fisio
A B Palma O
2023-10-09
library(readxl)
Analisi_muestreo1 <- read_excel("Analisi_muestreo1.xlsx")
#View(Analisi_muestreo1)datos <- read_excel("Analisi_muestreo1.xlsx")summary(datos)## TRATAMIENTOS TEMP_HOJA NUM_HOJAS SPAD
## Length:16 Min. : 18.28 Min. :3.00 Min. :31.20
## Class :character 1st Qu.: 19.27 1st Qu.:4.00 1st Qu.:41.77
## Mode :character Median : 19.42 Median :4.00 Median :43.90
## Mean : 133.44 Mean :4.25 Mean :42.80
## 3rd Qu.: 20.06 3rd Qu.:5.00 3rd Qu.:44.88
## Max. :1841.00 Max. :6.00 Max. :50.60
## ALTURA_AER DIAMETRO_R PF_AEREO PF_RAIZ
## Min. : 8.20 Min. :14.22 Min. :1.260 Min. :2.200
## 1st Qu.:10.05 1st Qu.:18.20 1st Qu.:2.252 1st Qu.:3.835
## Median :10.80 Median :19.41 Median :2.545 Median :4.130
## Mean :11.01 Mean :20.05 Mean :2.776 Mean :4.945
## 3rd Qu.:11.60 3rd Qu.:23.11 3rd Qu.:3.232 3rd Qu.:6.367
## Max. :15.80 Max. :24.63 Max. :4.940 Max. :8.420
## PS_AEREO PS_RAIZ ESTOMAS_A ESTOMAS_PA
## Min. :0.1400 Min. :0.1800 Min. : 4.00 Min. : 7.00
## 1st Qu.:0.2625 1st Qu.:0.2875 1st Qu.: 9.75 1st Qu.:12.00
## Median :0.2850 Median :0.3150 Median :15.50 Median :13.00
## Mean :0.3031 Mean :0.3306 Mean :14.00 Mean :15.19
## 3rd Qu.:0.3600 3rd Qu.:0.3750 3rd Qu.:19.25 3rd Qu.:17.50
## Max. :0.5100 Max. :0.4700 Max. :22.00 Max. :27.00
## ESTOMAS_C ESTOMAS_TOTALES AREA_FOLIAR A_DOSEL
## Min. : 3.00 Min. :24.00 Min. : 26.78 Min. : 26.86
## 1st Qu.: 9.00 1st Qu.:36.25 1st Qu.: 56.09 1st Qu.: 42.58
## Median :20.50 Median :50.00 Median : 66.14 Median : 62.84
## Mean :16.31 Mean :45.50 Mean : 68.76 Mean : 66.45
## 3rd Qu.:23.00 3rd Qu.:52.25 3rd Qu.: 86.74 3rd Qu.: 81.65
## Max. :30.00 Max. :64.00 Max. :108.71 Max. :146.80
## CRA
## Min. :64.75
## 1st Qu.:69.32
## Median :71.95
## Mean :72.90
## 3rd Qu.:75.43
## Max. :83.22TEMPERATURA
res_anova <- aov(TEMP_HOJA ~ TRATAMIENTOS, data = datos)
summary(res_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 622072 207357 1 0.426
## Residuals 12 2488091 207341res_tukey <- TukeyHSD(res_anova)
print(res_tukey)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TEMP_HOJA ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -455.6200 -1411.5438 500.3038 0.5141295
