9279189
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -20.47115 -23.607913 -17.334387 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -9.20000 -12.336763 -6.063237 0.0000081
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 11.27115 8.134387 14.407913 0.0000009
shapiro.test(Datos_muestreo_1$`Area foliar (cm2)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Area foliar (cm2)`
## W = 0.90612, p-value = 0.1008
Peso fresco hojas (g)
boxplot(`Peso fresco hojas (g)`~ Tratamientos)
aov9 = aov(Datos_muestreo_1$`Peso fresco hojas (g)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov9)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 19.934 6.645 49.77 4.81e-07 ***
## Residuals 12 1.602 0.134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov9)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Peso fresco hojas (g)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 1.22450 0.4574295 1.9915705 0.0023396
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -1.90375 -2.6708205 -1.1366795 0.0000445
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.36125 -1.1283205 0.4058205 0.5236522
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -3.12825 -3.8953205 -2.3611795 0.0000002
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.58575 -2.3528205 -0.8186795 0.0002549
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 1.54250 0.7754295 2.3095705 0.0003281
shapiro.test(Datos_muestreo_1$`Peso fresco hojas (g)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Peso fresco hojas (g)`
## W = 0.94789, p-value = 0.457
Peso seco hojas (g)
boxplot(`Peso seco hojas (g)`~ Tratamientos)
aov10 = aov(Datos_muestreo_1$`Peso seco hojas (g)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov10)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 0.3266 0.10887 86.08 2.2e-08 ***
## Residuals 12 0.0152 0.00126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov10)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Peso seco hojas (g)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.176 0.1013433 0.2506567 0.0000736
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.217 -0.2916567 -0.1423433 0.0000089
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.088 -0.1626567 -0.0133433 0.0198112
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.393 -0.4676567 -0.3183433 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.264 -0.3386567 -0.1893433 0.0000011
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.129 0.0543433 0.2036567 0.0012291
shapiro.test(Datos_muestreo_1$`Peso seco hojas (g)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Peso seco hojas (g)`
## W = 0.91962, p-value = 0.1663
CRA Peso freco (mg)
boxplot(`CRA Peso fresco (mg)`~ Tratamientos)
aov11 = aov(Datos_muestreo_1$`CRA Peso fresco (mg)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov11)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 5.591e-05 1.864e-05 119.9 3.27e-09 ***
## Residuals 12 1.860e-06 1.550e-07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov11)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`CRA Peso fresco (mg)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.001125 0.0002973828 0.0019526172
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.003900 -0.0047276172 -0.0030723828
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.001225 -0.0020526172 -0.0003973828
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.005025 -0.0058526172 -0.0041973828
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.002350 -0.0031776172 -0.0015223828
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.002675 0.0018473828 0.0035026172
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.0077653
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.0041886
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.0000114
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.0000029
shapiro.test(Datos_muestreo_1$`CRA Peso fresco (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`CRA Peso fresco (mg)`
## W = 0.91381, p-value = 0.1341
CRA Peso a saturacion (mg)
boxplot(`CRA Peso a saturación (mg)`~ Tratamientos)
aov12 = aov(Datos_muestreo_1$`CRA Peso a saturación (mg)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov12)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 6.250e-07 2.083e-07 0.62 0.616
## Residuals 12 4.035e-06 3.363e-07
TukeyHSD(aov12)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`CRA Peso a saturación (mg)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.000100 -0.00111734 0.00131734 0.9946230
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.000425 -0.00164234 0.00079234 0.7321465
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.000075 -0.00129234 0.00114234 0.9977040
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.000525 -0.00174234 0.00069234 0.5914449
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.000175 -0.00139234 0.00104234 0.9727605
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.000350 -0.00086734 0.00156734 0.8280546
shapiro.test(Datos_muestreo_1$`CRA Peso a saturación (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`CRA Peso a saturación (mg)`
## W = 0.95232, p-value = 0.5273
CRA Peso seco (mg)
boxplot(`CRA Peso seco (mg)`~ Tratamientos)
aov13 = aov(Datos_muestreo_1$`CRA Peso seco (mg)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov13)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 3.85e-07 1.283e-07 4.464 0.0252 *
## Residuals 12 3.45e-07 2.875e-08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov13)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`CRA Peso seco (mg)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.000050 -0.0003059587 4.059587e-04
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.000325 -0.0006809587 3.095874e-05
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.000225 -0.0005809587 1.309587e-04
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.000375 -0.0007309587 -1.904126e-05
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.000275 -0.0006309587 8.095874e-05
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.000100 -0.0002559587 4.559587e-04
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.9744930
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0777382
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.2877557
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0379638
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.1540794
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.8373821
shapiro.test(Datos_muestreo_1$`CRA Peso seco (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`CRA Peso seco (mg)`
## W = 0.93626, p-value = 0.3057
CRA (mg)
boxplot(`CRA (mg)`~ Tratamientos)
aov14 = aov(Datos_muestreo_1$`CRA (mg)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov14)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 0.15357 0.05119 107 6.31e-09 ***
## Residuals 12 0.00574 0.00048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov14)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`CRA (mg)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.05858252 0.01267094 0.10449411
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.20457856 -0.25049015 -0.15866698
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.06553618 -0.11144776 -0.01962459
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.26316109 -0.30907268 -0.21724950
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.12411870 -0.17003029 -0.07820711
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.13904239 0.09313080 0.18495397
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.0119477
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0000001
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.0054760
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.0000188
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.0000058
shapiro.test(Datos_muestreo_1$`CRA (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`CRA (mg)`
## W = 0.90147, p-value = 0.08485
Diametro Raiz tuberosa (mm)
boxplot(`Diametro Raiz tuberosa (mm)`~ Tratamientos)
aov15 = aov(Datos_muestreo_1$`Diametro Raiz tuberosa (mm)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov15)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 39.95 13.31 31 6.21e-06 ***
## Residuals 12 5.16 0.43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov15)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Diametro Raiz tuberosa (mm)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.950 -0.4259557 2.3259557 0.2240733
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -3.175 -4.5509557 -1.7990443 0.0000906
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -1.675 -3.0509557 -0.2990443 0.0162048
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -4.125 -5.5009557 -2.7490443 0.0000065
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -2.625 -4.0009557 -1.2490443 0.0005254
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 1.500 0.1240443 2.8759557 0.0314000
shapiro.test(Datos_muestreo_1$`Diametro Raiz tuberosa (mm)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Diametro Raiz tuberosa (mm)`
## W = 0.96842, p-value = 0.8123
Peso fresco raiz tuberosa
boxplot(`Peso fresco raiz tuberosa`~ Tratamientos)
aov16 = aov(Datos_muestreo_1$`Peso fresco raiz tuberosa`~Datos_muestreo_1$Tratamientos)
summary.aov(aov16)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 1.8369 0.6123 41.03 1.39e-06 ***
## Residuals 12 0.1791 0.0149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov16)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Peso fresco raiz tuberosa` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.15400 -0.1024667 0.410466671 0.3273960
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.73600 -0.9924667 -0.479533329 0.0000102
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.09925 -0.3557167 0.157216671 0.6680409
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.89000 -1.1464667 -0.633533329 0.0000014
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.25325 -0.5097167 0.003216671 0.0533156
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.63675 0.3802833 0.893216671 0.0000443
shapiro.test(Datos_muestreo_1$`Peso fresco raiz tuberosa`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Peso fresco raiz tuberosa`
## W = 0.86329, p-value = 0.0215
Peso seco raiz tuberosa (g)
boxplot(`Peso seco raiz tuberosa (g)`~ Tratamientos)
aov17 = aov(Datos_muestreo_1$`Peso seco raiz tuberosa (g)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov17)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 0.06459 0.021531 55.36 2.67e-07 ***
## Residuals 12 0.00467 0.000389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov17)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Peso seco raiz tuberosa (g)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.00000 -0.04140194 0.04140194 1.0000000
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.15125 -0.19265194 -0.10984806 0.0000008
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.08500 -0.12640194 -0.04359806 0.0002716
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.15125 -0.19265194 -0.10984806 0.0000008
