fisio
A B Palma O
2023-10-09
library(readxl)
Analisi_muestreo2 <- read_excel("Analisi_muestreo1.xlsx",
sheet = "muestreo2")
#View(Analisi_muestreo2)datos <- read_excel("Analisi_muestreo1.xlsx",
sheet = "muestreo2")summary(datos)## TRATAMIENTOS TEMP_HOJA NUM_HOJAS SPAD
## Length:16 Min. :11.66 Min. :5.00 Min. :39.40
## Class :character 1st Qu.:15.14 1st Qu.:5.00 1st Qu.:43.75
## Mode :character Median :16.07 Median :5.50 Median :45.20
## Mean :16.13 Mean :5.75 Mean :45.21
## 3rd Qu.:17.37 3rd Qu.:6.25 3rd Qu.:46.62
## Max. :20.00 Max. :7.00 Max. :50.50
## ALTURA_AER DIAMETRO_R PF_AEREO PF_RAIZ
## Min. : 9.20 Min. :13.82 Min. :2.540 Min. : 2.570
## 1st Qu.:10.43 1st Qu.:24.53 1st Qu.:3.650 1st Qu.: 7.735
## Median :10.95 Median :26.97 Median :4.295 Median :10.045
## Mean :11.81 Mean :27.57 Mean :4.613 Mean :11.748
## 3rd Qu.:13.82 3rd Qu.:29.02 3rd Qu.:4.718 3rd Qu.:11.335
## Max. :16.30 Max. :39.55 Max. :8.470 Max. :27.990
## PS_AEREO PS_RAIZ ESTOMAS_A ESTOMAS_PA
## Min. :0.2100 Min. :0.2200 Min. : 1.0 Min. : 3.00
## 1st Qu.:0.3600 1st Qu.:0.4075 1st Qu.: 7.0 1st Qu.: 8.00
## Median :0.4650 Median :0.5200 Median : 9.0 Median :13.00
## Mean :0.4725 Mean :0.5450 Mean : 9.5 Mean :13.44
## 3rd Qu.:0.5500 3rd Qu.:0.6275 3rd Qu.:12.0 3rd Qu.:16.25
## Max. :0.7600 Max. :0.9700 Max. :20.0 Max. :38.00
## ESTOMAS_C ESTOMAS_TOTALES AREA_FOLIAR A_DOSEL
## Min. : 2.000 Min. :14.00 Min. : 45.69 Min. : 59.45
## 1st Qu.: 5.000 1st Qu.:20.75 1st Qu.: 70.49 1st Qu.: 94.44
## Median : 6.500 Median :30.50 Median : 93.29 Median :116.84
## Mean : 9.375 Mean :32.31 Mean :103.82 Mean :121.57
## 3rd Qu.:12.750 3rd Qu.:41.75 3rd Qu.:113.59 3rd Qu.:129.58
## Max. :24.000 Max. :64.00 Max. :212.80 Max. :215.44
## CRA
## Min. :62.70
## 1st Qu.:71.98
## Median :75.54
## Mean :75.61
## 3rd Qu.:78.77
## Max. :86.96TEMPERATURA
res_anova <- aov(TEMP_HOJA ~ TRATAMIENTOS, data = datos)
summary(res_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 42.22 14.074 5.046 0.0173 *
## Residuals 12 33.47 2.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_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 -0.210 -3.7162322 3.2962322 0.9978888
## ROJA_CONTROL-AMARILLO_50_AS 3.855 0.3487678 7.3612322 0.0299176
## VERDE_50_AS1000-AMARILLO_50_AS 0.890 -2.6162322 4.3962322 0.8734229
## ROJA_CONTROL-AZUL_50_AS600 4.065 0.5587678 7.5712322 0.0219123
## VERDE_50_AS1000-AZUL_50_AS600 1.100 -2.4062322 4.6062322 0.7889541
## VERDE_50_AS1000-ROJA_CONTROL -2.965 -6.4712322 0.5412322 0.1085383library(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: 2.789458
