Datos de los 4 muestreos
rm(list=ls())
library(readxl)
GENERAL <- read_excel("d:/Users/Janus/Documents/Fisiologia vegetal basica/BASE DE DATOS FINAL.xlsx",
sheet = "GENERAL")
Muestreo1 <- read_excel("d:/Users/Janus/Documents/Fisiologia vegetal basica/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 1")
Muestreo2 <- read_excel("d:/Users/Janus/Documents/Fisiologia vegetal basica/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 2")
Muestreo3 <- read_excel("d:/Users/Janus/Documents/Fisiologia vegetal basica/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 3")
Muestreo4 <- read_excel("d:/Users/Janus/Documents/Fisiologia vegetal basica/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 4")Area_foliar
Determinación de la variable en los 4 muestreos
k1 <- aov(Area_foliar~Trat, data = Muestreo1)
k2 <- aov(Area_foliar~Trat, data = Muestreo2)
k3 <- aov(Area_foliar~Trat, data = Muestreo3)
k4 <- aov(Area_foliar~Trat, data = Muestreo4)
anova(k1)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 550.34 183.446 27.845 1.088e-05 ***
## Residuals 12 79.06 6.588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(k2)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1721.7 573.89 5.6601 0.01186 *
## Residuals 12 1216.7 101.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(k3)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 17500.9 5833.6 142.63 1.194e-09 ***
## Residuals 12 490.8 40.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(k4)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 16195.5 5398.5 289.65 1.863e-11 ***
## Residuals 12 223.7 18.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1shapiro test
shapiro.test(resid(k1))##
## Shapiro-Wilk normality test
##
## data: resid(k1)
## W = 0.9256, p-value = 0.2075shapiro.test(resid(k2))##
## Shapiro-Wilk normality test
##
## data: resid(k2)
## W = 0.87538, p-value = 0.03291shapiro.test(resid(k3))##
## Shapiro-Wilk normality test
##
## data: resid(k3)
## W = 0.82351, p-value = 0.005687shapiro.test(resid(k4))##
## Shapiro-Wilk normality test
##
## data: resid(k4)
## W = 0.96647, p-value = 0.7787*Homogeneidad de varianzas**
library(car)## Loading required package: carDatalibrary(carData)
leveneTest(Muestreo1$Area_foliar~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.1719 0.3611
## 12leveneTest(Muestreo2$Area_foliar~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.8594 0.08136 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo3$Area_foliar~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 8.539 0.002633 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo4$Area_foliar~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 0.4911 0.695
## 12Prueba de tukey
library(agricolae)
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionm1tukey <-HSD.test(Muestreo1$Area_foliar,Muestreo1$Trat, 12, 6.588, alpha = 0.05)
m2tukey <-HSD.test(Muestreo2$Area_foliar,Muestreo2$Trat, 12, 101.39, alpha = 0.05)
m3tukey <-HSD.test(Muestreo3$Area_foliar,Muestreo3$Trat, 12, 40.9, alpha = 0.05)
m4tukey <-HSD.test(Muestreo4$Area_foliar,Muestreo4$Trat, 12, 18.6, alpha = 0.05)
m1tukey## $statistics
## MSerror Df Mean CV MSD
## 6.588 12 59.42406 4.319311 5.388372
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$Area_foliar std r Min Max Q25 Q50 Q75
## T1 66.57400 1.953636 4 64.925 69.223 65.20250 66.0740 67.44550
## T2 52.04325 1.303762 4 50.238 53.351 51.73575 52.2920 52.59950
## T3 63.53700 2.607883 4 59.999 66.252 62.73875 63.9485 64.74675
## T4 55.54200 3.746375 4 50.356 58.346 54.01225 56.7330 58.26275
##
## $comparison
## NULL
##
## $groups
## Muestreo1$Area_foliar groups
## T1 66.57400 a
## T3 63.53700 a
## T4 55.54200 b
## T2 52.04325 b
##
## attr(,"class")
## [1] "group"m2tukey## $statistics
## MSerror Df Mean CV MSD
## 101.39 12 84.6375 11.89693 21.1387
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$Area_foliar std r Min Max Q25 Q50 Q75
## T1 92.775 18.570115 4 66.7 109.5 86.800 97.45 103.425
## T2 67.300 4.817330 4 61.2 71.9 64.650 68.05 70.700
## T3 92.500 3.221801 4 88.6 95.7 90.550 92.85 94.800
## T4 85.975 5.208567 4 81.5 93.3 82.775 84.55 87.750
##
## $comparison
## NULL
##
## $groups
## Muestreo2$Area_foliar groups
## T1 92.775 a
## T3 92.500 a
## T4 85.975 ab
## T2 67.300 b
##
## attr(,"class")
## [1] "group"m3tukey## $statistics
## MSerror Df Mean CV MSD
## 40.9 12 132.4688 4.827788 13.42587
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$Area_foliar std r Min Max Q25 Q50 Q75
## T1 161.575 1.447699 4 159.6 162.8 160.950 161.95 162.575
## T2 77.500 12.451774 4 61.2 88.6 71.175 80.10 86.425
## T3 154.675 1.936276 4 151.9 156.3 154.150 155.25 155.775
## T4 136.125 1.645955 4 134.3 138.0 135.050 136.10 137.175
##
## $comparison
## NULL
##
## $groups
## Muestreo3$Area_foliar groups
## T1 161.575 a
## T3 154.675 a
## T4 136.125 b
## T2 77.500 c
##
## attr(,"class")
## [1] "group"m4tukey## $statistics
## MSerror Df Mean CV MSD
## 18.6 12 158.0744 2.728317 9.053932
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$Area_foliar std r Min Max Q25 Q50 Q75
## T1 181.7325 5.107709 4 174.810 186.170 179.4750 182.9750 185.2325
## T2 103.9250 4.887191 4 98.670 109.750 100.7250 103.6400 106.8400
## T3 180.1078 3.965360 4 175.923 185.483 178.4873 179.5125 181.1330
## T4 166.5325 2.975739 4 163.360 169.360 164.3275 166.7050 168.9100
##
## $comparison
## NULL
##
## $groups
## Muestreo4$Area_foliar groups
## T1 181.7325 a
## T3 180.1078 a
## T4 166.5325 b
## T2 103.9250 c
##
## attr(,"class")
## [1] "group"Numero de hojas
Determinación de la variable en los 4 muestreos
ANOVA
m1 <- aov(PA_NHojas~Trat, data = Muestreo1)
m2 <- aov(PA_NHojas~Trat, data = Muestreo2)
m3 <- aov(PA_NHojas~Trat, data = Muestreo3)
m4 <- aov(PA_NHojas~Trat, data = Muestreo4)
anova(m1)## Analysis of Variance Table
##
## Response: PA_NHojas
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1.5 0.50000 2.4 0.1187
## Residuals 12 2.5 0.20833anova(m2)## Analysis of Variance Table
##
## Response: PA_NHojas
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3.5 1.1667 3.1111 0.06671 .
