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")
tratamiento1 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "TRATAMIENT 1")
tratamiento2 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "TRATAMIENTO 2")
tratamiento3 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "TRATAMIENTO 3")
tratamiento4 <- read_excel("~/Downloads/BASE DE DATOS FINAL.xlsx",
sheet = "TRATAMIENTO 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 ESTADÍSTICA ENTRE MUESTREOS
Temp
Determinación de la variable en los 4 muestreos
ANOVA
g1 <- aov(Temp~muestreo, data = tratamiento1)
g2 <- aov(Temp~muestreo, data = tratamiento2)
g3 <- aov(Temp~muestreo, data = tratamiento3)
g4 <- aov(Temp~muestreo, data = tratamiento4)
anova(g1)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 5.2319 1.74396 2.3042 0.1288
## Residuals 12 9.0825 0.75687anova(g2)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 23.913 7.9708 12.1 0.0006118 ***
## Residuals 12 7.905 0.6588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(g3)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 9.1425 3.04750 3.6884 0.04322 *
## Residuals 12 9.9150 0.82625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(g4)## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 49.273 16.4242 16.309 0.0001561 ***
## Residuals 12 12.085 1.0071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(g1))##
## Shapiro-Wilk normality test
##
## data: resid(g1)
## W = 0.96606, p-value = 0.7715shapiro.test(resid(g2))##
## Shapiro-Wilk normality test
##
## data: resid(g2)
## W = 0.93232, p-value = 0.2652shapiro.test(resid(g3))##
## Shapiro-Wilk normality test
##
## data: resid(g3)
## W = 0.93695, p-value = 0.3133shapiro.test(resid(g4))##
## Shapiro-Wilk normality test
##
## data: resid(g4)
## W = 0.95666, p-value = 0.6018*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$Temp~tratamiento1$muestreo, 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 1.2219 0.3442
## 12leveneTest(tratamiento2$Temp~tratamiento2$muestreo, 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 0.8676 0.4846
## 12leveneTest(tratamiento3$Temp~tratamiento3$muestreo, 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 0.4654 0.7117
## 12leveneTest(tratamiento4$Temp~tratamiento4$muestreo, 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 2.3569 0.1231
## 12Prueba de tukey
library(agricolae)
library(dplyr)
g1tukey <-HSD.test(tratamiento1$Temp,tratamiento1$muestreo, 12, 0.75687, alpha = 0.05)
g2tukey <-HSD.test(tratamiento2$Temp,tratamiento2$muestreo, 12, 0.6588, alpha = 0.05)
g3tukey <-HSD.test(tratamiento3$Temp,tratamiento3$muestreo, 12, 0.82625, alpha = 0.05)
g4tukey <-HSD.test(tratamiento4$Temp,tratamiento4$muestreo, 12, 1.0071, alpha = 0.05)
g1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.75687 12 17.36875 5.008897 1.826381
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$Temp std r Min Max Q25 Q50 Q75
## M1 18.225 0.6800735 4 17.3 18.9 17.975 18.35 18.600
## M2 17.225 1.2553220 4 16.0 18.9 16.450 17.00 17.775
## M3 17.400 0.4546061 4 16.8 17.9 17.250 17.45 17.600
## M4 16.625 0.8845903 4 15.9 17.9 16.125 16.35 16.850
##
## $comparison
## NULL
##
## $groups
## tratamiento1$Temp groups
## M1 18.225 a
## M3 17.400 a
## M2 17.225 a
## M4 16.625 a
##
## attr(,"class")
## [1] "group"g2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.6588 12 19.8375 4.091569 1.703953
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$Temp std r Min Max Q25 Q50 Q75
## M1 21.950 0.9882645 4 20.9 23.1 21.275 21.9 22.575
## M2 19.225 0.5188127 4 18.6 19.7 18.900 19.3 19.625
## M3 19.175 0.9464847 4 18.4 20.5 18.550 18.9 19.525
## M4 19.000 0.7023769 4 18.3 19.7 18.450 19.0 19.550
##
## $comparison
## NULL
##
## $groups
## tratamiento2$Temp groups
## M1 21.950 a
## M2 19.225 b
## M3 19.175 b
## M4 19.000 b
##
## attr(,"class")
## [1] "group"g3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.82625 12 17.3125 5.250443 1.908255
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$Temp std r Min Max Q25 Q50 Q75
## M1 18.350 0.7416198 4 17.4 19.2 18.075 18.40 18.675
## M2 17.225 0.9215024 4 16.3 18.5 16.825 17.05 17.450
## M3 17.450 0.7767453 4 16.5 18.4 17.175 17.45 17.725
## M4 16.225 1.1412712 4 15.1 17.7 15.475 16.05 16.800
##
## $comparison
## NULL
##
## $groups
## tratamiento3$Temp groups
## M1 18.350 a
## M3 17.450 ab
## M2 17.225 ab
## M4 16.225 b
##
## attr(,"class")
## [1] "group"g4tukey## $statistics
## MSerror Df Mean CV MSD
## 1.0071 12 18.1875 5.517766 2.10677
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$Temp std r Min Max Q25 Q50 Q75
## M1 21.000 1.5253415 4 19.3 22.8 20.050 20.95 21.900
## M2 18.275 0.8015610 4 17.1 18.9 18.150 18.55 18.675
## M3 17.050 0.8185353 4 16.3 17.9 16.375 17.00 17.675
## M4 16.425 0.6238322 4 15.6 17.0 16.125 16.55 16.850
##
## $comparison
## NULL
##
## $groups
## tratamiento4$Temp groups
## M1 21.000 a
## M2 18.275 b
## M3 17.050 b
## M4 16.425 b
##
## attr(,"class")
## [1] "group"ESTMAS ABIERTOS
Determinación de la variable en los 4 muestreos
ANOVA
h1 <- aov(E_Abierto~muestreo, data = tratamiento1)
h2 <- aov(E_Abierto~muestreo, data = tratamiento2)
h3 <- aov(E_Abierto~muestreo, data = tratamiento3)
h4 <- aov(E_Abierto~muestreo, data = tratamiento4)
anova(h1)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 388.5 129.500 16.984 0.0001287 ***
## Residuals 12 91.5 7.625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(h2)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 25.25 8.4167 8.08 0.00327 **
## Residuals 12 12.50 1.0417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(h3)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 579.69 193.229 26.576 1.385e-05 ***
## Residuals 12 87.25 7.271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(h4)## Analysis of Variance Table
##
## Response: E_Abierto
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 761.19 253.729 54.129 3.02e-07 ***
## Residuals 12 56.25 4.688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(h1))##
## Shapiro-Wilk normality test
##
## data: resid(h1)
## W = 0.94048, p-value = 0.3549shapiro.test(resid(h2))##
## Shapiro-Wilk normality test
##
## data: resid(h2)
## W = 0.9698, p-value = 0.8354shapiro.test(resid(h3))##
## Shapiro-Wilk normality test
##
## data: resid(h3)
## W = 0.96429, p-value = 0.7398shapiro.test(resid(h4))##
## Shapiro-Wilk normality test
##
## data: resid(h4)
## W = 0.9155, p-value = 0.1428*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$E_Abierto~tratamiento1$muestreo, 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 2.6901 0.09329 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$E_Abierto~tratamiento2$muestreo, 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 0.6667 0.5885
## 12leveneTest(tratamiento3$E_Abierto~tratamiento3$muestreo, 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 24.121 2.276e-05 ***
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$E_Abierto~tratamiento4$muestreo, 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 1.8182 0.1975
## 12Prueba de tukey
library(agricolae)
library(dplyr)
h1tukey <-HSD.test(tratamiento1$E_Abierto,tratamiento1$muestreo, 12, 7.625, alpha = 0.05)
h2tukey <-HSD.test(tratamiento2$E_Abierto,tratamiento2$muestreo, 12, 1.0417, alpha = 0.05)
h3tukey <-HSD.test(tratamiento3$E_Abierto,tratamiento3$muestreo, 12, 7.271, alpha = 0.05)
h4tukey <-HSD.test(tratamiento4$E_Abierto,tratamiento4$muestreo, 12, 4.688, alpha = 0.05)
h1tukey## $statistics
## MSerror Df Mean CV MSD
## 7.625 12 15 18.40894 5.796965
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$E_Abierto std r Min Max Q25 Q50 Q75
## M1 10.00 2.160247 4 7 12 9.25 10.5 11.25
## M2 10.75 1.707825 4 9 13 9.75 10.5 11.50
## M3 17.25 1.500000 4 15 18 17.25 18.0 18.00
## M4 22.00 4.546061 4 18 28 18.75 21.0 24.25
##
## $comparison
## NULL
##
## $groups
## tratamiento1$E_Abierto groups
## M4 22.00 a
## M3 17.25 a
## M2 10.75 b
## M1 10.00 b
##
## attr(,"class")
## [1] "group"h2tukey## $statistics
## MSerror Df Mean CV MSD
## 1.0417 12 6.125 16.66346 2.142654
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$E_Abierto std r Min Max Q25 Q50 Q75
## M1 4.00 0.8164966 4 3 5 3.75 4.0 4.25
## M2 6.75 0.9574271 4 6 8 6.00 6.5 7.25
## M3 6.50 1.2909944 4 5 8 5.75 6.5 7.25
## M4 7.25 0.9574271 4 6 8 6.75 7.5 8.00
##
## $comparison
## NULL
##
## $groups
## tratamiento2$E_Abierto groups
## M4 7.25 a
## M2 6.75 a
## M3 6.50 a
## M1 4.00 b
##
## attr(,"class")
## [1] "group"h3tukey## $statistics
## MSerror Df Mean CV MSD
## 7.271 12 15.0625 17.90194 5.6608
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$E_Abierto std r Min Max Q25 Q50 Q75
## M1 8.25 0.9574271 4 7 9 7.75 8.5 9.00
## M2 11.50 1.2909944 4 10 13 10.75 11.5 12.25
## M3 16.25 1.2583057 4 15 18 15.75 16.0 16.50
## M4 24.25 4.9916597 4 19 29 20.50 24.5 28.25
##
## $comparison
## NULL
##
## $groups
## tratamiento3$E_Abierto groups
## M4 24.25 a
## M3 16.25 b
## M2 11.50 bc
## M1 8.25 c
##
## attr(,"class")
## [1] "group"h4tukey## $statistics
## MSerror Df Mean CV MSD
## 4.688 12 14.3125 15.12789 4.545425
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$E_Abierto std r Min Max Q25 Q50 Q75
## M1 4.00 0.8164966 4 3 5 3.75 4.0 4.25
## M2 13.75 2.6299556 4 10 16 13.00 14.5 15.25
## M3 16.25 1.2583057 4 15 18 15.75 16.0 16.50
## M4 23.25 3.0956959 4 19 26 22.00 24.0 25.25
##
## $comparison
## NULL
##
## $groups
## tratamiento4$E_Abierto groups
## M4 23.25 a
## M3 16.25 b
## M2 13.75 b
## M1 4.00 c
##
## attr(,"class")
## [1] "group"ESTOMAS CERRADOS
Determinación de la variable en los 4 muestreos
ANOVA
i1 <- aov(E_Cerrado~muestreo, data = tratamiento1)
i2 <- aov(E_Cerrado~muestreo, data = tratamiento2)
i3 <- aov(E_Cerrado~muestreo, data = tratamiento3)
i4 <- aov(E_Cerrado~muestreo, data = tratamiento4)
anova(i1)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 30.687 10.229 0.7085 0.5652
## Residuals 12 173.250 14.438anova(i2)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 171.69 57.229 6.1454 0.008957 **
## Residuals 12 111.75 9.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(i3)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 77.5 25.833 2.8311 0.08323 .
