Datos de los 4 muestreos
rm(list=ls())
library(readxl)
GENERAL <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "GENERAL")
Muestreo1<- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 1")
Muestreo2 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 2")
Muestreo3 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 3")
Muestreo4 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "MUESTREO 4")TEMPERATURA
Determinación de la variable en los 4 muestreos
ANOVA
m1 <- aov(Temp~Trat, data = Muestreo1)
m2 <- aov(Temp~Trat, data = Muestreo2)
m3 <- aov(Temp~Trat, data = Muestreo3)
m4 <- aov(Temp~Trat, data = Muestreo4)
anova(m1)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 42.477 14.159 13.123 0.0004262 ***
## Residuals 12 12.948 1.079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m2)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 11.107 3.7025 4.4386 0.0256 *
## Residuals 12 10.010 0.8342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 10.9269 3.6423 6.1322 0.009024 **
## Residuals 12 7.1275 0.5940
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m4)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 20.2119 6.7373 9.0814 0.002058 **
## Residuals 12 8.9025 0.7419
## ---
## 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 = 0.98373, p-value = 0.9862shapiro.test(resid(m2))##
## Shapiro-Wilk normality test
##
## data: resid(m2)
## W = 0.96575, p-value = 0.766shapiro.test(resid(m3))##
## Shapiro-Wilk normality test
##
## data: resid(m3)
## W = 0.93912, p-value = 0.3384shapiro.test(resid(m4))##
## Shapiro-Wilk normality test
##
## data: resid(m4)
## W = 0.94762, p-value = 0.4529En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)## Loading required package: carDatalibrary(carData)
leveneTest(Muestreo1$Temp~Muestreo1$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 2.0138 0.1658
## 12leveneTest(Muestreo2$Temp~Muestreo2$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 0.7285 0.5545
## 12leveneTest(Muestreo3$Temp~Muestreo3$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 0.83 0.5026
## 12leveneTest(Muestreo4$Temp~Muestreo4$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 0.7169 0.5607
## 12En temperatura, todos los datos representan varianzas homogeneas
Prueba 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$Temp,Muestreo1$Trat, 12, 1.079, alpha = 0.05)
m2tukey <-HSD.test(Muestreo2$Temp,Muestreo2$Trat, 12, 0.8342, alpha = 0.05)
m3tukey <-HSD.test(Muestreo3$Temp,Muestreo3$Trat, 12, 0.5940, alpha = 0.05)
m4tukey <-HSD.test(Muestreo4$Temp,Muestreo4$Trat, 12, 0.7419, alpha = 0.05)
m1tukey## $statistics
## MSerror Df Mean CV MSD
## 1.079 12 19.88125 5.224768 2.180678
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$Temp std r Min Max Q25 Q50 Q75
## T1 18.225 0.6800735 4 17.3 18.9 17.975 18.35 18.600
## T2 21.950 0.9882645 4 20.9 23.1 21.275 21.90 22.575
## T3 18.350 0.7416198 4 17.4 19.2 18.075 18.40 18.675
## T4 21.000 1.5253415 4 19.3 22.8 20.050 20.95 21.900
##
## $comparison
## NULL
##
## $groups
## Muestreo1$Temp groups
## T2 21.950 a
## T4 21.000 a
## T3 18.350 b
## T1 18.225 b
##
## attr(,"class")
## [1] "group"m2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.8342 12 17.9875 5.077668 1.917414
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$Temp std r Min Max Q25 Q50 Q75
## T1 17.225 1.2553220 4 16.0 18.9 16.450 17.00 17.775
## T2 19.225 0.5188127 4 18.6 19.7 18.900 19.30 19.625
## T3 17.225 0.9215024 4 16.3 18.5 16.825 17.05 17.450
## T4 18.275 0.8015610 4 17.1 18.9 18.150 18.55 18.675
##
## $comparison
## NULL
##
## $groups
## Muestreo2$Temp groups
## T2 19.225 a
## T4 18.275 ab
## T1 17.225 b
## T3 17.225 b
##
## attr(,"class")
## [1] "group"m3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.594 12 17.76875 4.337469 1.617983
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$Temp std r Min Max Q25 Q50 Q75
## T1 17.400 0.4546061 4 16.8 17.9 17.250 17.45 17.600
## T2 19.175 0.9464847 4 18.4 20.5 18.550 18.90 19.525
## T3 17.450 0.7767453 4 16.5 18.4 17.175 17.45 17.725
## T4 17.050 0.8185353 4 16.3 17.9 16.375 17.00 17.675
##
## $comparison
## NULL
##
## $groups
## Muestreo3$Temp groups
## T2 19.175 a
## T3 17.450 b
## T1 17.400 b
## T4 17.050 b
##
## attr(,"class")
## [1] "group"m4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.7419 12 17.06875 5.046276 1.808229
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$Temp std r Min Max Q25 Q50 Q75
## T1 16.625 0.8845903 4 15.9 17.9 16.125 16.35 16.85
## T2 19.000 0.7023769 4 18.3 19.7 18.450 19.00 19.55
## T3 16.225 1.1412712 4 15.1 17.7 15.475 16.05 16.80
## T4 16.425 0.6238322 4 15.6 17.0 16.125 16.55 16.85
##
## $comparison
## NULL
##
## $groups
## Muestreo4$Temp groups
## T2 19.000 a
## T1 16.625 b
## T4 16.425 b
## T3 16.225 b
##
## attr(,"class")
## [1] "group"**Representacion de las diferencias estadisticas dentro de la variable temperatura en los cuatro muestreos
Estomas abiertos
Determinación de la variable en los 4 muestreos
ANOVA
n1 <- aov(E_Abierto~Trat, data = Muestreo1)
n2 <- aov(E_Abierto~Trat, data = Muestreo2)
n3 <- aov(E_Abierto~Trat, data = Muestreo3)
n4 <- aov(E_Abierto~Trat, data = Muestreo4)
anova(n1)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 111.19 37.063 21.434 4.127e-05 ***