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.08500 -0.12640194 -0.04359806 0.0002716
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.06625 0.02484806 0.10765194 0.0022956
shapiro.test(Datos_muestreo_1$`Peso seco raiz tuberosa (g)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Peso seco raiz tuberosa (g)`
## W = 0.91316, p-value = 0.1309
Perdida de electrolitos CE 60min
boxplot(`Perdida de electrolitos CE 60min`~ Tratamientos)
aov18 = aov(Datos_muestreo_1$`Perdida de electrolitos CE 60min`~Datos_muestreo_1$Tratamientos)
summary.aov(aov18)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 0.16200 0.05400 200.9 1.61e-10 ***
## Residuals 12 0.00323 0.00027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov18)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Perdida de electrolitos CE 60min` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.01200 -0.04641973 0.02241973 0.7329488
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.23975 0.20533027 0.27416973 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.06105 0.02663027 0.09546973 0.0009866
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.25175 0.21733027 0.28616973 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.07305 0.03863027 0.10746973 0.0002000
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -0.17870 -0.21311973 -0.14428027 0.0000000
shapiro.test(Datos_muestreo_1$`Perdida de electrolitos CE 60min`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Perdida de electrolitos CE 60min`
## W = 0.77296, p-value = 0.001218
Perdida de electrolitos CE max
boxplot(`Perdida de electrolitos CE max`~ Tratamientos)
aov19 = aov(Datos_muestreo_1$`Perdida de electrolitos CE max`~Datos_muestreo_1$Tratamientos)
summary.aov(aov19)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 0.01142 0.003807 0.92 0.461
## Residuals 12 0.04966 0.004138
TukeyHSD(aov19)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Perdida de electrolitos CE max` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.03100 -0.16604659 0.10404659 0.9021378
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.06100 -0.19604659 0.07404659 0.5563111
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.00575 -0.12929659 0.14079659 0.9992360
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.03000 -0.16504659 0.10504659 0.9101647
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.03675 -0.09829659 0.17179659 0.8495212
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.06675 -0.06829659 0.20179659 0.4849496
shapiro.test(Datos_muestreo_1$`Perdida de electrolitos CE max`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Perdida de electrolitos CE max`
## W = 0.87114, p-value = 0.02831
Perdida de electrolitos (%)
boxplot(`Perdida de electrolitos (%)`~ Tratamientos)
aov20 = aov(Datos_muestreo_1$`Perdida de electrolitos (%)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov20)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 1725.6 575.2 163.8 5.33e-10 ***
## Residuals 12 42.1 3.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov20)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Perdida de electrolitos (%)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -1.090731 -5.025001 2.843539 0.8425227
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 24.782498 20.848228 28.716769 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 5.854823 1.920553 9.789094 0.0040223
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 25.873229 21.938959 29.807500 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 6.945555 3.011284 10.879825 0.0010265
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -18.927675 -22.861945 -14.993405 0.0000000
shapiro.test(Datos_muestreo_1$`Perdida de electrolitos (%)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Perdida de electrolitos (%)`
## W = 0.76141, p-value = 0.0008749
MUESTREO 2
library(readxl)
datos2 <- read_excel("C:/Users/USER/Desktop/Kahomy/Fisiologia/datos2.xlsx")
View(datos2)
attach(datos2)
## The following objects are masked from Datos_muestreo_1:
##
## Contenido relativo de clorofila (SPAD), Tratamientos
names(datos2)
## [1] "Tratamientos"
## [2] "DDS"
## [3] "Repetición"
## [4] "TEMPERATURA (°C)"
## [5] "Abierto"
## [6] "Cerrado"
## [7] "Total"
## [8] "Contenido relativo de clorofila (SPAD)"
## [9] "#Hojas"
## [10] "Longitud (cm)"
## [11] "Área foliar (cm2)"
## [12] "Peso fresco hojas"
## [13] "Peso seco hojas"
## [14] "Peso fresco"
## [15] "Peso a saturación"
## [16] "Peso seco"
## [17] "CRA"
## [18] "Diámetro (mm)"
## [19] "Peso fresco raiz"
## [20] "Peso seco raiz"
## [21] "CE 60 min"
## [22] "CE max"
## [23] "% PE"
w = factor(Tratamientos)
summary(datos2)
## Tratamientos DDS Repetición TEMPERATURA (°C)
## Length:16 Min. :34 Min. :1.00 Min. :17.20
## Class :character 1st Qu.:34 1st Qu.:1.75 1st Qu.:17.60
## Mode :character Median :34 Median :2.50 Median :17.90
## Mean :34 Mean :2.50 Mean :18.20
## 3rd Qu.:34 3rd Qu.:3.25 3rd Qu.:18.68
## Max. :34 Max. :4.00 Max. :19.50
## NA's :12
## Abierto Cerrado Total
## Min. :12.00 Min. :18.00 Min. :38.00
## 1st Qu.:15.75 1st Qu.:20.75 1st Qu.:39.75
## Median :18.00 Median :22.00 Median :41.00
## Mean :18.06 Mean :22.88 Mean :40.94
## 3rd Qu.:21.00 3rd Qu.:24.75 3rd Qu.:42.00
## Max. :24.00 Max. :29.00 Max. :44.00
##
## Contenido relativo de clorofila (SPAD) #Hojas Longitud (cm)
## Min. :32.90 Min. :3.000 Min. : 8.500
## 1st Qu.:34.80 1st Qu.:4.000 1st Qu.: 9.000
## Median :37.50 Median :4.000 Median : 9.450
## Mean :36.88 Mean :4.312 Mean : 9.425
## 3rd Qu.:38.90 3rd Qu.:5.000 3rd Qu.: 9.925
## Max. :40.10 Max. :5.000 Max. :10.300
##
## Área foliar (cm2) Peso fresco hojas Peso seco hojas Peso fresco
## Min. : 71.38 Min. :5.142 Min. :0.5162 Min. :0.01190
## 1st Qu.: 79.08 1st Qu.:5.807 1st Qu.:0.6882 1st Qu.:0.01380
## Median : 88.61 Median :6.424 Median :0.7709 Median :0.01540
## Mean : 87.31 Mean :6.400 Mean :0.7155 Mean :0.01503
## 3rd Qu.: 97.53 3rd Qu.:6.948 3rd Qu.:0.7922 3rd Qu.:0.01650
## Max. :101.38 Max. :7.672 Max. :0.8152 Max. :0.01730
##
## Peso a saturación Peso seco CRA Diámetro (mm)
## Min. :0.01690 Min. :0.001800 Min. :0.6667 Min. :14.90
## 1st Qu.:0.01767 1st Qu.:0.001975 1st Qu.:0.7346 1st Qu.:17.35
## Median :0.01790 Median :0.002050 Median :0.8469 Median :21.50
## Mean :0.01784 Mean :0.002063 Mean :0.8201 Mean :20.68
## 3rd Qu.:0.01830 3rd Qu.:0.002125 3rd Qu.:0.8980 3rd Qu.:24.32
## Max. :0.01840 Max. :0.002300 Max. :0.9379 Max. :25.20
##
## Peso fresco raiz Peso seco raiz CE 60 min CE max
## Min. :6.018 Min. :0.4728 Min. :0.0648 Min. :0.9674
## 1st Qu.:6.364 1st Qu.:0.5537 1st Qu.:0.0845 1st Qu.:0.9833
## Median :6.750 Median :0.6551 Median :0.1210 Median :0.9863
## Mean :6.747 Mean :0.6367 Mean :0.1451 Mean :1.0176
## 3rd Qu.:7.207 3rd Qu.:0.7402 3rd Qu.:0.1778 3rd Qu.:1.0412
## Max. :7.372 Max. :0.7631 Max. :0.2746 Max. :1.1037
##
## % PE
## Min. : 6.641
## 1st Qu.: 8.584
## Median :11.875
## Mean :14.225
## 3rd Qu.:16.597
## Max. :27.907
##
TEMPERATURA
boxplot(datos2$`TEMPERATURA (°C)`~datos2$Tratamientos)
at2 <- aov(datos2$`TEMPERATURA (°C)`~datos2$Tratamientos)
summary(at2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 6.585 2.1950 12.69 0.000495 ***
## Residuals 12 2.075 0.1729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(at2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`TEMPERATURA (°C)` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.075 -0.9479697 0.7979697 0.9938647
## T_C-T_A 1.500 0.6270303 2.3729697 0.0012876
## T_D-T_A 0.175 -0.6979697 1.0479697 0.9316078
## T_C-T_B 1.575 0.7020303 2.4479697 0.0008534
## T_D-T_B 0.250 -0.6229697 1.1229697 0.8296760
## T_D-T_C -1.325 -2.1979697 -0.4520303 0.0034636
ESTOMAS
ABIERTOS
boxplot(datos2$Abierto~datos2$Tratamientos)
aa2 <- aov(datos2$Abierto~datos2$Tratamientos)
summary(aa2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 170.69 56.90 21.17 4.39e-05 ***
## Residuals 12 32.25 2.69
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aa2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$Abierto ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 1.50 -1.941557 4.941557 0.5835354
## T_C-T_A -7.00 -10.441557 -3.558443 0.0002958
## T_D-T_A -3.25 -6.691557 0.191557 0.0663935
## T_C-T_B -8.50 -11.941557 -5.058443 0.0000467
## T_D-T_B -4.75 -8.191557 -1.308443 0.0069751
## T_D-T_C 3.75 0.308443 7.191557 0.0314862
CERRRADOS
boxplot(datos2$Cerrado~datos2$Tratamientos)
ac2 <- aov(datos2$Cerrado~datos2$Tratamientos)
summary(ac2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 145.2 48.42 17.88 0.000101 ***
## Residuals 12 32.5 2.71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ac2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$Cerrado ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.25 -3.7048707 3.204871 0.9963019
## T_C-T_A 7.25 3.7951293 10.704871 0.0002220
## T_D-T_A 2.50 -0.9548707 5.954871 0.1931528
## T_C-T_B 7.50 4.0451293 10.954871 0.0001619
## T_D-T_B 2.75 -0.7048707 6.204871 0.1379278
## T_D-T_C -4.75 -8.2048707 -1.295129 0.0071684
TOTAL
boxplot(datos2$Total~datos2$Tratamientos)
ato2 <- aov(datos2$Total~datos2$Tratamientos)
summary(ato2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 8.19 2.729 0.845 0.495
## Residuals 12 38.75 3.229
TukeyHSD(ato2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$Total ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 1.25 -2.522471 5.022471 0.7611627
## T_C-T_A 0.25 -3.522471 4.022471 0.9971496
## T_D-T_A -0.75 -4.522471 3.022471 0.9331175
## T_C-T_B -1.00 -4.772471 2.772471 0.8589504
## T_D-T_B -2.00 -5.772471 1.772471 0.4278967
## T_D-T_C -1.00 -4.772471 2.772471 0.8589504
CONTENIDO RELATIVO DE CLOROFILA
boxplot(datos2$`Contenido relativo de clorofila (SPAD)` ~datos2$Tratamientos)
acl2 <- aov(datos2$`Contenido relativo de clorofila (SPAD)` ~datos2$Tratamientos)
summary(acl2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 89.57 29.857 57.23 2.21e-07 ***
## Residuals 12 6.26 0.522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(acl2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Contenido relativo de clorofila (SPAD)` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.25 -1.7662726 1.266273 0.9599563
## T_C-T_A -5.75 -7.2662726 -4.233727 0.0000005
## T_D-T_A -3.30 -4.8162726 -1.783727 0.0001581
## T_C-T_B -5.50 -7.0162726 -3.983727 0.0000008
## T_D-T_B -3.05 -4.5662726 -1.533727 0.0003272
## T_D-T_C 2.45 0.9337274 3.966273 0.0021247
Numero de hojas
boxplot(datos2$`#Hojas` ~datos2$Tratamientos)
ah2 <- aov(datos2$`#Hojas`~datos2$Tratamientos)
summary(ah2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 3.688 1.2292 3.933 0.0363 *
## Residuals 12 3.750 0.3125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ah2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`#Hojas` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.25 -1.4235612 0.92356121 0.9195619
## T_C-T_A -1.25 -2.4235612 -0.07643879 0.0357458
## T_D-T_A -0.25 -1.4235612 0.92356121 0.9195619
## T_C-T_B -1.00 -2.1735612 0.17356121 0.1051568
## T_D-T_B 0.00 -1.1735612 1.17356121 1.0000000
## T_D-T_C 1.00 -0.1735612 2.17356121 0.1051568
Longitud
boxplot(datos2$`Longitud (cm)`~datos2$Tratamientos)