## Residuals 12 109.5 9.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(i4)## Analysis of Variance Table
##
## Response: E_Cerrado
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 141.5 47.167 3.9719 0.03528 *
## Residuals 12 142.5 11.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(i1))##
## Shapiro-Wilk normality test
##
## data: resid(i1)
## W = 0.94964, p-value = 0.484shapiro.test(resid(i2))##
## Shapiro-Wilk normality test
##
## data: resid(i2)
## W = 0.97047, p-value = 0.8461shapiro.test(resid(i3))##
## Shapiro-Wilk normality test
##
## data: resid(i3)
## W = 0.96991, p-value = 0.8371shapiro.test(resid(i4))##
## Shapiro-Wilk normality test
##
## data: resid(i4)
## W = 0.97428, p-value = 0.9023*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$E_Cerrado~tratamiento1$muestreo, 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 1.4462 0.2783
## 12leveneTest(tratamiento2$E_Cerrado~tratamiento2$muestreo, 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 1.1049 0.3851
## 12leveneTest(tratamiento3$E_Cerrado~tratamiento3$muestreo, 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 1.6654 0.227
## 12leveneTest(tratamiento4$E_Cerrado~tratamiento4$muestreo, 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 8.7868 0.00235 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de tukey
library(agricolae)
library(dplyr)
i1tukey <-HSD.test(tratamiento1$E_Cerrado,tratamiento1$muestreo, 12, 14.438, alpha = 0.05)
i2tukey <-HSD.test(tratamiento2$E_Cerrado,tratamiento2$muestreo, 12, 9.312, alpha = 0.05)
i3tukey <-HSD.test(tratamiento3$E_Cerrado,tratamiento3$muestreo, 12, 9.125, alpha = 0.05)
i4tukey <-HSD.test(tratamiento4$E_Cerrado,tratamiento4$muestreo, 12, 11.875, alpha = 0.05)
i1tukey## $statistics
## MSerror Df Mean CV MSD
## 14.438 12 25.4375 14.93754 7.976902
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$E_Cerrado std r Min Max Q25 Q50 Q75
## M1 26.25 3.403430 4 23 31 24.50 25.5 27.25
## M2 27.25 4.787136 4 22 33 24.25 27.0 30.00
## M3 24.50 1.290994 4 23 26 23.75 24.5 25.25
## M4 23.75 4.645787 4 19 30 21.25 23.0 25.50
##
## $comparison
## NULL
##
## $groups
## tratamiento1$E_Cerrado groups
## M2 27.25 a
## M1 26.25 a
## M3 24.50 a
## M4 23.75 a
##
## attr(,"class")
## [1] "group"i2tukey## $statistics
## MSerror Df Mean CV MSD
## 9.312 12 27.3125 11.17275 6.406225
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$E_Cerrado std r Min Max Q25 Q50 Q75
## M1 31.50 1.290994 4 30 33 30.75 31.5 32.25
## M2 26.00 3.741657 4 22 31 24.25 25.5 27.25
## M3 22.75 1.707825 4 21 25 21.75 22.5 23.50
## M4 29.00 4.320494 4 23 33 27.50 30.0 31.50
##
## $comparison
## NULL
##
## $groups
## tratamiento2$E_Cerrado groups
## M1 31.50 a
## M4 29.00 ab
## M2 26.00 ab
## M3 22.75 b
##
## attr(,"class")
## [1] "group"i3tukey## $statistics
## MSerror Df Mean CV MSD
## 9.125 12 26.75 11.29257 6.341576
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$E_Cerrado std r Min Max Q25 Q50 Q75
## M1 28.50 2.081666 4 26 31 27.5 28.5 29.50
## M2 28.75 4.031129 4 23 32 27.5 30.0 31.25
## M3 26.50 1.000000 4 25 27 26.5 27.0 27.00
## M4 23.25 3.862210 4 21 29 21.0 21.5 23.75
##
## $comparison
## NULL
##
## $groups
## tratamiento3$E_Cerrado groups
## M2 28.75 a
## M1 28.50 a
## M3 26.50 a
## M4 23.25 a
##
## attr(,"class")
## [1] "group"i4tukey## $statistics
## MSerror Df Mean CV MSD
## 11.875 12 26.5 13.00382 7.234317
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$E_Cerrado std r Min Max Q25 Q50 Q75
## M1 31.25 1.707825 4 29 33 30.50 31.5 32.25
## M2 25.00 6.055301 4 19 32 20.50 24.5 29.00
## M3 26.50 1.000000 4 25 27 26.50 27.0 27.00
## M4 23.25 2.629956 4 21 27 21.75 22.5 24.00
##
## $comparison
## NULL
##
## $groups
## tratamiento4$E_Cerrado groups
## M1 31.25 a
## M3 26.50 ab
## M2 25.00 ab
## M4 23.25 b
##
## attr(,"class")
## [1] "group"ESTOMAS TOTALES
Determinación de la variable en los 4 muestreos
ANOVA
j1 <- aov(E_Total~muestreo, data = tratamiento1)
j2 <- aov(E_Total~muestreo, data = tratamiento2)
j3 <- aov(E_Total~muestreo, data = tratamiento3)
j4 <- aov(E_Total~muestreo, data = tratamiento4)
anova(j1)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 213.69 71.229 4.4003 0.02627 *
## Residuals 12 194.25 16.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(j2)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 120.69 40.229 3.9168 0.03668 *
## Residuals 12 123.25 10.271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(j3)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 245.19 81.729 3.5374 0.04828 *
## Residuals 12 277.25 23.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(j4)## Analysis of Variance Table
##
## Response: E_Total
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 285.19 95.063 5.7833 0.01103 *
## Residuals 12 197.25 16.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(j1))##
## Shapiro-Wilk normality test
##
## data: resid(j1)
## W = 0.93615, p-value = 0.3045shapiro.test(resid(j2))##
## Shapiro-Wilk normality test
##
## data: resid(j2)
## W = 0.92068, p-value = 0.173shapiro.test(resid(j3))##
## Shapiro-Wilk normality test
##
## data: resid(j3)
## W = 0.95597, p-value = 0.5897shapiro.test(resid(j4))##
## Shapiro-Wilk normality test
##
## data: resid(j4)
## W = 0.9117, p-value = 0.124*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$E_Total~tratamiento1$muestreo, 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 3.1682 0.06382 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$E_Total~tratamiento2$muestreo, 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 1.392 0.2928
## 12leveneTest(tratamiento3$E_Total~tratamiento3$muestreo, 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 3.8566 0.03829 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$E_Total~tratamiento4$muestreo, 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 5.3966 0.0139 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de tukey
library(agricolae)
library(dplyr)
j1tukey <-HSD.test(tratamiento1$E_Total,tratamiento1$muestreo, 12, 16.188, alpha = 0.05)
j2tukey <-HSD.test(tratamiento2$E_Total,tratamiento2$muestreo, 12, 10.271, alpha = 0.05)
j3tukey <-HSD.test(tratamiento3$E_Total,tratamiento3$muestreo, 12, 23.104, alpha = 0.05)
j4tukey <-HSD.test(tratamiento4$E_Total,tratamiento4$muestreo, 12, 16.438, alpha = 0.05)
j1tukey## $statistics
## MSerror Df Mean CV MSD
## 16.188 12 40.4375 9.949753 8.446511
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$E_Total std r Min Max Q25 Q50 Q75
## M1 36.25 1.500000 4 35 38 35.00 36.0 37.25
## M2 38.00 6.324555 4 32 46 33.50 37.0 41.50
## M3 41.75 2.629956 4 38 44 41.00 42.5 43.25
## M4 45.75 3.947573 4 40 49 45.25 47.0 47.50
##
## $comparison
## NULL
##
## $groups
## tratamiento1$E_Total groups
## M4 45.75 a
## M3 41.75 ab
## M2 38.00 ab
## M1 36.25 b
##
## attr(,"class")
## [1] "group"j2tukey## $statistics
## MSerror Df Mean CV MSD
## 10.271 12 33.4375 9.584568 6.728017
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$E_Total std r Min Max Q25 Q50 Q75
## M1 35.50 1.290994 4 34 37 34.75 35.5 36.25
## M2 32.75 4.573474 4 28 39 31.00 32.0 33.75
## M3 29.25 1.258306 4 28 31 28.75 29.0 29.50
## M4 36.25 4.112988 4 31 41 34.75 36.5 38.00
##
## $comparison
## NULL
##
## $groups
## tratamiento2$E_Total groups
## M4 36.25 a
## M1 35.50 ab
## M2 32.75 ab
## M3 29.25 b
##
## attr(,"class")
## [1] "group"j3tukey## $statistics
## MSerror Df Mean CV MSD
## 23.104 12 41.8125 11.49575 10.09077
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$E_Total std r Min Max Q25 Q50 Q75
## M1 36.75 1.892969 4 34 38 36.25 37.5 38.00
## M2 40.25 4.787136 4 34 44 37.75 41.5 44.00
## M3 42.75 1.707825 4 41 45 41.75 42.5 43.50
## M4 47.50 7.937254 4 40 58 42.25 46.0 51.25
##
## $comparison
## NULL
##
## $groups
## tratamiento3$E_Total groups
## M4 47.50 a
## M3 42.75 ab
## M2 40.25 ab
## M1 36.75 b
##
## attr(,"class")
## [1] "group"j4tukey## $statistics
## MSerror Df Mean CV MSD
## 16.438 12 40.8125 9.934163 8.511483
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$E_Total std r Min Max Q25 Q50 Q75
## M1 35.25 1.707825 4 33 37 34.50 35.5 36.25
## M2 38.75 7.500000 4 29 46 35.00 40.0 43.75
## M3 42.75 1.707825 4 41 45 41.75 42.5 43.50
## M4 46.50 1.914854 4 44 48 45.50 47.0 48.00
##
## $comparison
## NULL
##
## $groups
## tratamiento4$E_Total groups
## M4 46.50 a
## M3 42.75 ab
## M2 38.75 ab
## M1 35.25 b
##
## attr(,"class")
## [1] "group"CRC
Determinación de la variable en los 4 muestreos