## Residuals 12 4.5 0.3750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3)## Analysis of Variance Table
##
## Response: PA_NHojas
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3.6875 1.2292 3.9333 0.03626 *
## Residuals 12 3.7500 0.3125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m4)## Analysis of Variance Table
##
## Response: PA_NHojas
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 8.1875 2.72917 11.909 0.0006561 ***
## Residuals 12 2.7500 0.22917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(m1))##
## Shapiro-Wilk normality test
##
## data: resid(m1)
## W = 0.8514, p-value = 0.01429shapiro.test(resid(m2))##
## Shapiro-Wilk normality test
##
## data: resid(m2)
## W = 0.91698, p-value = 0.1508shapiro.test(resid(m3))##
## Shapiro-Wilk normality test
##
## data: resid(m3)
## W = 0.76781, p-value = 0.00105shapiro.test(resid(m4))##
## Shapiro-Wilk normality test
##
## data: resid(m4)
## W = 0.87974, p-value = 0.03844En todos los muestreos se puede observar normalidad en los datos de Numero de hojas
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$PA_NHojas~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 6 0.009731 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo2$PA_NHojas~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 6.8182 0.00619 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo3$PA_NHojas~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1 0.4262
## 12leveneTest(Muestreo4$PA_NHojas~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 14.333 0.0002852 ***
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
m1tukey <-HSD.test(Muestreo1$PA_NHojas,Muestreo1$Trat, 12, 0.20833, alpha = 0.05)
m2tukey <-HSD.test(Muestreo2$PA_NHojas,Muestreo2$Trat, 12, 0.3750, alpha = 0.05)
m3tukey <-HSD.test(Muestreo3$PA_NHojas,Muestreo3$Trat, 12, 0.3125, alpha = 0.05)
m4tukey <-HSD.test(Muestreo4$PA_NHojas,Muestreo4$Trat, 12, 0.22917, alpha = 0.05)
m1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.20833 12 3.5 13.04091 0.9582011
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$PA_NHojas std r Min Max Q25 Q50 Q75
## T1 3.75 0.5000000 4 3 4 3.75 4.0 4
## T2 3.00 0.0000000 4 3 3 3.00 3.0 3
## T3 3.75 0.5000000 4 3 4 3.75 4.0 4
## T4 3.50 0.5773503 4 3 4 3.00 3.5 4
##
## $comparison
## NULL
##
## $groups
## Muestreo1$PA_NHojas groups
## T1 3.75 a
## T3 3.75 a
## T4 3.50 a
## T2 3.00 a
##
## attr(,"class")
## [1] "group"m2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.375 12 4 15.30931 1.285572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$PA_NHojas std r Min Max Q25 Q50 Q75
## T1 4.25 0.9574271 4 3 5 3.75 4.5 5.00
## T2 3.25 0.5000000 4 3 4 3.00 3.0 3.25
## T3 4.50 0.5773503 4 4 5 4.00 4.5 5.00
## T4 4.00 0.0000000 4 4 4 4.00 4.0 4.00
##
## $comparison
## NULL
##
## $groups
## Muestreo2$PA_NHojas groups
## T3 4.50 a
## T1 4.25 a
## T4 4.00 a
## T2 3.25 a
##
## attr(,"class")
## [1] "group"m3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.3125 12 5.3125 10.52267 1.173561
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$PA_NHojas std r Min Max Q25 Q50 Q75
## T1 5.75 0.5000000 4 5 6 5.75 6.0 6
## T2 4.50 0.5773503 4 4 5 4.00 4.5 5
## T3 5.50 0.5773503 4 5 6 5.00 5.5 6
## T4 5.50 0.5773503 4 5 6 5.00 5.5 6
##
## $comparison
## NULL
##
## $groups
## Muestreo3$PA_NHojas groups
## T1 5.75 a
## T3 5.50 ab
## T4 5.50 ab
## T2 4.50 b
##
## attr(,"class")
## [1] "group"m4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.22917 12 6.9375 6.900426 1.004985
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$PA_NHojas std r Min Max Q25 Q50 Q75
## T1 7.50 0.5773503 4 7 8 7.00 7.5 8
## T2 5.75 0.5000000 4 5 6 5.75 6.0 6
## T3 7.50 0.5773503 4 7 8 7.00 7.5 8
## T4 7.00 0.0000000 4 7 7 7.00 7.0 7
##
## $comparison
## NULL
##
## $groups
## Muestreo4$PA_NHojas groups
## T1 7.50 a
## T3 7.50 a
## T4 7.00 a
## T2 5.75 b
##
## attr(,"class")
## [1] "group"**Representacion de las diferencias estadisticas dentro de la variable temperatura en los cuatro muestreos
PA longitud
Determinación de la variable en los 4 muestreos
ANOVA
n1 <- aov(PA_Longitud~Trat, data = Muestreo1)
n2 <- aov(PA_Longitud~Trat, data = Muestreo2)
n3 <- aov(PA_Longitud~Trat, data = Muestreo3)
n4 <- aov(PA_Longitud~Trat, data = Muestreo4)
anova(n1)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 4.2832 1.42772 5.3196 0.01456 *
## Residuals 12 3.2206 0.26839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(n2)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 51.782 17.2608 3.9041 0.03702 *
## Residuals 12 53.055 4.4212
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(n3)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 149.962 49.987 78.437 3.747e-08 ***
## Residuals 12 7.648 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(n4)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 123.142 41.047 59.471 1.787e-07 ***
## Residuals 12 8.282 0.690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(n1))##
## Shapiro-Wilk normality test
##
## data: resid(n1)
## W = 0.87853, p-value = 0.03682shapiro.test(resid(n2))##
## Shapiro-Wilk normality test
##
## data: resid(n2)
## W = 0.94037, p-value = 0.3536shapiro.test(resid(n3))##
## Shapiro-Wilk normality test
##
## data: resid(n3)
## W = 0.95714, p-value = 0.6103shapiro.test(resid(n4))##
## Shapiro-Wilk normality test
##
## data: resid(n4)
## W = 0.97054, p-value = 0.8473Todos los datos cumplen los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$PA_Longitud~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.773 0.08722 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo2$PA_Longitud~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 10.595 0.001087 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo3$PA_Longitud~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 0.6344 0.6069
## 12leveneTest(Muestreo4$PA_Longitud~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 0.32 0.8108
## 12todos los supuestos se cumplen (casi)
Pueba de tukey
library(agricolae)
library(dplyr)
n1tukey <-HSD.test(Muestreo1$PA_Longitud,Muestreo1$Trat, 12, 0.26839, alpha = 0.05)
n2tukey <-HSD.test(Muestreo2$PA_Longitud,Muestreo2$Trat, 12, 4.4212, alpha = 0.05)
n3tukey <-HSD.test(Muestreo3$PA_Longitud,Muestreo3$Trat, 12, 0.637, alpha = 0.05)
n4tukey <-HSD.test(Muestreo4$PA_Longitud,Muestreo4$Trat, 12, 0.690, alpha = 0.05)
n1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.26839 12 6.98 7.422116 1.087587
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$PA_Longitud std r Min Max Q25 Q50 Q75
## T1 7.5725 0.8942548 4 6.99 8.9 7.0725 7.2 7.700
## T2 6.2250 0.2362908 4 5.90 6.4 6.1250 6.3 6.400
## T3 7.3225 0.3022554 4 6.99 7.7 7.1475 7.3 7.475
## T4 6.8000 0.3559026 4 6.30 7.1 6.6750 6.9 7.025
##
## $comparison
## NULL
##
## $groups
## Muestreo1$PA_Longitud groups
## T1 7.5725 a
## T3 7.3225 a
## T4 6.8000 ab
## T2 6.2250 b
##
## attr(,"class")
## [1] "group"n2tukey## $statistics
## MSerror Df Mean CV MSD
## 4.4212 12 9.6125 21.87428 4.414188
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$PA_Longitud std r Min Max Q25 Q50 Q75