##
## TRATAMIENTOS, means
##
## TEMP_HOJA std r Min Max
## AMARILLO_50_AS 14.995 1.317915 4 13.06 15.86
## AZUL_50_AS600 14.785 2.206740 4 11.66 16.40
## ROJA_CONTROL 18.850 1.185214 4 17.19 20.00
## VERDE_50_AS1000 15.885 1.773838 4 13.62 17.90
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 2.573149 2.693348 2.766175
##
## Means with the same letter are not significantly different.
##
## TEMP_HOJA groups
## ROJA_CONTROL 18.850 a
## VERDE_50_AS1000 15.885 b
## AMARILLO_50_AS 14.995 b
## AZUL_50_AS600 14.785 bplot(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 3 1.0000 1.5 0.265
## 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 -1.7140959 1.7140959 1.0000000
## ROJA_CONTROL-AMARILLO_50_AS 1 -0.7140959 2.7140959 0.3502697
## VERDE_50_AS1000-AMARILLO_50_AS 0 -1.7140959 1.7140959 1.0000000
## ROJA_CONTROL-AZUL_50_AS600 1 -0.7140959 2.7140959 0.3502697
## VERDE_50_AS1000-AZUL_50_AS600 0 -1.7140959 1.7140959 1.0000000
## VERDE_50_AS1000-ROJA_CONTROL -1 -2.7140959 0.7140959 0.3502697dt2 <- 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 5.5 0.5773503 4 5 6
## AZUL_50_AS600 5.5 1.0000000 4 5 7
## ROJA_CONTROL 6.5 0.5773503 4 6 7
## VERDE_50_AS1000 5.5 1.0000000 4 5 7
##
## 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
## ROJA_CONTROL 6.5 a
## AMARILLO_50_AS 5.5 a
## AZUL_50_AS600 5.5 a
## VERDE_50_AS1000 5.5 aplot(dt2)
SPAD
res_anova3 <- aov(SPAD ~ TRATAMIENTOS, data = datos)
summary(res_anova3)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 7.09 2.362 0.225 0.877
## Residuals 12 125.91 10.493res_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 0.10 -6.700177 6.900177 0.9999683
## ROJA_CONTROL-AMARILLO_50_AS -1.35 -8.150177 5.450177 0.9333748
## VERDE_50_AS1000-AMARILLO_50_AS -1.20 -8.000177 5.600177 0.9516671
## ROJA_CONTROL-AZUL_50_AS600 -1.45 -8.250177 5.350177 0.9193587
## VERDE_50_AS1000-AZUL_50_AS600 -1.30 -8.100177 5.500177 0.9398385
## VERDE_50_AS1000-ROJA_CONTROL 0.15 -6.650177 6.950177 0.9998932dt3 <- duncan.test(res_anova3, 'TRATAMIENTOS', console = T)##
## Study: res_anova3 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for SPAD
##
## Mean Square Error: 10.4925
##
## TRATAMIENTOS, means
##
## SPAD std r Min Max
## AMARILLO_50_AS 45.825 4.5287047 4 39.4 49.8
## AZUL_50_AS600 45.925 3.4912987 4 42.0 50.5
## ROJA_CONTROL 44.475 0.9142392 4 43.3 45.2
## VERDE_50_AS1000 44.625 2.9044506 4 41.5 48.5
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 4.990504 5.223625 5.364870
##
## Means with the same letter are not significantly different.
##
## SPAD groups
## AZUL_50_AS600 45.925 a
## AMARILLO_50_AS 45.825 a
## VERDE_50_AS1000 44.625 a
## ROJA_CONTROL 44.475 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 45.08 15.026 9.092 0.00205 **
## Residuals 12 19.83 1.653
## ---
## 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 -0.100 -2.798851 2.5988506 0.9994955
## ROJA_CONTROL-AMARILLO_50_AS 4.025 1.326149 6.7238506 0.0039574
## VERDE_50_AS1000-AMARILLO_50_AS 0.800 -1.898851 3.4988506 0.8150990
## ROJA_CONTROL-AZUL_50_AS600 4.125 1.426149 6.8238506 0.0032835
## VERDE_50_AS1000-AZUL_50_AS600 0.900 -1.798851 3.5988506 0.7577373
## VERDE_50_AS1000-ROJA_CONTROL -3.225 -5.923851 -0.5261494 0.0182067dt4 <- duncan.test(res_anova4, 'TRATAMIENTOS', console = T)##