ANOVA
k1 <- aov(CRC~muestreo, data = tratamiento1)
k2 <- aov(CRC~muestreo, data = tratamiento2)
k3 <- aov(CRC~muestreo, data = tratamiento3)
k4 <- aov(CRC~muestreo, data = tratamiento4)
anova(k1)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 285.117 95.039 37.42 2.278e-06 ***
## Residuals 12 30.477 2.540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(k2)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 108.947 36.316 15.341 0.0002081 ***
## Residuals 12 28.407 2.367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(k3)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 188.612 62.871 16.838 0.0001341 ***
## Residuals 12 44.805 3.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(k4)## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 386.73 128.910 82.856 2.742e-08 ***
## Residuals 12 18.67 1.556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(k1))##
## Shapiro-Wilk normality test
##
## data: resid(k1)
## W = 0.96927, p-value = 0.8267shapiro.test(resid(k2))##
## Shapiro-Wilk normality test
##
## data: resid(k2)
## W = 0.97525, p-value = 0.9149shapiro.test(resid(k3))##
## Shapiro-Wilk normality test
##
## data: resid(k3)
## W = 0.97164, p-value = 0.8644shapiro.test(resid(k4))##
## Shapiro-Wilk normality test
##
## data: resid(k4)
## W = 0.92649, p-value = 0.2144*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$CRC~tratamiento1$muestreo, 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 2.153 0.1467
## 12leveneTest(tratamiento2$CRC~tratamiento2$muestreo, 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 1.5488 0.2528
## 12leveneTest(tratamiento3$CRC~tratamiento3$muestreo, 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 3.3626 0.05502 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$CRC~tratamiento4$muestreo, 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 1.8337 0.1947
## 12Prueba de tukey
library(agricolae)
library(dplyr)
k1tukey <-HSD.test(tratamiento1$CRC,tratamiento1$muestreo, 12, 2.540, alpha = 0.05)
k2tukey <-HSD.test(tratamiento2$CRC,tratamiento2$muestreo, 12, 2.367, alpha = 0.05)
k3tukey <-HSD.test(tratamiento3$CRC,tratamiento3$muestreo, 12, 3.734, alpha = 0.05)
k4tukey <-HSD.test(tratamiento4$CRC,tratamiento4$muestreo, 12, 1.556, alpha = 0.05)
k1tukey## $statistics
## MSerror Df Mean CV MSD
## 2.54 12 35.53125 4.485454 3.345782
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$CRC std r Min Max Q25 Q50 Q75
## M1 34.750 0.7852813 4 33.8 35.7 34.400 34.75 35.100
## M2 42.250 2.4893105 4 39.7 45.5 40.750 41.90 43.400
## M3 34.550 1.4387495 4 32.9 35.9 33.575 34.70 35.675
## M4 30.575 1.1295279 4 28.9 31.3 30.400 31.05 31.225
##
## $comparison
## NULL
##
## $groups
## tratamiento1$CRC groups
## M2 42.250 a
## M1 34.750 b
## M3 34.550 b
## M4 30.575 c
##
## attr(,"class")
## [1] "group"k2tukey## $statistics
## MSerror Df Mean CV MSD
## 2.367 12 23.46875 6.555551 3.229831
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$CRC std r Min Max Q25 Q50 Q75
## M1 24.700 0.6683313 4 23.8 25.3 24.400 24.85 25.150
## M2 26.650 2.1455380 4 25.1 29.7 25.175 25.90 27.375
## M3 22.975 1.9362765 4 20.2 24.4 22.375 23.65 24.250
## M4 19.550 0.8185353 4 18.5 20.5 19.325 19.60 19.825
##
## $comparison
## NULL
##
## $groups
## tratamiento2$CRC groups
## M2 26.650 a
## M1 24.700 ab
## M3 22.975 b
## M4 19.550 c
##
## attr(,"class")
## [1] "group"k3tukey## $statistics
## MSerror Df Mean CV MSD
## 3.734 12 33.8375 5.710694 4.056653
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$CRC std r Min Max Q25 Q50 Q75
## M1 33.475 1.379311 4 31.5 34.6 33.075 33.90 34.300
## M2 38.625 3.017035 4 34.9 41.5 36.850 39.05 40.825
## M3 34.300 1.009950 4 33.0 35.1 33.750 34.55 35.100
## M4 28.950 1.705872 4 27.0 30.9 27.900 28.95 30.000
##
## $comparison
## NULL
##
## $groups
## tratamiento3$CRC groups
## M2 38.625 a
## M3 34.300 b
## M1 33.475 b
## M4 28.950 c
##
## attr(,"class")
## [1] "group"k4tukey## $statistics
## MSerror Df Mean CV MSD
## 1.556 12 31.3 3.985295 2.618699
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$CRC std r Min Max Q25 Q50 Q75
## M1 25.825 0.6238322 4 25.2 26.5 25.350 25.80 26.275
## M2 39.025 1.2553220 4 37.5 40.4 38.325 39.10 39.800
## M3 31.675 1.9362765 4 30.1 34.5 30.775 31.05 31.950
## M4 28.675 0.7135592 4 27.9 29.5 28.200 28.65 29.125
##
## $comparison
## NULL
##
## $groups
## tratamiento4$CRC groups
## M2 39.025 a
## M3 31.675 b
## M4 28.675 c
## M1 25.825 d
##
## attr(,"class")
## [1] "group"CRA
Determinación de la variable en los 4 muestreos
ANOVA
l1 <- aov(CRA~muestreo, data = tratamiento1)
l2 <- aov(CRA~muestreo, data = tratamiento2)
l3 <- aov(CRA~muestreo, data = tratamiento3)
l4 <- aov(CRA~muestreo, data = tratamiento4)
anova(l1)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 134.3 44.767 2.4767 0.1113
## Residuals 12 216.9 18.075anova(l2)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 143.03 47.676 1.4528 0.2765
## Residuals 12 393.79 32.816anova(l3)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 415.70 138.567 14.469 0.0002731 ***
## Residuals 12 114.92 9.577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(l4)## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 691.57 230.522 16.631 0.0001422 ***
## Residuals 12 166.34 13.861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(l1))##
## Shapiro-Wilk normality test
##
## data: resid(l1)
## W = 0.96006, p-value = 0.6628shapiro.test(resid(l2))##
## Shapiro-Wilk normality test
##
## data: resid(l2)
## W = 0.91781, p-value = 0.1555shapiro.test(resid(l3))##
## Shapiro-Wilk normality test
##
## data: resid(l3)
## W = 0.97318, p-value = 0.8871shapiro.test(resid(l4))##
## Shapiro-Wilk normality test
##
## data: resid(l4)
## W = 0.96501, p-value = 0.7527*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$CRA~tratamiento1$muestreo, 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 1.8046 0.1999
## 12leveneTest(tratamiento2$CRA~tratamiento2$muestreo, 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 0.9515 0.4467
## 12leveneTest(tratamiento3$CRA~tratamiento3$muestreo, 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 1.4667 0.273
## 12leveneTest(tratamiento4$CRA~tratamiento4$muestreo, 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 0.2921 0.8303
## 12Prueba de tukey
library(agricolae)
library(dplyr)
l1tukey <-HSD.test(tratamiento1$CRA,tratamiento1$muestreo, 12, 18.075, alpha = 0.05)
l2tukey <-HSD.test(tratamiento2$CRA,tratamiento2$muestreo, 12, 32.816, alpha = 0.05)
l3tukey <-HSD.test(tratamiento3$CRA,tratamiento3$muestreo, 12, 9.577, alpha = 0.05)
l4tukey <-HSD.test(tratamiento4$CRA,tratamiento4$muestreo, 12, 13.861, alpha = 0.05)
l1tukey## $statistics
## MSerror Df Mean CV MSD
## 18.075 12 88.57787 4.799698 8.92524
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$CRA std r Min Max Q25 Q50 Q75
## M1 84.20468 3.542397 4 80.47337 87.86127 81.59375 84.24203 86.85296
## M2 88.14420 3.192775 4 85.31469 91.78082 85.54100 87.74065 90.34385
## M3 92.14515 6.904737 4 82.78146 99.31507 89.97017 93.24203 95.41700
## M4 89.81746 1.372111 4 88.46154 91.66667 89.01879 89.57083 90.36950
##
## $comparison
## NULL
##
## $groups
## tratamiento1$CRA groups
## M3 92.14515 a
## M4 89.81746 a
## M2 88.14420 a
## M1 84.20468 a
##
## attr(,"class")
## [1] "group"l2tukey## $statistics
## MSerror Df Mean CV MSD
## 32.816 12 63.27172 9.053847 12.02607
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$CRA std r Min Max Q25 Q50 Q75
## M1 65.56335 5.423975 4 60.59322 71.42857 61.14134 65.11580 69.53782
## M2 66.88609 4.913944 4 60.91371 72.92818 65.09545 66.85124 68.64188
## M3 60.34528 7.479434 4 54.27350 69.94536 54.47747 58.58114 64.44896
## M4 60.29217 4.664368 4 54.16667 65.21739 58.32840 60.89230 62.85607
##
## $comparison
## NULL
##
## $groups
## tratamiento2$CRA groups
## M2 66.88609 a
## M1 65.56335 a
## M3 60.34528 a
## M4 60.29217 a
##
## attr(,"class")
## [1] "group"l3tukey## $statistics
## MSerror Df Mean CV MSD
## 9.577 12 87.10249 3.55291 6.49674
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$CRA std r Min Max Q25 Q50 Q75
