dds= gl(1, 16, 16, c("29"))
Tratamiento= gl(4, 4, 16, c("-DH-M","DH-M","DH+150M","DH+250M"))
Planta= gl(4,1,16, c("A","B","C","D"))
CRC= c(38.33,36.92,38.10,37.12,33.16,35.70,38.32,33.28,34.74,40.6,33.45,42.72,41.06,36.22,36.8,33.76)
LPA= c(9.7,9.9,12.8,13.5,10,11.1,10.7,10.3,10.3,10.8,9.3,10.1,10.5,11,11.95,13.6)
PSPA=c(0.225,0.156,0.382,0.305,0.257,0.343,0.234,0.193,0.175,0.221,0.158,0.285,0.282,0.266,0.326,0.374)
DRT=c(4.3,2.2,8.5,7.3,2.4,1.25,1,2.5,1.5,2,0.7,1,2.5,1,1.5,2)
LRT=c(8,7.6,2.6,5,6.2,7.5,4.5,7.5,3.5,7,3.6,2.5,6,3,8,7.5)
PSRT=c(0.385,0.281,0.339,0.249,0.326,0.144,0.06,0.315,0.104,0.296,0.045,0.107,0.276,0.079,0.184,0.217)
PFPA=c(2.200,2.258,3.858,3.942,2.689,3.676,3.178,2.323,2.718,3.186,4.148,4.006,2.249,2.492,2.271,3.114)
PFRT=c(5.63,3.49,4.62,3.13,4.902,1.629,0.595,4.848,4.316,0.844,4.703,3.15,1.438,5.367,5.653,1.029)
df1=data.frame(dds,Tratamiento,Planta,CRC,LPA,PFPA,PSPA,DRT,LRT,PFRT,PSRT)
df1
## dds Tratamiento Planta CRC LPA PFPA PSPA DRT LRT PFRT PSRT
## 1 29 -DH-M A 38.33 9.70 2.200 0.225 4.30 8.0 5.630 0.385
## 2 29 -DH-M B 36.92 9.90 2.258 0.156 2.20 7.6 3.490 0.281
## 3 29 -DH-M C 38.10 12.80 3.858 0.382 8.50 2.6 4.620 0.339
## 4 29 -DH-M D 37.12 13.50 3.942 0.305 7.30 5.0 3.130 0.249
## 5 29 DH-M A 33.16 10.00 2.689 0.257 2.40 6.2 4.902 0.326
## 6 29 DH-M B 35.70 11.10 3.676 0.343 1.25 7.5 1.629 0.144
## 7 29 DH-M C 38.32 10.70 3.178 0.234 1.00 4.5 0.595 0.060
## 8 29 DH-M D 33.28 10.30 2.323 0.193 2.50 7.5 4.848 0.315
## 9 29 DH+150M A 34.74 10.30 2.718 0.175 1.50 3.5 4.316 0.104
## 10 29 DH+150M B 40.60 10.80 3.186 0.221 2.00 7.0 0.844 0.296
## 11 29 DH+150M C 33.45 9.30 4.148 0.158 0.70 3.6 4.703 0.045
## 12 29 DH+150M D 42.72 10.10 4.006 0.285 1.00 2.5 3.150 0.107
## 13 29 DH+250M A 41.06 10.50 2.249 0.282 2.50 6.0 1.438 0.276
## 14 29 DH+250M B 36.22 11.00 2.492 0.266 1.00 3.0 5.367 0.079
## 15 29 DH+250M C 36.80 11.95 2.271 0.326 1.50 8.0 5.653 0.184
## 16 29 DH+250M D 33.76 13.60 3.114 0.374 2.00 7.5 1.029 0.217
mod1a=aov(CRC~Tratamiento, df1)
summary(mod1a)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 18.64 6.213 0.695 0.572
## Residuals 12 107.21 8.934
mod1b=aov(LPA~Tratamiento, df1)
summary(mod1b)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 7.18 2.393 1.517 0.26
## Residuals 12 18.93 1.577
mod1c=aov(PFPA~Tratamiento, df1)
summary(mod1c)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 1.952 0.6507 1.367 0.3
## Residuals 12 5.710 0.4759
mod1d=aov(PSPA~Tratamiento, df1)
summary(mod1d)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 0.02112 0.007041 1.469 0.272
## Residuals 12 0.05751 0.004792
mod1e=aov(DRT~Tratamiento, df1)
summary(mod1e)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 47.69 15.898 6.677 0.00668 **
## Residuals 12 28.57 2.381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod1f=aov(LRT~Tratamiento, df1)
summary(mod1f)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 12.39 4.128 0.957 0.444
## Residuals 12 51.78 4.315
mod1g=aov(PFRT~Tratamiento, df1)
summary(mod1g)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 3.37 1.123 0.293 0.83
## Residuals 12 46.02 3.835
mod1h=aov(PSRT~Tratamiento, df1)
summary(mod1h)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 0.06522 0.021739 2.201 0.141
## Residuals 12 0.11852 0.009876
shapiro.test(mod1a$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1a$residuals
## W = 0.96522, p-value = 0.7565
bartlett.test(mod1a$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1a$residuals and df1$Tratamiento
## Bartlett's K-squared = 6.5014, df = 3, p-value = 0.08961
shapiro.test(mod1b$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1b$residuals
## W = 0.96776, p-value = 0.8011
bartlett.test(mod1b$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1b$residuals and df1$Tratamiento
## Bartlett's K-squared = 5.9949, df = 3, p-value = 0.1119
shapiro.test(mod1c$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1c$residuals
## W = 0.90825, p-value = 0.109
bartlett.test(mod1c$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1c$residuals and df1$Tratamiento