## Residuals 12 20.75 1.729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(n2)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 102.19 34.062 10.973 0.0009358 ***
## Residuals 12 37.25 3.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(n3)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 307.69 102.562 57.918 2.072e-07 ***
## Residuals 12 21.25 1.771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(n4)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 770.19 256.729 18.311 8.96e-05 ***
## Residuals 12 168.25 14.021
## ---
## 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 = 0.92462, p-value = 0.2001shapiro.test(resid(n2))##
## Shapiro-Wilk normality test
##
## data: resid(n2)
## W = 0.95152, p-value = 0.5142shapiro.test(resid(n3))##
## Shapiro-Wilk normality test
##
## data: resid(n3)
## W = 0.95234, p-value = 0.5277shapiro.test(resid(n4))##
## Shapiro-Wilk normality test
##
## data: resid(n4)
## W = 0.96748, p-value = 0.7963Todos los datos cumplen los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$E_Abierto~Muestreo1$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 1.4333 0.2817
## 12leveneTest(Muestreo2$E_Abierto~Muestreo2$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 1.0468 0.4074
## 12leveneTest(Muestreo3$E_Abierto~Muestreo3$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 0.1134 0.9506
## 12leveneTest(Muestreo4$E_Abierto~Muestreo4$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 4.602 0.02297 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1todos los supuestos se cumplen (casi)
Pueba de tukey
library(agricolae)
library(dplyr)
n1tukey <-HSD.test(Muestreo1$E_Abierto,Muestreo1$Trat, 12, 1.729, alpha = 0.05)
n2tukey <-HSD.test(Muestreo2$E_Abierto,Muestreo2$Trat, 12, 3.104, alpha = 0.05)
n3tukey <-HSD.test(Muestreo3$E_Abierto,Muestreo3$Trat, 12, 1.771, alpha = 0.05)
n4tukey <-HSD.test(Muestreo4$E_Abierto,Muestreo4$Trat, 12, 14.021, alpha = 0.05)
n1tukey## $statistics
## MSerror Df Mean CV MSD
## 1.729 12 6.5625 20.03679 2.760439
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$E_Abierto std r Min Max Q25 Q50 Q75
## T1 10.00 2.1602469 4 7 12 9.25 10.5 11.25
## T2 4.00 0.8164966 4 3 5 3.75 4.0 4.25
## T3 8.25 0.9574271 4 7 9 7.75 8.5 9.00
## T4 4.00 0.8164966 4 3 5 3.75 4.0 4.25
##
## $comparison
## NULL
##
## $groups
## Muestreo1$E_Abierto groups
## T1 10.00 a
## T3 8.25 a
## T2 4.00 b
## T4 4.00 b
##
## attr(,"class")
## [1] "group"n2tukey## $statistics
## MSerror Df Mean CV MSD
## 3.104 12 10.6875 16.48484 3.698636
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$E_Abierto std r Min Max Q25 Q50 Q75
## T1 10.75 1.7078251 4 9 13 9.75 10.5 11.50
## T2 6.75 0.9574271 4 6 8 6.00 6.5 7.25
## T3 11.50 1.2909944 4 10 13 10.75 11.5 12.25
## T4 13.75 2.6299556 4 10 16 13.00 14.5 15.25
##
## $comparison
## NULL
##
## $groups
## Muestreo2$E_Abierto groups
## T4 13.75 a
## T3 11.50 a
## T1 10.75 a
## T2 6.75 b
##
## attr(,"class")
## [1] "group"n3tukey## $statistics
## MSerror Df Mean CV MSD
## 1.771 12 14.0625 9.46339 2.793766
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$E_Abierto std r Min Max Q25 Q50 Q75
## T1 17.25 1.500000 4 15 18 17.25 18.0 18.00
## T2 6.50 1.290994 4 5 8 5.75 6.5 7.25
## T3 16.25 1.258306 4 15 18 15.75 16.0 16.50
## T4 16.25 1.258306 4 15 18 15.75 16.0 16.50
##
## $comparison
## NULL
##
## $groups
## Muestreo3$E_Abierto groups
## T1 17.25 a
## T3 16.25 a
## T4 16.25 a
## T2 6.50 b
##
## attr(,"class")
## [1] "group"n4tukey## $statistics
## MSerror Df Mean CV MSD
## 14.021 12 19.1875 19.51511 7.860863
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$E_Abierto std r Min Max Q25 Q50 Q75
## T1 22.00 4.5460606 4 18 28 18.75 21.0 24.25
## T2 7.25 0.9574271 4 6 8 6.75 7.5 8.00
## T3 24.25 4.9916597 4 19 29 20.50 24.5 28.25
## T4 23.25 3.0956959 4 19 26 22.00 24.0 25.25
##
## $comparison
## NULL
##
## $groups
## Muestreo4$E_Abierto groups
## T3 24.25 a
## T4 23.25 a
## T1 22.00 a
## T2 7.25 b
##
## attr(,"class")
## [1] "group"Estomas cerrados
Determinación de la variable en los 4 muestreos
ANOVA
o1 <- aov(E_Cerrado~Trat, data = Muestreo1)
o2 <- aov(E_Cerrado~Trat, data = Muestreo2)
o3 <- aov(E_Cerrado~Trat, data = Muestreo3)
o4 <- aov(E_Cerrado~Trat, data = Muestreo4)
anova(o1)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 74.25 24.750 4.8293 0.01981 *
## Residuals 12 61.50 5.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(o2)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 31.5 10.500 0.4675 0.7104
## Residuals 12 269.5 22.458anova(o3)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 39.187 13.0625 7.9367 0.003504 **
## Residuals 12 19.750 1.6458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(o4)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 94.187 31.396 2.0228 0.1645
## Residuals 12 186.250 15.521Prueba de normalidad de shapiro
shapiro.test(resid(o1))##
## Shapiro-Wilk normality test
##
## data: resid(o1)
## W = 0.97137, p-value = 0.8603shapiro.test(resid(o2))##
## Shapiro-Wilk normality test
##
## data: resid(o2)
## W = 0.94747, p-value = 0.4508shapiro.test(resid(o3))##
## Shapiro-Wilk normality test
##
## data: resid(o3)
## W = 0.89228, p-value = 0.06054shapiro.test(resid(o4))##
## Shapiro-Wilk normality test
##
## data: resid(o4)
## W = 0.9495, p-value = 0.4819se cumplen todos los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$E_Cerrado~Muestreo1$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 0.873 0.482
## 12leveneTest(Muestreo2$E_Cerrado~Muestreo2$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 1.0725 0.3974
## 12leveneTest(Muestreo3$E_Cerrado~Muestreo3$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 0.55 0.6577
## 12leveneTest(Muestreo4$E_Cerrado~Muestreo4$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 0.2875 0.8336
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
o1tukey <-HSD.test(Muestreo1$E_Cerrado,Muestreo1$Trat, 12, 5.125, alpha = 0.05)
o2tukey <-HSD.test(Muestreo2$E_Cerrado,Muestreo2$Trat, 12, 22.458, alpha = 0.05)
o3tukey <-HSD.test(Muestreo3$E_Cerrado,Muestreo3$Trat, 12, 1.6458, alpha = 0.05)
o4tukey <-HSD.test(Muestreo4$E_Cerrado,Muestreo4$Trat, 12, 15.521, alpha = 0.05)
o1tukey## $statistics
## MSerror Df Mean CV MSD
## 5.125 12 29.375 7.706711 4.752561
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$E_Cerrado std r Min Max Q25 Q50 Q75