## T1 10.450 3.8336232 4 6.5 15.0 7.775 10.15 12.825
## T2 6.750 0.4654747 4 6.3 7.2 6.375 6.75 7.125
## T3 11.625 1.5840349 4 9.6 13.2 10.800 11.85 12.675
## T4 9.625 0.5123475 4 8.9 10.1 9.500 9.75 9.875
##
## $comparison
## NULL
##
## $groups
## Muestreo2$PA_Longitud groups
## T3 11.625 a
## T1 10.450 ab
## T4 9.625 ab
## T2 6.750 b
##
## attr(,"class")
## [1] "group"n3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.637 12 12.40625 6.433232 1.675523
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$PA_Longitud std r Min Max Q25 Q50 Q75
## T1 15.100 0.5291503 4 14.6 15.8 14.75 15.00 15.350
## T2 8.000 0.8755950 4 7.2 9.1 7.35 7.85 8.500
## T3 15.425 0.8539126 4 14.3 16.3 15.05 15.55 15.925
## T4 11.100 0.8793937 4 10.1 12.1 10.55 11.10 11.650
##
## $comparison
## NULL
##
## $groups
## Muestreo3$PA_Longitud groups
## T3 15.425 a
## T1 15.100 a
## T4 11.100 b
## T2 8.000 c
##
## attr(,"class")
## [1] "group"n4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.69 12 16.11875 5.153392 1.743835
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$PA_Longitud std r Min Max Q25 Q50 Q75
## T1 17.600 0.5291503 4 16.9 18.1 17.350 17.70 17.95
## T2 11.525 0.7675719 4 10.4 12.1 11.375 11.80 11.95
## T3 18.825 0.8220908 4 17.9 19.7 18.275 18.85 19.40
## T4 16.525 1.1026483 4 15.2 17.9 16.175 16.50 16.85
##
## $comparison
## NULL
##
## $groups
## Muestreo4$PA_Longitud groups
## T3 18.825 a
## T1 17.600 ab
## T4 16.525 b
## T2 11.525 c
##
## attr(,"class")
## [1] "group"Hojas_Peso_fresco
Determinación de la variable en los 4 muestreos
ANOVA
o1 <- aov(Hojas_Peso_fresco~Trat, data = Muestreo1)
o2 <- aov(Hojas_Peso_fresco~Trat, data = Muestreo2)
o3 <- aov(Hojas_Peso_fresco~Trat, data = Muestreo3)
o4 <- aov(Hojas_Peso_fresco~Trat, data = Muestreo4)
anova(o1)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 5.4673 1.82242 28.939 8.901e-06 ***
## Residuals 12 0.7557 0.06297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(o2)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 16.9648 5.6549 10.976 0.0009349 ***
## Residuals 12 6.1828 0.5152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(o3)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 215.200 71.733 236.29 6.196e-11 ***
## Residuals 12 3.643 0.304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(o4)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 210.204 70.068 183.11 2.774e-10 ***
## Residuals 12 4.592 0.383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(o1))##
## Shapiro-Wilk normality test
##
## data: resid(o1)
## W = 0.97295, p-value = 0.8839shapiro.test(resid(o2))##
## Shapiro-Wilk normality test
##
## data: resid(o2)
## W = 0.96279, p-value = 0.7126shapiro.test(resid(o3))##
## Shapiro-Wilk normality test
##
## data: resid(o3)
## W = 0.95756, p-value = 0.6179shapiro.test(resid(o4))##
## Shapiro-Wilk normality test
##
## data: resid(o4)
## W = 0.92658, p-value = 0.2151se cumplen todos los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$Hojas_Peso_fresco~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.5854 0.1017
## 12leveneTest(Muestreo2$Hojas_Peso_fresco~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.2489 0.3355
## 12leveneTest(Muestreo3$Hojas_Peso_fresco~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.5082 0.2626
## 12leveneTest(Muestreo4$Hojas_Peso_fresco~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 6.4491 0.00756 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
o1tukey <-HSD.test(Muestreo1$Hojas_Peso_fresco,Muestreo1$Trat, 12, 0.06297, alpha = 0.05)
o2tukey <-HSD.test(Muestreo2$Hojas_Peso_fresco,Muestreo2$Trat, 12, 0.5152, alpha = 0.05)
o3tukey <-HSD.test(Muestreo3$Hojas_Peso_fresco,Muestreo3$Trat, 12, 0.304, alpha = 0.05)
o4tukey <-HSD.test(Muestreo4$Hojas_Peso_fresco,Muestreo4$Trat, 12, 0.383, alpha = 0.05)
o1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.06297 12 3.9295 6.386009 0.5268022
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$Hojas_Peso_fresco std r Min Max Q25 Q50 Q75
## T1 4.68800 0.3633492 4 4.223 5.023 4.49150 4.753 4.94950
## T2 3.09075 0.1257971 4 2.936 3.219 3.01850 3.104 3.17625
## T3 4.17550 0.2728864 4 3.902 4.510 3.98525 4.145 4.33525
## T4 3.76375 0.1719852 4 3.520 3.923 3.72400 3.806 3.84575
##
## $comparison
## NULL
##
## $groups
## Muestreo1$Hojas_Peso_fresco groups
## T1 4.68800 a
## T3 4.17550 ab
## T4 3.76375 b
## T2 3.09075 c
##
## attr(,"class")
## [1] "group"o2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.5152 12 5.8225 12.3276 1.506845
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$Hojas_Peso_fresco std r Min Max Q25 Q50 Q75
## T1 6.28075 1.0659588 4 4.841 7.234 5.80850 6.5240 6.99625
## T2 4.07075 0.7024127 4 3.138 4.812 3.80025 4.1665 4.43700
## T3 6.72025 0.4411314 4 6.162 7.193 6.50025 6.7630 6.98300
## T4 6.21825 0.4864945 4 5.872 6.917 5.89375 6.0420 6.36650
##
## $comparison
## NULL
##
## $groups
## Muestreo2$Hojas_Peso_fresco groups
## T3 6.72025 a
## T1 6.28075 a
## T4 6.21825 a
## T2 4.07075 b
##
## attr(,"class")
## [1] "group"o3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.304 12 12.95681 4.255383 1.157491
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$Hojas_Peso_fresco std r Min Max Q25 Q50
## T1 16.17300 0.7805583 4 15.232 16.961 15.70000 16.2495
## T2 6.72025 0.4411314 4 6.162 7.193 6.50025 6.7630
## T3 14.46700 0.4530350 4 13.933 14.994 14.21425 14.4705
## T4 14.46700 0.4530350 4 13.933 14.994 14.21425 14.4705
## Q75
## T1 16.72250
## T2 6.98300
## T3 14.72325
## T4 14.72325
##
## $comparison
## NULL
##
## $groups
## Muestreo3$Hojas_Peso_fresco groups
## T1 16.17300 a
## T3 14.46700 b
## T4 14.46700 b
## T2 6.72025 c
##
## attr(,"class")
## [1] "group"o4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.383 12 15.05237 4.111444 1.299212
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$Hojas_Peso_fresco std r Min Max Q25 Q50
## T1 17.9190 0.5463833 4 17.382 18.492 17.49150 17.9010
## T2 8.8765 0.7124729 4 8.078 9.531 8.37650 8.9485
## T3 17.3055 0.8271693 4 16.284 17.978 16.80750 17.4800
## T4 16.1085 0.2006830 4 15.939 16.364 15.95325 16.0655
## Q75
## T1 18.32850
## T2 9.44850
## T3 17.97800
## T4 16.22075
##
## $comparison
## NULL
##
## $groups
## Muestreo4$Hojas_Peso_fresco groups
## T1 17.9190 a
## T3 17.3055 ab
## T4 16.1085 b
## T2 8.8765 c
##
## attr(,"class")
## [1] "group"Hojas_Peso_seco
Determinación de la variable en los 4 muestreos
ANOVA
p1 <- aov(Hojas_Peso_seco~Trat, data = Muestreo1)
p2 <- aov(Hojas_Peso_seco~Trat, data = Muestreo2)
p3 <- aov(Hojas_Peso_seco~Trat, data = Muestreo3)
p4 <- aov(Hojas_Peso_seco~Trat, data = Muestreo4)
anova(p1)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.057738 0.019246 12.662 0.0005002 ***
