Datos de los índices de variables indirectas
rm(list=ls())
library(readxl)
General <- read_excel("~/Documents/INDICES.xlsx",
sheet = "General")
Muestreo1 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "Muestreo 1")
Muestreo2 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "Muestra 2")
Muestreo3 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "Muestra 3")
Muestreo4 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "Muestra 4")
tratamiento1 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "tratamiento 1")
tratamiento2 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "tratamiento 2")
tratamiento3 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "tratamiento 3")
tratamiento4 <- read_excel("~/Documents/INDICES.xlsx",
sheet = "tratamiento 4")ANÁLISIS TAN
Determinación de la variable en los 4 muestreos
ANOVA
m1 <- aov(TAN~Trat, data = Muestreo1)
m2 <- aov(TAN~Trat, data = Muestreo2)
m3 <- aov(TAN~Trat, data = Muestreo3)
m4 <- aov(TAN~Trat, data = Muestreo4)
anova(m1)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3.4732e-08 1.1577e-08 8.2652 0.002994 **
## Residuals 12 1.6809e-08 1.4007e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m2)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 3.3426e-08 1.1142e-08 1.5844 0.2446
## Residuals 12 8.4386e-08 7.0322e-09anova(m3)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 6.2270e-09 2.0756e-09 0.3183 0.812
## Residuals 12 7.8262e-08 6.5219e-09anova(m4)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 2.7874e-08 9.2913e-09 2.2208 0.1384
## Residuals 12 5.0205e-08 4.1837e-09Purea de normalidad de shapiro
shapiro.test(resid(m1))##
## Shapiro-Wilk normality test
##
## data: resid(m1)
## W = 0.96134, p-value = 0.6861shapiro.test(resid(m2))##
## Shapiro-Wilk normality test
##
## data: resid(m2)
## W = 0.90356, p-value = 0.09166shapiro.test(resid(m3))##
## Shapiro-Wilk normality test
##
## data: resid(m3)
## W = 0.91486, p-value = 0.1394shapiro.test(resid(m4))##
## Shapiro-Wilk normality test
##
## data: resid(m4)
## W = 0.94322, p-value = 0.3903En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)## Loading required package: carDatalibrary(carData)
leveneTest(Muestreo1$TAN~Muestreo1$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 0.7057 0.5668
## 12leveneTest(Muestreo2$TAN~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 2.2649 0.1332
## 12leveneTest(Muestreo3$TAN~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.1674 0.3627
## 12leveneTest(Muestreo4$TAN~Muestreo4$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 1.0816 0.3939
## 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$TAN,Muestreo1$Trat, 12, 0.0000000014007, alpha = 0.05)
m2tukey <-HSD.test(Muestreo2$TAN,Muestreo2$Trat, 12, 0.0000000070322, alpha = 0.05)
m3tukey <-HSD.test(Muestreo3$TAN,Muestreo3$Trat, 12, 0.0000000065219, alpha = 0.05)
m4tukey <-HSD.test(Muestreo4$TAN,Muestreo4$Trat, 12, 0.0000000041837, alpha = 0.05)
m1tukey## $statistics
## MSerror Df Mean CV MSD
## 1.4007e-09 12 0.000323703 11.56181 7.856938e-05
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$TAN std r Min Max Q25
## T1 0.0003624532 4.419217e-05 4 0.0003100611 0.0004101590 0.0003358606
## T2 0.0002453705 2.899676e-05 4 0.0002216400 0.0002853870 0.0002253693
## T3 0.0003320469 4.283163e-05 4 0.0002955064 0.0003879268 0.0003001174
## T4 0.0003549415 3.121939e-05 4 0.0003091650 0.0003756448 0.0003479960
## Q50 Q75
## T1 0.0003647963 0.0003913889
## T2 0.0002372276 0.0002572288
## T3 0.0003223773 0.0003543068
## T4 0.0003674781 0.0003744237
##
## $comparison
## NULL
##
## $groups
## Muestreo1$TAN groups
## T1 0.0003624532 a
## T4 0.0003549415 a
## T3 0.0003320469 a
## T2 0.0002453705 b
##
## attr(,"class")
## [1] "group"m2tukey## $statistics
## MSerror Df Mean CV MSD
## 7.0322e-09 12 0.0002116973 39.61231 0.0001760461
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$TAN std r Min Max Q25
## T1 0.0001695700 1.267257e-04 4 -7.432806e-06 0.0002924601 0.0001367035
## T2 0.0001655663 3.061887e-05 4 1.335902e-04 0.0001938145 0.0001423245
## T3 0.0002391105 1.028861e-04 4 1.056262e-04 0.0003488176 0.0001933087
## T4 0.0002725426 2.337250e-05 4 2.512514e-04 0.0003058799 0.0002620367
## Q50 Q75
## T1 0.0001966264 0.0002294929
## T2 0.0001674302 0.0001906719
## T3 0.0002509991 0.0002968008
## T4 0.0002665195 0.0002770254
##
## $comparison
## NULL
##
## $groups
## Muestreo2$TAN groups
## T4 0.0002725426 a
## T3 0.0002391105 a
## T1 0.0001695700 a
## T2 0.0001655663 a
##
## attr(,"class")
## [1] "group"m3tukey## $statistics
## MSerror Df Mean CV MSD
## 6.5219e-09 12 0.0002508313 32.19625 0.0001695383
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$TAN std r Min Max Q25
## T1 0.0002625136 1.271750e-04 4 0.0001663561 0.0004430050 0.0001771274
## T2 0.0002212328 5.181056e-05 4 0.0001709030 0.0002665815 0.0001793640
## T3 0.0002459390 6.599959e-05 4 0.0001938114 0.0003422950 0.0002113470
## T4 0.0002736398 5.360649e-05 4 0.0002192145 0.0003215005 0.0002318154
## Q50 Q75
## T1 0.0002203466 0.0003057328
## T2 0.0002237235 0.0002655923
## T3 0.0002238247 0.0002584167
## T4 0.0002769221 0.0003187464
##
## $comparison
## NULL
##
## $groups
## Muestreo3$TAN groups
## T4 0.0002736398 a
## T1 0.0002625136 a
## T3 0.0002459390 a
## T2 0.0002212328 a
##
## attr(,"class")
## [1] "group"m4tukey## $statistics
## MSerror Df Mean CV MSD
## 4.1837e-09 12 0.0002179291 29.68008 0.0001357879
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$TAN std r Min Max Q25
## T1 0.0002899928 6.408224e-05 4 0.0002220434 0.0003754579 0.0002581394
## T2 0.0001894569 3.294327e-05 4 0.0001684577 0.0002381012 0.0001695182
## T3 0.0001988293 4.359236e-05 4 0.0001623153 0.0002618206 0.0001760528
## T4 0.0001934372 9.819760e-05 4 0.0000845898 0.0003203372 0.0001443251
## Q50 Q75
## T1 0.0002812350 0.0003130884
## T2 0.0001756343 0.0001955730
## T3 0.0001855906 0.0002083671
## T4 0.0001844110 0.0002335232
##
## $comparison
## NULL
##
## $groups
## Muestreo4$TAN groups
## T1 0.0002899928 a
## T3 0.0001988293 a
## T4 0.0001934372 a
## T2 0.0001894569 a
##
## attr(,"class")
## [1] "group"Para este caso se las diferencias estadisticas se dan de la siguiente manera:
Muestreo1$TAN groups T1 0.0003624532 a T4 0.0003549415 a T3 0.0003320469 a T2 0.0002453705 b