## T1 26.25 3.403430 4 23 31 24.50 25.5 27.25
## T2 31.50 1.290994 4 30 33 30.75 31.5 32.25
## T3 28.50 2.081666 4 26 31 27.50 28.5 29.50
## T4 31.25 1.707825 4 29 33 30.50 31.5 32.25
##
## $comparison
## NULL
##
## $groups
## Muestreo1$E_Cerrado groups
## T2 31.50 a
## T4 31.25 a
## T3 28.50 ab
## T1 26.25 b
##
## attr(,"class")
## [1] "group"o2tukey## $statistics
## MSerror Df Mean CV MSD
## 22.458 12 26.75 17.71584 9.948699
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$E_Cerrado std r Min Max Q25 Q50 Q75
## T1 27.25 4.787136 4 22 33 24.25 27.0 30.00
## T2 26.00 3.741657 4 22 31 24.25 25.5 27.25
## T3 28.75 4.031129 4 23 32 27.50 30.0 31.25
## T4 25.00 6.055301 4 19 32 20.50 24.5 29.00
##
## $comparison
## NULL
##
## $groups
## Muestreo2$E_Cerrado groups
## T3 28.75 a
## T1 27.25 a
## T2 26.00 a
## T4 25.00 a
##
## attr(,"class")
## [1] "group"o3tukey## $statistics
## MSerror Df Mean CV MSD
## 1.6458 12 25.0625 5.118753 2.693204
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$E_Cerrado std r Min Max Q25 Q50 Q75
## T1 24.50 1.290994 4 23 26 23.75 24.5 25.25
## T2 22.75 1.707825 4 21 25 21.75 22.5 23.50
## T3 26.50 1.000000 4 25 27 26.50 27.0 27.00
## T4 26.50 1.000000 4 25 27 26.50 27.0 27.00
##
## $comparison
## NULL
##
## $groups
## Muestreo3$E_Cerrado groups
## T3 26.50 a
## T4 26.50 a
## T1 24.50 ab
## T2 22.75 b
##
## attr(,"class")
## [1] "group"o4tukey## $statistics
## MSerror Df Mean CV MSD
## 15.521 12 24.8125 15.87776 8.270668
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$E_Cerrado std r Min Max Q25 Q50 Q75
## T1 23.75 4.645787 4 19 30 21.25 23.0 25.50
## T2 29.00 4.320494 4 23 33 27.50 30.0 31.50
## T3 23.25 3.862210 4 21 29 21.00 21.5 23.75
## T4 23.25 2.629956 4 21 27 21.75 22.5 24.00
##
## $comparison
## NULL
##
## $groups
## Muestreo4$E_Cerrado groups
## T2 29.00 a
## T1 23.75 a
## T3 23.25 a
## T4 23.25 a
##
## attr(,"class")
## [1] "group"Estomas totales
Determinación de la variable en los 4 muestreos
ANOVA
p1 <- aov(E_Total~Trat, data = Muestreo1)
p2 <- aov(E_Total~Trat, data = Muestreo2)
p3 <- aov(E_Total~Trat, data = Muestreo3)
p4 <- aov(E_Total~Trat, data = Muestreo4)
anova(p1)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 5.6875 1.8958 0.728 0.5547
## Residuals 12 31.2500 2.6042anova(p2)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 127.69 42.562 1.2153 0.3464
## Residuals 12 420.25 35.021anova(p3)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 522.75 174.250 48.628 5.466e-07 ***
## Residuals 12 43.00 3.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(p4)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 326.5 108.833 4.3899 0.02645 *
## Residuals 12 297.5 24.792
## ---
## 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 = 0.92768, p-value = 0.224shapiro.test(resid(p2))##
## Shapiro-Wilk normality test
##
## data: resid(p2)
## W = 0.96291, p-value = 0.7147shapiro.test(resid(p3))##
## Shapiro-Wilk normality test
##
## data: resid(p3)
## W = 0.94324, p-value = 0.3906shapiro.test(resid(p4))##
## Shapiro-Wilk normality test
##
## data: resid(p4)
## W = 0.94194, p-value = 0.3735Se cumplen los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$E_Total~Muestreo1$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 0.1652 0.9178
## 12leveneTest(Muestreo2$E_Total~Muestreo2$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 0.7303 0.5535
## 12leveneTest(Muestreo3$E_Total~Muestreo3$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 0.6168 0.6172
## 12leveneTest(Muestreo4$E_Total~Muestreo4$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 2.1701 0.1446
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
p1tukey <-HSD.test(Muestreo1$E_Total,Muestreo1$Trat, 12, 2.6042, alpha = 0.05)
p2tukey <-HSD.test(Muestreo2$E_Total,Muestreo2$Trat, 12, 35.021, alpha = 0.05)
p3tukey <-HSD.test(Muestreo3$E_Total,Muestreo3$Trat, 12, 3.583, alpha = 0.05)
p4tukey <-HSD.test(Muestreo4$E_Total,Muestreo4$Trat, 12, 24.792, alpha = 0.05)
p1tukey## $statistics
## MSerror Df Mean CV MSD
## 2.6042 12 35.9375 4.490444 3.387801
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$E_Total std r Min Max Q25 Q50 Q75
## T1 36.25 1.500000 4 35 38 35.00 36.0 37.25
## T2 35.50 1.290994 4 34 37 34.75 35.5 36.25
## T3 36.75 1.892969 4 34 38 36.25 37.5 38.00
## T4 35.25 1.707825 4 33 37 34.50 35.5 36.25
##
## $comparison
## NULL
##
## $groups
## Muestreo1$E_Total groups
## T3 36.75 a
## T1 36.25 a
## T2 35.50 a
## T4 35.25 a
##
## attr(,"class")
## [1] "group"p2tukey## $statistics
## MSerror Df Mean CV MSD
## 35.021 12 37.4375 15.80729 12.42353
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$E_Total std r Min Max Q25 Q50 Q75
## T1 38.00 6.324555 4 32 46 33.50 37.0 41.50
## T2 32.75 4.573474 4 28 39 31.00 32.0 33.75
## T3 40.25 4.787136 4 34 44 37.75 41.5 44.00
## T4 38.75 7.500000 4 29 46 35.00 40.0 43.75
##
## $comparison
## NULL
##
## $groups
## Muestreo2$E_Total groups
## T3 40.25 a
## T4 38.75 a
## T1 38.00 a
## T2 32.75 a
##
## attr(,"class")
## [1] "group"p3tukey## $statistics
## MSerror Df Mean CV MSD
## 3.583 12 39.125 4.838036 3.973783
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$E_Total std r Min Max Q25 Q50 Q75
## T1 41.75 2.629956 4 38 44 41.00 42.5 43.25
## T2 29.25 1.258306 4 28 31 28.75 29.0 29.50
## T3 42.75 1.707825 4 41 45 41.75 42.5 43.50
## T4 42.75 1.707825 4 41 45 41.75 42.5 43.50
##
## $comparison
## NULL
##
## $groups
## Muestreo3$E_Total groups
## T3 42.75 a
## T4 42.75 a
## T1 41.75 a
## T2 29.25 b
##
## attr(,"class")
## [1] "group"p4tukey## $statistics
## MSerror Df Mean CV MSD
## 24.792 12 44 11.31626 10.45289
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$E_Total std r Min Max Q25 Q50 Q75