## Residuals 12 0.018240 0.001520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(p2)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.18608 0.062026 7.186 0.005104 **
## Residuals 12 0.10358 0.008632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(p3)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.68737 0.229122 38.146 2.054e-06 ***
## Residuals 12 0.07208 0.006006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(p4)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2.19644 0.73215 31.332 5.867e-06 ***
## Residuals 12 0.28041 0.02337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(p1))##
## Shapiro-Wilk normality test
##
## data: resid(p1)
## W = 0.96767, p-value = 0.7997shapiro.test(resid(p2))##
## Shapiro-Wilk normality test
##
## data: resid(p2)
## W = 0.93219, p-value = 0.264shapiro.test(resid(p3))##
## Shapiro-Wilk normality test
##
## data: resid(p3)
## W = 0.87473, p-value = 0.03214shapiro.test(resid(p4))##
## Shapiro-Wilk normality test
##
## data: resid(p4)
## W = 0.94936, p-value = 0.4796Se cumplen los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$Hojas_Peso_seco~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.1742 0.1441
## 12leveneTest(Muestreo2$Hojas_Peso_seco~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.3867 0.1201
## 12leveneTest(Muestreo3$Hojas_Peso_seco~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.6978 0.2203
## 12leveneTest(Muestreo4$Hojas_Peso_seco~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.2135 0.347
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
p1tukey <-HSD.test(Muestreo1$Hojas_Peso_seco,Muestreo1$Trat, 12, 0.001520, alpha = 0.05)
p2tukey <-HSD.test(Muestreo2$Hojas_Peso_seco,Muestreo2$Trat, 12, 0.008632, alpha = 0.05)
p3tukey <-HSD.test(Muestreo3$Hojas_Peso_seco,Muestreo3$Trat, 12, 0.006006, alpha = 0.05)
p4tukey <-HSD.test(Muestreo4$Hojas_Peso_seco,Muestreo4$Trat, 12, 0.02337, alpha = 0.05)
p1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00152 12 0.30575 12.75133 0.08184696
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$Hojas_Peso_seco std r Min Max Q25 Q50 Q75
## T1 0.37125 0.05314367 4 0.311 0.432 0.33800 0.3710 0.40425
## T2 0.20800 0.02080064 4 0.189 0.236 0.19425 0.2035 0.21725
## T3 0.32775 0.04495461 4 0.285 0.386 0.29700 0.3200 0.35075
## T4 0.31600 0.02831960 4 0.286 0.347 0.29575 0.3155 0.33575
##
## $comparison
## NULL
##
## $groups
## Muestreo1$Hojas_Peso_seco groups
## T1 0.37125 a
## T3 0.32775 a
## T4 0.31600 a
## T2 0.20800 b
##
## attr(,"class")
## [1] "group"p2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.008632 12 0.5531875 16.79513 0.1950457
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$Hojas_Peso_seco std r Min Max Q25 Q50 Q75
## T1 0.59850 0.14735332 4 0.387 0.713 0.5550 0.6470 0.69050
## T2 0.36725 0.03944933 4 0.311 0.401 0.3560 0.3785 0.38975
## T3 0.62525 0.09350356 4 0.515 0.712 0.5645 0.6370 0.69775
## T4 0.62175 0.05014230 4 0.581 0.692 0.5885 0.6070 0.64025
##
## $comparison
## NULL
##
## $groups
## Muestreo2$Hojas_Peso_seco groups
## T3 0.62525 a
## T4 0.62175 a
## T1 0.59850 a
## T2 0.36725 b
##
## attr(,"class")
## [1] "group"p3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.006006 12 0.98425 7.873852 0.1626947
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$Hojas_Peso_seco std r Min Max Q25 Q50 Q75
## T1 1.10425 0.04587937 4 1.059 1.156 1.0695 1.1010 1.13575
## T2 0.62525 0.09350356 4 0.515 0.712 0.5645 0.6370 0.69775
## T3 1.10375 0.08117214 4 1.003 1.173 1.0555 1.1195 1.16775
## T4 1.10375 0.08117214 4 1.003 1.173 1.0555 1.1195 1.16775
##
## $comparison
## NULL
##
## $groups
## Muestreo3$Hojas_Peso_seco groups
## T1 1.10425 a
## T3 1.10375 a
## T4 1.10375 a
## T2 0.62525 b
##
## attr(,"class")
## [1] "group"p4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.02337 12 1.50525 10.15595 0.3209298
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$Hojas_Peso_seco std r Min Max Q25 Q50 Q75
## T1 1.90650 0.16086537 4 1.727 2.094 1.80500 1.9025 2.00400
## T2 0.90075 0.08052484 4 0.799 0.973 0.85525 0.9155 0.96100
## T3 1.63875 0.16415516 4 1.485 1.870 1.55700 1.6000 1.68175
## T4 1.57500 0.18482604 4 1.376 1.782 1.44725 1.5710 1.69875
##
## $comparison
## NULL
##
## $groups
## Muestreo4$Hojas_Peso_seco groups
## T1 1.90650 a
## T3 1.63875 ab
## T4 1.57500 b
## T2 0.90075 c
##
## attr(,"class")
## [1] "group"RT_Longitud
Determinación de la variable en los 4 muestreos
ANOVA
q1 <- aov(RT_Longitud~Trat, data = Muestreo1)
q2 <- aov(RT_Longitud~Trat, data = Muestreo2)
q3 <- aov(RT_Longitud~Trat, data = Muestreo3)
q4 <- aov(RT_Longitud~Trat, data = Muestreo4)
anova(q1)## Analysis of Variance Table
##
## Response: RT_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 46.25 15.4167 5.5224 0.01288 *
## Residuals 12 33.50 2.7917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q2)## Analysis of Variance Table
##
## Response: RT_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 323.19 107.729 22.982 2.908e-05 ***
## Residuals 12 56.25 4.687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q3)## Analysis of Variance Table
##
## Response: RT_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1473.2 491.08 100.73 8.95e-09 ***
## Residuals 12 58.5 4.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q4)## Analysis of Variance Table
##
## Response: RT_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3459.7 1153.23 938.22 1.714e-14 ***
## Residuals 12 14.8 1.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(q1))##
## Shapiro-Wilk normality test
##
## data: resid(q1)
## W = 0.94539, p-value = 0.4204shapiro.test(resid(q2))##
## Shapiro-Wilk normality test
##
## data: resid(q2)
## W = 0.94633, p-value = 0.4338shapiro.test(resid(q3))##
## Shapiro-Wilk normality test
##
## data: resid(q3)
## W = 0.94294, p-value = 0.3865shapiro.test(resid(q4))##
## Shapiro-Wilk normality test
##
## data: resid(q4)
## W = 0.94651, p-value = 0.4365Se cumplen todos los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$RT_Longitud~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.503 0.2639
## 12leveneTest(Muestreo2$RT_Longitud~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.4124 0.2873
## 12leveneTest(Muestreo3$RT_Longitud~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 4.1441 0.03128 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo4$RT_Longitud~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 0.7857 0.5246
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
q1tukey <-HSD.test(Muestreo1$RT_Longitud,Muestreo1$Trat, 12, 2.7917, alpha = 0.05)
q2tukey <-HSD.test(Muestreo2$RT_Longitud,Muestreo2$Trat, 12, 4.687, alpha = 0.05)
q3tukey <-HSD.test(Muestreo3$RT_Longitud,Muestreo3$Trat, 12, 4.88, alpha = 0.05)
q4tukey <-HSD.test(Muestreo4$RT_Longitud,Muestreo4$Trat, 12, 1.23, alpha = 0.05)
q1tukey## $statistics
## MSerror Df Mean CV MSD
## 2.7917 12 15.125 11.04686 3.507641
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$RT_Longitud std r Min Max Q25 Q50 Q75