## Study: res_anova4 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ALTURA_AER
##
## Mean Square Error: 1.652708
##
## TRATAMIENTOS, means
##
## ALTURA_AER std r Min Max
## AMARILLO_50_AS 10.625 0.8616844 4 9.9 11.6
## AZUL_50_AS600 10.525 0.2500000 4 10.2 10.8
## ROJA_CONTROL 14.650 1.1561430 4 13.8 16.3
## VERDE_50_AS1000 11.425 2.1140404 4 9.2 14.2
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 1.980629 2.073150 2.129207
##
## Means with the same letter are not significantly different.
##
## ALTURA_AER groups
## ROJA_CONTROL 14.650 a
## VERDE_50_AS1000 11.425 b
## AMARILLO_50_AS 10.625 b
## AZUL_50_AS600 10.525 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 3 325.0 108.33 4.778 0.0205 *
## Residuals 12 272.1 22.67
## ---
## 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 0.6575 -9.3384454 10.653445 0.9972120
## ROJA_CONTROL-AMARILLO_50_AS 9.2575 -0.7384454 19.253445 0.0727871
## VERDE_50_AS1000-AMARILLO_50_AS -2.7925 -12.7884454 7.203445 0.8395734
## ROJA_CONTROL-AZUL_50_AS600 8.6000 -1.3959454 18.595945 0.1009884
## VERDE_50_AS1000-AZUL_50_AS600 -3.4500 -13.4459454 6.545945 0.7387215
## VERDE_50_AS1000-ROJA_CONTROL -12.0500 -22.0459454 -2.054055 0.0172353dt5 <- duncan.test(res_anova5, 'TRATAMIENTOS', console = T)##
## Study: res_anova5 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for DIAMETRO_R
##
## Mean Square Error: 22.67181
##
## TRATAMIENTOS, means
##
## DIAMETRO_R std r Min Max
## AMARILLO_50_AS 25.7875 2.260389 4 23.82 28.61
## AZUL_50_AS600 26.4450 4.349234 4 20.21 30.25
## ROJA_CONTROL 35.0450 5.318988 4 28.02 39.55
## VERDE_50_AS1000 22.9950 6.194387 4 13.82 26.75
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 7.335810 7.678487 7.886111
##
## Means with the same letter are not significantly different.
##
## DIAMETRO_R groups
## ROJA_CONTROL 35.0450 a
## AZUL_50_AS600 26.4450 b
## AMARILLO_50_AS 25.7875 b
## VERDE_50_AS1000 22.9950 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 32.29 10.763 10.56 0.0011 **
## Residuals 12 12.23 1.019
## ---
## 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 -0.2650 -2.384676 1.854676 0.9817174
## ROJA_CONTROL-AMARILLO_50_AS 3.1700 1.050324 5.289676 0.0038756
## VERDE_50_AS1000-AMARILLO_50_AS -0.0425 -2.162176 2.077176 0.9999197
## ROJA_CONTROL-AZUL_50_AS600 3.4350 1.315324 5.554676 0.0020757
## VERDE_50_AS1000-AZUL_50_AS600 0.2225 -1.897176 2.342176 0.9889746
## VERDE_50_AS1000-ROJA_CONTROL -3.2125 -5.332176 -1.092824 0.0035029dt6 <- duncan.test(res_anova6, 'TRATAMIENTOS', console = T)##