## M1 79.58223 3.087446 4 75.37688 82.60870 78.42755 80.17166 81.32633
## M2 85.32531 3.705073 4 81.76101 89.50617 82.45948 85.01704 87.88287
## M3 91.04734 3.621355 4 88.23529 96.32353 89.02311 89.81527 91.83950
## M4 92.45510 1.390754 4 91.04478 94.26752 91.59681 92.25405 93.11233
##
## $comparison
## NULL
##
## $groups
## tratamiento3$CRA groups
## M4 92.45510 a
## M3 91.04734 ab
## M2 85.32531 bc
## M1 79.58223 c
##
## attr(,"class")
## [1] "group"l4tukey## $statistics
## MSerror Df Mean CV MSD
## 13.861 12 82.80618 4.496085 7.815882
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$CRA std r Min Max Q25 Q50 Q75
## M1 75.00228 3.163968 4 71.17904 78.57143 73.27945 75.12933 76.85216
## M2 78.73208 4.051680 4 72.95918 82.12291 77.51399 79.92311 81.14119
## M3 85.22032 4.444089 4 80.85106 91.11111 82.45766 84.45956 87.22222
## M4 92.27002 3.044487 4 88.19444 95.52239 91.27938 92.68162 93.67226
##
## $comparison
## NULL
##
## $groups
## tratamiento4$CRA groups
## M4 92.27002 a
## M3 85.22032 ab
## M2 78.73208 bc
## M1 75.00228 c
##
## attr(,"class")
## [1] "group"DENSIDAD DE ESTOMAS ABIERTOS
Determinación de la variable en los 4 muestreos
ANOVA
q1 <- aov(densidad_estomática~muestreo, data = tratamiento1)
q2 <- aov(densidad_estomática~muestreo, data = tratamiento2)
q3 <- aov(densidad_estomática~muestreo, data = tratamiento3)
q4 <- aov(densidad_estomática~muestreo, data = tratamiento4)
anova(q1)## Analysis of Variance Table
##
## Response: densidad_estomática
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 1197.00 399.00 12.529 0.0005243 ***
## Residuals 12 382.15 31.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q2)## Analysis of Variance Table
##
## Response: densidad_estomática
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 295.95 98.650 9.1692 0.00198 **
## Residuals 12 129.11 10.759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q3)## Analysis of Variance Table
##
## Response: densidad_estomática
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 1827.75 609.25 63.922 1.192e-07 ***
## Residuals 12 114.37 9.53
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(q4)## Analysis of Variance Table
##
## Response: densidad_estomática
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3145.81 1048.60 58.66 1.929e-07 ***
## Residuals 12 214.51 17.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(q1))##
## Shapiro-Wilk normality test
##
## data: resid(q1)
## W = 0.91952, p-value = 0.1657shapiro.test(resid(q2))##
## Shapiro-Wilk normality test
##
## data: resid(q2)
## W = 0.95362, p-value = 0.5492shapiro.test(resid(q3))##
## Shapiro-Wilk normality test
##
## data: resid(q3)
## W = 0.95935, p-value = 0.65shapiro.test(resid(q4))##
## Shapiro-Wilk normality test
##
## data: resid(q4)
## W = 0.96743, p-value = 0.7954*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$densidad_estomática~tratamiento1$muestreo, 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 2.2978 0.1295
## 12leveneTest(tratamiento2$densidad_estomática~tratamiento2$muestreo, 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 2.1112 0.1522
## 12leveneTest(tratamiento3$densidad_estomática~tratamiento3$muestreo, 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 0.7489 0.5436
## 12leveneTest(tratamiento4$densidad_estomática~tratamiento4$muestreo, 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 1.5953 0.2422
## 12Prueba de tukey
library(agricolae)
library(dplyr)
q1tukey <-HSD.test(tratamiento1$densidad_estomática,tratamiento1$muestreo, 12, 31.85, alpha = 0.05)
q2tukey <-HSD.test(tratamiento2$densidad_estomática,tratamiento2$muestreo, 12, 10.759, alpha = 0.05)
q3tukey <-HSD.test(tratamiento3$densidad_estomática,tratamiento3$muestreo, 12, 9.53, alpha = 0.05)
q4tukey <-HSD.test(tratamiento4$densidad_estomática,tratamiento4$muestreo, 12, 17.88, alpha = 0.05)
q1tukey## $statistics
## MSerror Df Mean CV MSD
## 31.85 12 36.36724 15.51831 11.84774
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$densidad_estomática std r Min Max Q25
## M1 27.75198 6.691758 4 18.42105 34.28571 26.03383
## M2 28.37036 2.055154 4 26.47059 31.25000 27.24265
## M3 41.27510 1.440437 4 39.47368 42.85714 40.55024
## M4 48.07154 8.735282 4 38.77551 59.57447 43.44388
## Q50 Q75
## M1 29.15058 30.86873
## M2 27.88043 29.00815
## M3 41.38478 42.10963
## M4 46.96809 51.59574
##
## $comparison
## NULL
##
## $groups
## tratamiento1$densidad_estomática groups
## M4 48.07154 a
## M3 41.27510 a
## M2 28.37036 b
## M1 27.75198 b
##
## attr(,"class")
## [1] "group"q2tukey## $statistics
## MSerror Df Mean CV MSD
## 10.759 12 18.59456 17.64006 6.885995
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$densidad_estomática std r Min Max Q25
## M1 11.25779 2.259444 4 8.823529 14.28571 10.31399
## M2 20.64160 1.382671 4 18.750000 21.87500 20.07212
## M3 22.23403 4.461596 4 17.857143 27.58621 18.98041
## M4 20.24483 4.014089 4 16.216216 25.80645 18.63739
## Q50 Q75
## M1 10.96096 11.90476
## M2 20.97070 21.54018
## M3 21.74638 25.00000
## M4 19.47832 21.08576
##
## $comparison
## NULL
##
## $groups
## tratamiento2$densidad_estomática groups
## M3 22.23403 a
## M2 20.64160 a
## M4 20.24483 a
## M1 11.25779 b
##
## attr(,"class")
## [1] "group"q3tukey## $statistics
## MSerror Df Mean CV MSD
## 9.53 12 35.01245 8.817063 6.480779
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$densidad_estomática std r Min Max Q25
## M1 22.48975 2.734203 4 18.42105 24.32432 22.25232
## M2 28.70304 2.912797 4 25.64103 32.35294 26.86480
## M3 37.98699 1.905990 4 35.71429 40.00000 36.83555
## M4 50.87002 4.304834 4 47.50000 57.14286 48.50291
## Q50 Q75
## M1 23.60681 23.84424
## M2 28.40909 30.24733
## M3 38.11685 39.26829
## M4 49.41860 51.78571
##
## $comparison
## NULL
##
## $groups
## tratamiento3$densidad_estomática groups
## M4 50.87002 a
## M3 37.98699 b
## M2 28.70304 c
## M1 22.48975 c
##
## attr(,"class")
## [1] "group"q4tukey## $statistics
## MSerror Df Mean CV MSD
## 17.88 12 33.76351 12.5238 8.876965
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$densidad_estomática std r Min Max Q25
## M1 11.34916 2.189521 4 8.333333 13.51351 10.65476
## M2 35.76113 5.377610 4 30.434783 43.24324 33.47076
## M3 37.98699 1.905990 4 35.714286 40.00000 36.83555
## M4 49.95677 5.844472 4 41.304348 54.16667 49.38859
## Q50 Q75
## M1 11.77489 12.46929
## M2 34.68324 36.97360
## M3 38.11685 39.26829
## M4 52.17803 52.74621
##
## $comparison
## NULL
##
## $groups
## tratamiento4$densidad_estomática groups
## M4 49.95677 a
## M3 37.98699 b
## M2 35.76113 b
## M1 11.34916 c
##
## attr(,"class")
## [1] "group"LONGITUD DE PARTE AEREA
Determinación de la variable en los 4 muestreos
ANOVA
r1 <- aov(PA_Longitud~muestreo, data = tratamiento1)
r2 <- aov(PA_Longitud~muestreo, data = tratamiento2)
r3 <- aov(PA_Longitud~muestreo, data = tratamiento3)
r4 <- aov(PA_Longitud~muestreo, data = tratamiento4)
anova(r1)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 244.489 81.496 20.303 5.403e-05 ***
## Residuals 12 48.169 4.014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r2)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 68.305 22.7683 55.93 2.517e-07 ***
## Residuals 12 4.885 0.4071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r3)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 294.310 98.103 97.968 1.051e-08 ***
## Residuals 12 12.017 1.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(r4)## Analysis of Variance Table
##
## Response: PA_Longitud
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 200.263 66.754 112.27 4.79e-09 ***
## Residuals 12 7.135 0.595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(r1))##
## Shapiro-Wilk normality test
##
## data: resid(r1)
## W = 0.90173, p-value = 0.08567shapiro.test(resid(r2))##
## Shapiro-Wilk normality test
##
## data: resid(r2)
## W = 0.97115, p-value = 0.8569shapiro.test(resid(r3))##
## Shapiro-Wilk normality test
##
## data: resid(r3)
## W = 0.97286, p-value = 0.8825shapiro.test(resid(r4))##
## Shapiro-Wilk normality test
##
## data: resid(r4)
## W = 0.97505, p-value = 0.9122library(car)
library(carData)
leveneTest(tratamiento1$PA_Longitud~tratamiento1$muestreo, 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 12.355 0.0005578 ***