## T1 16.50 1.290994 4 15 18 15.75 16.5 17.25
## T2 12.50 2.516611 4 10 16 11.50 12.0 13.00
## T3 16.75 0.500000 4 16 17 16.75 17.0 17.00
## T4 14.75 1.707825 4 13 17 13.75 14.5 15.50
##
## $comparison
## NULL
##
## $groups
## Muestreo1$RT_Longitud groups
## T3 16.75 a
## T1 16.50 a
## T4 14.75 ab
## T2 12.50 b
##
## attr(,"class")
## [1] "group"q2tukey## $statistics
## MSerror Df Mean CV MSD
## 4.687 12 22.6875 9.542471 4.544941
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$RT_Longitud std r Min Max Q25 Q50 Q75
## T1 25.25 3.3040379 4 21 29 24.00 25.5 26.75
## T2 15.00 0.8164966 4 14 16 14.75 15.0 15.25
## T3 26.25 2.2173558 4 23 28 26.00 27.0 27.25
## T4 24.25 1.5000000 4 23 26 23.00 24.0 25.25
##
## $comparison
## NULL
##
## $groups
## Muestreo2$RT_Longitud groups
## T3 26.25 a
## T1 25.25 a
## T4 24.25 a
## T2 15.00 b
##
## attr(,"class")
## [1] "group"q3tukey## $statistics
## MSerror Df Mean CV MSD
## 4.88 12 30.125 7.33302 4.637572
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$RT_Longitud std r Min Max Q25 Q50 Q75
## T1 35.25 0.5000000 4 35 36 35.00 35.0 35.25
## T2 15.00 0.8164966 4 14 16 14.75 15.0 15.25
## T3 40.75 2.9860788 4 38 45 39.50 40.0 41.25
## T4 29.50 3.1091264 4 26 33 27.50 29.5 31.50
##
## $comparison
## NULL
##
## $groups
## Muestreo3$RT_Longitud groups
## T3 40.75 a
## T1 35.25 b
## T4 29.50 c
## T2 15.00 d
##
## attr(,"class")
## [1] "group"q4tukey## $statistics
## MSerror Df Mean CV MSD
## 1.23 12 41.8125 2.652445 2.32827
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$RT_Longitud std r Min Max Q25 Q50 Q75
## T1 49.50 1.2909944 4 48 51 48.75 49.5 50.25
## T2 18.00 0.8164966 4 17 19 17.75 18.0 18.25
## T3 57.25 0.9574271 4 56 58 56.75 57.5 58.00
## T4 42.50 1.2909944 4 41 44 41.75 42.5 43.25
##
## $comparison
## NULL
##
## $groups
## Muestreo4$RT_Longitud groups
## T3 57.25 a
## T1 49.50 b
## T4 42.50 c
## T2 18.00 d
##
## attr(,"class")
## [1] "group"RT_Diámetro
Determinación de la variable en los 4 muestreos
ANOVA
r1 <- aov(RT_Diámetro~Trat, data = Muestreo1)
r2 <- aov(RT_Diámetro~Trat, data = Muestreo2)
r3 <- aov(RT_Diámetro~Trat, data = Muestreo3)
r4 <- aov(RT_Diámetro~Trat, data = Muestreo4)
anova(r1)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 79.25 26.417 9.1884 0.001963 **
## Residuals 12 34.50 2.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r2)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 466.05 155.35 25.68 1.653e-05 ***
## Residuals 12 72.59 6.05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r3)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1571.19 523.73 138.89 1.394e-09 ***
## Residuals 12 45.25 3.77
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r4)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3654 1218.0 2436 < 2.2e-16 ***
## Residuals 12 6 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(r1))##
## Shapiro-Wilk normality test
##
## data: resid(r1)
## W = 0.98893, p-value = 0.9985shapiro.test(resid(r2))##
## Shapiro-Wilk normality test
##
## data: resid(r2)
## W = 0.91335, p-value = 0.1318shapiro.test(resid(r3))##
## Shapiro-Wilk normality test
##
## data: resid(r3)
## W = 0.92303, p-value = 0.1887shapiro.test(resid(r4))##
## Shapiro-Wilk normality test
##
## data: resid(r4)
## W = 0.929, p-value = 0.235Se cumplen todos los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$RT_Diámetro~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.1818 0.3577
## 12leveneTest(Muestreo2$RT_Diámetro~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.5917 0.243
## 12leveneTest(Muestreo3$RT_Diámetro~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 3.8065 0.03969 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo4$RT_Diámetro~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 0 1
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
r1tukey <-HSD.test(Muestreo1$RT_Diámetro,Muestreo1$Trat, 12, 2.875, alpha = 0.05)
r2tukey <-HSD.test(Muestreo2$RT_Diámetro,Muestreo2$Trat, 12, 6.05, alpha = 0.05)
r3tukey <-HSD.test(Muestreo3$RT_Diámetro,Muestreo3$Trat, 12, 3.77, alpha = 0.05)
r4tukey <-HSD.test(Muestreo4$RT_Diámetro,Muestreo4$Trat, 12, 0.5, alpha = 0.05)
r1tukey## $statistics
## MSerror Df Mean CV MSD
## 2.875 12 12.125 13.98419 3.559587
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$RT_Diámetro std r Min Max Q25 Q50 Q75
## T1 14.25 1.7078251 4 12 16 13.50 14.5 15.25
## T2 8.75 2.5000000 4 6 12 7.50 8.5 9.75
## T3 14.00 0.8164966 4 13 15 13.75 14.0 14.25
## T4 11.50 1.2909944 4 10 13 10.75 11.5 12.25
##
## $comparison
## NULL
##
## $groups
## Muestreo1$RT_Diámetro groups
## T1 14.25 a
## T3 14.00 a
## T4 11.50 ab
## T2 8.75 b
##
## attr(,"class")
## [1] "group"r2tukey## $statistics
## MSerror Df Mean CV MSD
## 6.05 12 21.225 11.58857 5.163669
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$RT_Diámetro std r Min Max Q25 Q50 Q75
## T1 25.000 3.5590261 4 20 28.0 23.75 26 27.250
## T2 12.000 0.8164966 4 11 13.0 11.75 12 12.250
## T3 25.025 2.8523382 4 21 27.1 24.00 26 27.025
## T4 22.875 1.6520190 4 21 24.5 21.75 23 24.125
##
## $comparison
## NULL
##
## $groups
## Muestreo2$RT_Diámetro groups
## T3 25.025 a
## T1 25.000 a
## T4 22.875 a
## T2 12.000 b
##
## attr(,"class")
## [1] "group"r3tukey## $statistics
## MSerror Df Mean CV MSD
## 3.77 12 28.8125 6.738911 4.076162
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$RT_Diámetro std r Min Max Q25 Q50 Q75
## T1 34.75 0.500000 4 34 35 34.75 35.0 35.00
## T2 13.25 0.500000 4 13 14 13.00 13.0 13.25
## T3 39.50 3.109126 4 37 44 37.75 38.5 40.25
## T4 27.75 2.217356 4 25 30 26.50 28.0 29.25
##
## $comparison
## NULL
##
## $groups
## Muestreo3$RT_Diámetro groups
## T3 39.50 a
## T1 34.75 b
## T4 27.75 c
## T2 13.25 d
##
## attr(,"class")
## [1] "group"r4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.5 12 42 1.683588 1.484451
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$RT_Diámetro std r Min Max Q25 Q50 Q75
## T1 49.5 0.5773503 4 49 50 49.00 49.5 50.00
## T2 17.5 0.5773503 4 17 18 17.00 17.5 18.00
## T3 58.0 0.8164966 4 57 59 57.75 58.0 58.25
## T4 43.0 0.8164966 4 42 44 42.75 43.0 43.25
##
## $comparison
## NULL
##
## $groups
## Muestreo4$RT_Diámetro groups
## T3 58.0 a
## T1 49.5 b
## T4 43.0 c
## T2 17.5 d
##
## attr(,"class")
## [1] "group"RT_Peso_fresco
Determinación de la variable en el muestreo 4
ANOVA
s1 <- aov(RT_Peso_fresco~Trat, data = Muestreo1)
s2 <- aov(RT_Peso_fresco~Trat, data = Muestreo2)
s3 <- aov(RT_Peso_fresco~Trat, data = Muestreo3)
s4 <- aov(RT_Peso_fresco~Trat, data = Muestreo4)
anova(s1)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 13.7767 4.5922 52.062 3.749e-07 ***