Muestreo2$TAN groups T4 0.0002725426 a T3 0.0002391105 a T1 0.0001695700 b T2 0.0001655663 b
Muestreo3$TAN groups T4 0.0002736398 a T1 0.0002625136 b T3 0.0002459390 c T2 0.0002212328 d
Muestreo4$TAN groups T1 0.0002899928 a T3 0.0001988293 b T4 0.0001934372 b T2 0.0001894569 c
TCC
Determinación de la variable en los 4 muestreos
ANOVA
a1 <- aov(TCC~Trat, data = Muestreo1)
a2 <- aov(TCC~Trat, data = Muestreo2)
a3 <- aov(TCC~Trat, data = Muestreo3)
a4 <- aov(TCC~Trat, data = Muestreo4)
anova(a1)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 9.5811e-09 3.1937e-09 6.6273 0.006859 **
## Residuals 12 5.7829e-09 4.8190e-10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(a2)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1.3514e-07 4.5047e-08 1.4975 0.2652
## Residuals 12 3.6099e-07 3.0082e-08anova(a3)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1.3423e-08 4.4744e-09 0.2253 0.877
## Residuals 12 2.3834e-07 1.9862e-08anova(a4)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1.3529e-07 4.5096e-08 2.0317 0.1632
## Residuals 12 2.6635e-07 2.2196e-08Purea de normalidad de shapiro
shapiro.test(resid(a1))##
## Shapiro-Wilk normality test
##
## data: resid(a1)
## W = 0.95174, p-value = 0.5178shapiro.test(resid(a2))##
## Shapiro-Wilk normality test
##
## data: resid(a2)
## W = 0.91214, p-value = 0.126shapiro.test(resid(a3))##
## Shapiro-Wilk normality test
##
## data: resid(a3)
## W = 0.93104, p-value = 0.2532shapiro.test(resid(a4))##
## Shapiro-Wilk normality test
##
## data: resid(a4)
## W = 0.94347, p-value = 0.3937En todos los muestreos se puede observar normalidad, almenos en casi todos.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$TCC~Muestreo1$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 0.3467 0.7922
## 12leveneTest(Muestreo2$TCC~Muestreo2$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 1.8167 0.1978
## 12leveneTest(Muestreo3$TCC~Muestreo3$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 0.6584 0.5932
## 12leveneTest(Muestreo4$TCC~Muestreo4$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 0.7774 0.5288
## 12En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
a1tukey <-HSD.test(Muestreo1$TCC,Muestreo1$Trat, 12, 0.00000000048190, alpha = 0.05)
a2tukey <-HSD.test(Muestreo2$TCC,Muestreo2$Trat, 12, 0.000000030082, alpha = 0.05)
a3tukey <-HSD.test(Muestreo3$TCC,Muestreo3$Trat, 12, 0.000000019862, alpha = 0.05)
a4tukey <-HSD.test(Muestreo4$TCC,Muestreo4$Trat, 12, 0.000000022196, alpha = 0.05)
a1tukey## $statistics
## MSerror Df Mean CV MSD
## 4.819e-10 12 0.000203474 10.78871 4.608496e-05
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$TCC std r Min Max Q25
## T1 0.0002236263 2.704882e-05 4 0.0001925549 0.0002551906 0.0002075775
## T2 0.0001641944 1.943114e-05 4 0.0001423360 0.0001858519 0.0001515753
## T3 0.0002015624 2.103139e-05 4 0.0001788001 0.0002288807 0.0001903329
## T4 0.0002245127 1.939317e-05 4 0.0001957322 0.0002363876 0.0002215323
## Q50 Q75
## T1 0.0002233798 0.0002394286
## T2 0.0001642948 0.0001769139
## T3 0.0001992844 0.0002105140
## T4 0.0002329656 0.0002359460
##
## $comparison
## NULL
##
## $groups
## Muestreo1$TCC groups
## T4 0.0002245127 a
## T1 0.0002236263 a
## T3 0.0002015624 ab
## T2 0.0001641944 b
##
## attr(,"class")
## [1] "group"a2tukey## $statistics
## MSerror Df Mean CV MSD
## 3.0082e-08 12 0.0004566662 37.97996 0.0003641112
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$TCC std r Min Max Q25
## T1 0.0003528799 2.585243e-04 4 -0.0000184035 0.0005760138 0.0002925578
## T2 0.0003854118 7.093073e-05 4 0.0003233501 0.0004493851 0.0003243440
## T3 0.0005080154 2.066039e-04 4 0.0002294965 0.0007168038 0.0004291213
## T4 0.0005803579 7.601281e-05 4 0.0004889043 0.0006742232 0.0005499561
## Q50 Q75
## T1 0.0004269546 0.0004872767
## T2 0.0003844560 0.0004455237
## T3 0.0005428806 0.0006217747
## T4 0.0005791521 0.0006095540
##
## $comparison
## NULL
##
## $groups
## Muestreo2$TCC groups
## T4 0.0005803579 a
## T3 0.0005080154 a
## T2 0.0003854118 a
## T1 0.0003528799 a
##
## attr(,"class")
## [1] "group"a3tukey## $statistics
## MSerror Df Mean CV MSD
## 1.9862e-08 12 0.0005169718 27.26118 0.0002958641
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$TCC std r Min Max Q25
## T1 0.0004868268 0.0001921006 4 0.0003311563 0.0007463121 0.0003467846
## T2 0.0005512016 0.0001178989 4 0.0004145499 0.0006824665 0.0004787337
## T3 0.0004897597 0.0001284566 4 0.0003976975 0.0006777217 0.0004131946
## T4 0.0005400991 0.0001101935 4 0.0004333485 0.0006498556 0.0004518613
## Q50 Q75
## T1 0.0004349193 0.0005749615
## T2 0.0005538950 0.0006263630
## T3 0.0004418098 0.0005183749
## T4 0.0005385962 0.0006268340
##
## $comparison
## NULL
##
## $groups
## Muestreo3$TCC groups
## T2 0.0005512016 a
## T4 0.0005400991 a
## T3 0.0004897597 a
## T1 0.0004868268 a
##
## attr(,"class")
## [1] "group"a4tukey## $statistics
## MSerror Df Mean CV MSD
## 2.2196e-08 12 0.0004883648 30.50654 0.000312765
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$TCC std r Min Max Q25
## T1 0.0006469903 0.0001405282 4 0.0004985909 0.0008340788 0.0005750375
## T2 0.0004272366 0.0001051782 4 0.0003401361 0.0005778021 0.0003656027
## T3 0.0004488395 0.0001006021 4 0.0003633342 0.0005945343 0.0004029956
## T4 0.0004303928 0.0002187499 4 0.0001955390 0.0007174863 0.0003160773
## Q50 Q75
## T1 0.0006276458 0.0006995986
## T2 0.0003955042 0.0004571381
## T3 0.0004187447 0.0004645887
## T4 0.0004042730 0.0005185886
##
## $comparison
## NULL
##
## $groups
## Muestreo4$TCC groups
## T1 0.0006469903 a
## T3 0.0004488395 a
## T4 0.0004303928 a
## T2 0.0004272366 a
##
## attr(,"class")
## [1] "group"para este caso las diferencias estadisticas se dan de la siguiente manera:
Muestreo1$TCC groups T4 0.0002245127 a T1 0.0002236263 a T3 0.0002015624 ab T2 0.0001641944 b