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$PA_Longitud~tratamiento2$muestreo, 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 2.7282 0.09044 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$PA_Longitud~tratamiento3$muestreo, 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 3.3332 0.05626 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$PA_Longitud~tratamiento4$muestreo, 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 0.9985 0.4268
## 12library(agricolae)
library(dplyr)
r1tukey <-HSD.test(tratamiento1$PA_Longitud,tratamiento1$muestreo, 12, 4.014, alpha = 0.05)
r2tukey <-HSD.test(tratamiento2$PA_Longitud,tratamiento2$muestreo, 12, 0.4071, alpha = 0.05)
r3tukey <-HSD.test(tratamiento3$PA_Longitud,tratamiento3$muestreo, 12, 1.001, alpha = 0.05)
r4tukey <-HSD.test(tratamiento4$PA_Longitud,tratamiento4$muestreo, 12, 0.595, alpha = 0.05)
r1tukey## $statistics
## MSerror Df Mean CV MSD
## 4.014 12 12.68062 15.79967 4.206001
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$PA_Longitud std r Min Max Q25 Q50 Q75
## M1 7.5725 0.8942548 4 6.99 8.9 7.0725 7.20 7.700
## M2 10.4500 3.8336232 4 6.50 15.0 7.7750 10.15 12.825
## M3 15.1000 0.5291503 4 14.60 15.8 14.7500 15.00 15.350
## M4 17.6000 0.5291503 4 16.90 18.1 17.3500 17.70 17.950
##
## $comparison
## NULL
##
## $groups
## tratamiento1$PA_Longitud groups
## M4 17.6000 a
## M3 15.1000 a
## M2 10.4500 b
## M1 7.5725 b
##
## attr(,"class")
## [1] "group"r2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.4071 12 8.125 7.852848 1.339465
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$PA_Longitud std r Min Max Q25 Q50 Q75
## M1 6.225 0.2362908 4 5.9 6.4 6.125 6.30 6.400
## M2 6.750 0.4654747 4 6.3 7.2 6.375 6.75 7.125
## M3 8.000 0.8755950 4 7.2 9.1 7.350 7.85 8.500
## M4 11.525 0.7675719 4 10.4 12.1 11.375 11.80 11.950
##
## $comparison
## NULL
##
## $groups
## tratamiento2$PA_Longitud groups
## M4 11.525 a
## M3 8.000 b
## M2 6.750 bc
## M1 6.225 c
##
## attr(,"class")
## [1] "group"r3tukey## $statistics
## MSerror Df Mean CV MSD
## 1.001 12 13.29937 7.522909 2.10038
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$PA_Longitud std r Min Max Q25 Q50 Q75
## M1 7.3225 0.3022554 4 6.99 7.7 7.1475 7.30 7.475
## M2 11.6250 1.5840349 4 9.60 13.2 10.8000 11.85 12.675
## M3 15.4250 0.8539126 4 14.30 16.3 15.0500 15.55 15.925
## M4 18.8250 0.8220908 4 17.90 19.7 18.2750 18.85 19.400
##
## $comparison
## NULL
##
## $groups
## tratamiento3$PA_Longitud groups
## M4 18.8250 a
## M3 15.4250 b
## M2 11.6250 c
## M1 7.3225 d
##
## attr(,"class")
## [1] "group"r4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.595 12 11.0125 7.004426 1.619344
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$PA_Longitud std r Min Max Q25 Q50 Q75
## M1 6.800 0.3559026 4 6.3 7.1 6.675 6.90 7.025
## M2 9.625 0.5123475 4 8.9 10.1 9.500 9.75 9.875
## M3 11.100 0.8793937 4 10.1 12.1 10.550 11.10 11.650
## M4 16.525 1.1026483 4 15.2 17.9 16.175 16.50 16.850
##
## $comparison
## NULL
##
## $groups
## tratamiento4$PA_Longitud groups
## M4 16.525 a
## M3 11.100 b
## M2 9.625 b
## M1 6.800 c
##
## attr(,"class")
## [1] "group"ÁREA FOLIAR
Determinación de la variable en los 4 muestreos
ANOVA
s1 <- aov(Area_foliar~muestreo, data = tratamiento1)
s2 <- aov(Area_foliar~muestreo, data = tratamiento2)
s3 <- aov(Area_foliar~muestreo, data = tratamiento3)
s4 <- aov(Area_foliar~muestreo, data = tratamiento4)
anova(s1)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 36026 12008.8 127.46 2.296e-09 ***
## Residuals 12 1131 94.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(s2)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 5716.2 1905.41 37.391 2.288e-06 ***
## Residuals 12 611.5 50.96
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(s3)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 34921 11640.5 1270.3 2.802e-15 ***
## Residuals 12 110 9.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(s4)## Analysis of Variance Table
##
## Response: Area_foliar
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 29667.8 9889.3 750.2 6.512e-14 ***
## Residuals 12 158.2 13.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(s1))##
## Shapiro-Wilk normality test
##
## data: resid(s1)
## W = 0.80397, p-value = 0.003076shapiro.test(resid(s2))##
## Shapiro-Wilk normality test
##
## data: resid(s2)
## W = 0.95525, p-value = 0.577shapiro.test(resid(s3))##
## Shapiro-Wilk normality test
##
## data: resid(s3)
## W = 0.97114, p-value = 0.8567shapiro.test(resid(s4))##
## Shapiro-Wilk normality test
##
## data: resid(s4)
## W = 0.96601, p-value = 0.7706library(car)
library(carData)
leveneTest(tratamiento1$Area_foliar~tratamiento1$muestreo, 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 3.9578 0.03563 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$Area_foliar~tratamiento2$muestreo, 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 5.4839 0.01318 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$Area_foliar~tratamiento3$muestreo, 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 0.5735 0.6432
## 12leveneTest(tratamiento4$Area_foliar~tratamiento4$muestreo, 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 1.0646 0.4004
## 12library(agricolae)
library(dplyr)
s1tukey <-HSD.test(tratamiento1$Area_foliar,tratamiento1$muestreo, 12, 94.2, alpha = 0.05)
s2tukey <-HSD.test(tratamiento2$Area_foliar,tratamiento2$muestreo, 12, 50.96, alpha = 0.05)
s3tukey <-HSD.test(tratamiento3$Area_foliar,tratamiento3$muestreo, 12, 9.2, alpha = 0.05)
s4tukey <-HSD.test(tratamiento4$Area_foliar,tratamiento4$muestreo, 12, 13.2, alpha = 0.05)
s1tukey## $statistics
## MSerror Df Mean CV MSD
## 94.2 12 125.6641 7.7235 20.3754
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$Area_foliar std r Min Max Q25 Q50
## M1 66.5740 1.953636 4 64.925 69.223 65.2025 66.074
## M2 92.7750 18.570115 4 66.700 109.500 86.8000 97.450
## M3 161.5750 1.447699 4 159.600 162.800 160.9500 161.950
## M4 181.7325 5.107709 4 174.810 186.170 179.4750 182.975
## Q75
## M1 67.4455
## M2 103.4250
## M3 162.5750
## M4 185.2325
##
## $comparison
## NULL
##
## $groups
## tratamiento1$Area_foliar groups
## M4 181.7325 a
## M3 161.5750 a
## M2 92.7750 b
## M1 66.5740 c
##
## attr(,"class")
## [1] "group"s2tukey## $statistics
## MSerror Df Mean CV MSD
## 50.96 12 75.19206 9.493858 14.98634
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$Area_foliar std r Min Max Q25 Q50
## M1 52.04325 1.303762 4 50.238 53.351 51.73575 52.292
## M2 67.30000 4.817330 4 61.200 71.900 64.65000 68.050
## M3 77.50000 12.451774 4 61.200 88.600 71.17500 80.100
## M4 103.92500 4.887191 4 98.670 109.750 100.72500 103.640
## Q75
## M1 52.5995
## M2 70.7000
## M3 86.4250
## M4 106.8400
##
## $comparison
## NULL
##
## $groups
## tratamiento2$Area_foliar groups
## M4 103.92500 a
## M3 77.50000 b
## M2 67.30000 b
## M1 52.04325 c
##
## attr(,"class")
## [1] "group"s3tukey## $statistics
## MSerror Df Mean CV MSD
## 9.2 12 122.7049 2.471906 6.367584
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$Area_foliar std r Min Max Q25 Q50
## M1 63.5370 2.607883 4 59.999 66.252 62.73875 63.9485
## M2 92.5000 3.221801 4 88.600 95.700 90.55000 92.8500
## M3 154.6750 1.936276 4 151.900 156.300 154.15000 155.2500
## M4 180.1078 3.965360 4 175.923 185.483 178.48725 179.5125
## Q75
## M1 64.74675
## M2 94.80000
## M3 155.77500
## M4 181.13300
##
## $comparison
## NULL
##
## $groups
## tratamiento3$Area_foliar groups
## M4 180.1078 a
## M3 154.6750 b
## M2 92.5000 c
## M1 63.5370 d
##
## attr(,"class")
## [1] "group"s4tukey## $statistics
## MSerror Df Mean CV MSD
## 13.2 12 111.0436 3.27185 7.627245
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$Area_foliar std r Min Max Q25 Q50
## M1 55.5420 3.746375 4 50.356 58.346 54.01225 56.733
## M2 85.9750 5.208567 4 81.500 93.300 82.77500 84.550
## M3 136.1250 1.645955 4 134.300 138.000 135.05000 136.100
## M4 166.5325 2.975739 4 163.360 169.360 164.32750 166.705
## Q75
## M1 58.26275
## M2 87.75000
## M3 137.17500
## M4 168.91000
##
## $comparison
## NULL
##
## $groups
## tratamiento4$Area_foliar groups
## M4 166.5325 a
## M3 136.1250 b
## M2 85.9750 c
## M1 55.5420 d
##
## attr(,"class")
## [1] "group"PESO FRESCO EN HOJAS
Determinación de la variable en los 4 muestreos
ANOVA
t1 <- aov(Hojas_Peso_fresco~muestreo, data = tratamiento1)
t2 <- aov(Hojas_Peso_fresco~muestreo, data = tratamiento2)
t3 <- aov(Hojas_Peso_fresco~muestreo, data = tratamiento3)
t4 <- aov(Hojas_Peso_fresco~muestreo, data = tratamiento4)