## ROJA_CONTROL-AMARILLO_50_AS -455.7775 -1411.7013 500.1463 0.5138541
## VERDE_50_AS1000-AMARILLO_50_AS -454.6950 -1410.6188 501.2288 0.5157476
## ROJA_CONTROL-AZUL_50_AS600 -0.1575 -956.0813 955.7663 1.0000000
## VERDE_50_AS1000-AZUL_50_AS600 0.9250 -954.9988 956.8488 1.0000000
## VERDE_50_AS1000-ROJA_CONTROL 1.0825 -954.8413 957.0063 1.0000000library(agricolae)
dt <- duncan.test(res_anova, 'TRATAMIENTOS', console = T)##
## Study: res_anova ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for TEMP_HOJA
##
## Mean Square Error: 207340.9
##
## TRATAMIENTOS, means
##
## TEMP_HOJA std r Min Max
## AMARILLO_50_AS 474.9600 910.6934413 4 19.28 1841.00
## AZUL_50_AS600 19.3400 0.4298837 4 18.78 19.80
## ROJA_CONTROL 19.1825 0.7040064 4 18.28 20.00
## VERDE_50_AS1000 20.2650 0.7558439 4 19.34 21.06
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 701.5320 734.3026 754.1579
##
## Means with the same letter are not significantly different.
##
## TEMP_HOJA groups
## AMARILLO_50_AS 474.9600 a
## VERDE_50_AS1000 20.2650 a
## AZUL_50_AS600 19.3400 a
## ROJA_CONTROL 19.1825 aplot(dt)
NUEMRRO DE HOJAS
res_anova2 <- aov(NUM_HOJAS ~ TRATAMIENTOS, data = datos)
summary(res_anova2)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 1 0.3333 0.5 0.689
## Residuals 12 8 0.6667res_tukey2 <- TukeyHSD(res_anova2)
print(res_tukey2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = NUM_HOJAS ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 0.5 -1.214096 2.214096 0.8220125
## ROJA_CONTROL-AMARILLO_50_AS 0.5 -1.214096 2.214096 0.8220125
## VERDE_50_AS1000-AMARILLO_50_AS 0.0 -1.714096 1.714096 1.0000000
## ROJA_CONTROL-AZUL_50_AS600 0.0 -1.714096 1.714096 1.0000000
## VERDE_50_AS1000-AZUL_50_AS600 -0.5 -2.214096 1.214096 0.8220125
## VERDE_50_AS1000-ROJA_CONTROL -0.5 -2.214096 1.214096 0.8220125dt2 <- duncan.test(res_anova2, 'TRATAMIENTOS', console = T)##
## Study: res_anova2 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for NUM_HOJAS
##
## Mean Square Error: 0.6666667
##
## TRATAMIENTOS, means
##
## NUM_HOJAS std r Min Max
## AMARILLO_50_AS 4.0 0.0000000 4 4 4
## AZUL_50_AS600 4.5 1.0000000 4 4 6
## ROJA_CONTROL 4.5 0.5773503 4 4 5
## VERDE_50_AS1000 4.0 1.1547005 4 3 5
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 1.257938 1.316700 1.352303
##
## Means with the same letter are not significantly different.
##
## NUM_HOJAS groups
## AZUL_50_AS600 4.5 a
## ROJA_CONTROL 4.5 a
## AMARILLO_50_AS 4.0 a
## VERDE_50_AS1000 4.0 aplot(dt2)
SPAD
res_anova3 <- aov(SPAD ~ TRATAMIENTOS, data = datos)
summary(res_anova3)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 88.31 29.43 1.752 0.21
## Residuals 12 201.63 16.80res_tukey3 <- TukeyHSD(res_anova3)
print(res_tukey3)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SPAD ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 2.025 -6.58044 10.63044 0.8956467
## ROJA_CONTROL-AMARILLO_50_AS -4.125 -12.73044 4.48044 0.5095905
## VERDE_50_AS1000-AMARILLO_50_AS 1.100 -7.50544 9.70544 0.9805139
## ROJA_CONTROL-AZUL_50_AS600 -6.150 -14.75544 2.45544 0.2011276
## VERDE_50_AS1000-AZUL_50_AS600 -0.925 -9.53044 7.68044 0.9881866
## VERDE_50_AS1000-ROJA_CONTROL 5.225 -3.38044 13.83044 0.3186913dt3 <- duncan.test(res_anova3, 'TRATAMIENTOS', console = T)##
## Study: res_anova3 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for SPAD
##
## Mean Square Error: 16.80292
##
## TRATAMIENTOS, means
##
## SPAD std r Min Max
## AMARILLO_50_AS 43.050 3.617089 4 37.7 45.4
## AZUL_50_AS600 45.075 1.359841 4 43.8 47.0
## ROJA_CONTROL 38.925 5.766209 4 31.2 44.2
## VERDE_50_AS1000 44.150 4.362339 4 41.1 50.6
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 6.315348 6.610356 6.789098
##
## Means with the same letter are not significantly different.
##
## SPAD groups
## AZUL_50_AS600 45.075 a
## VERDE_50_AS1000 44.150 a
## AMARILLO_50_AS 43.050 a
## ROJA_CONTROL 38.925 aplot(dt3)
ALTURA PARTE AEREA
res_anova4 <- aov(ALTURA_AER ~ TRATAMIENTOS, data = datos)
summary(res_anova4)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 19.21 6.402 2.934 0.0767 .