## T1 45.75 3.947573 4 40 49 45.25 47.0 47.50
## T2 36.25 4.112988 4 31 41 34.75 36.5 38.00
## T3 47.50 7.937254 4 40 58 42.25 46.0 51.25
## T4 46.50 1.914854 4 44 48 45.50 47.0 48.00
##
## $comparison
## NULL
##
## $groups
## Muestreo4$E_Total groups
## T3 47.50 a
## T4 46.50 ab
## T1 45.75 ab
## T2 36.25 b
##
## attr(,"class")
## [1] "group"Contenido relativo de clorofilas
Determinación de la variable en los 4 muestreos
ANOVA
q1 <- aov(CRC~Trat, data = Muestreo1)
q2 <- aov(CRC~Trat, data = Muestreo2)
q3 <- aov(CRC~Trat, data = Muestreo3)
q4 <- aov(CRC~Trat, data = Muestreo4)
anova(q1)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 319.07 106.358 126.8 2.366e-09 ***
## Residuals 12 10.07 0.839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q2)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 563.60 187.87 34.987 3.266e-06 ***
## Residuals 12 64.44 5.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q3)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 353.15 117.715 44.47 8.93e-07 ***
## Residuals 12 31.77 2.647
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q4)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 299.502 99.834 74.434 5.045e-08 ***
## Residuals 12 16.095 1.341
## ---
## 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 = 0.94647, p-value = 0.4359shapiro.test(resid(q2))##
## Shapiro-Wilk normality test
##
## data: resid(q2)
## W = 0.95547, p-value = 0.581shapiro.test(resid(q3))##
## Shapiro-Wilk normality test
##
## data: resid(q3)
## W = 0.97622, p-value = 0.9265shapiro.test(resid(q4))##
## Shapiro-Wilk normality test
##
## data: resid(q4)
## W = 0.96439, p-value = 0.7416Se cumplen todos los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$CRC~Muestreo1$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 0.9071 0.4664
## 12leveneTest(Muestreo2$CRC~Muestreo2$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 1.2515 0.3346
## 12leveneTest(Muestreo3$CRC~Muestreo3$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 0.4953 0.6923
## 12leveneTest(Muestreo4$CRC~Muestreo4$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 1.9169 0.1807
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
q1tukey <-HSD.test(Muestreo1$CRC,Muestreo1$Trat, 12, 0.839, alpha = 0.05)
q2tukey <-HSD.test(Muestreo2$CRC,Muestreo2$Trat, 12, 5.37, alpha = 0.05)
q3tukey <-HSD.test(Muestreo3$CRC,Muestreo3$Trat, 12, 2.647, alpha = 0.05)
q4tukey <-HSD.test(Muestreo4$CRC,Muestreo4$Trat, 12, 1.341, alpha = 0.05)
q1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.839 12 29.6875 3.085371 1.922922
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$CRC std r Min Max Q25 Q50 Q75
## T1 34.750 0.7852813 4 33.8 35.7 34.400 34.75 35.100
## T2 24.700 0.6683313 4 23.8 25.3 24.400 24.85 25.150
## T3 33.475 1.3793114 4 31.5 34.6 33.075 33.90 34.300
## T4 25.825 0.6238322 4 25.2 26.5 25.350 25.80 26.275
##
## $comparison
## NULL
##
## $groups
## Muestreo1$CRC groups
## T1 34.750 a
## T3 33.475 a
## T4 25.825 b
## T2 24.700 b
##
## attr(,"class")
## [1] "group"q2tukey## $statistics
## MSerror Df Mean CV MSD
## 5.37 12 36.6375 6.325011 4.864832
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$CRC std r Min Max Q25 Q50 Q75
## T1 42.250 2.489310 4 39.7 45.5 40.750 41.90 43.400
## T2 26.650 2.145538 4 25.1 29.7 25.175 25.90 27.375
## T3 38.625 3.017035 4 34.9 41.5 36.850 39.05 40.825
## T4 39.025 1.255322 4 37.5 40.4 38.325 39.10 39.800
##
## $comparison
## NULL
##
## $groups
## Muestreo2$CRC groups
## T1 42.250 a
## T4 39.025 a
## T3 38.625 a
## T2 26.650 b
##
## attr(,"class")
## [1] "group"q3tukey## $statistics
## MSerror Df Mean CV MSD
## 2.647 12 30.875 5.269507 3.415527
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$CRC std r Min Max Q25 Q50 Q75
## T1 34.550 1.438749 4 32.9 35.9 33.575 34.70 35.675
## T2 22.975 1.936276 4 20.2 24.4 22.375 23.65 24.250
## T3 34.300 1.009950 4 33.0 35.1 33.750 34.55 35.100
## T4 31.675 1.936276 4 30.1 34.5 30.775 31.05 31.950
##
## $comparison
## NULL
##
## $groups
## Muestreo3$CRC groups
## T1 34.550 a
## T3 34.300 a
## T4 31.675 a
## T2 22.975 b
##
## attr(,"class")
## [1] "group"q4tukey## $statistics
## MSerror Df Mean CV MSD
## 1.341 12 26.9375 4.298898 2.431057
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$CRC std r Min Max Q25 Q50 Q75
## T1 30.575 1.1295279 4 28.9 31.3 30.400 31.05 31.225
## T2 19.550 0.8185353 4 18.5 20.5 19.325 19.60 19.825
## T3 28.950 1.7058722 4 27.0 30.9 27.900 28.95 30.000
## T4 28.675 0.7135592 4 27.9 29.5 28.200 28.65 29.125
##
## $comparison
## NULL
##
## $groups
## Muestreo4$CRC groups
## T1 30.575 a
## T3 28.950 a
## T4 28.675 a
## T2 19.550 b
##
## attr(,"class")
## [1] "group"VARIABLES ASOCIADAS AL ESTADO HIDRICO
Contenido Relativo de agua
Determinación de la variable en los 4 muestreos
ANOVA
r1 <- aov(CRA~Trat, data = Muestreo1)
r2 <- aov(CRA~Trat, data = Muestreo2)
r3 <- aov(CRA~Trat, data = Muestreo3)
r4 <- aov(CRA~Trat, data = Muestreo4)
anova(r1)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 760.15 253.383 16.477 0.0001486 ***
## Residuals 12 184.53 15.378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r2)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1072.24 357.41 22.171 3.485e-05 ***
## Residuals 12 193.45 16.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r3)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2655.73 885.24 25.945 1.568e-05 ***
## Residuals 12 409.44 34.12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r4)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2941.79 980.60 112.58 4.715e-09 ***
## Residuals 12 104.53 8.71
## ---
## 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 = 0.91972, p-value = 0.1669shapiro.test(resid(r2))##
## Shapiro-Wilk normality test
##
## data: resid(r2)
## W = 0.96131, p-value = 0.6856shapiro.test(resid(r3))##
## Shapiro-Wilk normality test
##
## data: resid(r3)
## W = 0.98513, p-value = 0.9914shapiro.test(resid(r4))##
## Shapiro-Wilk normality test
##
## data: resid(r4)
## W = 0.95345, p-value = 0.5462Se cumplen todos los supuestos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$CRA~Muestreo1$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 2.6384 0.09733 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo2$CRA~Muestreo2$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 0.047 0.9858
## 12leveneTest(Muestreo3$CRA~Muestreo3$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 0.9474 0.4485
## 12leveneTest(Muestreo4$CRA~Muestreo4$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 1.6976 0.2204
## 12Se cumplen todos los supuestos
Pueba de tukey