Muestreo2$TCC groups T4 0.0005803579 a T3 0.0005080154 a T2 0.0003854118 b T1 0.0003528799 b
Muestreo3$TCC groups T2 0.0005512016 a T4 0.0005400991 a T3 0.0004897597 b T1 0.0004868268 b
Muestreo4$TCC groups T1 0.0006469903 a T3 0.0004488395 b T4 0.0004303928 b T2 0.0004272366 c
IAF
Determinación de la variable en los 4 muestreos
ANOVA
b1 <- aov(IAF~Trat, data = Muestreo1)
b2 <- aov(IAF~Trat, data = Muestreo2)
b3 <- aov(IAF~Trat, data = Muestreo3)
b4 <- aov(IAF~Trat, data = Muestreo4)
anova(b1)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.006698 0.0022327 2.0526 0.1602
## Residuals 12 0.013053 0.0010877anova(b2)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0040121 0.00133736 2.471 0.1118
## Residuals 12 0.0064946 0.00054122anova(b3)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.019103 0.0063676 3.691 0.04314 *
## Residuals 12 0.020702 0.0017252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(b4)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.019504 0.0065014 4.7777 0.02048 *
## Residuals 12 0.016329 0.0013608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(b1))##
## Shapiro-Wilk normality test
##
## data: resid(b1)
## W = 0.9739, p-value = 0.8971shapiro.test(resid(b2))##
## Shapiro-Wilk normality test
##
## data: resid(b2)
## W = 0.97073, p-value = 0.8504shapiro.test(resid(b3))##
## Shapiro-Wilk normality test
##
## data: resid(b3)
## W = 0.91657, p-value = 0.1485shapiro.test(resid(b4))##
## Shapiro-Wilk normality test
##
## data: resid(b4)
## W = 0.91992, p-value = 0.1682En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$IAF~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 6.3688 0.007903 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo2$IAF~Muestreo2$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 1.8208 0.197
## 12leveneTest(Muestreo3$IAF~Muestreo3$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 2.7588 0.08822 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(Muestreo4$IAF~Muestreo4$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 2.6038 0.1001
## 12En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
b1tukey <-HSD.test(Muestreo1$IAF,Muestreo1$Trat, 12, 0.0010877, alpha = 0.05)
b2tukey <-HSD.test(Muestreo2$IAF,Muestreo2$Trat, 12, 0.00054122, alpha = 0.05)
b3tukey <-HSD.test(Muestreo3$IAF,Muestreo3$Trat, 12, 0.0017252, alpha = 0.05)
b4tukey <-HSD.test(Muestreo4$IAF,Muestreo4$Trat, 12, 0.0013608, alpha = 0.05)
b1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0010877 12 1.118538 2.948518 0.06923653
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$IAF std r Min Max Q25 Q50 Q75
## T1 1.125077 0.01478559 4 1.109741 1.144958 1.117480 1.122804 1.130401
## T2 1.149001 0.05108794 4 1.103874 1.205655 1.106788 1.143237 1.185450
## T3 1.096939 0.03519133 4 1.066643 1.144604 1.072586 1.088254 1.112607
## T4 1.103135 0.01684830 4 1.081410 1.118061 1.094122 1.106536 1.115549
##
## $comparison
## NULL
##
## $groups
## Muestreo1$IAF groups
## T2 1.149001 a
## T1 1.125077 a
## T4 1.103135 a
## T3 1.096939 a
##
## attr(,"class")
## [1] "group"b2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00054122 12 1.12624 2.065646 0.0488391
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$IAF std r Min Max Q25 Q50 Q75
## T1 1.106880 0.02650974 4 1.074074 1.134709 1.092240 1.109368 1.124009
## T2 1.146038 0.03448256 4 1.107744 1.190397 1.128513 1.143004 1.160529
## T3 1.115155 0.01136026 4 1.103362 1.130112 1.108684 1.113572 1.120042
## T4 1.136888 0.01200040 4 1.124346 1.152758 1.130298 1.135224 1.141815
##
## $comparison
## NULL
##
## $groups
## Muestreo2$IAF groups
## T2 1.146038 a
## T4 1.136888 a
## T3 1.115155 a
## T1 1.106880 a
##
## attr(,"class")
## [1] "group"b3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0017252 12 1.10422 3.761525 0.08719678
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$IAF std r Min Max Q25 Q50 Q75
## T1 1.081718 0.03489206 4 1.046362 1.116865 1.054841 1.081823 1.108700
## T2 1.158887 0.07031378 4 1.098743 1.260313 1.123761 1.138247 1.173373
## T3 1.107677 0.01987336 4 1.079806 1.126893 1.103345 1.112006 1.116339
## T4 1.068598 0.01855749 4 1.045136 1.089968 1.060929 1.069644 1.077313
##
## $comparison
## NULL
##
## $groups
## Muestreo3$IAF groups
## T2 1.158887 a
## T3 1.107677 ab
## T1 1.081718 ab
## T4 1.068598 b
##
## attr(,"class")
## [1] "group"b4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0013608 12 1.069528 3.449092 0.07744224
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$IAF std r Min Max Q25 Q50 Q75
## T1 1.027511 0.007982756 4 1.020252 1.036141 1.020971 1.026826 1.033367
## T2 1.123669 0.063782710 4 1.062205 1.213243 1.096236 1.109615 1.137048
## T3 1.056190 0.018988757 4 1.030529 1.076311 1.050927 1.058959 1.064221
## T4 1.070744 0.030831918 4 1.031667 1.101491 1.054251 1.074908 1.091401
##
## $comparison
## NULL
##
## $groups
## Muestreo4$IAF groups
## T2 1.123669 a
## T4 1.070744 ab
## T3 1.056190 ab
## T1 1.027511 b
##
## attr(,"class")
## [1] "group"En este caso se pueden dejar las mismas diferencias estadisticas de la prueba
AFE
Determinación de la variable en los 4 muestreos
ANOVA
c1 <- aov(AFE~Trat, data = Muestreo1)
c2 <- aov(AFE~Trat, data = Muestreo2)
c3 <- aov(AFE~Trat, data = Muestreo3)
c4 <- aov(AFE~Trat, data = Muestreo4)
anova(c1)## Analysis of Variance Table
##
## Response: AFE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 14528.7 4842.9 8.6668 0.002483 **
## Residuals 12 6705.4 558.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(c2)## Analysis of Variance Table
##
## Response: AFE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 4356.5 1452.18 6.9049 0.005912 **
## Residuals 12 2523.8 210.31
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(c3)## Analysis of Variance Table