## Residuals 12 1.0585 0.0882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(s2)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 144.150 48.050 6.8756 0.006004 **
## Residuals 12 83.861 6.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(s3)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 400.13 133.377 59.925 1.712e-07 ***
## Residuals 12 26.71 2.226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(s4)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 667.64 222.548 215.37 1.069e-10 ***
## Residuals 12 12.40 1.033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(s1))##
## Shapiro-Wilk normality test
##
## data: resid(s1)
## W = 0.92893, p-value = 0.2345shapiro.test(resid(s2))##
## Shapiro-Wilk normality test
##
## data: resid(s2)
## W = 0.93226, p-value = 0.2647shapiro.test(resid(s3))##
## Shapiro-Wilk normality test
##
## data: resid(s3)
## W = 0.98114, p-value = 0.972shapiro.test(resid(s4))##
## Shapiro-Wilk normality test
##
## data: resid(s4)
## W = 0.96446, p-value = 0.7428*Homogeneidad de varianzas**
leveneTest(Muestreo1$RT_Peso_fresco~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.5923 0.2428
## 12leveneTest(Muestreo2$RT_Peso_fresco~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.9572 0.07526 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo3$RT_Peso_fresco~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 3.9532 0.03575 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo4$RT_Peso_fresco~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.2387 0.3387
## 12Pueba de tukey
s1tukey <-HSD.test(Muestreo1$RT_Peso_fresco,Muestreo1$Trat, 12, 0.0882, alpha = 0.05)
s2tukey <-HSD.test(Muestreo2$RT_Peso_fresco,Muestreo2$Trat, 12, 6.988, alpha = 0.05)
s3tukey <-HSD.test(Muestreo3$RT_Peso_fresco,Muestreo3$Trat, 12, 2.226, alpha = 0.05)
s4tukey <-HSD.test(Muestreo4$RT_Peso_fresco,Muestreo4$Trat, 12, 1.033, alpha = 0.05)
s1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0882 12 2.6625 11.15436 0.6234692
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$RT_Peso_fresco std r Min Max Q25 Q50 Q75
## T1 3.47075 0.2578648 4 3.122 3.734 3.37850 3.5135 3.60575
## T2 1.08900 0.1629438 4 0.922 1.298 0.98650 1.0680 1.17050
## T3 3.15050 0.1493106 4 2.927 3.238 3.14300 3.2185 3.22600
## T4 2.93975 0.4873297 4 2.251 3.361 2.78425 3.0735 3.22900
##
## $comparison
## NULL
##
## $groups
## Muestreo1$RT_Peso_fresco groups
## T1 3.47075 a
## T3 3.15050 a
## T4 2.93975 a
## T2 1.08900 b
##
## attr(,"class")
## [1] "group"s2tukey## $statistics
## MSerror Df Mean CV MSD
## 6.988 12 7.363 35.90225 5.549543
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$RT_Peso_fresco std r Min Max Q25 Q50 Q75
## T1 8.84200 2.7575440 4 6.494 12.805 7.36400 8.0345 9.51250
## T2 2.44875 0.2434822 4 2.201 2.762 2.29475 2.4160 2.57000
## T3 10.45900 4.1825588 4 4.284 13.521 9.89175 12.0155 12.58275
## T4 7.70225 1.6723252 4 5.935 9.961 6.96475 7.4565 8.19400
##
## $comparison
## NULL
##
## $groups
## Muestreo2$RT_Peso_fresco groups
## T3 10.45900 a
## T1 8.84200 a
## T4 7.70225 ab
## T2 2.44875 b
##
## attr(,"class")
## [1] "group"s3tukey## $statistics
## MSerror Df Mean CV MSD
## 2.226 12 11.473 13.00426 3.132156
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$RT_Peso_fresco std r Min Max Q25 Q50 Q75
## T1 13.4670 0.6758555 4 12.483 13.958 13.30050 13.7135 13.88000
## T2 3.5880 0.8404864 4 2.917 4.817 3.18250 3.3090 3.71450
## T3 17.2935 1.6845360 4 15.187 19.215 16.51525 17.3860 18.16425
## T4 11.5435 2.2140449 4 9.215 13.958 9.94400 11.5005 13.10000
##
## $comparison
## NULL
##
## $groups
## Muestreo3$RT_Peso_fresco groups
## T3 17.2935 a
## T1 13.4670 b
## T4 11.5435 b
## T2 3.5880 c
##
## attr(,"class")
## [1] "group"s4tukey## $statistics
## MSerror Df Mean CV MSD
## 1.033 12 15.55156 6.53546 2.133688
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$RT_Peso_fresco std r Min Max Q25 Q50 Q75
## T1 16.81800 1.1545998 4 15.223 17.738 16.3450 17.1555 17.62850
## T2 5.37575 0.5681974 4 4.782 5.888 4.9440 5.4165 5.84825
## T3 23.32825 1.4290231 4 21.568 24.957 22.6165 23.3940 24.10575
## T4 16.68425 0.6597991 4 15.822 17.375 16.3920 16.7700 17.06225
##
## $comparison
## NULL
##
## $groups
## Muestreo4$RT_Peso_fresco groups
## T3 23.32825 a
## T1 16.81800 b
## T4 16.68425 b
## T2 5.37575 c
##
## attr(,"class")
## [1] "group"RT_Peso_seco
Determinación de la variable en el muestreo 4
ANOVA
u1 <- aov(RT_Peso_seco~Trat, data = Muestreo1)
u2 <- aov(RT_Peso_seco~Trat, data = Muestreo2)
u3 <- aov(RT_Peso_seco~Trat, data = Muestreo3)
u4 <- aov(RT_Peso_seco~Trat, data = Muestreo4)
anova(u1)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.255710 0.085237 39.772 1.64e-06 ***
## Residuals 12 0.025718 0.002143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(u2)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.22673 0.075577 6.3322 0.008066 **
## Residuals 12 0.14323 0.011935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(u3)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3.04438 1.01479 72.359 5.922e-08 ***
## Residuals 12 0.16829 0.01402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(u4)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 10.130 3.3765 72.879 5.687e-08 ***
## Residuals 12 0.556 0.0463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(u1))##
## Shapiro-Wilk normality test
##
## data: resid(u1)
## W = 0.96667, p-value = 0.7822shapiro.test(resid(u2))##
## Shapiro-Wilk normality test
##
## data: resid(u2)
## W = 0.86349, p-value = 0.02165shapiro.test(resid(u3))##
## Shapiro-Wilk normality test
##
## data: resid(u3)
## W = 0.9194, p-value = 0.165shapiro.test(resid(u4))##
## Shapiro-Wilk normality test
##
## data: resid(u4)
## W = 0.82528, p-value = 0.006021*Homogeneidad de varianzas**
leveneTest(Muestreo1$RT_Peso_seco~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.4348 0.2813
## 12leveneTest(Muestreo2$RT_Peso_seco~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 3.4493 0.05155 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo3$RT_Peso_seco~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.6144 0.2379
## 12leveneTest(Muestreo4$RT_Peso_seco~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 3.8377 0.03881 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Pueba de tukey
u1tukey <-HSD.test(Muestreo1$RT_Peso_seco,Muestreo1$Trat, 12, 0.002143, alpha = 0.05)
u2tukey <-HSD.test(Muestreo2$RT_Peso_seco,Muestreo2$Trat, 12, 0.011935, alpha = 0.05)
u3tukey <-HSD.test(Muestreo3$RT_Peso_seco,Muestreo3$Trat, 12, 0.01402, alpha = 0.05)
u4tukey <-HSD.test(Muestreo4$RT_Peso_seco,Muestreo4$Trat, 12, 0.0463, alpha = 0.05)
u1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.002143 12 0.2714375 17.05459 0.09718334
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$RT_Peso_seco std r Min Max Q25 Q50 Q75
## T1 0.40550 0.06477911 4 0.342 0.493 0.36750 0.3935 0.4315
## T2 0.06800 0.02294922 4 0.039 0.095 0.06000 0.0690 0.0770
## T3 0.33900 0.03787699 4 0.293 0.385 0.32300 0.3390 0.3550
## T4 0.27325 0.04914180 4 0.229 0.331 0.23425 0.2665 0.3055
##
## $comparison
## NULL
##
## $groups
## Muestreo1$RT_Peso_seco groups
## T1 0.40550 a
## T3 0.33900 ab
## T4 0.27325 b
## T2 0.06800 c
##
## attr(,"class")
## [1] "group"u2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.011935 12 0.44175 24.7306 0.2293464
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$RT_Peso_seco std r Min Max Q25 Q50 Q75