library(agricolae)
library(dplyr)
r1tukey <-HSD.test(Muestreo1$CRA,Muestreo1$Trat, 12, 15.378, alpha = 0.05)
r2tukey <-HSD.test(Muestreo2$CRA,Muestreo2$Trat, 12, 16.12, alpha = 0.05)
r3tukey <-HSD.test(Muestreo3$CRA,Muestreo3$Trat, 12, 34.12, alpha = 0.05)
r4tukey <-HSD.test(Muestreo4$CRA,Muestreo4$Trat, 12, 8.71, alpha = 0.05)
r1tukey## $statistics
## MSerror Df Mean CV MSD
## 15.378 12 76.08813 5.153865 8.23248
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$CRA std r Min Max Q25 Q50 Q75
## T1 84.20468 3.542397 4 80.47337 87.86127 81.59375 84.24203 86.85296
## T2 65.56335 5.423975 4 60.59322 71.42857 61.14134 65.11580 69.53782
## T3 79.58223 3.087446 4 75.37688 82.60870 78.42755 80.17166 81.32633
## T4 75.00228 3.163968 4 71.17904 78.57143 73.27945 75.12933 76.85216
##
## $comparison
## NULL
##
## $groups
## Muestreo1$CRA groups
## T1 84.20468 a
## T3 79.58223 ab
## T4 75.00228 b
## T2 65.56335 c
##
## attr(,"class")
## [1] "group"r2tukey## $statistics
## MSerror Df Mean CV MSD
## 16.12 12 79.77192 5.033064 8.428752
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$CRA std r Min Max Q25 Q50 Q75
## T1 88.14420 3.192775 4 85.31469 91.78082 85.54100 87.74065 90.34385
## T2 66.88609 4.913944 4 60.91371 72.92818 65.09545 66.85124 68.64188
## T3 85.32531 3.705073 4 81.76101 89.50617 82.45948 85.01704 87.88287
## T4 78.73208 4.051680 4 72.95918 82.12291 77.51399 79.92311 81.14119
##
## $comparison
## NULL
##
## $groups
## Muestreo2$CRA groups
## T1 88.14420 a
## T3 85.32531 ab
## T4 78.73208 b
## T2 66.88609 c
##
## attr(,"class")
## [1] "group"r3tukey## $statistics
## MSerror Df Mean CV MSD
## 34.12 12 82.18952 7.107028 12.26268
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$CRA std r Min Max Q25 Q50 Q75
## T1 92.14515 6.904737 4 82.78146 99.31507 89.97017 93.24203 95.41700
## T2 60.34528 7.479434 4 54.27350 69.94536 54.47747 58.58114 64.44896
## T3 91.04734 3.621355 4 88.23529 96.32353 89.02311 89.81527 91.83950
## T4 85.22032 4.444089 4 80.85106 91.11111 82.45766 84.45956 87.22222
##
## $comparison
## NULL
##
## $groups
## Muestreo3$CRA groups
## T1 92.14515 a
## T3 91.04734 a
## T4 85.22032 a
## T2 60.34528 b
##
## attr(,"class")
## [1] "group"r4tukey## $statistics
## MSerror Df Mean CV MSD
## 8.71 12 83.70869 3.525645 6.195692
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$CRA std r Min Max Q25 Q50 Q75
## T1 89.81746 1.372111 4 88.46154 91.66667 89.01879 89.57083 90.36950
## T2 60.29217 4.664368 4 54.16667 65.21739 58.32840 60.89230 62.85607
## T3 92.45510 1.390754 4 91.04478 94.26752 91.59681 92.25405 93.11233
## T4 92.27002 3.044487 4 88.19444 95.52239 91.27938 92.68162 93.67226
##
## $comparison
## NULL
##
## $groups
## Muestreo4$CRA groups
## T3 92.45510 a
## T4 92.27002 a
## T1 89.81746 a
## T2 60.29217 b
##
## attr(,"class")
## [1] "group"Perdida de electrolitos
Determinación de la variable en el muestreo 4
ANOVA
s4 <- aov(`% PE`~Trat, data = Muestreo4)
anova(s4)## Analysis of Variance Table
##
## Response: % PE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2889.92 963.31 127.38 2.305e-09 ***
## Residuals 12 90.75 7.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(s4))##
## Shapiro-Wilk normality test
##
## data: resid(s4)
## W = 0.81811, p-value = 0.004787*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo4$`% PE`~Muestreo4$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 3.1694 0.06376 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Pueba de tukey
library(agricolae)
library(dplyr)
s4tukey <-HSD.test(Muestreo4$`% PE`,Muestreo4$Trat, 12, 7.56, alpha = 0.05)
s4tukey## $statistics
## MSerror Df Mean CV MSD
## 7.56 12 21.68116 12.68173 5.772203
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$`% PE` std r Min Max Q25 Q50 Q75
## T1 14.01317 0.579645 4 13.19290 14.46908 13.81387 14.19535 14.39465
## T2 44.93670 5.214482 4 39.73974 52.13675 42.43720 43.93515 46.43465
## T3 13.05812 1.101852 4 11.60896 14.20765 12.58578 13.20793 13.68026
## T4 14.71663 1.228119 4 13.78601 16.50485 14.00039 14.28784 15.00408
##
## $comparison
## NULL
##
## $groups
## Muestreo4$`% PE` groups
## T2 44.93670 a
## T4 14.71663 b
## T1 14.01317 b
## T3 13.05812 b
##
## attr(,"class")
## [1] "group"Densidad estomatica de abiertos/totales
ANOVA
y1 <- aov(densidad_estomatica~Trat, data = Muestreo1)
y2 <- aov(densidad_estomatica~Trat, data = Muestreo2)
y3 <- aov(densidad_estomatica~Trat, data = Muestreo3)
y4 <- aov(densidad_estomatica~Trat, data = Muestreo4)
anova(y1)## Analysis of Variance Table
##
## Response: densidad_estomatica
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 819.08 273.027 17.571 0.0001093 ***
## Residuals 12 186.46 15.539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(y2)## Analysis of Variance Table
##
## Response: densidad_estomatica
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 457.87 152.624 14.022 0.0003154 ***
## Residuals 12 130.62 10.885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(y3)## Analysis of Variance Table
##
## Response: densidad_estomatica
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 880.50 293.499 40.142 1.559e-06 ***