## Residuals 12 26.19 2.183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey4 <- TukeyHSD(res_anova4)
print(res_tukey4)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ALTURA_AER ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 0.30 -2.801401 3.40140057 0.9913136
## ROJA_CONTROL-AMARILLO_50_AS 2.00 -1.101401 5.10140057 0.2727674
## VERDE_50_AS1000-AMARILLO_50_AS -1.05 -4.151401 2.05140057 0.7495008
## ROJA_CONTROL-AZUL_50_AS600 1.70 -1.401401 4.80140057 0.4006626
## VERDE_50_AS1000-AZUL_50_AS600 -1.35 -4.451401 1.75140057 0.5845050
## VERDE_50_AS1000-ROJA_CONTROL -3.05 -6.151401 0.05140057 0.0544253dt4 <- duncan.test(res_anova4, 'TRATAMIENTOS', console = T)##
## Study: res_anova4 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ALTURA_AER
##
## Mean Square Error: 2.1825
##
## TRATAMIENTOS, means
##
## ALTURA_AER std r Min Max
## AMARILLO_50_AS 10.70 1.6832508 4 9.1 13.0
## AZUL_50_AS600 11.00 0.6271629 4 10.2 11.5
## ROJA_CONTROL 12.70 2.1055482 4 11.1 15.8
## VERDE_50_AS1000 9.65 1.0344080 4 8.2 10.6
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 2.276051 2.382372 2.446791
##
## Means with the same letter are not significantly different.
##
## ALTURA_AER groups
## ROJA_CONTROL 12.70 a
## AZUL_50_AS600 11.00 ab
## AMARILLO_50_AS 10.70 ab
## VERDE_50_AS1000 9.65 bplot(dt4)
DIAMETRO DE LA RAIZ
res_anova5 <- aov(DIAMETRO_R ~ TRATAMIENTOS, data = datos)
summary(res_anova5)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 94.42 31.47 6.544 0.00718 **
## Residuals 12 57.72 4.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey5 <- TukeyHSD(res_anova5)
print(res_tukey5)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DIAMETRO_R ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -0.5275 -5.131637 4.076637 0.9857858
## ROJA_CONTROL-AMARILLO_50_AS 5.6575 1.053363 10.261637 0.0152678
## VERDE_50_AS1000-AMARILLO_50_AS 2.0600 -2.544137 6.664137 0.5636259
## ROJA_CONTROL-AZUL_50_AS600 6.1850 1.580863 10.789137 0.0084313
## VERDE_50_AS1000-AZUL_50_AS600 2.5875 -2.016637 7.191637 0.3803692
## VERDE_50_AS1000-ROJA_CONTROL -3.5975 -8.201637 1.006637 0.1478252dt5 <- duncan.test(res_anova5, 'TRATAMIENTOS', console = T)##