## Study: res_anova6 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PF_AEREO
##
## Mean Square Error: 1.019477
##
## TRATAMIENTOS, means
##
## PF_AEREO std r Min Max
## AMARILLO_50_AS 3.8975 0.9218957 4 2.54 4.57
## AZUL_50_AS600 3.6325 0.5144171 4 3.11 4.30
## ROJA_CONTROL 7.0675 1.4697024 4 5.13 8.47
## VERDE_50_AS1000 3.8550 0.8963072 4 2.56 4.58
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 1.555585 1.628251 1.672278
##
## Means with the same letter are not significantly different.
##
## PF_AEREO groups
## ROJA_CONTROL 7.0675 a
## AMARILLO_50_AS 3.8975 b
## VERDE_50_AS1000 3.8550 b
## AZUL_50_AS600 3.6325 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 3 453.7 151.2 7.875 0.00361 **
## Residuals 12 230.4 19.2
## ---
## 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 -0.4500 -9.649637 8.749637 0.9988443
## ROJA_CONTROL-AMARILLO_50_AS 11.6625 2.462863 20.862137 0.0124718
## VERDE_50_AS1000-AMARILLO_50_AS -1.3100 -10.509637 7.889637 0.9734845
## ROJA_CONTROL-AZUL_50_AS600 12.1125 2.912863 21.312137 0.0096794
## VERDE_50_AS1000-AZUL_50_AS600 -0.8600 -10.059637 8.339637 0.9921399
## VERDE_50_AS1000-ROJA_CONTROL -12.9725 -22.172137 -3.772863 0.0059832dt7 <- duncan.test(res_anova7, 'TRATAMIENTOS', console = T)##
## Study: res_anova7 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PF_RAIZ
##
## Mean Square Error: 19.20348
##
## TRATAMIENTOS, means
##
## PF_RAIZ std r Min Max
## AMARILLO_50_AS 9.2725 1.869409 4 6.67 11.12
## AZUL_50_AS600 8.8225 1.429717 4 7.33 10.11
## ROJA_CONTROL 20.9350 7.356333 4 10.76 27.99
## VERDE_50_AS1000 7.9625 4.142402 4 2.57 11.98
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 6.751416 7.066794 7.257878
##
## Means with the same letter are not significantly different.
##
## PF_RAIZ groups
## ROJA_CONTROL 20.9350 a
## AMARILLO_50_AS 9.2725 b
## AZUL_50_AS600 8.8225 b
## VERDE_50_AS1000 7.9625 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 0.1971 0.06568 4.397 0.0263 *
## Residuals 12 0.1792 0.01494
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_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 -0.0250 -0.281578165 0.23157817 0.9911271
## ROJA_CONTROL-AMARILLO_50_AS 0.2525 -0.004078165 0.50907817 0.0542369
## VERDE_50_AS1000-AMARILLO_50_AS 0.0225 -0.234078165 0.27907817 0.9934839
## ROJA_CONTROL-AZUL_50_AS600 0.2775 0.020921835 0.53407817 0.0328336
## VERDE_50_AS1000-AZUL_50_AS600 0.0475 -0.209078165 0.30407817 0.9448767
## VERDE_50_AS1000-ROJA_CONTROL -0.2300 -0.486578165 0.02657817 0.0844716dt8 <- duncan.test(res_anova8, 'TRATAMIENTOS', console = T)##
## Study: res_anova8 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PS_AEREO
##
## Mean Square Error: 0.0149375
##
## TRATAMIENTOS, means
##
## PS_AEREO std r Min Max
## AMARILLO_50_AS 0.4100 0.1344123 4 0.21 0.50
## AZUL_50_AS600 0.3850 0.1072381 4 0.30 0.54
## ROJA_CONTROL 0.6625 0.1184272 4 0.50 0.76
## VERDE_50_AS1000 0.4325 0.1271154 4 0.27 0.58
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 0.1882972 0.1970931 0.2024225
##
## Means with the same letter are not significantly different.
##
## PS_AEREO groups
## ROJA_CONTROL 0.6625 a
## VERDE_50_AS1000 0.4325 b
## AMARILLO_50_AS 0.4100 b
## AZUL_50_AS600 0.3850 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 0.1670 0.05568 1.69 0.222
## Residuals 12 0.3953 0.03295res_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 0.0275 -0.3535491 0.4085491 0.9963309
## ROJA_CONTROL-AMARILLO_50_AS 0.2375 -0.1435491 0.6185491 0.2984441
## VERDE_50_AS1000-AMARILLO_50_AS -0.0150 -0.3960491 0.3660491 0.9993955
## ROJA_CONTROL-AZUL_50_AS600 0.2100 -0.1710491 0.5910491 0.3962598
## VERDE_50_AS1000-AZUL_50_AS600 -0.0425 -0.4235491 0.3385491 0.9868498
## VERDE_50_AS1000-ROJA_CONTROL -0.2525 -0.6335491 0.1285491 0.2528907dt9 <- duncan.test(res_anova9, 'TRATAMIENTOS', console = T)##
## Study: res_anova9 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for PS_RAIZ
##
## Mean Square Error: 0.03294583
##
## TRATAMIENTOS, means
##
## PS_RAIZ std r Min Max
## AMARILLO_50_AS 0.4825 0.09844626 4 0.40 0.61
## AZUL_50_AS600 0.5100 0.03464102 4 0.46 0.54
## ROJA_CONTROL 0.7200 0.28118796 4 0.33 0.97
## VERDE_50_AS1000 0.4675 0.20451161 4 0.22 0.65
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 0.2796438 0.2927068 0.3006215