## Residuals 12 87.74 7.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(y4)## Analysis of Variance Table
##
## Response: densidad_estomatica
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2607.25 869.08 23.957 2.356e-05 ***
## Residuals 12 435.32 36.28
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(y1))##
## Shapiro-Wilk normality test
##
## data: resid(y1)
## W = 0.91197, p-value = 0.1252shapiro.test(resid(y2))##
## Shapiro-Wilk normality test
##
## data: resid(y2)
## W = 0.94194, p-value = 0.3735shapiro.test(resid(y3))##
## Shapiro-Wilk normality test
##
## data: resid(y3)
## W = 0.96831, p-value = 0.8106shapiro.test(resid(y4))##
## Shapiro-Wilk normality test
##
## data: resid(y4)
## W = 0.97646, p-value = 0.9292Homogeneidad de varianzas
library(car)
library(carData)
leveneTest(Muestreo1$densidad_estomatica~Muestreo1$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 1.6954 0.2208
## 12leveneTest(Muestreo2$densidad_estomatica~Muestreo2$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 1.6494 0.2304
## 12leveneTest(Muestreo3$densidad_estomatica~Muestreo3$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 5.3333 0.01444 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo4$densidad_estomatica~Muestreo4$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 0.8234 0.5058
## 12Pueba de tukey
library(agricolae)
library(dplyr)
y1tukey <-HSD.test(Muestreo1$densidad_estomatica,Muestreo1$Trat, 12, 15.539, alpha = 0.05)
y2tukey <-HSD.test(Muestreo2$densidad_estomatica,Muestreo2$Trat, 12, 10.885, alpha = 0.05)
y3tukey <-HSD.test(Muestreo3$densidad_estomatica,Muestreo3$Trat, 12, 7.312, alpha = 0.05)
y4tukey <-HSD.test(Muestreo4$densidad_estomatica,Muestreo4$Trat, 12, 36.28, alpha = 0.05)
y1tukey## $statistics
## MSerror Df Mean CV MSD
## 15.539 12 18.21217 21.64461 8.275462
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$densidad_estomatica std r Min Max Q25
## T1 27.75198 6.691758 4 18.421053 34.28571 26.03383
## T2 11.25779 2.259444 4 8.823529 14.28571 10.31399
## T3 22.48975 2.734203 4 18.421053 24.32432 22.25232
## T4 11.34916 2.189521 4 8.333333 13.51351 10.65476
## Q50 Q75
## T1 29.15058 30.86873
## T2 10.96096 11.90476
## T3 23.60681 23.84424
## T4 11.77489 12.46929
##
## $comparison
## NULL
##
## $groups
## Muestreo1$densidad_estomatica groups
## T1 27.75198 a
## T3 22.48975 a
## T4 11.34916 b
## T2 11.25779 b
##
## attr(,"class")
## [1] "group"y2tukey## $statistics
## MSerror Df Mean CV MSD
## 10.885 12 28.36903 11.62973 6.926199
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$densidad_estomatica std r Min Max Q25 Q50
## T1 28.37036 2.055154 4 26.47059 31.25000 27.24265 27.88043
## T2 20.64160 1.382671 4 18.75000 21.87500 20.07212 20.97070
## T3 28.70304 2.912797 4 25.64103 32.35294 26.86480 28.40909
## T4 35.76113 5.377610 4 30.43478 43.24324 33.47076 34.68324
## Q75
## T1 29.00815
## T2 21.54018
## T3 30.24733
## T4 36.97360
##
## $comparison
## NULL
##
## $groups
## Muestreo2$densidad_estomatica groups
## T4 35.76113 a
## T3 28.70304 b
## T1 28.37036 b
## T2 20.64160 c
##
## attr(,"class")
## [1] "group"y3tukey## $statistics
## MSerror Df Mean CV MSD
## 7.312 12 34.87078 7.754547 5.676738
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$densidad_estomatica std r Min Max Q25 Q50
## T1 41.27510 1.440437 4 39.47368 42.85714 40.55024 41.38478
## T2 22.23403 4.461596 4 17.85714 27.58621 18.98041 21.74638
## T3 37.98699 1.905990 4 35.71429 40.00000 36.83555 38.11685
## T4 37.98699 1.905990 4 35.71429 40.00000 36.83555 38.11685
## Q75
## T1 42.10963
## T2 25.00000
## T3 39.26829
## T4 39.26829
##
## $comparison
## NULL
##
## $groups
## Muestreo3$densidad_estomatica groups
## T1 41.27510 a
## T3 37.98699 a
## T4 37.98699 a
## T2 22.23403 b
##
## attr(,"class")
## [1] "group"y4tukey## $statistics
## MSerror Df Mean CV MSD
## 36.28 12 42.28579 14.24424 12.64487
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$densidad_estomatica std r Min Max Q25 Q50
## T1 48.07154 8.735282 4 38.77551 59.57447 43.44388 46.96809
## T2 20.24483 4.014089 4 16.21622 25.80645 18.63739 19.47832
## T3 50.87002 4.304834 4 47.50000 57.14286 48.50291 49.41860
## T4 49.95677 5.844472 4 41.30435 54.16667 49.38859 52.17803
## Q75
## T1 51.59574
## T2 21.08576
## T3 51.78571
## T4 52.74621
##
## $comparison
## NULL
##
## $groups
## Muestreo4$densidad_estomatica groups
## T3 50.87002 a
## T4 49.95677 a
## T1 48.07154 a
## T2 20.24483 b
##
## attr(,"class")
## [1] "group"Diferencias estadisticas entre muestreos
TEMPERATURA
t1 <- aov(Temp~conjunto, data = GENERAL)
anova(t1)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 154.087 10.2725 12.647 6.607e-12 ***
## Residuals 48 38.987 0.8122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t1tukey <-HSD.test(GENERAL$Temp,GENERAL$conjunto, 48, 0.8122, alpha = 0.05)
t1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.8122 48 18.17656 4.958151 2.302223
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$Temp std r Min Max Q25 Q50 Q75
## M1T1 18.225 0.6800735 4 17.3 18.9 17.975 18.35 18.600
## M1T2 21.950 0.9882645 4 20.9 23.1 21.275 21.90 22.575
## M1T3 18.350 0.7416198 4 17.4 19.2 18.075 18.40 18.675
## M1T4 21.000 1.5253415 4 19.3 22.8 20.050 20.95 21.900
## M2T1 17.225 1.2553220 4 16.0 18.9 16.450 17.00 17.775
## M2T2 19.225 0.5188127 4 18.6 19.7 18.900 19.30 19.625
## M2T3 17.225 0.9215024 4 16.3 18.5 16.825 17.05 17.450
## M2T4 18.275 0.8015610 4 17.1 18.9 18.150 18.55 18.675
## M3T1 17.400 0.4546061 4 16.8 17.9 17.250 17.45 17.600
## M3T2 19.175 0.9464847 4 18.4 20.5 18.550 18.90 19.525
## M3T3 17.450 0.7767453 4 16.5 18.4 17.175 17.45 17.725
## M3T4 17.050 0.8185353 4 16.3 17.9 16.375 17.00 17.675
## M4T1 16.625 0.8845903 4 15.9 17.9 16.125 16.35 16.850
## M4T2 19.000 0.7023769 4 18.3 19.7 18.450 19.00 19.550
## M4T3 16.225 1.1412712 4 15.1 17.7 15.475 16.05 16.800
## M4T4 16.425 0.6238322 4 15.6 17.0 16.125 16.55 16.850
##
## $comparison
## NULL
##
## $groups
## GENERAL$Temp groups
## M1T2 21.950 a
## M1T4 21.000 ab
## M2T2 19.225 bc
## M3T2 19.175 bc
## M4T2 19.000 bc
## M1T3 18.350 cd
## M2T4 18.275 cd
## M1T1 18.225 cd
## M3T3 17.450 cd
## M3T1 17.400 cd
## M2T1 17.225 cd
## M2T3 17.225 cd
## M3T4 17.050 cd
## M4T1 16.625 d
## M4T4 16.425 d
## M4T3 16.225 d
##
## attr(,"class")
## [1] "group"ESTOMAS ABIERTOS
t2 <- aov(E_Abierto~conjunto, data = GENERAL)