## Study: res_anova5 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for DIAMETRO_R
##
## Mean Square Error: 4.809887
##
## TRATAMIENTOS, means
##
## DIAMETRO_R std r Min Max
## AMARILLO_50_AS 18.2475 2.6078136 4 15.07 21.26
## AZUL_50_AS600 17.7200 2.4308983 4 14.22 19.72
## ROJA_CONTROL 23.9050 0.6783067 4 23.01 24.63
## VERDE_50_AS1000 20.3075 2.4636338 4 18.25 23.39
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 3.378877 3.536714 3.632346
##
## Means with the same letter are not significantly different.
##
## DIAMETRO_R groups
## ROJA_CONTROL 23.9050 a
## VERDE_50_AS1000 20.3075 b
## AMARILLO_50_AS 18.2475 b
## AZUL_50_AS600 17.7200 bplot(dt5)
PESO FRESCO *PARTE AEREA
res_anova6 <- aov(PF_AEREO ~ TRATAMIENTOS, data = datos)
summary(res_anova6)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 8.00 2.6666 5.461 0.0134 *
## Residuals 12 5.86 0.4883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey6 <- TukeyHSD(res_anova6)
print(res_tukey6)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PF_AEREO ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -0.2625 -1.729485938 1.2044859 0.9497811
## ROJA_CONTROL-AMARILLO_50_AS 1.4725 0.005514062 2.9394859 0.0490456
## VERDE_50_AS1000-AMARILLO_50_AS -0.1750 -1.641985938 1.2919859 0.9840266
## ROJA_CONTROL-AZUL_50_AS600 1.7350 0.268014062 3.2019859 0.0194062
## VERDE_50_AS1000-AZUL_50_AS600 0.0875 -1.379485938 1.5544859 0.9979146
## VERDE_50_AS1000-ROJA_CONTROL -1.6475 -3.114485938 -0.1805141 0.0264684dt6 <- duncan.test(res_anova6, 'TRATAMIENTOS', console = T)##
## Study: res_anova6 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PF_AEREO
##
## Mean Square Error: 0.4883042
##
## TRATAMIENTOS, means
##
## PF_AEREO std r Min Max
## AMARILLO_50_AS 2.5175 0.4518388 4 2.17 3.18
## AZUL_50_AS600 2.2550 0.4224137 4 1.65 2.56
## ROJA_CONTROL 3.9900 0.7825599 4 3.08 4.94
## VERDE_50_AS1000 2.3425 0.9788897 4 1.26 3.39
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 1.076589 1.126880 1.157351
##
## Means with the same letter are not significantly different.
##
## PF_AEREO groups
## ROJA_CONTROL 3.9900 a
## AMARILLO_50_AS 2.5175 b
## VERDE_50_AS1000 2.3425 b
## AZUL_50_AS600 2.2550 bplot(dt6)
*RAIZ TUBEROSA
res_anova7 <- aov(PF_RAIZ ~ TRATAMIENTOS, data = datos)
summary(res_anova7)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 50.05 16.682 22.81 3.02e-05 ***
## Residuals 12 8.78 0.731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey7 <- TukeyHSD(res_anova7)
print(res_tukey7)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PF_RAIZ ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 0.0675 -1.7279234 1.862923 0.9994731
## ROJA_CONTROL-AMARILLO_50_AS 4.3425 2.5470766 6.137923 0.0000573
## VERDE_50_AS1000-AMARILLO_50_AS 1.0500 -0.7454234 2.845423 0.3483248
## ROJA_CONTROL-AZUL_50_AS600 4.2750 2.4795766 6.070423 0.0000668
## VERDE_50_AS1000-AZUL_50_AS600 0.9825 -0.8129234 2.777923 0.4020239
## VERDE_50_AS1000-ROJA_CONTROL -3.2925 -5.0879234 -1.497077 0.0007419dt7 <- duncan.test(res_anova7, 'TRATAMIENTOS', console = T)##