## T1 0.54325 0.07347278 4 0.474 0.636 0.49050 0.5315 0.58425
## T2 0.24375 0.03933086 4 0.186 0.274 0.23775 0.2575 0.26350
## T3 0.52550 0.19077474 4 0.242 0.649 0.50000 0.6055 0.63100
## T4 0.45450 0.06634506 4 0.391 0.524 0.40150 0.4515 0.50450
##
## $comparison
## NULL
##
## $groups
## Muestreo2$RT_Peso_seco groups
## T1 0.54325 a
## T3 0.52550 a
## T4 0.45450 ab
## T2 0.24375 b
##
## attr(,"class")
## [1] "group"u3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.01402 12 1.059688 11.17368 0.2485735
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$RT_Peso_seco std r Min Max Q25 Q50 Q75
## T1 1.31800 0.06694774 4 1.248 1.387 1.26825 1.3185 1.36825
## T2 0.37675 0.04431986 4 0.317 0.418 0.35750 0.3860 0.40525
## T3 1.53450 0.18227909 4 1.362 1.792 1.45875 1.4920 1.56775
## T4 1.00950 0.12816266 4 0.892 1.191 0.94450 0.9775 1.04250
##
## $comparison
## NULL
##
## $groups
## Muestreo3$RT_Peso_seco groups
## T3 1.53450 a
## T1 1.31800 a
## T4 1.00950 b
## T2 0.37675 c
##
## attr(,"class")
## [1] "group"u4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0463 12 1.75325 12.27288 0.451722
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$RT_Peso_seco std r Min Max Q25 Q50 Q75
## T1 1.99175 0.40493240 4 1.638 2.573 1.78875 1.878 2.08100
## T2 0.71250 0.06927000 4 0.628 0.782 0.67150 0.720 0.76100
## T3 2.88600 0.11920570 4 2.720 2.978 2.83925 2.923 2.96975
## T4 1.42275 0.04842434 4 1.376 1.467 1.38350 1.424 1.46325
##
## $comparison
## NULL
##
## $groups
## Muestreo4$RT_Peso_seco groups
## T3 2.88600 a
## T1 1.99175 b
## T4 1.42275 c
## T2 0.71250 d
##
## attr(,"class")
## [1] "group"Diferencias estadisticas entre muestreos
Area_foliar
x1 <- aov(Area_foliar~conjunto, data = GENERAL)
anova(x1)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 132127 8808.5 210.33 < 2.2e-16 ***
## Residuals 48 2010 41.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1x1tukey <-HSD.test(GENERAL$Area_foliar,GENERAL$conjunto, 48, 41.9, alpha = 0.05)
x1tukey## $statistics
## MSerror Df Mean CV MSD
## 41.9 48 108.6512 5.957616 16.53571
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$Area_foliar std r Min Max Q25 Q50
## M1T1 66.57400 1.953636 4 64.925 69.223 65.20250 66.0740
## M1T2 52.04325 1.303762 4 50.238 53.351 51.73575 52.2920
## M1T3 63.53700 2.607883 4 59.999 66.252 62.73875 63.9485
## M1T4 55.54200 3.746375 4 50.356 58.346 54.01225 56.7330
## M2T1 92.77500 18.570115 4 66.700 109.500 86.80000 97.4500
## M2T2 67.30000 4.817330 4 61.200 71.900 64.65000 68.0500
## M2T3 92.50000 3.221801 4 88.600 95.700 90.55000 92.8500
## M2T4 85.97500 5.208567 4 81.500 93.300 82.77500 84.5500
## M3T1 161.57500 1.447699 4 159.600 162.800 160.95000 161.9500
## M3T2 77.50000 12.451774 4 61.200 88.600 71.17500 80.1000
## M3T3 154.67500 1.936276 4 151.900 156.300 154.15000 155.2500
## M3T4 136.12500 1.645955 4 134.300 138.000 135.05000 136.1000
## M4T1 181.73250 5.107709 4 174.810 186.170 179.47500 182.9750
## M4T2 103.92500 4.887191 4 98.670 109.750 100.72500 103.6400
## M4T3 180.10775 3.965360 4 175.923 185.483 178.48725 179.5125
## M4T4 166.53250 2.975739 4 163.360 169.360 164.32750 166.7050
## Q75
## M1T1 67.44550
## M1T2 52.59950
## M1T3 64.74675
## M1T4 58.26275
## M2T1 103.42500
## M2T2 70.70000
## M2T3 94.80000
## M2T4 87.75000
## M3T1 162.57500
## M3T2 86.42500
## M3T3 155.77500
## M3T4 137.17500
## M4T1 185.23250
## M4T2 106.84000
## M4T3 181.13300
## M4T4 168.91000
##
## $comparison
## NULL
##
## $groups
## GENERAL$Area_foliar groups
## M4T1 181.73250 a
## M4T3 180.10775 a
## M4T4 166.53250 ab
## M3T1 161.57500 b
## M3T3 154.67500 b
## M3T4 136.12500 c
## M4T2 103.92500 d
## M2T1 92.77500 de
## M2T3 92.50000 de
## M2T4 85.97500 e
## M3T2 77.50000 ef
## M2T2 67.30000 fg
## M1T1 66.57400 fg
## M1T3 63.53700 fg
## M1T4 55.54200 g
## M1T2 52.04325 g
##
## attr(,"class")
## [1] "group"RT_Peso:fresco
t1 <- aov(RT_Peso_fresco~conjunto, data = GENERAL)
anova(t1)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 2691.41 179.427 69.44 < 2.2e-16 ***
## Residuals 48 124.03 2.584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t1tukey <-HSD.test(GENERAL$RT_Peso_fresco,GENERAL$conjunto, 48, 2.584, alpha = 0.05)
t1tukey## $statistics
## MSerror Df Mean CV MSD
## 2.584 48 9.262516 17.35471 4.106409
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$RT_Peso_fresco std r Min Max Q25 Q50 Q75
## M1T1 3.47075 0.2578648 4 3.122 3.734 3.37850 3.5135 3.60575
## M1T2 1.08900 0.1629438 4 0.922 1.298 0.98650 1.0680 1.17050
## M1T3 3.15050 0.1493106 4 2.927 3.238 3.14300 3.2185 3.22600
## M1T4 2.93975 0.4873297 4 2.251 3.361 2.78425 3.0735 3.22900
## M2T1 8.84200 2.7575440 4 6.494 12.805 7.36400 8.0345 9.51250
## M2T2 2.44875 0.2434822 4 2.201 2.762 2.29475 2.4160 2.57000
## M2T3 10.45900 4.1825588 4 4.284 13.521 9.89175 12.0155 12.58275
## M2T4 7.70225 1.6723252 4 5.935 9.961 6.96475 7.4565 8.19400
## M3T1 13.46700 0.6758555 4 12.483 13.958 13.30050 13.7135 13.88000
## M3T2 3.58800 0.8404864 4 2.917 4.817 3.18250 3.3090 3.71450
## M3T3 17.29350 1.6845360 4 15.187 19.215 16.51525 17.3860 18.16425
## M3T4 11.54350 2.2140449 4 9.215 13.958 9.94400 11.5005 13.10000
## M4T1 16.81800 1.1545998 4 15.223 17.738 16.34500 17.1555 17.62850
## M4T2 5.37575 0.5681974 4 4.782 5.888 4.94400 5.4165 5.84825
## M4T3 23.32825 1.4290231 4 21.568 24.957 22.61650 23.3940 24.10575
## M4T4 16.68425 0.6597991 4 15.822 17.375 16.39200 16.7700 17.06225
##
## $comparison
## NULL
##
## $groups
## GENERAL$RT_Peso_fresco groups
## M4T3 23.32825 a
## M3T3 17.29350 b
## M4T1 16.81800 b
## M4T4 16.68425 b
## M3T1 13.46700 bc
## M3T4 11.54350 cd
## M2T3 10.45900 cd
## M2T1 8.84200 de
## M2T4 7.70225 de
## M4T2 5.37575 ef
## M3T2 3.58800 fg
## M1T1 3.47075 fg
## M1T3 3.15050 fg
## M1T4 2.93975 fg
## M2T2 2.44875 fg
## M1T2 1.08900 g
##
## attr(,"class")
## [1] "group"RT_Peso_seco
t2 <- aov(RT_Peso_seco~conjunto, data = GENERAL)
anova(t2)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 35.372 2.35817 126.73 < 2.2e-16 ***
## Residuals 48 0.893 0.01861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t2tukey <-HSD.test(GENERAL$RT_Peso_seco,GENERAL$conjunto, 48, 0.01861, alpha = 0.05)
t2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.01861 48 0.8815312 15.47517 0.348489
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$RT_Peso_seco std r Min Max Q25 Q50 Q75