anova(t2)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 2661.5 177.433 34.411 < 2.2e-16 ***
## Residuals 48 247.5 5.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t2tukey <-HSD.test(GENERAL$E_Abierto,GENERAL$conjunto, 48, 5.126, alpha = 0.05)
t2tukey## $statistics
## MSerror Df Mean CV MSD
## 5.126 48 12.625 17.93321 5.783693
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$E_Abierto std r Min Max Q25 Q50 Q75
## M1T1 10.00 2.1602469 4 7 12 9.25 10.5 11.25
## M1T2 4.00 0.8164966 4 3 5 3.75 4.0 4.25
## M1T3 8.25 0.9574271 4 7 9 7.75 8.5 9.00
## M1T4 4.00 0.8164966 4 3 5 3.75 4.0 4.25
## M2T1 10.75 1.7078251 4 9 13 9.75 10.5 11.50
## M2T2 6.75 0.9574271 4 6 8 6.00 6.5 7.25
## M2T3 11.50 1.2909944 4 10 13 10.75 11.5 12.25
## M2T4 13.75 2.6299556 4 10 16 13.00 14.5 15.25
## M3T1 17.25 1.5000000 4 15 18 17.25 18.0 18.00
## M3T2 6.50 1.2909944 4 5 8 5.75 6.5 7.25
## M3T3 16.25 1.2583057 4 15 18 15.75 16.0 16.50
## M3T4 16.25 1.2583057 4 15 18 15.75 16.0 16.50
## M4T1 22.00 4.5460606 4 18 28 18.75 21.0 24.25
## M4T2 7.25 0.9574271 4 6 8 6.75 7.5 8.00
## M4T3 24.25 4.9916597 4 19 29 20.50 24.5 28.25
## M4T4 23.25 3.0956959 4 19 26 22.00 24.0 25.25
##
## $comparison
## NULL
##
## $groups
## GENERAL$E_Abierto groups
## M4T3 24.25 a
## M4T4 23.25 a
## M4T1 22.00 ab
## M3T1 17.25 bc
## M3T3 16.25 bcd
## M3T4 16.25 bcd
## M2T4 13.75 cde
## M2T3 11.50 cdef
## M2T1 10.75 def
## M1T1 10.00 ef
## M1T3 8.25 efg
## M4T2 7.25 fg
## M2T2 6.75 fg
## M3T2 6.50 fg
## M1T2 4.00 g
## M1T4 4.00 g
##
## attr(,"class")
## [1] "group"ESTOMAS CERRADOS
t3 <- aov(E_Cerrado~conjunto, data = GENERAL)
anova(t3)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 451 30.067 2.6875 0.004809 **
## Residuals 48 537 11.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t3tukey <-HSD.test(GENERAL$E_Cerrado,GENERAL$conjunto, 48, 11.188, alpha = 0.05)
t3tukey## $statistics
## MSerror Df Mean CV MSD
## 11.188 48 26.5 12.62206 8.544608
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$E_Cerrado std r Min Max Q25 Q50 Q75
## M1T1 26.25 3.403430 4 23 31 24.50 25.5 27.25
## M1T2 31.50 1.290994 4 30 33 30.75 31.5 32.25
## M1T3 28.50 2.081666 4 26 31 27.50 28.5 29.50
## M1T4 31.25 1.707825 4 29 33 30.50 31.5 32.25
## M2T1 27.25 4.787136 4 22 33 24.25 27.0 30.00
## M2T2 26.00 3.741657 4 22 31 24.25 25.5 27.25
## M2T3 28.75 4.031129 4 23 32 27.50 30.0 31.25
## M2T4 25.00 6.055301 4 19 32 20.50 24.5 29.00
## M3T1 24.50 1.290994 4 23 26 23.75 24.5 25.25
## M3T2 22.75 1.707825 4 21 25 21.75 22.5 23.50
## M3T3 26.50 1.000000 4 25 27 26.50 27.0 27.00
## M3T4 26.50 1.000000 4 25 27 26.50 27.0 27.00
## M4T1 23.75 4.645787 4 19 30 21.25 23.0 25.50
## M4T2 29.00 4.320494 4 23 33 27.50 30.0 31.50
## M4T3 23.25 3.862210 4 21 29 21.00 21.5 23.75
## M4T4 23.25 2.629956 4 21 27 21.75 22.5 24.00
##
## $comparison
## NULL
##
## $groups
## GENERAL$E_Cerrado groups
## M1T2 31.50 a
## M1T4 31.25 ab
## M4T2 29.00 ab
## M2T3 28.75 ab
## M1T3 28.50 ab
## M2T1 27.25 ab
## M3T3 26.50 ab
## M3T4 26.50 ab
## M1T1 26.25 ab
## M2T2 26.00 ab
## M2T4 25.00 ab
## M3T1 24.50 ab
## M4T1 23.75 ab
## M4T3 23.25 ab
## M4T4 23.25 ab
## M3T2 22.75 b
##
## attr(,"class")
## [1] "group"ESTOMAS TOTALES
t4 <- aov(E_Total~conjunto, data = GENERAL)
anova(t4)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 1571 104.73 6.3475 4.13e-07 ***
## Residuals 48 792 16.50
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t4tukey <-HSD.test(GENERAL$E_Total,GENERAL$conjunto, 48, 16.50, alpha = 0.05)
t4tukey## $statistics
## MSerror Df Mean CV MSD
## 16.5 48 39.125 10.38216 10.37667
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$E_Total std r Min Max Q25 Q50 Q75
## M1T1 36.25 1.500000 4 35 38 35.00 36.0 37.25
## M1T2 35.50 1.290994 4 34 37 34.75 35.5 36.25
## M1T3 36.75 1.892969 4 34 38 36.25 37.5 38.00
## M1T4 35.25 1.707825 4 33 37 34.50 35.5 36.25
## M2T1 38.00 6.324555 4 32 46 33.50 37.0 41.50
## M2T2 32.75 4.573474 4 28 39 31.00 32.0 33.75
## M2T3 40.25 4.787136 4 34 44 37.75 41.5 44.00
## M2T4 38.75 7.500000 4 29 46 35.00 40.0 43.75
## M3T1 41.75 2.629956 4 38 44 41.00 42.5 43.25
## M3T2 29.25 1.258306 4 28 31 28.75 29.0 29.50
## M3T3 42.75 1.707825 4 41 45 41.75 42.5 43.50
## M3T4 42.75 1.707825 4 41 45 41.75 42.5 43.50
## M4T1 45.75 3.947573 4 40 49 45.25 47.0 47.50
## M4T2 36.25 4.112988 4 31 41 34.75 36.5 38.00
## M4T3 47.50 7.937254 4 40 58 42.25 46.0 51.25
## M4T4 46.50 1.914854 4 44 48 45.50 47.0 48.00
##
## $comparison
## NULL
##
## $groups
## GENERAL$E_Total groups
## M4T3 47.50 a
## M4T4 46.50 ab
## M4T1 45.75 abc
## M3T3 42.75 abcd
## M3T4 42.75 abcd
## M3T1 41.75 abcd
## M2T3 40.25 abcd
## M2T4 38.75 abcde
## M2T1 38.00 abcde
## M1T3 36.75 bcde
## M1T1 36.25 bcde
## M4T2 36.25 bcde
## M1T2 35.50 cde
## M1T4 35.25 de
## M2T2 32.75 de
## M3T2 29.25 e
##
## attr(,"class")
## [1] "group"CONTENIDO RELATIVO DE CLOROFILAS
t5 <- aov(CRC~conjunto, data = GENERAL)
anova(t5)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 2335.62 155.708 61.082 < 2.2e-16 ***
## Residuals 48 122.36 2.549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t5tukey <-HSD.test(GENERAL$CRC,GENERAL$conjunto, 48, 2.549, alpha = 0.05)
t5tukey## $statistics
## MSerror Df Mean CV MSD
## 2.549 48 31.03438 5.144485 4.078503
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$CRC std r Min Max Q25 Q50 Q75