anova(t1)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 545.86 181.952 334.46 7.951e-12 ***
## Residuals 12 6.53 0.544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(t2)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 82.373 27.4577 90.663 1.639e-08 ***
## Residuals 12 3.634 0.3029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(t3)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 464.90 154.97 535.06 4.88e-13 ***
## Residuals 12 3.48 0.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(t4)## Analysis of Variance Table
##
## Response: Hojas_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 441.53 147.177 1150.3 5.07e-15 ***
## Residuals 12 1.54 0.128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(t1))##
## Shapiro-Wilk normality test
##
## data: resid(t1)
## W = 0.96018, p-value = 0.6651shapiro.test(resid(t2))##
## Shapiro-Wilk normality test
##
## data: resid(t2)
## W = 0.96431, p-value = 0.7401shapiro.test(resid(t3))##
## Shapiro-Wilk normality test
##
## data: resid(t3)
## W = 0.96391, p-value = 0.733shapiro.test(resid(t4))##
## Shapiro-Wilk normality test
##
## data: resid(t4)
## W = 0.96637, p-value = 0.7769library(car)
library(carData)
leveneTest(tratamiento1$Hojas_Peso_fresco~tratamiento1$muestreo, 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 1.8597 0.1902
## 12leveneTest(tratamiento2$Hojas_Peso_fresco~tratamiento2$muestreo, 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 3.0203 0.07161 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$Hojas_Peso_fresco~tratamiento3$muestreo, 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 3.3122 0.05716 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$Hojas_Peso_fresco~tratamiento4$muestreo, 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 1.7217 0.2156
## 12library(agricolae)
library(dplyr)
t1tukey <-HSD.test(tratamiento1$Hojas_Peso_fresco,tratamiento1$muestreo, 12, 0.544, alpha = 0.05)
t2tukey <-HSD.test(tratamiento2$Hojas_Peso_fresco,tratamiento2$muestreo, 12, 0.3029, alpha = 0.05)
t3tukey <-HSD.test(tratamiento3$Hojas_Peso_fresco,tratamiento3$muestreo, 12, 0.29, alpha = 0.05)
t4tukey <-HSD.test(tratamiento4$Hojas_Peso_fresco,tratamiento4$muestreo, 12, 0.128, alpha = 0.05)
t1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.544 12 11.26519 6.547282 1.548389
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$Hojas_Peso_fresco std r Min Max Q25 Q50
## M1 4.68800 0.3633492 4 4.223 5.023 4.4915 4.7530
## M2 6.28075 1.0659588 4 4.841 7.234 5.8085 6.5240
## M3 16.17300 0.7805583 4 15.232 16.961 15.7000 16.2495
## M4 17.91900 0.5463833 4 17.382 18.492 17.4915 17.9010
## Q75
## M1 4.94950
## M2 6.99625
## M3 16.72250
## M4 18.32850
##
## $comparison
## NULL
##
## $groups
## tratamiento1$Hojas_Peso_fresco groups
## M4 17.91900 a
## M3 16.17300 b
## M2 6.28075 c
## M1 4.68800 d
##
## attr(,"class")
## [1] "group"t2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.3029 12 5.689563 9.673213 1.155395
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$Hojas_Peso_fresco std r Min Max Q25 Q50
## M1 3.09075 0.1257971 4 2.936 3.219 3.01850 3.1040
## M2 4.07075 0.7024127 4 3.138 4.812 3.80025 4.1665
## M3 6.72025 0.4411314 4 6.162 7.193 6.50025 6.7630
## M4 8.87650 0.7124729 4 8.078 9.531 8.37650 8.9485
## Q75
## M1 3.17625
## M2 4.43700
## M3 6.98300
## M4 9.44850
##
## $comparison
## NULL
##
## $groups
## tratamiento2$Hojas_Peso_fresco groups
## M4 8.87650 a
## M3 6.72025 b
## M2 4.07075 c
## M1 3.09075 c
##
## attr(,"class")
## [1] "group"t3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.29 12 10.66706 5.048405 1.130524
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$Hojas_Peso_fresco std r Min Max Q25 Q50
## M1 4.17550 0.2728864 4 3.902 4.510 3.98525 4.1450
## M2 6.72025 0.4411314 4 6.162 7.193 6.50025 6.7630
## M3 14.46700 0.4530350 4 13.933 14.994 14.21425 14.4705
## M4 17.30550 0.8271693 4 16.284 17.978 16.80750 17.4800
## Q75
## M1 4.33525
## M2 6.98300
## M3 14.72325
## M4 17.97800
##
## $comparison
## NULL
##
## $groups
## tratamiento3$Hojas_Peso_fresco groups
## M4 17.30550 a
## M3 14.46700 b
## M2 6.72025 c
## M1 4.17550 d
##
## attr(,"class")
## [1] "group"t4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.128 12 10.13937 3.52853 0.7510792
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$Hojas_Peso_fresco std r Min Max Q25 Q50
## M1 3.76375 0.1719852 4 3.520 3.923 3.72400 3.8060
## M2 6.21825 0.4864945 4 5.872 6.917 5.89375 6.0420
## M3 14.46700 0.4530350 4 13.933 14.994 14.21425 14.4705
## M4 16.10850 0.2006830 4 15.939 16.364 15.95325 16.0655
## Q75
## M1 3.84575
## M2 6.36650
## M3 14.72325
## M4 16.22075
##
## $comparison
## NULL
##
## $groups
## tratamiento4$Hojas_Peso_fresco groups
## M4 16.10850 a
## M3 14.46700 b
## M2 6.21825 c
## M1 3.76375 d
##
## attr(,"class")
## [1] "group"HOJAS PESO SECO
Determinación de la variable en los 4 muestreos
ANOVA
u1 <- aov(Hojas_Peso_seco~muestreo, data = tratamiento1)
u2 <- aov(Hojas_Peso_seco~muestreo, data = tratamiento2)
u3 <- aov(Hojas_Peso_seco~muestreo, data = tratamiento3)
u4 <- aov(Hojas_Peso_seco~muestreo, data = tratamiento4)
anova(u1)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 5.5562 1.85206 141.06 1.274e-09 ***
## Residuals 12 0.1576 0.01313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(u2)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 1.10645 0.36882 85.691 2.263e-08 ***
## Residuals 12 0.05165 0.00430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(u3)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.9518 1.31726 118.94 3.43e-09 ***
## Residuals 12 0.1329 0.01107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(u4)## Analysis of Variance Table
##
## Response: Hojas_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.6622 1.22073 110.81 5.166e-09 ***
## Residuals 12 0.1322 0.01102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(u1))##
## Shapiro-Wilk normality test
##
## data: resid(u1)
## W = 0.96748, p-value = 0.7963shapiro.test(resid(u2))##
## Shapiro-Wilk normality test
##
## data: resid(u2)
## W = 0.9622, p-value = 0.7018shapiro.test(resid(u3))##
## Shapiro-Wilk normality test
##
## data: resid(u3)
## W = 0.94751, p-value = 0.4514shapiro.test(resid(u4))##
## Shapiro-Wilk normality test
##
## data: resid(u4)
## W = 0.9709, p-value = 0.853library(car)
library(carData)
leveneTest(tratamiento1$Hojas_Peso_seco~tratamiento1$muestreo, 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 2.7935 0.08578 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$Hojas_Peso_seco~tratamiento2$muestreo, 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 5.7341 0.01136 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$Hojas_Peso_seco~tratamiento3$muestreo, 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 1.6034 0.2403
## 12leveneTest(tratamiento4$Hojas_Peso_seco~tratamiento4$muestreo, 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 10.135 0.00131 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
u1tukey <-HSD.test(tratamiento1$densidad_estomática,tratamiento1$muestreo, 12, 0.01313, alpha = 0.05)
u2tukey <-HSD.test(tratamiento2$densidad_estomática,tratamiento2$muestreo, 12, 0.00430, alpha = 0.05)
u3tukey <-HSD.test(tratamiento3$densidad_estomática,tratamiento3$muestreo, 12, 0.01107, alpha = 0.05)
u4tukey <-HSD.test(tratamiento4$densidad_estomática,tratamiento4$muestreo, 12, 0.01102, alpha = 0.05)
u1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.01313 12 36.36724 0.3150808 0.2405543
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$densidad_estomática std r Min Max Q25
## M1 27.75198 6.691758 4 18.42105 34.28571 26.03383
## M2 28.37036 2.055154 4 26.47059 31.25000 27.24265
## M3 41.27510 1.440437 4 39.47368 42.85714 40.55024
## M4 48.07154 8.735282 4 38.77551 59.57447 43.44388
## Q50 Q75
## M1 29.15058 30.86873
## M2 27.88043 29.00815
## M3 41.38478 42.10963
## M4 46.96809 51.59574
##
## $comparison
## NULL
##
## $groups
## tratamiento1$densidad_estomática groups
## M4 48.07154 a
## M3 41.27510 b
## M2 28.37036 c
## M1 27.75198 d
##
## attr(,"class")
## [1] "group"u2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0043 12 18.59456 0.3526536 0.1376623
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$densidad_estomática std r Min Max Q25