##
## Response: AFE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 870.04 290.013 3.5319 0.04848 *
## Residuals 12 985.34 82.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(c4)## Analysis of Variance Table
##
## Response: AFE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1450.1 483.35 1.5807 0.2455
## Residuals 12 3669.4 305.79Purea de normalidad de shapiro
shapiro.test(resid(b1))##
## Shapiro-Wilk normality test
##
## data: resid(b1)
## W = 0.9739, p-value = 0.8971shapiro.test(resid(b2))##
## Shapiro-Wilk normality test
##
## data: resid(b2)
## W = 0.97073, p-value = 0.8504shapiro.test(resid(b3))##
## Shapiro-Wilk normality test
##
## data: resid(b3)
## W = 0.91657, p-value = 0.1485shapiro.test(resid(b4))##
## Shapiro-Wilk normality test
##
## data: resid(b4)
## W = 0.91992, p-value = 0.1682En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$AFE~Muestreo1$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 0.4455 0.7249
## 12leveneTest(Muestreo2$AFE~Muestreo2$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 1.4863 0.268
## 12leveneTest(Muestreo3$AFE~Muestreo3$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 0.4844 0.6994
## 12leveneTest(Muestreo4$AFE~Muestreo4$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 3.0347 0.07081 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
b1tukey <-HSD.test(Muestreo1$AFE,Muestreo1$Trat, 12, 558.8, alpha = 0.05)
b2tukey <-HSD.test(Muestreo2$AFE,Muestreo2$Trat, 12, 210.31, alpha = 0.05)
b3tukey <-HSD.test(Muestreo3$AFE,Muestreo3$Trat, 12, 82.112, alpha = 0.05)
b4tukey <-HSD.test(Muestreo4$AFE,Muestreo4$Trat, 12, 305.79, alpha = 0.05)
b1tukey## $statistics
## MSerror Df Mean CV MSD
## 558.8 12 201.7165 11.7189 49.62596
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$AFE std r Min Max Q25 Q50 Q75
## T1 181.6047 21.52385 4 160.2384 208.7621 166.9957 178.7091 193.3180
## T2 252.4025 29.33681 4 212.8729 276.9788 238.8877 259.8791 273.3939
## T3 196.2077 24.03891 4 166.4378 220.1063 182.4325 199.1434 212.9187
## T4 176.6511 18.25775 4 166.3584 204.0070 167.4578 168.1195 177.3128
##
## $comparison
## NULL
##
## $groups
## Muestreo1$AFE groups
## T2 252.4025 a
## T3 196.2077 b
## T1 181.6047 b
## T4 176.6511 b
##
## attr(,"class")
## [1] "group"b2tukey## $statistics
## MSerror Df Mean CV MSD
## 210.31 12 157.4971 9.207831 30.44463
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$AFE std r Min Max Q25 Q50 Q75
## T1 156.8546 10.582479 4 148.4627 172.3514 151.8865 153.3021 158.2702
## T2 183.9311 9.800889 4 175.3117 196.7846 176.8468 181.8140 188.8982
## T3 149.9574 18.022698 4 132.7247 172.0388 136.7526 147.5330 160.7378
## T4 139.2455 17.560987 4 120.2312 160.5852 128.1718 138.0827 149.1565
##
## $comparison
## NULL
##
## $groups
## Muestreo2$AFE groups
## T2 183.9311 a
## T1 156.8546 ab
## T3 149.9574 b
## T4 139.2455 b
##
## attr(,"class")
## [1] "group"b3tukey## $statistics
## MSerror Df Mean CV MSD
## 82.112 12 107.2268 8.450841 19.02322
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$AFE std r Min Max Q25 Q50 Q75
## T1 95.75701 7.206865 4 86.45177 101.6767 91.87130 97.44979 101.3355
## T2 115.82625 7.794443 4 108.80781 126.9212 111.87301 113.78802 117.7413
## T3 110.64734 10.047685 4 95.90481 118.4667 109.21482 114.10894 115.5415
## T4 106.67666 10.714389 4 94.70258 118.7209 99.69001 106.64157 113.6282
##
## $comparison
## NULL
##
## $groups
## Muestreo3$AFE groups
## T2 115.82625 a
## T3 110.64734 ab
## T4 106.67666 ab
## T1 95.75701 b
##
## attr(,"class")
## [1] "group"b4tukey## $statistics
## MSerror Df Mean CV MSD
## 305.79 12 134.3563 13.01529 36.71068
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$AFE std r Min Max Q25 Q50 Q75
## T1 146.5117 6.268268 4 140.8304 152.4079 141.2306 146.4043 151.6854
## T2 126.4186 31.663918 4 104.6348 172.0388 105.1604 114.5004 135.7586
## T3 140.6989 10.429163 4 132.8473 155.1346 133.1479 137.4069 144.9579
## T4 123.7958 8.513902 4 115.3453 133.8983 117.6013 122.9698 129.1642
##
## $comparison
## NULL
##
## $groups
## Muestreo4$AFE groups
## T1 146.5117 a
## T3 140.6989 a
## T2 126.4186 a
## T4 123.7958 a
##
## attr(,"class")
## [1] "group"Para este caso se dejan los mismos de la prueba a exepción del cuarto muestreo donde se reemplaza por el siguiente:
Muestreo4$AFE groups T1 146.5117 a T3 140.6989 a T2 126.4186 b T4 123.7958 b
TRC
Determinación de la variable en los 4 muestreos
ANOVA
d1 <- aov(TRC~Trat, data = Muestreo1)
d2 <- aov(TRC~Trat, data = Muestreo2)
d3 <- aov(TRC~Trat, data = Muestreo3)
d4 <- aov(TRC~Trat, data = Muestreo4)
anova(d1)## Warning in anova.lm(d1): ANOVA F-tests on an essentially perfect fit are
## unreliable## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 5.7778e-34 1.9259e-34 1 0.4262
## Residuals 12 2.3111e-33 1.9259e-34anova(d2)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0020962 0.00069875 0.7471 0.5446
## Residuals 12 0.0112227 0.00093522anova(d3)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0003423 0.00011410 0.1906 0.9008
## Residuals 12 0.0071836 0.00059863anova(d4)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 0.0016975 0.00056583 2.2016 0.1407
## Residuals 12 0.0030842 0.00025701Purea de normalidad de shapiro
shapiro.test(resid(d1))##
## Shapiro-Wilk normality test
##
## data: resid(d1)
## W = 0.56474, p-value = 7.665e-06shapiro.test(resid(d2))##
## Shapiro-Wilk normality test
##
## data: resid(d2)
## W = 0.91228, p-value = 0.1267shapiro.test(resid(d3))##
## Shapiro-Wilk normality test
##
## data: resid(d3)
## W = 0.92541, p-value = 0.206shapiro.test(resid(d4))##
## Shapiro-Wilk normality test
##
## data: resid(d4)
## W = 0.94597, p-value = 0.4286En todos los muestreos se puede observar normalidad en los datos
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo1$TRC~Muestreo1$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3
## 12leveneTest(Muestreo2$TRC~Muestreo2$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.9549 0.1747
## 12leveneTest(Muestreo3$TRC~Muestreo3$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 1.4912 0.2668
## 12leveneTest(Muestreo4$TRC~Muestreo4$Trat, center=mean)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 3 0.7725 0.5313
## 12En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
d1tukey <-HSD.test(Muestreo1$TRC,Muestreo1$Trat, 12, 0.0000000000000000000000000000000019259, alpha = 0.05)