## M1T1 0.40550 0.06477911 4 0.342 0.493 0.36750 0.3935 0.43150
## M1T2 0.06800 0.02294922 4 0.039 0.095 0.06000 0.0690 0.07700
## M1T3 0.33900 0.03787699 4 0.293 0.385 0.32300 0.3390 0.35500
## M1T4 0.27325 0.04914180 4 0.229 0.331 0.23425 0.2665 0.30550
## M2T1 0.54325 0.07347278 4 0.474 0.636 0.49050 0.5315 0.58425
## M2T2 0.24375 0.03933086 4 0.186 0.274 0.23775 0.2575 0.26350
## M2T3 0.52550 0.19077474 4 0.242 0.649 0.50000 0.6055 0.63100
## M2T4 0.45450 0.06634506 4 0.391 0.524 0.40150 0.4515 0.50450
## M3T1 1.31800 0.06694774 4 1.248 1.387 1.26825 1.3185 1.36825
## M3T2 0.37675 0.04431986 4 0.317 0.418 0.35750 0.3860 0.40525
## M3T3 1.53450 0.18227909 4 1.362 1.792 1.45875 1.4920 1.56775
## M3T4 1.00950 0.12816266 4 0.892 1.191 0.94450 0.9775 1.04250
## M4T1 1.99175 0.40493240 4 1.638 2.573 1.78875 1.8780 2.08100
## M4T2 0.71250 0.06927000 4 0.628 0.782 0.67150 0.7200 0.76100
## M4T3 2.88600 0.11920570 4 2.720 2.978 2.83925 2.9230 2.96975
## M4T4 1.42275 0.04842434 4 1.376 1.467 1.38350 1.4240 1.46325
##
## $comparison
## NULL
##
## $groups
## GENERAL$RT_Peso_seco groups
## M4T3 2.88600 a
## M4T1 1.99175 b
## M3T3 1.53450 c
## M4T4 1.42275 c
## M3T1 1.31800 cd
## M3T4 1.00950 de
## M4T2 0.71250 ef
## M2T1 0.54325 fg
## M2T3 0.52550 fg
## M2T4 0.45450 fg
## M1T1 0.40550 fgh
## M3T2 0.37675 fgh
## M1T3 0.33900 gh
## M1T4 0.27325 gh
## M2T2 0.24375 gh
## M1T2 0.06800 h
##
## attr(,"class")
## [1] "group"Hojas_peso_fresco
t3 <- aov(Hojas_Peso_fresco~conjunto, data = GENERAL)
anova(t3)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 1844.93 122.996 389.09 < 2.2e-16 ***
## Residuals 48 15.17 0.316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t3tukey <-HSD.test(GENERAL$Hojas_Peso_fresco,GENERAL$conjunto, 48, 0.316, alpha = 0.05)
t3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.316 48 9.440297 5.954673 1.436017
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$Hojas_Peso_fresco std r Min Max Q25 Q50
## M1T1 4.68800 0.3633492 4 4.223 5.023 4.49150 4.7530
## M1T2 3.09075 0.1257971 4 2.936 3.219 3.01850 3.1040
## M1T3 4.17550 0.2728864 4 3.902 4.510 3.98525 4.1450
## M1T4 3.76375 0.1719852 4 3.520 3.923 3.72400 3.8060
## M2T1 6.28075 1.0659588 4 4.841 7.234 5.80850 6.5240
## M2T2 4.07075 0.7024127 4 3.138 4.812 3.80025 4.1665
## M2T3 6.72025 0.4411314 4 6.162 7.193 6.50025 6.7630
## M2T4 6.21825 0.4864945 4 5.872 6.917 5.89375 6.0420
## M3T1 16.17300 0.7805583 4 15.232 16.961 15.70000 16.2495
## M3T2 6.72025 0.4411314 4 6.162 7.193 6.50025 6.7630
## M3T3 14.46700 0.4530350 4 13.933 14.994 14.21425 14.4705
## M3T4 14.46700 0.4530350 4 13.933 14.994 14.21425 14.4705
## M4T1 17.91900 0.5463833 4 17.382 18.492 17.49150 17.9010
## M4T2 8.87650 0.7124729 4 8.078 9.531 8.37650 8.9485
## M4T3 17.30550 0.8271693 4 16.284 17.978 16.80750 17.4800
## M4T4 16.10850 0.2006830 4 15.939 16.364 15.95325 16.0655
## Q75
## M1T1 4.94950
## M1T2 3.17625
## M1T3 4.33525
## M1T4 3.84575
## M2T1 6.99625
## M2T2 4.43700
## M2T3 6.98300
## M2T4 6.36650
## M3T1 16.72250
## M3T2 6.98300
## M3T3 14.72325
## M3T4 14.72325
## M4T1 18.32850
## M4T2 9.44850
## M4T3 17.97800
## M4T4 16.22075
##
## $comparison
## NULL
##
## $groups
## GENERAL$Hojas_Peso_fresco groups
## M4T1 17.91900 a
## M4T3 17.30550 ab
## M3T1 16.17300 b
## M4T4 16.10850 b
## M3T3 14.46700 c
## M3T4 14.46700 c
## M4T2 8.87650 d
## M2T3 6.72025 e
## M3T2 6.72025 e
## M2T1 6.28075 e
## M2T4 6.21825 e
## M1T1 4.68800 f
## M1T3 4.17550 fg
## M2T2 4.07075 fg
## M1T4 3.76375 fg
## M1T2 3.09075 g
##
## attr(,"class")
## [1] "group"Hojas_peso_seco
t4 <- aov(Hojas_Peso_seco~conjunto, data = GENERAL)
anova(t4)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 16.4239 1.09493 110.81 < 2.2e-16 ***
## Residuals 48 0.4743 0.00988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t4tukey <-HSD.test(GENERAL$Hojas_Peso_seco,GENERAL$conjunto, 48, 0.00988, alpha = 0.05)
t4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00988 48 0.8371094 11.87398 0.2539185
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$Hojas_Peso_seco std r Min Max Q25 Q50 Q75
## M1T1 0.37125 0.05314367 4 0.311 0.432 0.33800 0.3710 0.40425
## M1T2 0.20800 0.02080064 4 0.189 0.236 0.19425 0.2035 0.21725
## M1T3 0.32775 0.04495461 4 0.285 0.386 0.29700 0.3200 0.35075
## M1T4 0.31600 0.02831960 4 0.286 0.347 0.29575 0.3155 0.33575
## M2T1 0.59850 0.14735332 4 0.387 0.713 0.55500 0.6470 0.69050
## M2T2 0.36725 0.03944933 4 0.311 0.401 0.35600 0.3785 0.38975
## M2T3 0.62525 0.09350356 4 0.515 0.712 0.56450 0.6370 0.69775
## M2T4 0.62175 0.05014230 4 0.581 0.692 0.58850 0.6070 0.64025
## M3T1 1.10425 0.04587937 4 1.059 1.156 1.06950 1.1010 1.13575
## M3T2 0.62525 0.09350356 4 0.515 0.712 0.56450 0.6370 0.69775
## M3T3 1.10375 0.08117214 4 1.003 1.173 1.05550 1.1195 1.16775
## M3T4 1.10375 0.08117214 4 1.003 1.173 1.05550 1.1195 1.16775
## M4T1 1.90650 0.16086537 4 1.727 2.094 1.80500 1.9025 2.00400
## M4T2 0.90075 0.08052484 4 0.799 0.973 0.85525 0.9155 0.96100
## M4T3 1.63875 0.16415516 4 1.485 1.870 1.55700 1.6000 1.68175
## M4T4 1.57500 0.18482604 4 1.376 1.782 1.44725 1.5710 1.69875
##
## $comparison
## NULL
##
## $groups
## GENERAL$Hojas_Peso_seco groups
## M4T1 1.90650 a
## M4T3 1.63875 b
## M4T4 1.57500 b
## M3T1 1.10425 c
## M3T3 1.10375 c
## M3T4 1.10375 c
## M4T2 0.90075 c
## M2T3 0.62525 d
## M3T2 0.62525 d
## M2T4 0.62175 de
## M2T1 0.59850 def
## M1T1 0.37125 efg
## M2T2 0.36725 fg
## M1T3 0.32775 g
## M1T4 0.31600 g
## M1T2 0.20800 g
##
## attr(,"class")
## [1] "group"PA_Longitud
t5 <- aov(PA_Longitud~conjunto, data = GENERAL)
anova(t5)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 1064.41 70.961 47.172 < 2.2e-16 ***
## Residuals 48 72.21 1.504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t5tukey <-HSD.test(GENERAL$PA_Longitud,GENERAL$conjunto, 48, 1.504, alpha = 0.05)
t5tukey## $statistics
## MSerror Df Mean CV MSD
## 1.504 48 11.27937 10.87274 3.132852
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$PA_Longitud std r Min Max Q25 Q50 Q75
## M1T1 7.5725 0.8942548 4 6.99 8.9 7.0725 7.20 7.700
## M1T2 6.2250 0.2362908 4 5.90 6.4 6.1250 6.30 6.400
## M1T3 7.3225 0.3022554 4 6.99 7.7 7.1475 7.30 7.475
## M1T4 6.8000 0.3559026 4 6.30 7.1 6.6750 6.90 7.025
## M2T1 10.4500 3.8336232 4 6.50 15.0 7.7750 10.15 12.825
## M2T2 6.7500 0.4654747 4 6.30 7.2 6.3750 6.75 7.125
## M2T3 11.6250 1.5840349 4 9.60 13.2 10.8000 11.85 12.675
## M2T4 9.6250 0.5123475 4 8.90 10.1 9.5000 9.75 9.875
## M3T1 15.1000 0.5291503 4 14.60 15.8 14.7500 15.00 15.350
## M3T2 8.0000 0.8755950 4 7.20 9.1 7.3500 7.85 8.500
## M3T3 15.4250 0.8539126 4 14.30 16.3 15.0500 15.55 15.925
## M3T4 11.1000 0.8793937 4 10.10 12.1 10.5500 11.10 11.650
## M4T1 17.6000 0.5291503 4 16.90 18.1 17.3500 17.70 17.950
## M4T2 11.5250 0.7675719 4 10.40 12.1 11.3750 11.80 11.950
## M4T3 18.8250 0.8220908 4 17.90 19.7 18.2750 18.85 19.400
## M4T4 16.5250 1.1026483 4 15.20 17.9 16.1750 16.50 16.850
##
## $comparison
## NULL
##
## $groups
## GENERAL$PA_Longitud groups
## M4T3 18.8250 a
## M4T1 17.6000 ab
## M4T4 16.5250 ab
## M3T3 15.4250 b
## M3T1 15.1000 b
## M2T3 11.6250 c
## M4T2 11.5250 c
## M3T4 11.1000 cd
## M2T1 10.4500 cde
## M2T4 9.6250 cdef
## M3T2 8.0000 defg
## M1T1 7.5725 efg
## M1T3 7.3225 efg
## M1T4 6.8000 fg
## M2T2 6.7500 fg
## M1T2 6.2250 g
##
## attr(,"class")
## [1] "group"fin :)