al2 <- aov(datos2$`Longitud (cm)`~datos2$Tratamientos)
summary(al2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 4.59 1.5300 31.66 5.56e-06 ***
## Residuals 12 0.58 0.0483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(al2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Longitud (cm)` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 6.000000e-01 0.1384656 1.0615344 0.0105486
## T_C-T_A -9.000000e-01 -1.3615344 -0.4384656 0.0004326
## T_D-T_A 1.776357e-15 -0.4615344 0.4615344 1.0000000
## T_C-T_B -1.500000e+00 -1.9615344 -1.0384656 0.0000028
## T_D-T_B -6.000000e-01 -1.0615344 -0.1384656 0.0105486
## T_D-T_C 9.000000e-01 0.4384656 1.3615344 0.0004326
Área foliar
boxplot(datos2$`Área foliar (cm2)` ~datos2$Tratamientos)
aaf2 <- aov(datos2$`Área foliar (cm2)` ~datos2$Tratamientos)
summary(aaf2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 1799.4 599.8 161.2 5.85e-10 ***
## Residuals 12 44.7 3.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aaf2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Área foliar (cm2)` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 3.92675 -0.1231428 7.976643 0.0584041
## T_C-T_A -23.00150 -27.0513928 -18.951607 0.0000000
## T_D-T_A -12.31975 -16.3696428 -8.269857 0.0000056
## T_C-T_B -26.92825 -30.9781428 -22.878357 0.0000000
## T_D-T_B -16.24650 -20.2963928 -12.196607 0.0000003
## T_D-T_C 10.68175 6.6318572 14.731643 0.0000242
Peso fresco hojas
boxplot(datos2$`Peso fresco hojas`~datos2$Tratamientos)
apfh2 <- aov(datos2$`Peso fresco hojas`~datos2$Tratamientos)
summary(apfh2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 10.570 3.523 213.9 1.11e-10 ***
## Residuals 12 0.198 0.016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apfh2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso fresco hojas` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.75125 0.481786 1.020714 0.0000138
## T_C-T_A -1.46350 -1.732964 -1.194036 0.0000000
## T_D-T_A -0.61250 -0.881964 -0.343036 0.0001047
## T_C-T_B -2.21475 -2.484214 -1.945286 0.0000000
## T_D-T_B -1.36375 -1.633214 -1.094286 0.0000000
## T_D-T_C 0.85100 0.581536 1.120464 0.0000037
Peso seco hojas
boxplot(datos2$`Peso seco hojas` ~datos2$Tratamientos)
apsh2 <- aov(datos2$`Peso seco hojas` ~datos2$Tratamientos)
summary(apsh2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 0.20131 0.0671 693.5 1.04e-13 ***
## Residuals 12 0.00116 0.0001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apsh2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso seco hojas` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.002850 -0.01780097 0.02350097 0.9757421
## T_C-T_A -0.269650 -0.29030097 -0.24899903 0.0000000
## T_D-T_A -0.046725 -0.06737597 -0.02607403 0.0001094
## T_C-T_B -0.272500 -0.29315097 -0.25184903 0.0000000
## T_D-T_B -0.049575 -0.07022597 -0.02892403 0.0000617
## T_D-T_C 0.222925 0.20227403 0.24357597 0.0000000
Peso fresco
boxplot(datos2$`Peso fresco` ~datos2$Tratamientos)
apf2 <- aov(datos2$`Peso fresco`~datos2$Tratamientos)
summary(apf2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 4.817e-05 1.606e-05 95.14 1.24e-08 ***
## Residuals 12 2.020e-06 1.690e-07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apf2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso fresco` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.000150 -0.001012388 0.0007123878 0.9535545
## T_C-T_A -0.004275 -0.005137388 -0.0034126122 0.0000000
## T_D-T_A -0.002075 -0.002937388 -0.0012126122 0.0000603
## T_C-T_B -0.004125 -0.004987388 -0.0032626122 0.0000000
## T_D-T_B -0.001925 -0.002787388 -0.0010626122 0.0001244
## T_D-T_C 0.002200 0.001337612 0.0030623878 0.0000338
Peso saturación
boxplot(datos2$`Peso a saturación` ~datos2$Tratamientos)
apsa2 <- aov(datos2$`Peso a saturación` ~datos2$Tratamientos)
summary(apsa2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 3.207e-06 1.069e-06 22.51 3.23e-05 ***
## Residuals 12 5.700e-07 4.750e-08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apsa2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso a saturación` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.00020 -0.0006575384 0.0002575384 0.5813409
## T_C-T_A -0.00115 -0.0016075384 -0.0006924616 0.0000392
## T_D-T_A -0.00020 -0.0006575384 0.0002575384 0.5813409
## T_C-T_B -0.00095 -0.0014075384 -0.0004924616 0.0002449
## T_D-T_B 0.00000 -0.0004575384 0.0004575384 1.0000000
## T_D-T_C 0.00095 0.0004924616 0.0014075384 0.0002449
Peso seco
boxplot(datos2$`Peso seco` ~datos2$Tratamientos)
aps2<- aov(datos2$`Peso seco`~datos2$Tratamientos)
summary(aps2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 2.425e-07 8.083e-08 8.435 0.00276 **
## Residuals 12 1.150e-07 9.580e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aps2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso seco` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.000100 -0.0001055129 3.055129e-04 0.4975804
## T_C-T_A -0.000225 -0.0004305129 -1.948713e-05 0.0306483
## T_D-T_A -0.000125 -0.0003305129 8.051287e-05 0.3173297
## T_C-T_B -0.000325 -0.0005305129 -1.194871e-04 0.0025196
## T_D-T_B -0.000225 -0.0004305129 -1.948713e-05 0.0306483
## T_D-T_C 0.000100 -0.0001055129 3.055129e-04 0.4975804
Contenido relativo de agua
boxplot(datos2$CRA ~datos2$Tratamientos)
acra2 <- aov(datos2$CRA ~datos2$Tratamientos)
summary(acra2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 0.12709 0.04236 73.44 5.45e-08 ***
## Residuals 12 0.00692 0.00058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(acra2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$CRA ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.001010114 -0.04941072 0.05143095 0.9999199
## T_C-T_A -0.212053654 -0.26247449 -0.16163282 0.0000002
## T_D-T_A -0.117251990 -0.16767282 -0.06683116 0.0000840
## T_C-T_B -0.213063767 -0.26348460 -0.16264294 0.0000002
## T_D-T_B -0.118262104 -0.16868294 -0.06784127 0.0000773
## T_D-T_C 0.094801664 0.04438083 0.14522250 0.0005971
Diametro
boxplot(datos2$`Diámetro (mm)`~datos2$Tratamientos)
ad2 <- aov(datos2$`Diámetro (mm)` ~datos2$Tratamientos)
summary(ad2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 248.85 82.95 646.4 1.58e-13 ***
## Residuals 12 1.54 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ad2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Diámetro (mm)` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.90 0.1479435 1.652057 0.0180402
## T_C-T_A -8.65 -9.4020565 -7.897943 0.0000000
## T_D-T_A -5.55 -6.3020565 -4.797943 0.0000000
## T_C-T_B -9.55 -10.3020565 -8.797943 0.0000000
## T_D-T_B -6.45 -7.2020565 -5.697943 0.0000000
## T_D-T_C 3.10 2.3479435 3.852057 0.0000002
Peso fresco raíz
boxplot(datos2$`Peso fresco raiz` ~datos2$Tratamientos)
apfr2 <- aov(datos2$`Peso fresco raiz` ~datos2$Tratamientos)
summary(apfr2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 3.763 1.2544 201.9 1.56e-10 ***
## Residuals 12 0.075 0.0062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apfr2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso fresco raiz` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.199125 0.03365031 0.3645997 0.0174276
## T_C-T_A -0.981575 -1.14704969 -0.8161003 0.0000000
## T_D-T_A -0.695575 -0.86104969 -0.5301003 0.0000002
## T_C-T_B -1.180700 -1.34617469 -1.0152253 0.0000000
## T_D-T_B -0.894700 -1.06017469 -0.7292253 0.0000000
## T_D-T_C 0.286000 0.12052531 0.4514747 0.0012264
Peso seco raíz
boxplot(datos2$`Peso seco raiz` ~datos2$Tratamientos)
apsr2 <- aov(datos2$`Peso seco raiz` ~datos2$Tratamientos)
summary(apsr2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 0.1915 0.06384 851.2 3.06e-14 ***
## Residuals 12 0.0009 0.00008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apsr2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`Peso seco raiz` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A 0.029950 0.01176828 0.04813172 0.0018198
## T_C-T_A -0.241450 -0.25963172 -0.22326828 0.0000000
## T_D-T_A -0.139775 -0.15795672 -0.12159328 0.0000000
## T_C-T_B -0.271400 -0.28958172 -0.25321828 0.0000000
## T_D-T_B -0.169725 -0.18790672 -0.15154328 0.0000000
## T_D-T_C 0.101675 0.08349328 0.11985672 0.0000000
CE min
boxplot(datos2$`CE 60 min`~datos2$Tratamientos)
acm2 <- aov(datos2$`CE 60 min` ~datos2$Tratamientos)
summary(acm2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 0.08659 0.028864 189.6 2.26e-10 ***
## Residuals 12 0.00183 0.000152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(acm2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`CE 60 min` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.018865 -0.04476929 0.007039295 0.1891219
## T_C-T_A 0.169935 0.14403071 0.195839295 0.0000000
## T_D-T_A 0.055710 0.02980571 0.081614295 0.0001767
## T_C-T_B 0.188800 0.16289571 0.214704295 0.0000000
## T_D-T_B 0.074575 0.04867071 0.100479295 0.0000099
## T_D-T_C -0.114225 -0.14012929 -0.088320705 0.0000001
CE max
boxplot(datos2$`CE max` ~datos2$Tratamientos)
acmax2 <- aov(datos2$`CE max`~datos2$Tratamientos)
summary(acmax2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 0.00408 0.001359 0.508 0.684
## Residuals 12 0.03210 0.002675
TukeyHSD(acmax2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`CE max` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -0.037875 -0.14645207 0.07070207 0.7326288
## T_C-T_A -0.001700 -0.11027707 0.10687707 0.9999618
## T_D-T_A -0.001425 -0.11000207 0.10715207 0.9999775
## T_C-T_B 0.036175 -0.07240207 0.14475207 0.7582241
## T_D-T_B 0.036450 -0.07212707 0.14502707 0.7541305
## T_D-T_C 0.000275 -0.10830207 0.10885207 0.9999998
%PE
boxplot(datos2$`% PE` ~datos2$Tratamientos)
ape2 <- aov(datos2$`% PE`~datos2$Tratamientos)
summary(ape2)
## Df Sum Sq Mean Sq F value Pr(>F)
## datos2$Tratamientos 3 820.5 273.48 108 6e-09 ***
## Residuals 12 30.4 2.53
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ape2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = datos2$`% PE` ~ datos2$Tratamientos)
##
## $`datos2$Tratamientos`
## diff lwr upr p adj
## T_B-T_A -1.539030 -4.879886 1.801827 0.5410214
## T_C-T_A 16.715169 13.374312 20.056026 0.0000000
## T_D-T_A 5.457216 2.116360 8.798073 0.0019473
## T_C-T_B 18.254199 14.913342 21.595055 0.0000000
## T_D-T_B 6.996246 3.655389 10.337103 0.0002263
## T_D-T_C -11.257953 -14.598809 -7.917096 0.0000019
MUESTREO 3
library(readxl)
datos3 <- data.frame(read_excel("C:/Users/USER/Desktop/Kahomy/Fisiologia/datos3.xlsx"))
View(datos3)
attach(datos3)
## The following objects are masked from datos2:
##
## Abierto, Cerrado, CRA, DDS, Repetición, Total, Tratamientos
## The following object is masked from Datos_muestreo_1:
##
## Tratamientos
names(datos3)
## [1] "Tratamientos"
## [2] "DDS"
## [3] "Repetición"
## [4] "TEMPERATURA...C."
## [5] "Abierto"
## [6] "Cerrado"
## [7] "Total"
## [8] "Contenido.relativo.de.clorofila..SPAD."
## [9] "X.Hojas"
## [10] "Longitud..cm."
## [11] "Área.foliar..cm2."
## [12] "Peso.fresco.hojas"
## [13] "Peso.seco.hojas"
## [14] "Peso.fresco"
## [15] "Peso.a.saturación"
## [16] "Peso.seco"
## [17] "CRA"
## [18] "Diámetro..mm."
## [19] "Peso.fresco.raiz"
## [20] "Peso.seco.raiz"
## [21] "CE.60.min"
## [22] "CE.max"
## [23] "X..PE"
f = factor(Tratamientos)
summary(datos3)