## M1 11.25779 2.259444 4 8.823529 14.28571 10.31399
## M2 20.64160 1.382671 4 18.750000 21.87500 20.07212
## M3 22.23403 4.461596 4 17.857143 27.58621 18.98041
## M4 20.24483 4.014089 4 16.216216 25.80645 18.63739
## Q50 Q75
## M1 10.96096 11.90476
## M2 20.97070 21.54018
## M3 21.74638 25.00000
## M4 19.47832 21.08576
##
## $comparison
## NULL
##
## $groups
## tratamiento2$densidad_estomática groups
## M3 22.23403 a
## M2 20.64160 b
## M4 20.24483 c
## M1 11.25779 d
##
## attr(,"class")
## [1] "group"u3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.01107 12 35.01245 0.3005047 0.2208791
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$densidad_estomática std r Min Max Q25
## M1 22.48975 2.734203 4 18.42105 24.32432 22.25232
## M2 28.70304 2.912797 4 25.64103 32.35294 26.86480
## M3 37.98699 1.905990 4 35.71429 40.00000 36.83555
## M4 50.87002 4.304834 4 47.50000 57.14286 48.50291
## Q50 Q75
## M1 23.60681 23.84424
## M2 28.40909 30.24733
## M3 38.11685 39.26829
## M4 49.41860 51.78571
##
## $comparison
## NULL
##
## $groups
## tratamiento3$densidad_estomática groups
## M4 50.87002 a
## M3 37.98699 b
## M2 28.70304 c
## M1 22.48975 d
##
## attr(,"class")
## [1] "group"u4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.01102 12 33.76351 0.3109161 0.2203797
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$densidad_estomática std r Min Max Q25
## M1 11.34916 2.189521 4 8.333333 13.51351 10.65476
## M2 35.76113 5.377610 4 30.434783 43.24324 33.47076
## M3 37.98699 1.905990 4 35.714286 40.00000 36.83555
## M4 49.95677 5.844472 4 41.304348 54.16667 49.38859
## Q50 Q75
## M1 11.77489 12.46929
## M2 34.68324 36.97360
## M3 38.11685 39.26829
## M4 52.17803 52.74621
##
## $comparison
## NULL
##
## $groups
## tratamiento4$densidad_estomática groups
## M4 49.95677 a
## M3 37.98699 b
## M2 35.76113 c
## M1 11.34916 d
##
## attr(,"class")
## [1] "group"DIAMETRO DE RAIZ TUBEROSA
Determinación de la variable en los 4 muestreos
ANOVA
v1 <- aov(RT_Diámetro~muestreo, data = tratamiento1)
v2 <- aov(RT_Diámetro~muestreo, data = tratamiento2)
v3 <- aov(RT_Diámetro~muestreo, data = tratamiento3)
v4 <- aov(RT_Diámetro~muestreo, data = tratamiento4)
anova(v1)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 2691.2 897.08 221.96 8.956e-11 ***
## Residuals 12 48.5 4.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(v2)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 157.25 52.417 27.956 1.066e-05 ***
## Residuals 12 22.50 1.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(v3)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 4346.9 1448.98 302.88 1.43e-11 ***
## Residuals 12 57.4 4.78
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(v4)## Analysis of Variance Table
##
## Response: RT_Diámetro
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 2047.05 682.35 273.51 2.614e-11 ***
## Residuals 12 29.94 2.49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(v1))##
## Shapiro-Wilk normality test
##
## data: resid(v1)
## W = 0.88487, p-value = 0.04624shapiro.test(resid(v2))##
## Shapiro-Wilk normality test
##
## data: resid(v2)
## W = 0.89138, p-value = 0.05858shapiro.test(resid(v3))##
## Shapiro-Wilk normality test
##
## data: resid(v3)
## W = 0.95915, p-value = 0.6463shapiro.test(resid(v4))##
## Shapiro-Wilk normality test
##
## data: resid(v4)
## W = 0.9729, p-value = 0.8831library(car)
library(carData)
leveneTest(tratamiento1$RT_Diámetro~tratamiento1$muestreo, 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 2.9124 0.07799 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$RT_Diámetro~tratamiento2$muestreo, 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 2.626 0.09833 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$RT_Diámetro~tratamiento3$muestreo, 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 2.3091 0.1283
## 12leveneTest(tratamiento4$RT_Diámetro~tratamiento4$muestreo, 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 2.6707 0.09478 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
v1tukey <-HSD.test(tratamiento1$RT_Diámetro,tratamiento1$muestreo, 12, 4.04, alpha = 0.05)
v2tukey <-HSD.test(tratamiento2$RT_Diámetro,tratamiento2$muestreo, 12, 1.875, alpha = 0.05)
v3tukey <-HSD.test(tratamiento3$RT_Diámetro,tratamiento3$muestreo, 12, 4.78, alpha = 0.05)
v4tukey <-HSD.test(tratamiento4$RT_Diámetro,tratamiento4$muestreo, 12, 2.49, alpha = 0.05)
v1tukey## $statistics
## MSerror Df Mean CV MSD
## 4.04 12 30.875 6.510041 4.219601
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$RT_Diámetro std r Min Max Q25 Q50 Q75
## M1 14.25 1.7078251 4 12 16 13.50 14.5 15.25
## M2 25.00 3.5590261 4 20 28 23.75 26.0 27.25
## M3 34.75 0.5000000 4 34 35 34.75 35.0 35.00
## M4 49.50 0.5773503 4 49 50 49.00 49.5 50.00
##
## $comparison
## NULL
##
## $groups
## tratamiento1$RT_Diámetro groups
## M4 49.50 a
## M3 34.75 b
## M2 25.00 c
## M1 14.25 d
##
## attr(,"class")
## [1] "group"v2tukey## $statistics
## MSerror Df Mean CV MSD
## 1.875 12 12.875 10.63539 2.874626
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$RT_Diámetro std r Min Max Q25 Q50 Q75
## M1 8.75 2.5000000 4 6 12 7.50 8.5 9.75
## M2 12.00 0.8164966 4 11 13 11.75 12.0 12.25
## M3 13.25 0.5000000 4 13 14 13.00 13.0 13.25
## M4 17.50 0.5773503 4 17 18 17.00 17.5 18.00
##
## $comparison
## NULL
##
## $groups
## tratamiento2$RT_Diámetro groups
## M4 17.50 a
## M3 13.25 b
## M2 12.00 b
## M1 8.75 c
##
## attr(,"class")
## [1] "group"v3tukey## $statistics
## MSerror Df Mean CV MSD
## 4.78 12 34.13125 6.405629 4.58981
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$RT_Diámetro std r Min Max Q25 Q50 Q75
## M1 14.000 0.8164966 4 13 15.0 13.75 14.0 14.250
## M2 25.025 2.8523382 4 21 27.1 24.00 26.0 27.025
## M3 39.500 3.1091264 4 37 44.0 37.75 38.5 40.250
## M4 58.000 0.8164966 4 57 59.0 57.75 58.0 58.250
##
## $comparison
## NULL
##
## $groups
## tratamiento3$RT_Diámetro groups
## M4 58.000 a
## M3 39.500 b
## M2 25.025 c
## M1 14.000 d
##
## attr(,"class")
## [1] "group"v4tukey## $statistics
## MSerror Df Mean CV MSD
## 2.49 12 26.28125 6.004179 3.312687
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$RT_Diámetro std r Min Max Q25 Q50 Q75
## M1 11.500 1.2909944 4 10 13.0 10.75 11.5 12.250
## M2 22.875 1.6520190 4 21 24.5 21.75 23.0 24.125
## M3 27.750 2.2173558 4 25 30.0 26.50 28.0 29.250
## M4 43.000 0.8164966 4 42 44.0 42.75 43.0 43.250
##
## $comparison
## NULL
##
## $groups
## tratamiento4$RT_Diámetro groups
## M4 43.000 a
## M3 27.750 b
## M2 22.875 c
## M1 11.500 d
##
## attr(,"class")
## [1] "group"PESO FRESCO DE RAÍZ TUBEROSA
Determinación de la variable en los 4 muestreos
ANOVA
w1 <- aov(RT_Peso_fresco~muestreo, data = tratamiento1)
w2 <- aov(RT_Peso_fresco~muestreo, data = tratamiento2)
w3 <- aov(RT_Peso_fresco~muestreo, data = tratamiento3)
w4 <- aov(RT_Peso_fresco~muestreo, data = tratamiento4)
anova(w1)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 403.16 134.387 56.821 2.305e-07 ***
## Residuals 12 28.38 2.365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(w2)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 39.531 13.1771 47.268 6.39e-07 ***
## Residuals 12 3.345 0.2788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(w3)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 909.33 303.109 54.137 3.018e-07 ***
## Residuals 12 67.19 5.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(w4)## Analysis of Variance Table
##
## Response: RT_Peso_fresco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 407.48 135.825 64.899 1.095e-07 ***
## Residuals 12 25.11 2.093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(w1))##
## Shapiro-Wilk normality test
##
## data: resid(w1)
## W = 0.87814, p-value = 0.0363shapiro.test(resid(w2))##
## Shapiro-Wilk normality test
##
## data: resid(w2)
## W = 0.93321, p-value = 0.2739shapiro.test(resid(w3))##
## Shapiro-Wilk normality test
##
## data: resid(w3)
## W = 0.86399, p-value = 0.02203shapiro.test(resid(w4))##
## Shapiro-Wilk normality test
##
## data: resid(w4)
## W = 0.97053, p-value = 0.8471library(car)
library(carData)