## M1T1 34.750 0.7852813 4 33.8 35.7 34.400 34.75 35.100
## M1T2 24.700 0.6683313 4 23.8 25.3 24.400 24.85 25.150
## M1T3 33.475 1.3793114 4 31.5 34.6 33.075 33.90 34.300
## M1T4 25.825 0.6238322 4 25.2 26.5 25.350 25.80 26.275
## M2T1 42.250 2.4893105 4 39.7 45.5 40.750 41.90 43.400
## M2T2 26.650 2.1455380 4 25.1 29.7 25.175 25.90 27.375
## M2T3 38.625 3.0170350 4 34.9 41.5 36.850 39.05 40.825
## M2T4 39.025 1.2553220 4 37.5 40.4 38.325 39.10 39.800
## M3T1 34.550 1.4387495 4 32.9 35.9 33.575 34.70 35.675
## M3T2 22.975 1.9362765 4 20.2 24.4 22.375 23.65 24.250
## M3T3 34.300 1.0099505 4 33.0 35.1 33.750 34.55 35.100
## M3T4 31.675 1.9362765 4 30.1 34.5 30.775 31.05 31.950
## M4T1 30.575 1.1295279 4 28.9 31.3 30.400 31.05 31.225
## M4T2 19.550 0.8185353 4 18.5 20.5 19.325 19.60 19.825
## M4T3 28.950 1.7058722 4 27.0 30.9 27.900 28.95 30.000
## M4T4 28.675 0.7135592 4 27.9 29.5 28.200 28.65 29.125
##
## $comparison
## NULL
##
## $groups
## GENERAL$CRC groups
## M2T1 42.250 a
## M2T4 39.025 a
## M2T3 38.625 ab
## M1T1 34.750 bc
## M3T1 34.550 bcd
## M3T3 34.300 cd
## M1T3 33.475 cd
## M3T4 31.675 cde
## M4T1 30.575 def
## M4T3 28.950 efg
## M4T4 28.675 efgh
## M2T2 26.650 fghi
## M1T4 25.825 ghi
## M1T2 24.700 hi
## M3T2 22.975 ij
## M4T2 19.550 j
##
## attr(,"class")
## [1] "group"CONTENIDO RELATIVO DE AGUA
t6 <- aov(CRA~conjunto, data = GENERAL)
anova(t6)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 7960 530.67 28.557 < 2.2e-16 ***
## Residuals 48 892 18.58
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t6tukey <-HSD.test(GENERAL$CRA,GENERAL$conjunto, 48, 18.58, alpha = 0.05)
t6tukey## $statistics
## MSerror Df Mean CV MSD
## 18.58 48 80.43957 5.358622 11.0113
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$CRA std r Min Max Q25 Q50 Q75
## M1T1 84.20468 3.542397 4 80.47337 87.86127 81.59375 84.24203 86.85296
## M1T2 65.56335 5.423975 4 60.59322 71.42857 61.14134 65.11580 69.53782
## M1T3 79.58223 3.087446 4 75.37688 82.60870 78.42755 80.17166 81.32633
## M1T4 75.00228 3.163968 4 71.17904 78.57143 73.27945 75.12933 76.85216
## M2T1 88.14420 3.192775 4 85.31469 91.78082 85.54100 87.74065 90.34385
## M2T2 66.88609 4.913944 4 60.91371 72.92818 65.09545 66.85124 68.64188
## M2T3 85.32531 3.705073 4 81.76101 89.50617 82.45948 85.01704 87.88287
## M2T4 78.73208 4.051680 4 72.95918 82.12291 77.51399 79.92311 81.14119
## M3T1 92.14515 6.904737 4 82.78146 99.31507 89.97017 93.24203 95.41700
## M3T2 60.34528 7.479434 4 54.27350 69.94536 54.47747 58.58114 64.44896
## M3T3 91.04734 3.621355 4 88.23529 96.32353 89.02311 89.81527 91.83950
## M3T4 85.22032 4.444089 4 80.85106 91.11111 82.45766 84.45956 87.22222
## M4T1 89.81746 1.372111 4 88.46154 91.66667 89.01879 89.57083 90.36950
## M4T2 60.29217 4.664368 4 54.16667 65.21739 58.32840 60.89230 62.85607
## M4T3 92.45510 1.390754 4 91.04478 94.26752 91.59681 92.25405 93.11233
## M4T4 92.27002 3.044487 4 88.19444 95.52239 91.27938 92.68162 93.67226
##
## $comparison
## NULL
##
## $groups
## GENERAL$CRA groups
## M4T3 92.45510 a
## M4T4 92.27002 a
## M3T1 92.14515 a
## M3T3 91.04734 a
## M4T1 89.81746 ab
## M2T1 88.14420 abc
## M2T3 85.32531 abcd
## M3T4 85.22032 abcd
## M1T1 84.20468 abcd
## M1T3 79.58223 bcd
## M2T4 78.73208 cd
## M1T4 75.00228 de
## M2T2 66.88609 ef
## M1T2 65.56335 ef
## M3T2 60.34528 f
## M4T2 60.29217 f
##
## attr(,"class")
## [1] "group"densidad de estomas abiertos
t7 <- aov(densidad_estomática~conjunto, data = GENERAL)
anova(t7)## Analysis of Variance Table
##
## Response: densidad_estomática
## Df Sum Sq Mean Sq F value Pr(>F)
## conjunto 15 9769.3 651.28 37.21 < 2.2e-16 ***
## Residuals 48 840.1 17.50
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
t7tukey <-HSD.test(GENERAL$densidad_estomática,GENERAL$conjunto, 48, 17.50, alpha = 0.05)
t7tukey## $statistics
## MSerror Df Mean CV MSD
## 17.5 48 30.93444 13.52311 10.68649
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey GENERAL$conjunto 16 5.109118 0.05
##
## $means
## GENERAL$densidad_estomática std r Min Max Q25
## M1T1 27.75198 6.691758 4 18.421053 34.28571 26.03383
## M1T2 11.25779 2.259444 4 8.823529 14.28571 10.31399
## M1T3 22.48975 2.734203 4 18.421053 24.32432 22.25232
## M1T4 11.34916 2.189521 4 8.333333 13.51351 10.65476
## M2T1 28.37036 2.055154 4 26.470588 31.25000 27.24265
## M2T2 20.64160 1.382671 4 18.750000 21.87500 20.07212
## M2T3 28.70304 2.912797 4 25.641026 32.35294 26.86480
## M2T4 35.76113 5.377610 4 30.434783 43.24324 33.47076
## M3T1 41.27510 1.440437 4 39.473684 42.85714 40.55024
## M3T2 22.23403 4.461596 4 17.857143 27.58621 18.98041
## M3T3 37.98699 1.905990 4 35.714286 40.00000 36.83555
## M3T4 37.98699 1.905990 4 35.714286 40.00000 36.83555
## M4T1 48.07154 8.735282 4 38.775510 59.57447 43.44388
## M4T2 20.24483 4.014089 4 16.216216 25.80645 18.63739
## M4T3 50.87002 4.304834 4 47.500000 57.14286 48.50291
## M4T4 49.95677 5.844472 4 41.304348 54.16667 49.38859
## Q50 Q75
## M1T1 29.15058 30.86873
## M1T2 10.96096 11.90476
## M1T3 23.60681 23.84424
## M1T4 11.77489 12.46929
## M2T1 27.88043 29.00815
## M2T2 20.97070 21.54018
## M2T3 28.40909 30.24733
## M2T4 34.68324 36.97360
## M3T1 41.38478 42.10963
## M3T2 21.74638 25.00000
## M3T3 38.11685 39.26829
## M3T4 38.11685 39.26829
## M4T1 46.96809 51.59574
## M4T2 19.47832 21.08576
## M4T3 49.41860 51.78571
## M4T4 52.17803 52.74621
##
## $comparison
## NULL
##
## $groups
## GENERAL$densidad_estomática groups
## M4T3 50.87002 a
## M4T4 49.95677 a
## M4T1 48.07154 ab
## M3T1 41.27510 abc
## M3T3 37.98699 bcd
## M3T4 37.98699 bcd
## M2T4 35.76113 cd
## M2T3 28.70304 de
## M2T1 28.37036 de
## M1T1 27.75198 de
## M1T3 22.48975 e
## M3T2 22.23403 e
## M2T2 20.64160 ef
## M4T2 20.24483 ef
## M1T4 11.34916 f
## M1T2 11.25779 f
##
## attr(,"class")
## [1] "group"fin :)