## Tratamientos DDS Repetición TEMPERATURA...C.
## Length:16 Min. :41 Min. :1.00 Min. :17.10
## Class :character 1st Qu.:41 1st Qu.:1.75 1st Qu.:17.20
## Mode :character Median :41 Median :2.50 Median :17.60
## Mean :41 Mean :2.50 Mean :17.80
## 3rd Qu.:41 3rd Qu.:3.25 3rd Qu.:18.02
## Max. :41 Max. :4.00 Max. :19.10
## NA's :12
## Abierto Cerrado Total
## Min. :14.00 Min. :20.00 Min. :39.00
## 1st Qu.:16.00 1st Qu.:21.00 1st Qu.:41.00
## Median :19.00 Median :23.00 Median :42.00
## Mean :18.94 Mean :23.31 Mean :42.25
## 3rd Qu.:21.25 3rd Qu.:24.50 3rd Qu.:43.25
## Max. :24.00 Max. :28.00 Max. :45.00
##
## Contenido.relativo.de.clorofila..SPAD. X.Hojas Longitud..cm.
## Min. :35.80 Min. :5.000 Min. : 9.90
## 1st Qu.:37.92 1st Qu.:6.000 1st Qu.:11.40
## Median :38.95 Median :6.500 Median :12.20
## Mean :38.64 Mean :6.375 Mean :11.87
## 3rd Qu.:39.73 3rd Qu.:7.000 3rd Qu.:12.60
## Max. :41.10 Max. :7.000 Max. :13.20
##
## Área.foliar..cm2. Peso.fresco.hojas Peso.seco.hojas Peso.fresco
## Min. :128.4 Min. :6.297 Min. :0.7180 Min. :0.01210
## 1st Qu.:133.8 1st Qu.:7.504 1st Qu.:0.7817 1st Qu.:0.01620
## Median :146.2 Median :8.092 Median :0.8190 Median :0.01770
## Mean :144.8 Mean :7.870 Mean :0.8424 Mean :0.01671
## 3rd Qu.:157.0 3rd Qu.:8.463 3rd Qu.:0.9038 3rd Qu.:0.01823
## Max. :157.5 Max. :9.012 Max. :0.9810 Max. :0.01840
##
## Peso.a.saturación Peso.seco CRA Diámetro..mm.
## Min. :0.01890 Min. :0.001900 Min. :0.5714 Min. :23.90
## 1st Qu.:0.01935 1st Qu.:0.002100 1st Qu.:0.7731 1st Qu.:29.23
## Median :0.01970 Median :0.002100 Median :0.8942 Median :33.00
## Mean :0.01971 Mean :0.002131 Mean :0.8304 Mean :31.35
## 3rd Qu.:0.01980 3rd Qu.:0.002200 3rd Qu.:0.9173 3rd Qu.:35.10
## Max. :0.02180 Max. :0.002400 Max. :0.9357 Max. :35.40
##
## Peso.fresco.raiz Peso.seco.raiz CE.60.min CE.max
## Min. : 8.372 Min. :1.249 Min. :0.07400 Min. :0.982
## 1st Qu.:10.318 1st Qu.:1.534 1st Qu.:0.09135 1st Qu.:1.028
## Median :11.608 Median :1.627 Median :0.09950 Median :1.056
## Mean :10.961 Mean :1.541 Mean :0.11396 Mean :1.067
## 3rd Qu.:12.149 3rd Qu.:1.635 3rd Qu.:0.13150 3rd Qu.:1.123
## Max. :12.472 Max. :1.673 Max. :0.17500 Max. :1.174
##
## X..PE
## Min. : 7.411
## 1st Qu.: 8.397
## Median : 9.564
## Mean :10.708
## 3rd Qu.:11.858
## Max. :16.427
##
TEMPERATURA
boxplot(TEMPERATURA...C.~Tratamientos)
at <- aov(TEMPERATURA...C.~Tratamientos)
summary(at)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 6.755 2.2517 47.82 5.99e-07 ***
## Residuals 12 0.565 0.0471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(at)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TEMPERATURA...C. ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.100 -0.35552722 0.5555272 0.9129199
## T_C-T_A 1.625 1.16947278 2.0805272 0.0000010
## T_D-T_A 0.375 -0.08052722 0.8305272 0.1210275
## T_C-T_B 1.525 1.06947278 1.9805272 0.0000020
## T_D-T_B 0.275 -0.18052722 0.7305272 0.3231858
## T_D-T_C -1.250 -1.70552722 -0.7944728 0.0000162
ESTOMAS
ABIERTOS
boxplot(Abierto~Tratamientos)
aa <- aov(Abierto~Tratamientos)
summary(aa)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 149.19 49.73 43.4 1.02e-06 ***
## Residuals 12 13.75 1.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aa)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Abierto ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 1.25 -0.9971986 3.497199 0.3887108
## T_C-T_A -6.25 -8.4971986 -4.002801 0.0000141
## T_D-T_A -4.25 -6.4971986 -2.002801 0.0005672
## T_C-T_B -7.50 -9.7471986 -5.252801 0.0000021
## T_D-T_B -5.50 -7.7471986 -3.252801 0.0000510
## T_D-T_C 2.00 -0.2471986 4.247199 0.0872142
CERRRADOS
boxplot(Cerrado~Tratamientos)
ac <- aov(Cerrado~Tratamientos)
summary(ac)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 78.19 26.062 33.81 3.92e-06 ***
## Residuals 12 9.25 0.771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ac)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Cerrado ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.25 -1.5931513 2.093151 0.9769109
## T_C-T_A 5.50 3.6568487 7.343151 0.0000068
## T_D-T_A 2.50 0.6568487 4.343151 0.0078842
## T_C-T_B 5.25 3.4068487 7.093151 0.0000110
## T_D-T_B 2.25 0.4068487 4.093151 0.0159207
## T_D-T_C -3.00 -4.8431513 -1.156849 0.0020041
TOTAL
boxplot(Total~Tratamientos)
ato <- aov(Total~Tratamientos)
summary(ato)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 22.5 7.500 3.673 0.0437 *
## Residuals 12 24.5 2.042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ato)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Total ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 1.50 -1.499668 4.4996678 0.4755365
## T_C-T_A -0.75 -3.749668 2.2496678 0.8781579
## T_D-T_A -1.75 -4.749668 1.2496678 0.3502697
## T_C-T_B -2.25 -5.249668 0.7496678 0.1710975
## T_D-T_B -3.25 -6.249668 -0.2503322 0.0325098
## T_D-T_C -1.00 -3.999668 1.9996678 0.7579067
CONTENIDO RELATIVO DE CLOROFILA
boxplot(Contenido.relativo.de.clorofila..SPAD.~Tratamientos)
acl <- aov(Contenido.relativo.de.clorofila..SPAD.~Tratamientos)
summary(acl)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 29.73 9.909 21.05 4.52e-05 ***
## Residuals 12 5.65 0.471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(acl)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Contenido.relativo.de.clorofila..SPAD. ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A -0.50 -1.940504 0.9405036 0.7354886
## T_C-T_A -3.55 -4.990504 -2.1094964 0.0000477
## T_D-T_A -1.10 -2.540504 0.3405036 0.1606648
## T_C-T_B -3.05 -4.490504 -1.6094964 0.0002044
## T_D-T_B -0.60 -2.040504 0.8405036 0.6169737
## T_D-T_C 2.45 1.009496 3.8905036 0.0014014
Numero de hojas
boxplot(X.Hojas~Tratamientos)
ah <- aov(X.Hojas~Tratamientos)
summary(ah)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 4.25 1.4167 4.857 0.0195 *
## Residuals 12 3.50 0.2917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ah)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = X.Hojas ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.00 -1.1337678 1.1337678 1.0000000
## T_C-T_A -1.25 -2.3837678 -0.1162322 0.0294480
## T_D-T_A -0.25 -1.3837678 0.8837678 0.9118967
## T_C-T_B -1.25 -2.3837678 -0.1162322 0.0294480
## T_D-T_B -0.25 -1.3837678 0.8837678 0.9118967
## T_D-T_C 1.00 -0.1337678 2.1337678 0.0907414
Longitud
boxplot(Longitud..cm.~Tratamientos)
al <- aov(Longitud..cm.~Tratamientos)
summary(al)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 20.682 6.894 429.8 1.8e-12 ***
## Residuals 12 0.192 0.016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(al)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Longitud..cm. ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.650 0.3841079 0.9158921 0.0000516
## T_C-T_A -2.400 -2.6658921 -2.1341079 0.0000000
## T_D-T_A -0.475 -0.7408921 -0.2091079 0.0009285
## T_C-T_B -3.050 -3.3158921 -2.7841079 0.0000000
## T_D-T_B -1.125 -1.3908921 -0.8591079 0.0000002
## T_D-T_C 1.925 1.6591079 2.1908921 0.0000000
Área foliar
boxplot(Área.foliar..cm2. ~Tratamientos)
aaf <- aov(Área.foliar..cm2. ~Tratamientos)
summary(aaf)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 2465 821.7 2468 <2e-16 ***
## Residuals 12 4 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aaf)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Área.foliar..cm2. ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.47450 -0.7367955 1.685795 0.6598772
## T_C-T_A -27.14000 -28.3512955 -25.928705 0.0000000
## T_D-T_A -21.35225 -22.5635455 -20.140955 0.0000000
## T_C-T_B -27.61450 -28.8257955 -26.403205 0.0000000
## T_D-T_B -21.82675 -23.0380455 -20.615455 0.0000000
## T_D-T_C 5.78775 4.5764545 6.999045 0.0000000
Peso fresco hojas
boxplot(Peso.fresco.hojas~Tratamientos)
apfh <- aov(Peso.fresco.hojas~Tratamientos)
summary(apfh)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 14.949 4.983 4623 <2e-16 ***
## Residuals 12 0.013 0.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apfh)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.fresco.hojas ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.65150 0.5825762 0.7204238 0
## T_C-T_A -1.97350 -2.0424238 -1.9045762 0
## T_D-T_A -0.41325 -0.4821738 -0.3443262 0
## T_C-T_B -2.62500 -2.6939238 -2.5560762 0
## T_D-T_B -1.06475 -1.1336738 -0.9958262 0
## T_D-T_C 1.56025 1.4913262 1.6291738 0
Peso seco hojas
boxplot(Peso.seco.hojas ~Tratamientos)
apsh <- aov(Peso.seco.hojas ~Tratamientos)
summary(apsh)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 0.1369 0.04565 116.5 3.86e-09 ***
## Residuals 12 0.0047 0.00039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apsh)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.seco.hojas ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.1190 0.07744864 0.16055136 0.0000104
## T_C-T_A -0.1380 -0.17955136 -0.09644864 0.0000022
## T_D-T_A -0.0455 -0.08705136 -0.00394864 0.0306151
## T_C-T_B -0.2570 -0.29855136 -0.21544864 0.0000000
## T_D-T_B -0.1645 -0.20605136 -0.12294864 0.0000003
## T_D-T_C 0.0925 0.05094864 0.13405136 0.0001277
Peso fresco