##
## Means with the same letter are not significantly different.
##
## PS_RAIZ groups
## ROJA_CONTROL 0.7200 a
## AZUL_50_AS600 0.5100 a
## AMARILLO_50_AS 0.4825 a
## VERDE_50_AS1000 0.4675 aplot(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 57.5 19.17 0.776 0.53
## Residuals 12 296.5 24.71res_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 2.00 -8.43524 12.43524 0.9394219
## ROJA_CONTROL-AMARILLO_50_AS -2.25 -12.68524 8.18524 0.9169494
## VERDE_50_AS1000-AMARILLO_50_AS -2.75 -13.18524 7.68524 0.8609849
## ROJA_CONTROL-AZUL_50_AS600 -4.25 -14.68524 6.18524 0.6329985
## VERDE_50_AS1000-AZUL_50_AS600 -4.75 -15.18524 5.68524 0.5503477
## VERDE_50_AS1000-ROJA_CONTROL -0.50 -10.93524 9.93524 0.9989133dt10 <- duncan.test(res_anova10, 'TRATAMIENTOS', console = T)##
## Study: res_anova10 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_A
##
## Mean Square Error: 24.70833
##
## TRATAMIENTOS, means
##
## ESTOMAS_A std r Min Max
## AMARILLO_50_AS 10.25 3.304038 4 7 14
## AZUL_50_AS600 12.25 5.315073 4 8 20
## ROJA_CONTROL 8.00 6.164414 4 1 16
## VERDE_50_AS1000 7.50 4.654747 4 3 12
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 7.658199 8.015936 8.232684
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_A groups
## AZUL_50_AS600 12.25 a
## AMARILLO_50_AS 10.25 a
## ROJA_CONTROL 8.00 a
## VERDE_50_AS1000 7.50 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 57.5 19.17 0.776 0.53
## Residuals 12 296.5 24.71res_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 2.00 -8.43524 12.43524 0.9394219
## ROJA_CONTROL-AMARILLO_50_AS -2.25 -12.68524 8.18524 0.9169494
## VERDE_50_AS1000-AMARILLO_50_AS -2.75 -13.18524 7.68524 0.8609849
## ROJA_CONTROL-AZUL_50_AS600 -4.25 -14.68524 6.18524 0.6329985
## VERDE_50_AS1000-AZUL_50_AS600 -4.75 -15.18524 5.68524 0.5503477
## VERDE_50_AS1000-ROJA_CONTROL -0.50 -10.93524 9.93524 0.9989133dt101 <- duncan.test(res_anova101, 'TRATAMIENTOS', console = T)##
## Study: res_anova101 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_A
##
## Mean Square Error: 24.70833
##
## TRATAMIENTOS, means
##
## ESTOMAS_A std r Min Max
## AMARILLO_50_AS 10.25 3.304038 4 7 14
## AZUL_50_AS600 12.25 5.315073 4 8 20
## ROJA_CONTROL 8.00 6.164414 4 1 16
## VERDE_50_AS1000 7.50 4.654747 4 3 12
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 7.658199 8.015936 8.232684
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_A groups
## AZUL_50_AS600 12.25 a
## AMARILLO_50_AS 10.25 a
## ROJA_CONTROL 8.00 a
## VERDE_50_AS1000 7.50 aplot(dt101)
*CERRADOS
res_anova11 <- aov(ESTOMAS_C ~ TRATAMIENTOS, data = datos)
summary(res_anova11)## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTOS 3 303.2 101.08 3.422 0.0526 .