d2tukey <-HSD.test(Muestreo2$TRC,Muestreo2$Trat, 12, 0.00093522, alpha = 0.05)
d3tukey <-HSD.test(Muestreo3$TRC,Muestreo3$Trat, 12, 0.00059863, alpha = 0.05)
d4tukey <-HSD.test(Muestreo4$TRC,Muestreo4$Trat, 12, 0.25701, alpha = 0.05)
d1tukey## $statistics
## MSerror Df Mean CV MSD
## 1.9259e-33 12 0.07142857 6.143911e-14 9.212926e-17
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo1$Trat 4 4.19866 0.05
##
## $means
## Muestreo1$TRC std r Min Max Q25 Q50 Q75
## T1 0.07142857 0 4 0.07142857 0.07142857 0.07142857 0.07142857 0.07142857
## T2 0.07142857 0 4 0.07142857 0.07142857 0.07142857 0.07142857 0.07142857
## T3 0.07142857 0 4 0.07142857 0.07142857 0.07142857 0.07142857 0.07142857
## T4 0.07142857 0 4 0.07142857 0.07142857 0.07142857 0.07142857 0.07142857
##
## $comparison
## NULL
##
## $groups
## Muestreo1$TRC groups
## T1 0.07142857 a
## T2 0.07142857 a
## T3 0.07142857 a
## T4 0.07142857 a
##
## attr(,"class")
## [1] "group"d2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00093522 12 0.08077309 37.86083 0.06420038
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo2$Trat 4 4.19866 0.05
##
## $means
## Muestreo2$TRC std r Min Max Q25 Q50
## T1 0.06307387 0.048347230 4 -0.002922908 0.11216518 0.04750758 0.07152660
## T2 0.07880880 0.014408861 4 0.063544363 0.09327442 0.06817181 0.07920822
## T3 0.08799591 0.034187199 4 0.040906929 0.12236710 0.07825394 0.09435480
## T4 0.09321380 0.005202046 4 0.087060583 0.09936472 0.09043398 0.09321494
## Q75
## T1 0.08709289
## T2 0.08984522
## T3 0.10409676
## T4 0.09599476
##
## $comparison
## NULL
##
## $groups
## Muestreo2$TRC groups
## T4 0.09321380 a
## T3 0.08799591 a
## T2 0.07880880 a
## T1 0.06307387 a
##
## attr(,"class")
## [1] "group"d3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00059863 12 0.07987059 30.6332 0.05136413
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo3$Trat 4 4.19866 0.05
##
## $means
## Muestreo3$TRC std r Min Max Q25 Q50
## T1 0.08667804 0.03906578 4 0.05759079 0.14239422 0.06064995 0.07336357
## T2 0.07362032 0.01250520 4 0.05761560 0.08646617 0.06732676 0.07519976
## T3 0.07951047 0.02206774 4 0.06147873 0.11137441 0.06717251 0.07259438
## T4 0.07967351 0.01500086 4 0.06167544 0.09567422 0.07080513 0.08067219
## Q75
## T1 0.09939166
## T2 0.08149332
## T3 0.08493235
## T4 0.08954057
##
## $comparison
## NULL
##
## $groups
## Muestreo3$TRC groups
## T1 0.08667804 a
## T4 0.07967351 a
## T3 0.07951047 a
## T2 0.07362032 a
##
## attr(,"class")
## [1] "group"d4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.25701 12 0.05824819 870.3473 1.06428
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$TRC std r Min Max Q25 Q50
## T1 0.07571455 0.013923084 4 0.05982393 0.09378823 0.07088869 0.07462302
## T2 0.05223761 0.014417745 4 0.04425604 0.07384552 0.04491494 0.04542443
## T3 0.05548323 0.008436143 4 0.04564061 0.06625259 0.05242641 0.05501987
## T4 0.04955738 0.023561831 4 0.02275402 0.07991793 0.03921276 0.04777878
## Q75
## T1 0.07944888
## T2 0.05274709
## T3 0.05807670
## T4 0.05812340
##
## $comparison
## NULL
##
## $groups
## Muestreo4$TRC groups
## T1 0.07571455 a
## T3 0.05548323 a
## T2 0.05223761 a
## T4 0.04955738 a
##
## attr(,"class")
## [1] "group"para este caso tenemos que:
Muestreo1$TRC groups T1 0.07142857 a T2 0.07142857 a T3 0.07142857 a T4 0.07142857 a
Muestreo2$TRC groups T4 0.09321380 a T3 0.08799591 a T2 0.07880880 b T1 0.06307387 b
Muestreo3$TRC groups T1 0.08667804 a T4 0.07967351 a T3 0.07951047 a T2 0.07362032 a
Muestreo4$TRC groups T1 0.07571455 a T3 0.05548323 b T2 0.05223761 b T4 0.04955738 b
IC
Determinación de la variable en el muestreo 4
ANOVA
e4 <- aov(IC~Trat, data = Muestreo4)
anova(e4)## Analysis of Variance Table
##
## Response: IC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 789.13 263.04 21.49 4.074e-05 ***
## Residuals 12 146.88 12.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Prueba de normalidad de shapiro
shapiro.test(resid(e4))##
## Shapiro-Wilk normality test
##
## data: resid(e4)
## W = 0.96312, p-value = 0.7185*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(Muestreo4$IC~Muestreo4$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 1.8635 0.1896
## 12Pueba de tukey
library(agricolae)
library(dplyr)
e4tukey <-HSD.test(Muestreo4$IC,Muestreo4$Trat, 12, 12.24, alpha = 0.05)
e4tukey## $statistics
## MSerror Df Mean CV MSD
## 12.24 12 44.6059 7.843293 7.344656
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Muestreo4$Trat 4 4.19866 0.05
##
## $means
## Muestreo4$IC std r Min Max Q25 Q50 Q75
## T1 45.43426 1.005231 4 44.45209 46.81693 44.89347 45.23401 45.77480
## T2 34.54970 5.027384 4 28.85262 41.06564 32.41647 34.14028 36.27351
## T3 54.36240 2.167290 4 52.15316 57.31886 53.29817 53.98879 55.05301
## T4 44.07723 4.240062 4 39.17443 48.81956 41.46960 44.15746 46.76509
##
## $comparison
## NULL
##
## $groups
## Muestreo4$IC groups
## T3 54.36240 a
## T1 45.43426 b
## T4 44.07723 b
## T2 34.54970 c
##
## attr(,"class")
## [1] "group"DIFERENCIAS ESTADISTICAS ENTRE MUESTREOS
TAN
Determinación de la variable en los 4 muestreos
ANOVA
f1 <- aov(TAN~muestreo, data = tratamiento1)
f2 <- aov(TAN~muestreo, data = tratamiento2)
f3 <- aov(TAN~muestreo, data = tratamiento3)
f4 <- aov(TAN~muestreo, data = tratamiento4)
anova(f1)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 7.6338e-08 2.5446e-08 2.6581 0.09577 .
## Residuals 12 1.1488e-07 9.5731e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(f2)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 1.4757e-08 4.919e-09 3.5465 0.04796 *
## Residuals 12 1.6644e-08 1.387e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(f3)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.7687e-08 1.2562e-08 2.6906 0.09326 .