boxplot(Peso.fresco ~Tratamientos)
apf <- aov(Peso.fresco ~Tratamientos)
summary(apf)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 7.199e-05 2.40e-05 94.8 1.27e-08 ***
## Residuals 12 3.040e-06 2.53e-07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apf)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.fresco ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.000175 -0.0008812051 0.0012312051 0.9594066
## T_C-T_A -0.004950 -0.0060062051 -0.0038937949 0.0000000
## T_D-T_A -0.000400 -0.0014562051 0.0006562051 0.6822407
## T_C-T_B -0.005125 -0.0061812051 -0.0040687949 0.0000000
## T_D-T_B -0.000575 -0.0016312051 0.0004812051 0.4062416
## T_D-T_C 0.004550 0.0034937949 0.0056062051 0.0000001
Peso saturación
boxplot(Peso.a.saturación ~Tratamientos)
apsa <- aov(Peso.a.saturación ~Tratamientos)
summary(apsa)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 1.572e-06 5.242e-07 1.326 0.312
## Residuals 12 4.745e-06 3.954e-07
TukeyHSD(apsa)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.a.saturación ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A -0.000525 -0.001845104 0.0007951042 0.6495753
## T_C-T_A -0.000275 -0.001595104 0.0010451042 0.9242015
## T_D-T_A -0.000850 -0.002170104 0.0004701042 0.2738980
## T_C-T_B 0.000250 -0.001070104 0.0015701042 0.9413589
## T_D-T_B -0.000325 -0.001645104 0.0009951042 0.8828427
## T_D-T_C -0.000575 -0.001895104 0.0007451042 0.5840143
Peso seco
boxplot(Peso.seco ~Tratamientos)
aps<- aov(Peso.seco ~Tratamientos)
summary(aps)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 5.187e-08 1.729e-08 1.137 0.373
## Residuals 12 1.825e-07 1.521e-08
TukeyHSD(aps)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.seco ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A -0.000025 -0.0002838937 0.0002338937 0.9913571
## T_C-T_A -0.000050 -0.0003088937 0.0002088937 0.9381530
## T_D-T_A 0.000100 -0.0001588937 0.0003588937 0.6692981
## T_C-T_B -0.000025 -0.0002838937 0.0002338937 0.9913571
## T_D-T_B 0.000125 -0.0001338937 0.0003838937 0.5038115
## T_D-T_C 0.000150 -0.0001088937 0.0004088937 0.3557899
Contenido relativo de agua
boxplot(CRA ~Tratamientos)
acra <- aov(CRA ~Tratamientos)
summary(acra)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 0.24458 0.08153 83.52 2.62e-08 ***
## Residuals 12 0.01171 0.00098
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(acra)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRA ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.03473929 -0.03085103 0.10032962 0.4286922
## T_C-T_A -0.26664129 -0.33223162 -0.20105096 0.0000002
## T_D-T_A 0.01769151 -0.04789881 0.08328184 0.8527621
## T_C-T_B -0.30138059 -0.36697091 -0.23579026 0.0000001
## T_D-T_B -0.01704778 -0.08263811 0.04854255 0.8656867
## T_D-T_C 0.28433280 0.21874248 0.34992313 0.0000001
Diametro
boxplot(Diámetro..mm. ~Tratamientos)
ad <- aov(Diámetro..mm. ~Tratamientos)
summary(ad)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 317.5 105.83 2374 <2e-16 ***
## Residuals 12 0.5 0.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ad)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Diámetro..mm. ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A -0.050 -0.4932686 0.3932686 0.9864131
## T_C-T_A -10.925 -11.3682686 -10.4817314 0.0000000
## T_D-T_A -4.125 -4.5682686 -3.6817314 0.0000000
## T_C-T_B -10.875 -11.3182686 -10.4317314 0.0000000
## T_D-T_B -4.075 -4.5182686 -3.6317314 0.0000000
## T_D-T_C 6.800 6.3567314 7.2432686 0.0000000
Peso fresco raíz
boxplot(Peso.fresco.raiz ~Tratamientos)
apfr <- aov(Peso.fresco.raiz ~Tratamientos)
summary(apfr)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 38.31 12.771 1518 9.65e-16 ***
## Residuals 12 0.10 0.008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apfr)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.fresco.raiz ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A -0.20375 -0.3962966 -0.01120345 0.037054
## T_C-T_A -3.88525 -4.0777966 -3.69270345 0.000000
## T_D-T_A -1.22350 -1.4160466 -1.03095345 0.000000
## T_C-T_B -3.68150 -3.8740466 -3.48895345 0.000000
## T_D-T_B -1.01975 -1.2122966 -0.82720345 0.000000
## T_D-T_C 2.66175 2.4692034 2.85429655 0.000000
Peso seco raíz
boxplot(Peso.seco.raiz ~Tratamientos)
apsr <- aov(Peso.seco.raiz ~Tratamientos)
summary(apsr)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 0.4190 0.1397 713.2 8.81e-14 ***
## Residuals 12 0.0024 0.0002
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(apsr)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Peso.seco.raiz ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.01350 -0.01587969 0.042879686 0.5430053
## T_C-T_A -0.37200 -0.40137969 -0.342620314 0.0000000
## T_D-T_A -0.00975 -0.03912969 0.019629686 0.7603361
## T_C-T_B -0.38550 -0.41487969 -0.356120314 0.0000000
## T_D-T_B -0.02325 -0.05262969 0.006129686 0.1409882
## T_D-T_C 0.36225 0.33287031 0.391629686 0.0000000
CE min
boxplot(CE.60.min ~Tratamientos)
acm <- aov(CE.60.min ~Tratamientos)
summary(acm)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 0.017172 0.005724 97.86 1.06e-08 ***
## Residuals 12 0.000702 0.000058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(acm)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CE.60.min ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 0.01510 -0.0009557585 0.03115576 0.0677050
## T_C-T_A 0.08650 0.0704442415 0.10255576 0.0000000
## T_D-T_A 0.02825 0.0121942415 0.04430576 0.0010559
## T_C-T_B 0.07140 0.0553442415 0.08745576 0.0000001
## T_D-T_B 0.01315 -0.0029057585 0.02920576 0.1235070
## T_D-T_C -0.05825 -0.0743057585 -0.04219424 0.0000008
CE max
boxplot(CE.max ~Tratamientos)
acmax <- aov(CE.max ~Tratamientos)
summary(acmax)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 0.00222 0.000740 0.164 0.918
## Residuals 12 0.05413 0.004511
TukeyHSD(acmax)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CE.max ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A -0.01875 -0.1597477 0.1222477 0.9781786
## T_C-T_A -0.02950 -0.1704977 0.1114977 0.9233202
## T_D-T_A -0.00400 -0.1449977 0.1369977 0.9997731
## T_C-T_B -0.01075 -0.1517477 0.1302477 0.9956846
## T_D-T_B 0.01475 -0.1262477 0.1557477 0.9890834
## T_D-T_C 0.02550 -0.1154977 0.1664977 0.9482912
%PE
boxplot(X..PE ~Tratamientos)
ape <- aov(X..PE ~Tratamientos)
summary(ape)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamientos 3 163.59 54.53 264.5 3.18e-11 ***
## Residuals 12 2.47 0.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(ape)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = X..PE ~ Tratamientos)
##
## $Tratamientos
## diff lwr upr p adj
## T_B-T_A 1.564843 0.6117023 2.517983 0.0018695
## T_C-T_A 8.459442 7.5063020 9.412583 0.0000000
## T_D-T_A 2.627077 1.6739370 3.580218 0.0000155
## T_C-T_B 6.894600 5.9414595 7.847740 0.0000000
## T_D-T_B 1.062235 0.1090945 2.015375 0.0276773
## T_D-T_C -5.832365 -6.7855052 -4.879225 0.0000000
MUESTREO 4
library(readxl)
Datos_muestreo_4 <- read_excel("C:/Users/USER/Desktop/Kahomy/Fisiologia/Datos muestreo 4.xlsx")
View(Datos_muestreo_4)
attach(Datos_muestreo_4)
## The following object is masked from datos3:
##
## Tratamientos
## The following objects are masked from datos2:
##
## Contenido relativo de clorofila (SPAD), Tratamientos
## The following objects are masked from Datos_muestreo_1:
##
## Area foliar (cm2), Contenido relativo de clorofila (SPAD), CRA
## (mg), CRA Peso a saturación (mg), CRA Peso fresco (mg), CRA Peso
## seco (mg), Diametro Raiz tuberosa (mm), Estomas enves abiertos,
## Estomas enves cerrados, Estomas enves total, Longitud parte aerea
## (cm), Numero de hojas, Perdida de electrolitos (%), Perdida de
## electrolitos CE 60min, Perdida de electrolitos CE max, Peso fresco
## hojas (g), Peso fresco raiz tuberosa, Peso seco hojas (g), Peso
## seco raiz tuberosa (g), Temperatura (c°), Tratamientos
names(Datos_muestreo_4)
## [1] "Tratamientos"
## [2] "Temperatura (c°)"
## [3] "Estomas enves abiertos"
## [4] "Estomas enves cerrados"
## [5] "Estomas enves total"
## [6] "Contenido relativo de clorofila (SPAD)"
## [7] "Numero de hojas"
## [8] "Longitud parte aerea (cm)"
## [9] "Area foliar (cm2)"
## [10] "Peso fresco hojas (g)"
## [11] "Peso seco hojas (g)"
## [12] "CRA Peso fresco (mg)"
## [13] "CRA Peso a saturación (mg)"
## [14] "CRA Peso seco (mg)"
## [15] "CRA (mg)"
## [16] "Diametro Raiz tuberosa (mm)"
## [17] "Peso fresco raiz tuberosa"
## [18] "Peso seco raiz tuberosa (g)"
## [19] "Perdida de electrolitos CE 60min"
## [20] "Perdida de electrolitos CE max"
## [21] "Perdida de electrolitos (%)"
summary(Datos_muestreo_4)