## Residuals 12 354.5 29.54
## ---
## 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 9.50 -1.910331 20.9103307 0.1156647
## ROJA_CONTROL-AMARILLO_50_AS -1.75 -13.160331 9.6603307 0.9672971
## VERDE_50_AS1000-AMARILLO_50_AS 0.75 -10.660331 12.1603307 0.9972179
## ROJA_CONTROL-AZUL_50_AS600 -11.25 -22.660331 0.1603307 0.0537284
## VERDE_50_AS1000-AZUL_50_AS600 -8.75 -20.160331 2.6603307 0.1582601
## VERDE_50_AS1000-ROJA_CONTROL 2.50 -8.910331 13.9103307 0.9133639dt11 <- duncan.test(res_anova11, 'TRATAMIENTOS', console = T)##
## Study: res_anova11 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_C
##
## Mean Square Error: 29.54167
##
## TRATAMIENTOS, means
##
## ESTOMAS_C std r Min Max
## AMARILLO_50_AS 7.25 2.629956 4 5 11
## AZUL_50_AS600 16.75 7.632169 4 6 24
## ROJA_CONTROL 5.50 3.316625 4 2 10
## VERDE_50_AS1000 8.00 6.480741 4 2 15
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 8.373797 8.764962 9.001963
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_C groups
## AZUL_50_AS600 16.75 a
## VERDE_50_AS1000 8.00 b
## AMARILLO_50_AS 7.25 b
## ROJA_CONTROL 5.50 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 57.5 19.17 0.776 0.53
## Residuals 12 296.5 24.71res_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 2.00 -8.43524 12.43524 0.9394219
## ROJA_CONTROL-AMARILLO_50_AS -2.25 -12.68524 8.18524 0.9169494
## VERDE_50_AS1000-AMARILLO_50_AS -2.75 -13.18524 7.68524 0.8609849
## ROJA_CONTROL-AZUL_50_AS600 -4.25 -14.68524 6.18524 0.6329985
## VERDE_50_AS1000-AZUL_50_AS600 -4.75 -15.18524 5.68524 0.5503477
## VERDE_50_AS1000-ROJA_CONTROL -0.50 -10.93524 9.93524 0.9989133dt112 <- duncan.test(res_anova112, 'TRATAMIENTOS', console = T)##
## Study: res_anova112 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for ESTOMAS_A
##
## Mean Square Error: 24.70833
##
## TRATAMIENTOS, means
##
## ESTOMAS_A std r Min Max
## AMARILLO_50_AS 10.25 3.304038 4 7 14
## AZUL_50_AS600 12.25 5.315073 4 8 20
## ROJA_CONTROL 8.00 6.164414 4 1 16
## VERDE_50_AS1000 7.50 4.654747 4 3 12
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 7.658199 8.015936 8.232684
##
## Means with the same letter are not significantly different.
##
## ESTOMAS_A groups
## AZUL_50_AS600 12.25 a
## AMARILLO_50_AS 10.25 a
## ROJA_CONTROL 8.00 a
## VERDE_50_AS1000 7.50 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 22765 7588 7.888 0.00359 **
## Residuals 12 11544 962
## ---
## 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 -8.6125 -73.72476 56.49976 0.9785096
## ROJA_CONTROL-AMARILLO_50_AS 81.0850 15.97274 146.19726 0.0140108
## VERDE_50_AS1000-AMARILLO_50_AS -8.3600 -73.47226 56.75226 0.9802648
## ROJA_CONTROL-AZUL_50_AS600 89.6975 24.58524 154.80976 0.0070692
## VERDE_50_AS1000-AZUL_50_AS600 0.2525 -64.85976 65.36476 0.9999994
## VERDE_50_AS1000-ROJA_CONTROL -89.4450 -154.55726 -24.33274 0.0072115dt12 <- duncan.test(res_anova12, 'TRATAMIENTOS', console = T)##