## Study: res_anova7 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PF_RAIZ
##
## Mean Square Error: 0.7314292
##
## TRATAMIENTOS, means
##
## PF_RAIZ std r Min Max
## AMARILLO_50_AS 3.5800 0.8010826 4 2.39 4.13
## AZUL_50_AS600 3.6475 1.0389859 4 2.20 4.65
## ROJA_CONTROL 7.9225 0.3756217 4 7.53 8.42
## VERDE_50_AS1000 4.6300 1.0312129 4 3.59 5.98
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 1.317623 1.379173 1.416465
##
## Means with the same letter are not significantly different.
##
## PF_RAIZ groups
## ROJA_CONTROL 7.9225 a
## VERDE_50_AS1000 4.6300 b
## AZUL_50_AS600 3.6475 b
## AMARILLO_50_AS 3.5800 bplot(dt7)
PESO SECO *PARTE AEREA
res_anova8 <- aov(PS_AEREO ~ TRATAMIENTOS, data = datos)
summary(res_anova8)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 0.04897 0.016323 2.592 0.101
## Residuals 12 0.07557 0.006298res_tukey8 <- TukeyHSD(res_anova8)
print(res_tukey8)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_AEREO ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -0.0425 -0.20910161 0.12410161 0.8718295
## ROJA_CONTROL-AMARILLO_50_AS 0.0975 -0.06910161 0.26410161 0.3477680
## VERDE_50_AS1000-AMARILLO_50_AS -0.0325 -0.19910161 0.13410161 0.9364515
## ROJA_CONTROL-AZUL_50_AS600 0.1400 -0.02660161 0.30660161 0.1113864
## VERDE_50_AS1000-AZUL_50_AS600 0.0100 -0.15660161 0.17660161 0.9978751
## VERDE_50_AS1000-ROJA_CONTROL -0.1300 -0.29660161 0.03660161 0.1485652dt8 <- duncan.test(res_anova8, 'TRATAMIENTOS', console = T)##
## Study: res_anova8 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PS_AEREO
##
## Mean Square Error: 0.006297917
##
## TRATAMIENTOS, means
##
## PS_AEREO std r Min Max
## AMARILLO_50_AS 0.2975 0.04193249 4 0.27 0.36
## AZUL_50_AS600 0.2550 0.05066228 4 0.19 0.30
## ROJA_CONTROL 0.3950 0.09609024 4 0.28 0.51
## VERDE_50_AS1000 0.2650 0.10785793 4 0.14 0.36
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 0.1222653 0.1279767 0.1314372
##
## Means with the same letter are not significantly different.
##
## PS_AEREO groups
## ROJA_CONTROL 0.3950 a
## AMARILLO_50_AS 0.2975 ab
## VERDE_50_AS1000 0.2650 b
## AZUL_50_AS600 0.2550 bplot(dt8)
*RAIZ TUBEROSA
res_anova9 <- aov(PS_RAIZ ~ TRATAMIENTOS, data = datos)
summary(res_anova9)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 0.05017 0.016723 4.809 0.0201 *
## Residuals 12 0.04172 0.003477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey9 <- TukeyHSD(res_anova9)
print(res_tukey9)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_RAIZ ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 0.0050 -0.118790776 0.12879078 0.9993473
## ROJA_CONTROL-AMARILLO_50_AS 0.1375 0.013709224 0.26129078 0.0282163
## VERDE_50_AS1000-AMARILLO_50_AS 0.0700 -0.053790776 0.19379078 0.3753766
## ROJA_CONTROL-AZUL_50_AS600 0.1325 0.008709224 0.25629078 0.0347928
## VERDE_50_AS1000-AZUL_50_AS600 0.0650 -0.058790776 0.18879078 0.4357590
## VERDE_50_AS1000-ROJA_CONTROL -0.0675 -0.191290776 0.05629078 0.4049345dt9 <- duncan.test(res_anova9, 'TRATAMIENTOS', console = T)##
## Study: res_anova9 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PS_RAIZ
##
## Mean Square Error: 0.003477083
##
## TRATAMIENTOS, means
##
## PS_RAIZ std r Min Max
## AMARILLO_50_AS 0.2775 0.03403430 4 0.23 0.31
## AZUL_50_AS600 0.2825 0.07500000 4 0.18 0.36
## ROJA_CONTROL 0.4150 0.04932883 4 0.36 0.47
## VERDE_50_AS1000 0.3475 0.06849574 4 0.27 0.43
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 0.09084739 0.09509114 0.09766238