## Tratamientos Temperatura (c°) Estomas enves abiertos
## Length:16 Min. :16.90 Min. :19.00
## Class :character 1st Qu.:17.30 1st Qu.:21.00
## Mode :character Median :17.50 Median :23.00
## Mean :17.63 Mean :22.62
## 3rd Qu.:17.77 3rd Qu.:24.00
## Max. :18.60 Max. :25.00
## Estomas enves cerrados Estomas enves total
## Min. :18.00 Min. :40.00
## 1st Qu.:20.75 1st Qu.:42.00
## Median :21.00 Median :44.00
## Mean :21.25 Mean :43.88
## 3rd Qu.:23.00 3rd Qu.:45.00
## Max. :24.00 Max. :48.00
## Contenido relativo de clorofila (SPAD) Numero de hojas
## Min. :37.20 Min. :6.0
## 1st Qu.:38.62 1st Qu.:7.0
## Median :39.85 Median :7.5
## Mean :39.48 Mean :7.5
## 3rd Qu.:40.23 3rd Qu.:8.0
## Max. :41.20 Max. :9.0
## Longitud parte aerea (cm) Area foliar (cm2) Peso fresco hojas (g)
## Min. :13.10 Min. :184.9 Min. : 8.739
## 1st Qu.:14.03 1st Qu.:200.4 1st Qu.: 9.695
## Median :14.61 Median :218.7 Median :10.219
## Mean :14.35 Mean :213.6 Mean :10.036
## 3rd Qu.:14.72 3rd Qu.:230.9 3rd Qu.:10.637
## Max. :15.20 Max. :234.2 Max. :10.935
## Peso seco hojas (g) CRA Peso fresco (mg) CRA Peso a saturación (mg)
## Min. :0.889 Min. :0.01210 Min. :0.01820
## 1st Qu.:1.006 1st Qu.:0.01588 1st Qu.:0.01837
## Median :1.149 Median :0.01725 Median :0.01920
## Mean :1.110 Mean :0.01653 Mean :0.01935
## 3rd Qu.:1.209 3rd Qu.:0.01825 3rd Qu.:0.01980
## Max. :1.302 Max. :0.01930 Max. :0.02180
## CRA Peso seco (mg) CRA (mg) Diametro Raiz tuberosa (mm)
## Min. :0.001700 Min. :0.5833 Min. :35.70
## 1st Qu.:0.001900 1st Qu.:0.7736 1st Qu.:43.40
## Median :0.002100 Median :0.9088 Median :47.10
## Mean :0.002031 Mean :0.8389 Mean :44.74
## 3rd Qu.:0.002100 3rd Qu.:0.9335 3rd Qu.:48.42
## Max. :0.002300 Max. :0.9556 Max. :49.20
## Peso fresco raiz tuberosa Peso seco raiz tuberosa (g)
## Min. :15.39 Min. :1.638
## 1st Qu.:17.99 1st Qu.:1.866
## Median :19.20 Median :1.976
## Mean :18.52 Mean :1.943
## 3rd Qu.:19.73 3rd Qu.:2.096
## Max. :20.12 Max. :2.184
## Perdida de electrolitos CE 60min Perdida de electrolitos CE max
## Min. :0.05930 Min. :0.978
## 1st Qu.:0.07340 1st Qu.:1.010
## Median :0.09115 Median :1.055
## Mean :0.09733 Mean :1.051
## 3rd Qu.:0.12275 3rd Qu.:1.101
## Max. :0.15200 Max. :1.131
## Perdida de electrolitos (%)
## Min. : 5.785
## 1st Qu.: 6.814
## Median : 8.639
## Mean : 9.297
## 3rd Qu.:12.394
## Max. :14.829
Temperatura
boxplot(`Temperatura (c°)`~ Tratamientos)
aov1 = aov(Datos_muestreo_4$`Temperatura (c°)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov1)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 3.992 1.3306 44.05 9.41e-07 ***
## Residuals 12 0.363 0.0302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov1)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Temperatura (c°)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.300 -0.06487501 0.664875 0.1216268
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 1.250 0.88512499 1.614875 0.0000016
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.075 -0.28987501 0.439875 0.9268586
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.950 0.58512499 1.314875 0.0000276
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.225 -0.58987501 0.139875 0.3066660
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -1.175 -1.53987501 -0.810125 0.0000030
shapiro.test(Datos_muestreo_4$`Temperatura (c°)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Temperatura (c°)`
## W = 0.85445, p-value = 0.01585
Contenido relativo de clorofila
boxplot(`Contenido relativo de clorofila (SPAD)`~ Tratamientos)
aov2 = aov(Datos_muestreo_4$`Contenido relativo de clorofila (SPAD)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 18.845 6.282 29.39 8.21e-06 ***
## Residuals 12 2.565 0.214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Contenido relativo de clorofila (SPAD)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.875 -0.0955855 1.8455855 0.0823471
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -1.925 -2.8955855 -0.9544145 0.0003716
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.550 -0.4205855 1.5205855 0.3736650
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -2.800 -3.7705855 -1.8294145 0.0000097
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.325 -1.2955855 0.6455855 0.7555175
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 2.475 1.5044145 3.4455855 0.0000340
shapiro.test(Datos_muestreo_4$`Contenido relativo de clorofila (SPAD)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Contenido relativo de clorofila (SPAD)`
## W = 0.88537, p-value = 0.04708
Numero de Hojas
boxplot(`Numero de hojas`~ Tratamientos)
aov3 = aov(Datos_muestreo_4$`Numero de hojas`~Datos_muestreo_4$Tratamientos)
summary.aov(aov3)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 5 1.667 6.667 0.00671 **
## Residuals 12 3 0.250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov3)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Numero de hojas` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.5 -0.5496651 1.54966506 0.5146067
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -1.0 -2.0496651 0.04966506 0.0636452
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.5 -1.5496651 0.54966506 0.5146067
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -1.5 -2.5496651 -0.45033494 0.0054319
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.0 -2.0496651 0.04966506 0.0636452
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.5 -0.5496651 1.54966506 0.5146067
shapiro.test(Datos_muestreo_4$`Numero de hojas`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Numero de hojas`
## W = 0.8555, p-value = 0.01643
Estomas enves abiertos
boxplot(`Estomas enves abiertos`~ Tratamientos)
aov4 = aov(Datos_muestreo_4$`Estomas enves abiertos`~Datos_muestreo_4$Tratamientos)
summary.aov(aov4)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 39.25 13.083 12.56 0.000519 ***
## Residuals 12 12.50 1.042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov4)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Estomas enves abiertos` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.25 -1.8926198 2.3926198 0.9850160
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -3.50 -5.6426198 -1.3573802 0.0019470
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -2.25 -4.3926198 -0.1073802 0.0386320
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -3.75 -5.8926198 -1.6073802 0.0011041
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -2.50 -4.6426198 -0.3573802 0.0210808
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 1.25 -0.8926198 3.3926198 0.3502697
shapiro.test(Datos_muestreo_4$`Estomas enves abiertos`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Estomas enves abiertos`
## W = 0.93701, p-value = 0.314
Estomas enves cerrados
boxplot(`Estomas enves cerrados`~ Tratamientos)
aov5 = aov(Datos_muestreo_4$`Estomas enves cerrados`~Datos_muestreo_4$Tratamientos)
summary.aov(aov5)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 18.5 6.167 3.02 0.0716 .
## Residuals 12 24.5 2.042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov5)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Estomas enves cerrados` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.50 -2.4996678 3.499668 0.9587232
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 2.75 -0.2496678 5.749668 0.0762928
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 1.75 -1.2496678 4.749668 0.3502697
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 2.25 -0.7496678 5.249668 0.1710975
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 1.25 -1.7496678 4.249668 0.6166413
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -1.00 -3.9996678 1.999668 0.7579067
shapiro.test(Datos_muestreo_4$`Estomas enves cerrados`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Estomas enves cerrados`
## W = 0.93157, p-value = 0.2581
Estomas enves total
boxplot(`Estomas enves total`~ Tratamientos)
aov6 = aov(Datos_muestreo_4$`Estomas enves total`~Datos_muestreo_4$Tratamientos)
summary.aov(aov6)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 5.25 1.750 0.385 0.766
## Residuals 12 54.50 4.542
TukeyHSD(aov6)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Estomas enves total` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.75 -3.723922 5.223922 0.9580640
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.75 -5.223922 3.723922 0.9580640
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.50 -4.973922 3.973922 0.9867729
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -1.50 -5.973922 2.973922 0.7548267
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.25 -5.723922 3.223922 0.8395264
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.25 -4.223922 4.723922 0.9982822
shapiro.test(Datos_muestreo_4$`Estomas enves total`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Estomas enves total`
## W = 0.96308, p-value = 0.7179
Longitud de la parte aerea