## Study: res_anova12 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for AREA_FOLIAR
##
## Mean Square Error: 961.9756
##
## TRATAMIENTOS, means
##
## AREA_FOLIAR std r Min Max
## AMARILLO_50_AS 87.7875 31.41195 4 45.69 120.72
## AZUL_50_AS600 79.1750 15.63701 4 60.54 97.07
## ROJA_CONTROL 168.8725 44.17845 4 107.52 212.80
## VERDE_50_AS1000 79.4275 25.78645 4 56.81 111.22
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 47.78449 50.01665 51.36908
##
## Means with the same letter are not significantly different.
##
## AREA_FOLIAR groups
## ROJA_CONTROL 168.8725 a
## AMARILLO_50_AS 87.7875 b
## VERDE_50_AS1000 79.4275 b
## AZUL_50_AS600 79.1750 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 17232 5744 7.062 0.00544 **
## Residuals 12 9761 813
## ---
## 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 -11.9675 -71.84006 47.905059 0.9321405
## ROJA_CONTROL-AMARILLO_50_AS 73.1950 13.32244 133.067559 0.0157742
## VERDE_50_AS1000-AMARILLO_50_AS 11.7025 -48.17006 71.575059 0.9361157
## ROJA_CONTROL-AZUL_50_AS600 85.1625 25.28994 145.035059 0.0056189
## VERDE_50_AS1000-AZUL_50_AS600 23.6700 -36.20256 83.542559 0.6536464
## VERDE_50_AS1000-ROJA_CONTROL -61.4925 -121.36506 -1.619941 0.0435111dt13 <- duncan.test(res_anova13, 'TRATAMIENTOS', console = T)##
## Study: res_anova13 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for A_DOSEL
##
## Mean Square Error: 813.3813
##
## TRATAMIENTOS, means
##
## A_DOSEL std r Min Max
## AMARILLO_50_AS 103.3375 31.19703 4 59.45 128.38
## AZUL_50_AS600 91.3700 17.09741 4 70.14 111.58
## ROJA_CONTROL 176.5325 41.45552 4 118.92 215.44
## VERDE_50_AS1000 115.0400 16.41308 4 93.30 133.18
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 43.93919 45.99171 47.23531
##
## Means with the same letter are not significantly different.
##
## A_DOSEL groups
## ROJA_CONTROL 176.5325 a
## VERDE_50_AS1000 115.0400 b
## AMARILLO_50_AS 103.3375 b
## AZUL_50_AS600 91.3700 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 3 225.3 75.11 2.897 0.0789 .
## Residuals 12 311.1 25.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res_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 -9.90125 -20.590516 0.7880158 0.0727306
## ROJA_CONTROL-AMARILLO_50_AS -2.07000 -12.759266 8.6192658 0.9376986
## VERDE_50_AS1000-AMARILLO_50_AS -5.53375 -16.223016 5.1555158 0.4473290
## ROJA_CONTROL-AZUL_50_AS600 7.83125 -2.858016 18.5205158 0.1853911
## VERDE_50_AS1000-AZUL_50_AS600 4.36750 -6.321766 15.0567658 0.6307225
## VERDE_50_AS1000-ROJA_CONTROL -3.46375 -14.153016 7.2255158 0.7728185dt14 <- duncan.test(res_anova14, 'TRATAMIENTOS', console = T)##
## Study: res_anova14 ~ "TRATAMIENTOS"
##
## Duncan's new multiple range test
## for CRA
##
## Mean Square Error: 25.92593
##
## TRATAMIENTOS, means
##
## CRA std r Min Max
## AMARILLO_50_AS 79.98125 3.407018 4 77.150 84.920
## AZUL_50_AS600 70.08000 5.597903 4 62.705 76.285
## ROJA_CONTROL 77.91125 6.106404 4 73.610 86.960
## VERDE_50_AS1000 74.44750 4.844713 4 70.420 81.435
##
## Alpha: 0.05 ; DF Error: 12
##
## Critical Range
## 2 3 4
## 7.844623 8.211068 8.433093
##
## Means with the same letter are not significantly different.
##
## CRA groups
## AMARILLO_50_AS 79.98125 a
## ROJA_CONTROL 77.91125 ab
## VERDE_50_AS1000 74.44750 ab
## AZUL_50_AS600 70.08000 bplot(dt14)