## Residuals 12 5.6029e-08 4.6691e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(f4)## Analysis of Variance Table
##
## Response: TAN
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 5.2174e-08 1.7392e-08 4.9558 0.01827 *
## Residuals 12 4.2112e-08 3.5093e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Purea de normalidad de shapiro
shapiro.test(resid(f1))##
## Shapiro-Wilk normality test
##
## data: resid(f1)
## W = 0.98839, p-value = 0.998shapiro.test(resid(f2))##
## Shapiro-Wilk normality test
##
## data: resid(f2)
## W = 0.9007, p-value = 0.08248shapiro.test(resid(f3))##
## Shapiro-Wilk normality test
##
## data: resid(f3)
## W = 0.9415, p-value = 0.3678shapiro.test(resid(f4))##
## Shapiro-Wilk normality test
##
## data: resid(f4)
## W = 0.96752, p-value = 0.7969En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$TAN~tratamiento1$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 1.0536 0.4047
## 12leveneTest(tratamiento2$TAN~tratamiento2$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 3.0782 0.06844 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$TAN~tratamiento3$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 1.2275 0.3424
## 12leveneTest(tratamiento4$TAN~tratamiento4$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 2.4033 0.1184
## 12todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
f1tukey <-HSD.test(tratamiento1$TAN,tratamiento1$muestreo, 12, 0.0000000095731, alpha = 0.05)
f2tukey <-HSD.test(tratamiento2$TAN,tratamiento2$muestreo, 12, 0.000000001387, alpha = 0.05)
f3tukey <-HSD.test(tratamiento3$TAN,tratamiento3$muestreo, 12, 0.0000000046691, alpha = 0.05)
f4tukey <-HSD.test(tratamiento4$TAN,tratamiento4$muestreo, 12, 0.0000000035093, alpha = 0.05)
f1tukey## $statistics
## MSerror Df Mean CV MSD
## 9.5731e-09 12 0.0002711324 36.08651 0.0002054031
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$TAN std r Min Max Q25
## M1 0.0003624532 4.419217e-05 4 3.100611e-04 0.0004101590 0.0003358606
## M2 0.0001695700 1.267257e-04 4 -7.432806e-06 0.0002924601 0.0001367035
## M3 0.0002625136 1.271750e-04 4 1.663561e-04 0.0004430050 0.0001771274
## M4 0.0002899928 6.408224e-05 4 2.220434e-04 0.0003754579 0.0002581394
## Q50 Q75
## M1 0.0003647963 0.0003913889
## M2 0.0001966264 0.0002294929
## M3 0.0002203466 0.0003057328
## M4 0.0002812350 0.0003130884
##
## $comparison
## NULL
##
## $groups
## tratamiento1$TAN groups
## M1 0.0003624532 a
## M4 0.0002899928 a
## M3 0.0002625136 a
## M2 0.0001695700 a
##
## attr(,"class")
## [1] "group"f2tukey## $statistics
## MSerror Df Mean CV MSD
## 1.387e-09 12 0.0002054066 18.13108 7.818419e-05
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$TAN std r Min Max Q25
## M1 0.0002453705 2.899676e-05 4 0.0002216400 0.0002853870 0.0002253693
## M2 0.0001655663 3.061887e-05 4 0.0001335902 0.0001938145 0.0001423245
## M3 0.0002212328 5.181056e-05 4 0.0001709030 0.0002665815 0.0001793640
## M4 0.0001894569 3.294327e-05 4 0.0001684577 0.0002381012 0.0001695182
## Q50 Q75
## M1 0.0002372276 0.0002572288
## M2 0.0001674302 0.0001906719
## M3 0.0002237235 0.0002655923
## M4 0.0001756343 0.0001955730
##
## $comparison
## NULL
##
## $groups
## tratamiento2$TAN groups
## M1 0.0002453705 a
## M3 0.0002212328 ab
## M4 0.0001894569 ab
## M2 0.0001655663 b
##
## attr(,"class")
## [1] "group"f3tukey## $statistics
## MSerror Df Mean CV MSD
## 4.6691e-09 12 0.0002539814 26.90386 0.0001434489
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$TAN std r Min Max Q25
## M1 0.0003320469 4.283163e-05 4 0.0002955064 0.0003879268 0.0003001174
## M2 0.0002391105 1.028861e-04 4 0.0001056262 0.0003488176 0.0001933087
## M3 0.0002459390 6.599959e-05 4 0.0001938114 0.0003422950 0.0002113470
## M4 0.0001988293 4.359236e-05 4 0.0001623153 0.0002618206 0.0001760528
## Q50 Q75
## M1 0.0003223773 0.0003543068
## M2 0.0002509991 0.0002968008
## M3 0.0002238247 0.0002584167
## M4 0.0001855906 0.0002083671
##
## $comparison
## NULL
##
## $groups
## tratamiento3$TAN groups
## M1 0.0003320469 a
## M3 0.0002459390 a
## M2 0.0002391105 a
## M4 0.0001988293 a
##
## attr(,"class")
## [1] "group"f4tukey## $statistics
## MSerror Df Mean CV MSD
## 3.5093e-09 12 0.0002736403 21.64862 0.0001243629
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$TAN std r Min Max Q25
## M1 0.0003549415 3.121939e-05 4 0.0003091650 0.0003756448 0.0003479960
## M2 0.0002725426 2.337250e-05 4 0.0002512514 0.0003058799 0.0002620367
## M3 0.0002736398 5.360649e-05 4 0.0002192145 0.0003215005 0.0002318154
## M4 0.0001934372 9.819760e-05 4 0.0000845898 0.0003203372 0.0001443251
## Q50 Q75
## M1 0.0003674781 0.0003744237
## M2 0.0002665195 0.0002770254
## M3 0.0002769221 0.0003187464
## M4 0.0001844110 0.0002335232
##
## $comparison
## NULL
##
## $groups
## tratamiento4$TAN groups
## M1 0.0003549415 a
## M3 0.0002736398 ab
## M2 0.0002725426 ab
## M4 0.0001934372 b
##
## attr(,"class")
## [1] "group"usar estas diferencias de la prueba
TCC
Determinación de la variable en los 4 muestreos
ANOVA
g1 <- aov(TCC~muestreo, data = tratamiento1)
g2 <- aov(TCC~muestreo, data = tratamiento2)
g3 <- aov(TCC~muestreo, data = tratamiento3)
g4 <- aov(TCC~muestreo, data = tratamiento4)
anova(g1)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.9531e-07 1.3177e-07 4.2432 0.02922 *
## Residuals 12 3.7265e-07 3.1054e-08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(g2)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.1251e-07 1.0417e-07 13.719 0.0003485 ***
## Residuals 12 9.1114e-08 7.5930e-09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(g3)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 2.4363e-07 8.1209e-08 4.6572 0.02215 *
## Residuals 12 2.0925e-07 1.7437e-08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(g4)## Analysis of Variance Table
##
## Response: TCC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 3.0475e-07 1.0158e-07 6.1428 0.00897 **
## Residuals 12 1.9845e-07 1.6537e-08
## ---
## 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 = 0.95446, p-value = 0.5635shapiro.test(resid(g2))##
## Shapiro-Wilk normality test
##
## data: resid(g2)
## W = 0.96887, p-value = 0.82shapiro.test(resid(g3))##
## Shapiro-Wilk normality test
##
## data: resid(g3)
## W = 0.92305, p-value = 0.1888shapiro.test(resid(g4))##
## Shapiro-Wilk normality test
##
## data: resid(g4)
## W = 0.94202, p-value = 0.3744En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$TCC~tratamiento1$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 2.1704 0.1445
## 12leveneTest(tratamiento2$TCC~tratamiento2$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 3.1136 0.06658 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$TCC~tratamiento3$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 2.0794 0.1565
## 12leveneTest(tratamiento4$TCC~tratamiento4$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 3.1063 0.06696 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
g1tukey <-HSD.test(tratamiento1$TCC,tratamiento1$muestreo, 12, 0.000000031054, alpha = 0.05)