boxplot(`Longitud parte aerea (cm)`~ Tratamientos)
aov7 = aov(Datos_muestreo_4$`Longitud parte aerea (cm)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov7)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 6.354 2.1181 69.27 7.58e-08 ***
## Residuals 12 0.367 0.0306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov7)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Longitud parte aerea (cm)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.25925 -0.1078551 0.6263551 0.2089134
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -1.39075 -1.7578551 -1.0236449 0.0000005
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.24075 -0.6078551 0.1263551 0.2604027
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -1.65000 -2.0171051 -1.2828949 0.0000001
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.50000 -0.8671051 -0.1328949 0.0076587
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 1.15000 0.7828949 1.5171051 0.0000041
shapiro.test(Datos_muestreo_4$`Longitud parte aerea (cm)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Longitud parte aerea (cm)`
## W = 0.84691, p-value = 0.01227
Area foliar (cm2)
boxplot(`Area foliar (cm2)`~ Tratamientos)
aov8 = aov(Datos_muestreo_4$`Area foliar (cm2)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov8)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 5771 1923.7 1041 9.19e-15 ***
## Residuals 12 22 1.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov8)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Area foliar (cm2)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.74900 -2.104285 3.602285 0.8623255
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -45.10850 -47.961785 -42.255215 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -23.97925 -26.832535 -21.125965 0.0000000
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -45.85750 -48.710785 -43.004215 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -24.72825 -27.581535 -21.874965 0.0000000
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 21.12925 18.275965 23.982535 0.0000000
shapiro.test(Datos_muestreo_4$`Area foliar (cm2)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Area foliar (cm2)`
## W = 0.80518, p-value = 0.003193
Peso fresco hojas (g)
boxplot(`Peso fresco hojas (g)`~ Tratamientos)
aov9 = aov(Datos_muestreo_1$`Peso fresco hojas (g)`~Datos_muestreo_1$Tratamientos)
summary.aov(aov9)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_1$Tratamientos 3 19.934 6.645 49.77 4.81e-07 ***
## Residuals 12 1.602 0.134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov9)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_1$`Peso fresco hojas (g)` ~ Datos_muestreo_1$Tratamientos)
##
## $`Datos_muestreo_1$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 1.22450 0.4574295 1.9915705 0.0023396
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -1.90375 -2.6708205 -1.1366795 0.0000445
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.36125 -1.1283205 0.4058205 0.5236522
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -3.12825 -3.8953205 -2.3611795 0.0000002
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.58575 -2.3528205 -0.8186795 0.0002549
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 1.54250 0.7754295 2.3095705 0.0003281
shapiro.test(Datos_muestreo_1$`Peso fresco hojas (g)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_1$`Peso fresco hojas (g)`
## W = 0.94789, p-value = 0.457
Peso seco hojas (g)
boxplot(`Peso seco hojas (g)`~ Tratamientos)
aov10 = aov(Datos_muestreo_4$`Peso seco hojas (g)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov10)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 0.3220 0.10732 126.5 2.4e-09 ***
## Residuals 12 0.0102 0.00085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov10)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Peso seco hojas (g)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.06475 -0.1259007 -0.003599315 0.0369250
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.37000 -0.4311507 -0.308849315 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.20150 -0.2626507 -0.140349315 0.0000024
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.30525 -0.3664007 -0.244099315 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.13675 -0.1979007 -0.075599315 0.0001223
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.16850 0.1073493 0.229650685 0.0000155
shapiro.test(Datos_muestreo_4$`Peso seco hojas (g)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Peso seco hojas (g)`
## W = 0.89138, p-value = 0.05858
CRA Peso freco (mg)
boxplot(`CRA Peso fresco (mg)`~ Tratamientos)
aov11 = aov(Datos_muestreo_4$`CRA Peso fresco (mg)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov11)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 9.101e-05 3.034e-05 101.1 8.75e-09 ***
## Residuals 12 3.600e-06 3.000e-07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov11)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`CRA Peso fresco (mg)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.00055 -0.0005998505 0.0016998505
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.00550 -0.0066498505 -0.0043501495
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.00095 -0.0020998505 0.0001998505
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.00605 -0.0071998505 -0.0049001495
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.00150 -0.0026498505 -0.0003501495
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.00455 0.0034001495 0.0056998505
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.5112991
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.1193002
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.0103054
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.0000003
shapiro.test(Datos_muestreo_4$`CRA Peso fresco (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`CRA Peso fresco (mg)`
## W = 0.80197, p-value = 0.002893
CRA Peso a saturacion (mg)
boxplot(`CRA Peso a saturación (mg)`~ Tratamientos)
aov12 = aov(Datos_muestreo_4$`CRA Peso a saturación (mg)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov12)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 5.735e-06 1.912e-06 2.119 0.151
## Residuals 12 1.083e-05 9.021e-07
TukeyHSD(aov12)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`CRA Peso a saturación (mg)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.000625 -0.002618903 0.0013689032
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.000800 -0.002793903 0.0011939032
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.001675 -0.003668903 0.0003189032
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.000175 -0.002168903 0.0018189032
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.001050 -0.003043903 0.0009439032
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -0.000875 -0.002868903 0.0011189032
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.7893737
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.6434750
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.1115315
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.9934675
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.4333883
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.5783836
shapiro.test(Datos_muestreo_4$`CRA Peso a saturación (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`CRA Peso a saturación (mg)`
## W = 0.89882, p-value = 0.07696
CRA Peso seco (mg)
boxplot(`CRA Peso seco (mg)`~ Tratamientos)
aov13 = aov(Datos_muestreo_4$`CRA Peso seco (mg)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov13)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 3.319e-07 1.106e-07 10.84 0.000987 ***
## Residuals 12 1.225e-07 1.021e-08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov13)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`CRA Peso seco (mg)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.000050 -1.621085e-04 2.621085e-04
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.000325 -5.371085e-04 -1.128915e-04
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.000100 -3.121085e-04 1.121085e-04
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.000375 -5.871085e-04 -1.628915e-04
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.000150 -3.621085e-04 6.210854e-05
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.000225 1.289146e-05 4.371085e-04
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.8951748
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0032213
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.5227964
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0010140
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.2079957
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.0365606
shapiro.test(Datos_muestreo_4$`CRA Peso seco (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`CRA Peso seco (mg)`