##
## Means with the same letter are not significantly different.
##
## PS_RAIZ groups
## ROJA_CONTROL 0.4150 a
## VERDE_50_AS1000 0.3475 ab
## AZUL_50_AS600 0.2825 b
## AMARILLO_50_AS 0.2775 bplot(dt9)
NUMERO DE ESTOMAS *ABIERTOS
res_anova10 <- aov(ESTOMAS_A ~ TRATAMIENTOS, data = datos)
summary(res_anova10)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 155.5 51.83 1.697 0.22
## Residuals 12 366.5 30.54res_tukey10 <- TukeyHSD(res_anova10)
print(res_tukey10)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ESTOMAS_A ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 8.50 -3.101846 20.101846 0.1853788
## ROJA_CONTROL-AMARILLO_50_AS 5.25 -6.351846 16.851846 0.5549126
## VERDE_50_AS1000-AMARILLO_50_AS 6.25 -5.351846 17.851846 0.4148246
## ROJA_CONTROL-AZUL_50_AS600 -3.25 -14.851846 8.351846 0.8385065
## VERDE_50_AS1000-AZUL_50_AS600 -2.25 -13.851846 9.351846 0.9374523
## VERDE_50_AS1000-ROJA_CONTROL 1.00 -10.601846 12.601846 0.9938059dt10 <- duncan.test(res_anova10, 'TRATAMIENTOS', console = T)##
## Study: res_anova10 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_A
##
## Mean Square Error: 30.54167
##
## TRATAMIENTOS, means
##
## ESTOMAS_A std r Min Max
## AMARILLO_50_AS 9.00 6.633250 4 4 18
## AZUL_50_AS600 17.50 4.358899 4 11 20
## ROJA_CONTROL 14.25 5.123475 4 9 20
## VERDE_50_AS1000 15.25 5.737305 4 8 22
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 8.514346 8.912076 9.153056
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_A groups
## AZUL_50_AS600 17.50 a
## VERDE_50_AS1000 15.25 a
## ROJA_CONTROL 14.25 a
## AMARILLO_50_AS 9.00 aplot(dt10)
*PARCIALMENTE ABIERTOS
res_anova101 <- aov(ESTOMAS_A ~ TRATAMIENTOS, data = datos)
summary(res_anova101)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 155.5 51.83 1.697 0.22
## Residuals 12 366.5 30.54res_tukey101 <- TukeyHSD(res_anova101)
print(res_tukey101)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ESTOMAS_A ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 8.50 -3.101846 20.101846 0.1853788
## ROJA_CONTROL-AMARILLO_50_AS 5.25 -6.351846 16.851846 0.5549126
## VERDE_50_AS1000-AMARILLO_50_AS 6.25 -5.351846 17.851846 0.4148246
## ROJA_CONTROL-AZUL_50_AS600 -3.25 -14.851846 8.351846 0.8385065
## VERDE_50_AS1000-AZUL_50_AS600 -2.25 -13.851846 9.351846 0.9374523
## VERDE_50_AS1000-ROJA_CONTROL 1.00 -10.601846 12.601846 0.9938059dt101 <- duncan.test(res_anova101, 'TRATAMIENTOS', console = T)##
## Study: res_anova101 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_A
##
## Mean Square Error: 30.54167
##
## TRATAMIENTOS, means
##
## ESTOMAS_A std r Min Max
## AMARILLO_50_AS 9.00 6.633250 4 4 18
## AZUL_50_AS600 17.50 4.358899 4 11 20
## ROJA_CONTROL 14.25 5.123475 4 9 20
## VERDE_50_AS1000 15.25 5.737305 4 8 22
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 8.514346 8.912076 9.153056
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_A groups
## AZUL_50_AS600 17.50 a
## VERDE_50_AS1000 15.25 a
## ROJA_CONTROL 14.25 a
## AMARILLO_50_AS 9.00 aplot(dt101)
*CERRADOS
res_anova11 <- aov(ESTOMAS_C ~ TRATAMIENTOS, data = datos)
summary(res_anova11)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 549.2 183.06 3.624 0.0453 *
## Residuals 12 606.2 50.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey11 <- TukeyHSD(res_anova11)
print(res_tukey11)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ESTOMAS_C ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -7.50 -22.4216204 7.42162 0.4713759