leveneTest(tratamiento1$RT_Peso_fresco~tratamiento1$muestreo, 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 3.4124 0.053 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$RT_Peso_fresco~tratamiento2$muestreo, 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 3.8501 0.03846 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$RT_Peso_fresco~tratamiento3$muestreo, 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 4.0137 0.03426 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$RT_Peso_fresco~tratamiento4$muestreo, 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 4.5 0.02457 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
library(dplyr)
w1tukey <-HSD.test(tratamiento1$RT_Peso_fresco,tratamiento1$muestreo, 12, 2.365, alpha = 0.05)
w2tukey <-HSD.test(tratamiento2$RT_Peso_fresco,tratamiento2$muestreo, 12, 0.2788, alpha = 0.05)
w3tukey <-HSD.test(tratamiento3$RT_Peso_fresco,tratamiento3$muestreo, 12, 0.001, alpha = 0.05)
w4tukey <-HSD.test(tratamiento4$RT_Peso_fresco,tratamiento4$muestreo, 12, 5.599, alpha = 0.05)
w1tukey## $statistics
## MSerror Df Mean CV MSD
## 2.365 12 10.64944 14.44072 3.228467
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$RT_Peso_fresco std r Min Max Q25 Q50
## M1 3.47075 0.2578648 4 3.122 3.734 3.3785 3.5135
## M2 8.84200 2.7575440 4 6.494 12.805 7.3640 8.0345
## M3 13.46700 0.6758555 4 12.483 13.958 13.3005 13.7135
## M4 16.81800 1.1545998 4 15.223 17.738 16.3450 17.1555
## Q75
## M1 3.60575
## M2 9.51250
## M3 13.88000
## M4 17.62850
##
## $comparison
## NULL
##
## $groups
## tratamiento1$RT_Peso_fresco groups
## M4 16.81800 a
## M3 13.46700 b
## M2 8.84200 c
## M1 3.47075 d
##
## attr(,"class")
## [1] "group"w2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.2788 12 3.125375 16.89446 1.108478
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$RT_Peso_fresco std r Min Max Q25 Q50 Q75
## M1 1.08900 0.1629438 4 0.922 1.298 0.98650 1.0680 1.17050
## M2 2.44875 0.2434822 4 2.201 2.762 2.29475 2.4160 2.57000
## M3 3.58800 0.8404864 4 2.917 4.817 3.18250 3.3090 3.71450
## M4 5.37575 0.5681974 4 4.782 5.888 4.94400 5.4165 5.84825
##
## $comparison
## NULL
##
## $groups
## tratamiento2$RT_Peso_fresco groups
## M4 5.37575 a
## M3 3.58800 b
## M2 2.44875 c
## M1 1.08900 d
##
## attr(,"class")
## [1] "group"w3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.001 12 13.55781 0.2332439 0.06638665
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$RT_Peso_fresco std r Min Max Q25 Q50
## M1 3.15050 0.1493106 4 2.927 3.238 3.14300 3.2185
## M2 10.45900 4.1825588 4 4.284 13.521 9.89175 12.0155
## M3 17.29350 1.6845360 4 15.187 19.215 16.51525 17.3860
## M4 23.32825 1.4290231 4 21.568 24.957 22.61650 23.3940
## Q75
## M1 3.22600
## M2 12.58275
## M3 18.16425
## M4 24.10575
##
## $comparison
## NULL
##
## $groups
## tratamiento3$RT_Peso_fresco groups
## M4 23.32825 a
## M3 17.29350 b
## M2 10.45900 c
## M1 3.15050 d
##
## attr(,"class")
## [1] "group"w4tukey## $statistics
## MSerror Df Mean CV MSD
## 5.599 12 9.717437 24.35025 4.967478
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$RT_Peso_fresco std r Min Max Q25 Q50
## M1 2.93975 0.4873297 4 2.251 3.361 2.78425 3.0735
## M2 7.70225 1.6723252 4 5.935 9.961 6.96475 7.4565
## M3 11.54350 2.2140449 4 9.215 13.958 9.94400 11.5005
## M4 16.68425 0.6597991 4 15.822 17.375 16.39200 16.7700
## Q75
## M1 3.22900
## M2 8.19400
## M3 13.10000
## M4 17.06225
##
## $comparison
## NULL
##
## $groups
## tratamiento4$RT_Peso_fresco groups
## M4 16.68425 a
## M3 11.54350 b
## M2 7.70225 bc
## M1 2.93975 c
##
## attr(,"class")
## [1] "group"PESO SECO DE RAÍZ TUBEROSA
Determinación de la variable en los 4 muestreos
ANOVA
x1 <- aov(RT_Peso_seco~muestreo, data = tratamiento1)
x2 <- aov(RT_Peso_seco~muestreo, data = tratamiento2)
x3 <- aov(RT_Peso_seco~muestreo, data = tratamiento3)
x4 <- aov(RT_Peso_seco~muestreo, data = tratamiento4)
anova(x1)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 6.5201 2.17338 48.827 5.344e-07 ***
## Residuals 12 0.5341 0.04451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(x2)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.89174 0.297246 134.56 1.676e-09 ***
## Residuals 12 0.02651 0.002209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(x3)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 16.3678 5.4559 255.95 3.867e-11 ***
## Residuals 12 0.2558 0.0213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(x4)## Analysis of Variance Table
##
## Response: RT_Peso_seco
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.3126 1.1042 172.62 3.919e-10 ***
## Residuals 12 0.0768 0.0064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(x1))##
## Shapiro-Wilk normality test
##
## data: resid(x1)
## W = 0.79083, p-value = 0.002063shapiro.test(resid(x2))##
## Shapiro-Wilk normality test
##
## data: resid(x2)
## W = 0.96035, p-value = 0.6682shapiro.test(resid(x3))##
## Shapiro-Wilk normality test
##
## data: resid(x3)
## W = 0.9627, p-value = 0.711shapiro.test(resid(x4))##
## Shapiro-Wilk normality test
##
## data: resid(x4)
## W = 0.91653, p-value = 0.1483library(car)
library(carData)
leveneTest(tratamiento1$RT_Peso_seco~tratamiento1$muestreo, 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 4.1823 0.03047 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$RT_Peso_seco~tratamiento2$muestreo, 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 2.3943 0.1193
## 12leveneTest(tratamiento3$RT_Peso_seco~tratamiento3$muestreo, 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 1.7061 0.2187
## 12leveneTest(tratamiento4$RT_Peso_seco~tratamiento4$muestreo, 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 1.5005 0.2645
## 12library(agricolae)
library(dplyr)
x1tukey <-HSD.test(tratamiento1$RT_Peso_seco,tratamiento1$muestreo, 12, 0.04451, alpha = 0.05)
x2tukey <-HSD.test(tratamiento2$RT_Peso_seco,tratamiento2$muestreo, 12, 0.002209, alpha = 0.05)
x3tukey <-HSD.test(tratamiento3$RT_Peso_seco,tratamiento3$muestreo, 12, 0.0213, alpha = 0.05)
x4tukey <-HSD.test(tratamiento4$RT_Peso_seco,tratamiento4$muestreo, 12, 0.0064, alpha = 0.05)
x1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.04451 12 1.064625 19.81674 0.4429039
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$RT_Peso_seco std r Min Max Q25 Q50 Q75
## M1 0.40550 0.06477911 4 0.342 0.493 0.36750 0.3935 0.43150
## M2 0.54325 0.07347278 4 0.474 0.636 0.49050 0.5315 0.58425
## M3 1.31800 0.06694774 4 1.248 1.387 1.26825 1.3185 1.36825
## M4 1.99175 0.40493240 4 1.638 2.573 1.78875 1.8780 2.08100
##
## $comparison
## NULL
##
## $groups
## tratamiento1$RT_Peso_seco groups
## M4 1.99175 a
## M3 1.31800 b
## M2 0.54325 c
## M1 0.40550 c
##
## attr(,"class")
## [1] "group"x2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.002209 12 0.35025 13.41899 0.09866852
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$RT_Peso_seco std r Min Max Q25 Q50 Q75
## M1 0.06800 0.02294922 4 0.039 0.095 0.06000 0.0690 0.07700
## M2 0.24375 0.03933086 4 0.186 0.274 0.23775 0.2575 0.26350
## M3 0.37675 0.04431986 4 0.317 0.418 0.35750 0.3860 0.40525
## M4 0.71250 0.06927000 4 0.628 0.782 0.67150 0.7200 0.76100
##
## $comparison
## NULL
##
## $groups
## tratamiento2$RT_Peso_seco groups
## M4 0.71250 a
## M3 0.37675 b
## M2 0.24375 c
## M1 0.06800 d
##
## attr(,"class")
## [1] "group"x3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0213 12 1.32125 11.04599 0.3063871
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$RT_Peso_seco std r Min Max Q25 Q50 Q75
## M1 0.3390 0.03787699 4 0.293 0.385 0.32300 0.3390 0.35500
## M2 0.5255 0.19077474 4 0.242 0.649 0.50000 0.6055 0.63100
## M3 1.5345 0.18227909 4 1.362 1.792 1.45875 1.4920 1.56775
## M4 2.8860 0.11920570 4 2.720 2.978 2.83925 2.9230 2.96975
##
## $comparison
## NULL
##
## $groups
## tratamiento3$RT_Peso_seco groups
## M4 2.8860 a
## M3 1.5345 b
## M2 0.5255 c
## M1 0.3390 c
##
## attr(,"class")
## [1] "group"x4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0064 12 0.79 10.12658 0.1679464
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$RT_Peso_seco std r Min Max Q25 Q50 Q75
## M1 0.27325 0.04914180 4 0.229 0.331 0.23425 0.2665 0.30550
## M2 0.45450 0.06634506 4 0.391 0.524 0.40150 0.4515 0.50450
## M3 1.00950 0.12816266 4 0.892 1.191 0.94450 0.9775 1.04250
## M4 1.42275 0.04842434 4 1.376 1.467 1.38350 1.4240 1.46325
##
## $comparison
## NULL
##
## $groups
## tratamiento4$RT_Peso_seco groups
## M4 1.42275 a
## M3 1.00950 b
## M2 0.45450 c
## M1 0.27325 d
##
## attr(,"class")
## [1] "group"fin :)