## W = 0.93676, p-value = 0.3112
CRA (mg)
boxplot(`CRA (mg)`~ Tratamientos)
aov14 = aov(Datos_muestreo_4$`CRA (mg)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov14)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 0.28289 0.09430 108.2 5.92e-09 ***
## Residuals 12 0.01046 0.00087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov14)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`CRA (mg)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.06090023 -0.001067927 0.12286838
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.27231898 -0.334287130 -0.21035082
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.03121809 -0.030750061 0.09318625
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.33321920 -0.395187357 -0.27125105
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.02968213 -0.091650287 0.03228602
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.30353707 0.241568916 0.36550522
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.0546091
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0000001
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.4695200
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.5101868
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.0000000
shapiro.test(Datos_muestreo_4$`CRA (mg)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`CRA (mg)`
## W = 0.73532, p-value = 0.0004251
Diametro Raiz tuberosa (mm)
boxplot(`Diametro Raiz tuberosa (mm)`~ Tratamientos)
aov15 = aov(Datos_muestreo_4$`Diametro Raiz tuberosa (mm)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov15)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 428.7 142.91 1563 8.12e-16 ***
## Residuals 12 1.1 0.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov15)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Diametro Raiz tuberosa (mm)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.475 -0.1598811 1.109881 0.1726158
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -12.300 -12.9348811 -11.665119 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -2.200 -2.8348811 -1.565119 0.0000014
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -12.775 -13.4098811 -12.140119 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -2.675 -3.3098811 -2.040119 0.0000002
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 10.100 9.4651189 10.734881 0.0000000
shapiro.test(Datos_muestreo_4$`Diametro Raiz tuberosa (mm)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Diametro Raiz tuberosa (mm)`
## W = 0.7066, p-value = 0.0001997
Peso fresco raiz tuberosa
boxplot(`Peso fresco raiz tuberosa`~ Tratamientos)
aov16 = aov(Datos_muestreo_4$`Peso fresco raiz tuberosa`~Datos_muestreo_4$Tratamientos)
summary.aov(aov16)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 48.65 16.218 1401 1.56e-15 ***
## Residuals 12 0.14 0.012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov16)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Peso fresco raiz tuberosa` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.4420 0.2161065 0.6678935 0.0004196
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -4.0055 -4.2313935 -3.7796065 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.7305 -0.9563935 -0.5046065 0.0000029
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -4.4475 -4.6733935 -4.2216065 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -1.1725 -1.3983935 -0.9466065 0.0000000
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 3.2750 3.0491065 3.5008935 0.0000000
shapiro.test(Datos_muestreo_4$`Peso fresco raiz tuberosa`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Peso fresco raiz tuberosa`
## W = 0.75056, p-value = 0.0006452
Peso seco raiz tuberosa (g)
boxplot(`Peso seco raiz tuberosa (g)`~ Tratamientos)
aov17 = aov(Datos_muestreo_4$`Peso seco raiz tuberosa (g)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov17)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 0.5084 0.16946 106.3 6.55e-09 ***
## Residuals 12 0.0191 0.00159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov17)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Peso seco raiz tuberosa (g)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.00875 -0.09255794 0.07505794 0.9891460
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.44050 -0.52430794 -0.35669206 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM -0.16825 -0.25205794 -0.08444206 0.0003331
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.43175 -0.51555794 -0.34794206 0.0000000
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM -0.15950 -0.24330794 -0.07569206 0.0005367
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.27225 0.18844206 0.35605794 0.0000028
shapiro.test(Datos_muestreo_4$`Peso seco raiz tuberosa (g)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Peso seco raiz tuberosa (g)`
## W = 0.85469, p-value = 0.01598
Perdida de electrolitos CE 60min
boxplot(`Perdida de electrolitos CE 60min`~ Tratamientos)
aov18 = aov(Datos_muestreo_4$`Perdida de electrolitos CE 60min`~Datos_muestreo_4$Tratamientos)
summary.aov(aov18)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 0.011973 0.003991 44.73 8.64e-07 ***
## Residuals 12 0.001071 0.000089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov18)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Perdida de electrolitos CE 60min` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.013825 -0.03365374 0.00600374
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.058175 0.03834626 0.07800374
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.024675 0.00484626 0.04450374
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.072000 0.05217126 0.09182874
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.038500 0.01867126 0.05832874
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -0.033500 -0.05332874 -0.01367126
## p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM 0.2174409
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 0.0000081
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.0140769
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 0.0000008
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.0004495
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.0014806
shapiro.test(Datos_muestreo_4$`Perdida de electrolitos CE 60min`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Perdida de electrolitos CE 60min`
## W = 0.92821, p-value = 0.2284
Perdida de electrolitos CE max
boxplot(`Perdida de electrolitos CE max`~ Tratamientos)
aov19 = aov(Datos_muestreo_4$`Perdida de electrolitos CE max`~Datos_muestreo_4$Tratamientos)
summary.aov(aov19)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 0.00284 0.0009472 0.308 0.819
## Residuals 12 0.03686 0.0030715
TukeyHSD(aov19)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Perdida de electrolitos CE max` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -0.01550 -0.13184682 0.10084682 0.9780651
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM -0.03050 -0.14684682 0.08584682 0.8627911
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 0.00275 -0.11359682 0.11909682 0.9998686
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM -0.01500 -0.13134682 0.10134682 0.9800298
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 0.01825 -0.09809682 0.13459682 0.9651646
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM 0.03325 -0.08309682 0.14959682 0.8305251
shapiro.test(Datos_muestreo_4$`Perdida de electrolitos CE max`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Perdida de electrolitos CE max`
## W = 0.91412, p-value = 0.1356
Perdida de electrolitos (%)
boxplot(`Perdida de electrolitos (%)`~ Tratamientos)
aov20 = aov(Datos_muestreo_4$`Perdida de electrolitos (%)`~Datos_muestreo_4$Tratamientos)
summary.aov(aov20)
## Df Sum Sq Mean Sq F value Pr(>F)
## Datos_muestreo_4$Tratamientos 3 116.71 38.90 38.43 1.97e-06 ***
## Residuals 12 12.15 1.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov20)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Datos_muestreo_4$`Perdida de electrolitos (%)` ~ Datos_muestreo_4$Tratamientos)
##
## $`Datos_muestreo_4$Tratamientos`
## diff lwr upr p adj
## 100% CM - Tr 20 mM-100% CM - Tr 0 mM -1.224266 -3.3364757 0.8879436 0.3554778
## 50% CM - Tr 0 mM-100% CM - Tr 0 mM 5.862960 3.7507499 7.9751692 0.0000144
## 50% CM - Tr 20 mM-100% CM - Tr 0 mM 2.342664 0.2304546 4.4548739 0.0284576
## 50% CM - Tr 0 mM-100% CM - Tr 20 mM 7.087226 4.9750159 9.1994352 0.0000020
## 50% CM - Tr 20 mM-100% CM - Tr 20 mM 3.566930 1.4547206 5.6791400 0.0014860
## 50% CM - Tr 20 mM-50% CM - Tr 0 mM -3.520295 -5.6325049 -1.4080856 0.0016550
shapiro.test(Datos_muestreo_4$`Perdida de electrolitos (%)`)
##
## Shapiro-Wilk normality test
##
## data: Datos_muestreo_4$`Perdida de electrolitos (%)`
## W = 0.88526, p-value = 0.04691