## ROJA_CONTROL-AMARILLO_50_AS -10.50 -25.4216204 4.42162 0.2112823
## VERDE_50_AS1000-AMARILLO_50_AS 4.25 -10.6716204 19.17162 0.8318936
## ROJA_CONTROL-AZUL_50_AS600 -3.00 -17.9216204 11.92162 0.9310698
## VERDE_50_AS1000-AZUL_50_AS600 11.75 -3.1716204 26.67162 0.1436312
## VERDE_50_AS1000-ROJA_CONTROL 14.75 -0.1716204 29.67162 0.0530326dt11 <- duncan.test(res_anova11, 'TRATAMIENTOS', console = T)##
## Study: res_anova11 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_C
##
## Mean Square Error: 50.52083
##
## TRATAMIENTOS, means
##
## ESTOMAS_C std r Min Max
## AMARILLO_50_AS 19.75 5.188127 4 12 23
## AZUL_50_AS600 12.25 8.301606 4 3 23
## ROJA_CONTROL 9.25 9.251126 4 3 23
## VERDE_50_AS1000 24.00 4.546061 4 19 30
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 10.95066 11.46219 11.77213
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_C groups
## VERDE_50_AS1000 24.00 a
## AMARILLO_50_AS 19.75 ab
## AZUL_50_AS600 12.25 b
## ROJA_CONTROL 9.25 bplot(dt11)
*ESTOMAS TOTALES
res_anova112 <- aov(ESTOMAS_A ~ TRATAMIENTOS, data = datos)
summary(res_anova112)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 155.5 51.83 1.697 0.22
## Residuals 12 366.5 30.54res_tukey112 <- TukeyHSD(res_anova112)
print(res_tukey112)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ESTOMAS_A ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 8.50 -3.101846 20.101846 0.1853788
## ROJA_CONTROL-AMARILLO_50_AS 5.25 -6.351846 16.851846 0.5549126
## VERDE_50_AS1000-AMARILLO_50_AS 6.25 -5.351846 17.851846 0.4148246
## ROJA_CONTROL-AZUL_50_AS600 -3.25 -14.851846 8.351846 0.8385065
## VERDE_50_AS1000-AZUL_50_AS600 -2.25 -13.851846 9.351846 0.9374523
## VERDE_50_AS1000-ROJA_CONTROL 1.00 -10.601846 12.601846 0.9938059dt112 <- duncan.test(res_anova112, 'TRATAMIENTOS', console = T)##
## Study: res_anova112 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_A
##
## Mean Square Error: 30.54167
##
## TRATAMIENTOS, means
##
## ESTOMAS_A std r Min Max
## AMARILLO_50_AS 9.00 6.633250 4 4 18
## AZUL_50_AS600 17.50 4.358899 4 11 20
## ROJA_CONTROL 14.25 5.123475 4 9 20
## VERDE_50_AS1000 15.25 5.737305 4 8 22
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 8.514346 8.912076 9.153056
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_A groups
## AZUL_50_AS600 17.50 a
## VERDE_50_AS1000 15.25 a
## ROJA_CONTROL 14.25 a
## AMARILLO_50_AS 9.00 aplot(dt112)
AREA FOLIAR
res_anova12 <- aov(AREA_FOLIAR ~ TRATAMIENTOS, data = datos)
summary(res_anova12)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 4223 1407.6 8.526 0.00265 **
## Residuals 12 1981 165.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey12 <- TukeyHSD(res_anova12)
print(res_tukey12)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AREA_FOLIAR ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -10.9575 -37.932608 16.017608 0.6348450
## ROJA_CONTROL-AMARILLO_50_AS 24.5050 -2.470108 51.480108 0.0795494
## VERDE_50_AS1000-AMARILLO_50_AS -18.4725 -45.447608 8.502608 0.2296943
## ROJA_CONTROL-AZUL_50_AS600 35.4625 8.487392 62.437608 0.0097795
## VERDE_50_AS1000-AZUL_50_AS600 -7.5150 -34.490108 19.460108 0.8406491
## VERDE_50_AS1000-ROJA_CONTROL -42.9775 -69.952608 -16.002392 0.0023759dt12 <- duncan.test(res_anova12, 'TRATAMIENTOS', console = T)##