g2tukey <-HSD.test(tratamiento2$TCC,tratamiento2$muestreo, 12, 0.0000000075930, alpha = 0.05)
g3tukey <-HSD.test(tratamiento3$TCC,tratamiento3$muestreo, 12, 0.000000017437, alpha = 0.05)
g4tukey <-HSD.test(tratamiento4$TAN,tratamiento4$muestreo, 12, 0.000000016537, alpha = 0.05)
g1tukey## $statistics
## MSerror Df Mean CV MSD
## 3.1054e-08 12 0.0004275808 41.2136 0.000369947
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$TCC std r Min Max Q25
## M1 0.0002236263 2.704882e-05 4 0.0001925549 0.0002551906 0.0002075775
## M2 0.0003528799 2.585243e-04 4 -0.0000184035 0.0005760138 0.0002925578
## M3 0.0004868268 1.921006e-04 4 0.0003311563 0.0007463121 0.0003467846
## M4 0.0006469903 1.405282e-04 4 0.0004985909 0.0008340788 0.0005750375
## Q50 Q75
## M1 0.0002233798 0.0002394286
## M2 0.0004269546 0.0004872767
## M3 0.0004349193 0.0005749615
## M4 0.0006276458 0.0006995986
##
## $comparison
## NULL
##
## $groups
## tratamiento1$TCC groups
## M4 0.0006469903 a
## M3 0.0004868268 ab
## M2 0.0003528799 ab
## M1 0.0002236263 b
##
## attr(,"class")
## [1] "group"g2tukey## $statistics
## MSerror Df Mean CV MSD
## 7.593e-09 12 0.0003820111 22.81028 0.0001829311
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$TCC std r Min Max Q25
## M1 0.0001641944 1.943114e-05 4 0.0001423360 0.0001858519 0.0001515753
## M2 0.0003854118 7.093073e-05 4 0.0003233501 0.0004493851 0.0003243440
## M3 0.0005512016 1.178989e-04 4 0.0004145499 0.0006824665 0.0004787337
## M4 0.0004272366 1.051782e-04 4 0.0003401361 0.0005778021 0.0003656027
## Q50 Q75
## M1 0.0001642948 0.0001769139
## M2 0.0003844560 0.0004455237
## M3 0.0005538950 0.0006263630
## M4 0.0003955042 0.0004571381
##
## $comparison
## NULL
##
## $groups
## tratamiento2$TCC groups
## M3 0.0005512016 a
## M4 0.0004272366 a
## M2 0.0003854118 a
## M1 0.0001641944 b
##
## attr(,"class")
## [1] "group"g3tukey## $statistics
## MSerror Df Mean CV MSD
## 1.7437e-08 12 0.0004120443 32.04734 0.0002772149
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$TCC std r Min Max Q25
## M1 0.0002015624 2.103139e-05 4 0.0001788001 0.0002288807 0.0001903329
## M2 0.0005080154 2.066039e-04 4 0.0002294965 0.0007168038 0.0004291213
## M3 0.0004897597 1.284566e-04 4 0.0003976975 0.0006777217 0.0004131946
## M4 0.0004488395 1.006021e-04 4 0.0003633342 0.0005945343 0.0004029956
## Q50 Q75
## M1 0.0001992844 0.0002105140
## M2 0.0005428806 0.0006217747
## M3 0.0004418098 0.0005183749
## M4 0.0004187447 0.0004645887
##
## $comparison
## NULL
##
## $groups
## tratamiento3$TCC groups
## M2 0.0005080154 a
## M3 0.0004897597 a
## M4 0.0004488395 ab
## M1 0.0002015624 b
##
## attr(,"class")
## [1] "group"g4tukey## $statistics
## MSerror Df Mean CV MSD
## 1.6537e-08 12 0.0002736403 46.99464 0.000269966
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$TAN std r Min Max Q25
## M1 0.0003549415 3.121939e-05 4 0.0003091650 0.0003756448 0.0003479960
## M2 0.0002725426 2.337250e-05 4 0.0002512514 0.0003058799 0.0002620367
## M3 0.0002736398 5.360649e-05 4 0.0002192145 0.0003215005 0.0002318154
## M4 0.0001934372 9.819760e-05 4 0.0000845898 0.0003203372 0.0001443251
## Q50 Q75
## M1 0.0003674781 0.0003744237
## M2 0.0002665195 0.0002770254
## M3 0.0002769221 0.0003187464
## M4 0.0001844110 0.0002335232
##
## $comparison
## NULL
##
## $groups
## tratamiento4$TAN groups
## M1 0.0003549415 a
## M3 0.0002736398 a
## M2 0.0002725426 a
## M4 0.0001934372 a
##
## attr(,"class")
## [1] "group"IAF
Determinación de la variable en los 4 muestreos
ANOVA
h1 <- aov(IAF~muestreo, data = tratamiento1)
h2 <- aov(IAF~muestreo, data = tratamiento2)
h3 <- aov(IAF~muestreo, data = tratamiento3)
h4 <- aov(IAF~muestreo, data = tratamiento4)
anova(h1)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0216009 0.0072003 13.076 0.000433 ***
## Residuals 12 0.0066077 0.0005506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(h2)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.002654 0.0008847 0.2762 0.8415
## Residuals 12 0.038434 0.0032028anova(h3)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0082914 0.00276381 5.2073 0.01561 *
## Residuals 12 0.0063690 0.00053075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(h4)## Analysis of Variance Table
##
## Response: IAF
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0124245 0.0041415 9.6154 0.00163 **
## Residuals 12 0.0051686 0.0004307
## ---
## 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 = 0.97292, p-value = 0.8834shapiro.test(resid(h2))##
## Shapiro-Wilk normality test
##
## data: resid(h2)
## W = 0.91225, p-value = 0.1265shapiro.test(resid(h3))##
## Shapiro-Wilk normality test
##
## data: resid(h3)
## W = 0.93971, p-value = 0.3454shapiro.test(resid(h4))##
## Shapiro-Wilk normality test
##
## data: resid(h4)
## W = 0.97839, p-value = 0.9495En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$IAF~tratamiento1$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 6.7979 0.006257 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$IAF~tratamiento2$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 0.6031 0.6254
## 12leveneTest(tratamiento3$IAF~tratamiento3$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 1.521 0.2595
## 12leveneTest(tratamiento4$IAF~tratamiento4$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 1.7353 0.2129
## 12En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
h1tukey <-HSD.test(tratamiento1$IAF,tratamiento1$muestreo, 12, 0.0005506, alpha = 0.05)
h2tukey <-HSD.test(tratamiento2$IAF,tratamiento2$muestreo, 12, 0.0032028, alpha = 0.05)
h3tukey <-HSD.test(tratamiento3$IAF,tratamiento3$muestreo, 12, 0.00053075, alpha = 0.05)
h4tukey <-HSD.test(tratamiento4$IAF,tratamiento4$muestreo, 12, 0.0004307, alpha = 0.05)
h1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0005506 12 1.085296 2.16207 0.0492605
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$IAF std r Min Max Q25 Q50 Q75
## M1 1.125077 0.014785590 4 1.109741 1.144958 1.117480 1.122804 1.130401
## M2 1.106880 0.026509742 4 1.074074 1.134709 1.092240 1.109368 1.124009
## M3 1.081718 0.034892065 4 1.046362 1.116865 1.054841 1.081823 1.108700
## M4 1.027511 0.007982756 4 1.020252 1.036141 1.020971 1.026826 1.033367
##
## $comparison
## NULL
##
## $groups
## tratamiento1$IAF groups
## M1 1.125077 a
## M2 1.106880 a
## M3 1.081718 a
## M4 1.027511 b
##
## attr(,"class")
## [1] "group"h2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0032028 12 1.144399 4.945242 0.118808
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$IAF std r Min Max Q25 Q50 Q75
## M1 1.149001 0.05108794 4 1.103874 1.205655 1.106788 1.143237 1.185450
## M2 1.146038 0.03448256 4 1.107744 1.190397 1.128513 1.143004 1.160529
## M3 1.158887 0.07031378 4 1.098743 1.260313 1.123761 1.138247 1.173373
## M4 1.123669 0.06378271 4 1.062205 1.213243 1.096236 1.109615 1.137048
##
## $comparison
## NULL
##
## $groups
## tratamiento2$IAF groups
## M3 1.158887 a
## M1 1.149001 a
## M2 1.146038 a
## M4 1.123669 a
##
## attr(,"class")
## [1] "group"h3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00053075 12 1.09399 2.10587 0.04836439
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$IAF std r Min Max Q25 Q50 Q75
## M1 1.096939 0.03519133 4 1.066643 1.144604 1.072586 1.088254 1.112607
## M2 1.115155 0.01136026 4 1.103362 1.130112 1.108684 1.113572 1.120042
## M3 1.107677 0.01987336 4 1.079806 1.126893 1.103345 1.112006 1.116339
## M4 1.056190 0.01898876 4 1.030529 1.076311 1.050927 1.058959 1.064221
##
## $comparison
## NULL
##
## $groups
## tratamiento3$IAF groups
## M2 1.115155 a
## M3 1.107677 a
## M1 1.096939 ab
## M4 1.056190 b
##
## attr(,"class")
## [1] "group"h4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.0004307 12 1.094841 1.895554 0.04356805
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$IAF std r Min Max Q25 Q50 Q75
## M1 1.103135 0.01684830 4 1.081410 1.118061 1.094122 1.106536 1.115549
## M2 1.136888 0.01200040 4 1.124346 1.152758 1.130298 1.135224 1.141815
## M3 1.068598 0.01855749 4 1.045136 1.089968 1.060929 1.069644 1.077313
## M4 1.070744 0.03083192 4 1.031667 1.101491 1.054251 1.074908 1.091401
##
## $comparison
## NULL
##
## $groups
## tratamiento4$IAF groups
## M2 1.136888 a
## M1 1.103135 ab
## M4 1.070744 b
## M3 1.068598 b
##
## attr(,"class")
## [1] "group"TRC
Determinación de la variable en los 4 muestreos
ANOVA
i1 <- aov(TRC~muestreo, data = tratamiento1)
i2 <- aov(TRC~muestreo, data = tratamiento2)
i3 <- aov(TRC~muestreo, data = tratamiento3)
i4 <- aov(TRC~muestreo, data = tratamiento4)
anova(i1)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0011579 0.00038595 0.3805 0.7689
## Residuals 12 0.0121723 0.00101436anova(i2)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0016177 0.00053924 3.7718 0.04069 *
## Residuals 12 0.0017156 0.00014297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(i3)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0023004 0.00076681 1.7761 0.2052
## Residuals 12 0.0051808 0.00043173anova(i4)## Analysis of Variance Table
##
## Response: TRC
## Df Sum Sq Mean Sq F value Pr(>F)
## muestreo 3 0.0040171 0.00133904 6.6351 0.00683 **
## Residuals 12 0.0024217 0.00020181
## ---
## 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 = 0.90288, p-value = 0.08939shapiro.test(resid(i2))##
## Shapiro-Wilk normality test
##
## data: resid(i2)
## W = 0.95895, p-value = 0.6428shapiro.test(resid(i3))##
## Shapiro-Wilk normality test
##
## data: resid(i3)
## W = 0.88668, p-value = 0.04937shapiro.test(resid(i4))##
## Shapiro-Wilk normality test
##
## data: resid(i4)
## W = 0.91914, p-value = 0.1634En todos los muestreos se puede observar normalidad en los datos de temperatura.
*Homogeneidad de varianzas**
library(car)
library(carData)
leveneTest(tratamiento1$TRC~tratamiento1$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 2.6462 0.09671 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento2$TRC~tratamiento2$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 5.0194 0.01755 *
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento3$TRC~tratamiento3$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 2.9172 0.07769 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tratamiento4$TRC~tratamiento4$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 3.1841 0.06304 .
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1En temperatura, todos los datos representan varianzas homogeneas
Prueba de tukey
library(agricolae)
library(dplyr)
i1tukey <-HSD.test(tratamiento1$TRC,tratamiento1$muestreo, 12, 0.00101436, alpha = 0.05)
i2tukey <-HSD.test(tratamiento2$TRC,tratamiento2$muestreo, 12, 0.00014297, alpha = 0.05)
i3tukey <-HSD.test(tratamiento3$TRC,tratamiento3$muestreo, 12, 0.00043173, alpha = 0.05)
i4tukey <-HSD.test(tratamiento4$TRC,tratamiento4$muestreo, 12, 0.00020181, alpha = 0.05)
i1tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00101436 12 0.07422376 42.90947 0.0668616
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento1$muestreo 4 4.19866 0.05
##
## $means
## tratamiento1$TRC std r Min Max Q25 Q50
## M1 0.07142857 0.00000000 4 0.071428571 0.07142857 0.07142857 0.07142857
## M2 0.06307387 0.04834723 4 -0.002922908 0.11216518 0.04750758 0.07152660
## M3 0.08667804 0.03906578 4 0.057590785 0.14239422 0.06064995 0.07336357
## M4 0.07571455 0.01392308 4 0.059823930 0.09378823 0.07088869 0.07462302
## Q75
## M1 0.07142857
## M2 0.08709289
## M3 0.09939166
## M4 0.07944888
##
## $comparison
## NULL
##
## $groups
## tratamiento1$TRC groups
## M3 0.08667804 a
## M4 0.07571455 a
## M1 0.07142857 a
## M2 0.06307387 a
##
## attr(,"class")
## [1] "group"i2tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00014297 12 0.06902383 17.32301 0.0251017
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento2$muestreo 4 4.19866 0.05
##
## $means
## tratamiento2$TRC std r Min Max Q25 Q50
## M1 0.07142857 0.00000000 4 0.07142857 0.07142857 0.07142857 0.07142857
## M2 0.07880880 0.01440886 4 0.06354436 0.09327442 0.06817181 0.07920822
## M3 0.07362032 0.01250520 4 0.05761560 0.08646617 0.06732676 0.07519976
## M4 0.05223761 0.01441775 4 0.04425604 0.07384552 0.04491494 0.04542443
## Q75
## M1 0.07142857
## M2 0.08984522
## M3 0.08149332
## M4 0.05274709
##
## $comparison
## NULL
##
## $groups
## tratamiento2$TRC groups
## M2 0.07880880 a
## M3 0.07362032 ab
## M1 0.07142857 ab
## M4 0.05223761 b
##
## attr(,"class")
## [1] "group"i3tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00043173 12 0.07360455 28.22939 0.04362012
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento3$muestreo 4 4.19866 0.05
##
## $means
## tratamiento3$TRC std r Min Max Q25 Q50
## M1 0.07142857 0.000000000 4 0.07142857 0.07142857 0.07142857 0.07142857
## M2 0.08799591 0.034187199 4 0.04090693 0.12236710 0.07825394 0.09435480
## M3 0.07951047 0.022067738 4 0.06147873 0.11137441 0.06717251 0.07259438
## M4 0.05548323 0.008436143 4 0.04564061 0.06625259 0.05242641 0.05501987
## Q75
## M1 0.07142857
## M2 0.10409676
## M3 0.08493235
## M4 0.05807670
##
## $comparison
## NULL
##
## $groups
## tratamiento3$TRC groups
## M2 0.08799591 a
## M3 0.07951047 a
## M1 0.07142857 a
## M4 0.05548323 a
##
## attr(,"class")
## [1] "group"i4tukey## $statistics
## MSerror Df Mean CV MSD
## 0.00020181 12 0.07346831 19.33621 0.02982305
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey tratamiento4$muestreo 4 4.19866 0.05
##
## $means
## tratamiento4$TRC std r Min Max Q25 Q50
## M1 0.07142857 0.000000000 4 0.07142857 0.07142857 0.07142857 0.07142857
## M2 0.09321380 0.005202046 4 0.08706058 0.09936472 0.09043398 0.09321494
## M3 0.07967351 0.015000860 4 0.06167544 0.09567422 0.07080513 0.08067219
## M4 0.04955738 0.023561831 4 0.02275402 0.07991793 0.03921276 0.04777878
## Q75
## M1 0.07142857
## M2 0.09599476
## M3 0.08954057
## M4 0.05812340
##
## $comparison
## NULL
##
## $groups
## tratamiento4$TRC groups
## M2 0.09321380 a
## M3 0.07967351 a
## M1 0.07142857 ab
## M4 0.04955738 b
##
## attr(,"class")
## [1] "group"