## Study: res_anova12 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for AREA_FOLIAR
##
## Mean Square Error: 165.1068
##
## TRATAMIENTOS, means
##
## AREA_FOLIAR std r Min Max
## AMARILLO_50_AS 69.9900 13.001769 4 56.30 87.36
## AZUL_50_AS600 59.0325 8.898945 4 51.25 71.77
## ROJA_CONTROL 94.4950 10.361346 4 86.69 108.71
## VERDE_50_AS1000 51.5175 17.459451 4 26.78 66.50
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 19.79645 20.72120 21.28150
##
## Means with the same letter are not significantly different.
##
## AREA_FOLIAR groups
## ROJA_CONTROL 94.4950 a
## AMARILLO_50_AS 69.9900 b
## AZUL_50_AS600 59.0325 b
## VERDE_50_AS1000 51.5175 bplot(dt12)
AREA BAJO EL DOSEL
res_anova13 <- aov(A_DOSEL ~ TRATAMIENTOS, data = datos)
summary(res_anova13)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 9529 3176 7.185 0.00511 **
## Residuals 12 5305 442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_tukey13 <- TukeyHSD(res_anova13)
print(res_tukey13)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = A_DOSEL ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS -2.5625 -46.70228 41.57728 0.9980756
## ROJA_CONTROL-AMARILLO_50_AS 54.6075 10.46772 98.74728 0.0146180
## VERDE_50_AS1000-AMARILLO_50_AS -2.5375 -46.67728 41.60228 0.9981309
## ROJA_CONTROL-AZUL_50_AS600 57.1700 13.03022 101.30978 0.0108146
## VERDE_50_AS1000-AZUL_50_AS600 0.0250 -44.11478 44.16478 1.0000000
## VERDE_50_AS1000-ROJA_CONTROL -57.1450 -101.28478 -13.00522 0.0108464dt13 <- duncan.test(res_anova13, 'TRATAMIENTOS', console = T)##
## Study: res_anova13 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for A_DOSEL
##
## Mean Square Error: 442.078
##
## TRATAMIENTOS, means
##
## A_DOSEL std r Min Max
## AMARILLO_50_AS 54.0775 16.75190 4 34.96 71.77
## AZUL_50_AS600 51.5150 16.14043 4 32.80 68.49
## ROJA_CONTROL 108.6850 26.06204 4 89.53 146.80
## VERDE_50_AS1000 51.5400 23.40818 4 26.86 79.02
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 32.39324 33.90642 34.82324
##
## Means with the same letter are not significantly different.
##
## A_DOSEL groups
## ROJA_CONTROL 108.6850 a
## AMARILLO_50_AS 54.0775 b
## VERDE_50_AS1000 51.5400 b
## AZUL_50_AS600 51.5150 bplot(dt13)
CONTENIDO RELATIVO DE AGUA
res_anova14 <- aov(CRA ~ TRATAMIENTOS, data = datos)
summary(res_anova14)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 157.5 52.51 2.107 0.153
## Residuals 12 299.1 24.92res_tukey14 <- TukeyHSD(res_anova14)
print(res_tukey14)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRA ~ TRATAMIENTOS, data = datos)
##
## $TRATAMIENTOS
## diff lwr upr p adj
## AZUL_50_AS600-AMARILLO_50_AS 5.24375 -5.23634 15.72384 0.4750532
## ROJA_CONTROL-AMARILLO_50_AS 7.39750 -3.08259 17.87759 0.2092301
## VERDE_50_AS1000-AMARILLO_50_AS 0.48500 -9.99509 10.96509 0.9990203
## ROJA_CONTROL-AZUL_50_AS600 2.15375 -8.32634 12.63384 0.9268977
## VERDE_50_AS1000-AZUL_50_AS600 -4.75875 -15.23884 5.72134 0.5522466
## VERDE_50_AS1000-ROJA_CONTROL -6.91250 -17.39259 3.56759 0.2562321dt14 <- duncan.test(res_anova14, 'TRATAMIENTOS', console = T)##
## Study: res_anova14 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for CRA
##
## Mean Square Error: 24.92118
##
## TRATAMIENTOS, means
##
## CRA std r Min Max
## AMARILLO_50_AS 69.61375 3.483046 4 64.750 72.895
## AZUL_50_AS600 74.85750 4.481678 4 70.260 80.910
## ROJA_CONTROL 77.01125 7.823067 4 66.050 83.225
## VERDE_50_AS1000 70.09875 2.503456 4 68.125 73.765
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 7.691113 8.050388 8.268067
##
## Means with the same letter are not significantly different.
##
## CRA groups
## ROJA_CONTROL 77.01125 a
## AZUL_50_AS600 74.85750 a
## VERDE_50_AS1000 70.09875 a
## AMARILLO_50_AS 69.61375 aplot(dt14)