## Bartlett's K-squared = 1.9685, df = 3, p-value = 0.579
shapiro.test(mod1d$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1d$residuals
## W = 0.96813, p-value = 0.8074
bartlett.test(mod1d$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1d$residuals and df1$Tratamiento
## Bartlett's K-squared = 1.5635, df = 3, p-value = 0.6677
shapiro.test(mod1e$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1e$residuals
## W = 0.94257, p-value = 0.3817
bartlett.test(mod1e$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1e$residuals and df1$Tratamiento
## Bartlett's K-squared = 10.214, df = 3, p-value = 0.01684
shapiro.test(mod1f$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1f$residuals
## W = 0.95291, p-value = 0.5372
bartlett.test(mod1f$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1f$residuals and df1$Tratamiento
## Bartlett's K-squared = 0.86679, df = 3, p-value = 0.8334
shapiro.test(mod1g$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1g$residuals
## W = 0.88941, p-value = 0.05451
bartlett.test(mod1g$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1g$residuals and df1$Tratamiento
## Bartlett's K-squared = 1.6213, df = 3, p-value = 0.6546
shapiro.test(mod1h$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod1h$residuals
## W = 0.97349, p-value = 0.8915
bartlett.test(mod1h$residuals, df1$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod1h$residuals and df1$Tratamiento
## Bartlett's K-squared = 1.6475, df = 3, p-value = 0.6487
TukeyHSD(mod1e)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DRT ~ Tratamiento, data = df1)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -3.7875 -7.02672 -0.5482797 0.0208119
## DH+150M--DH-M -4.2750 -7.51422 -1.0357797 0.0095238
## DH+250M--DH-M -3.8250 -7.06422 -0.5857797 0.0195947
## DH+150M-DH-M -0.4875 -3.72672 2.7517203 0.9689916
## DH+250M-DH-M -0.0375 -3.27672 3.2017203 0.9999845
## DH+250M-DH+150M 0.4500 -2.78922 3.6892203 0.9752819
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
df1 %>%
group_by(Tratamiento) %>%
summarise(med_LPA = mean(LPA),
med_PFPA = mean(PFPA),
med_PSPA = mean(PSPA),
med_DRT = mean(DRT),
med_LRT = mean(LRT),
med_PFRT = mean(PFRT),
med_PSRT = mean(PSRT))
## # A tibble: 4 × 8
## Tratamiento med_LPA med_PFPA med_PSPA med_DRT med_LRT med_PFRT med_PSRT
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -DH-M 11.5 3.06 0.267 5.58 5.8 4.22 0.314
## 2 DH-M 10.5 2.97 0.257 1.79 6.42 2.99 0.211
## 3 DH+150M 10.1 3.51 0.210 1.3 4.15 3.25 0.138
## 4 DH+250M 11.8 2.53 0.312 1.75 6.12 3.37 0.189
library(ggplot2)
ggplot(df1, aes(x = Tratamiento, y = CRC, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = LPA, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = PFPA, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = PSPA, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = DRT, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = LRT, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = PFRT, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df1, aes(x = Tratamiento, y = PSRT, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
dds2= gl(1, 16, 16, c("36"))
TH2= c(13.05,16.15,14.7,15.16,17.15,17.8,16.83,18.8,27.75,25.55,28.9,26.95,20.4,23.2,22.05,19.45)
CRC2= c(38.8,34.8,35.8,34.5,46,46.2,36.9,38.1,46.9,43.2,42.4,46.4,40.1,43.7,35.7,36.5)
AF2= c(112.65,136.59,106.61,89.175,80.664,88.777,67.01,72.012,65.977,87.999,90.618,121.526,94.095,98.685,117.813,53.669)
LPA2= c(12.5,12,11.5,12.5,9.5,11,8.5,13,9.5,10.5,10.5,13.5,11,10.5,13,9.5)
PSPA2=c(0.447,0.536,0.409,0.455,0.364,0.385,0.268,0.302,0.287,0.34,0.34,0.547,0.365,0.474,0.557,0.283)
DRT2=c(7,5,4,8.5,5,4,3.5,2,5,4.5,5.5,3,6,4.2,2,5)
LRT2=c(10,9.5,7,10,7.5,5,4.3,3.4,5,6,8.5,6,7,7,5.5,8.3)
PSRT2=c(2.328,3.631,0.448,1.761,0.787,0.626,0.394,0.239,1.085,0.699,1.83,1.048,0.834,0.988,0.395,0.684)
PFRT2=c(20.563,20.037,6.065,22.05,9.812,8.019,4.883,3.084,11.954,8.094,13.723,9.778,9.149,8.274,3.210,11.307)
PFPA2=c(3.946,4.746,4.112,3.507,2.965,3.339,2.627,2.568,2.748,3.509,2.825,5.557,3.187,3.500,4.774,2.775)
df2=data.frame(dds2,Tratamiento,Planta,TH2,CRC2,AF2,LPA2,PFPA2,PSPA2,DRT2,LRT2,PFRT2,PSRT2)
df2
## dds2 Tratamiento Planta TH2 CRC2 AF2 LPA2 PFPA2 PSPA2 DRT2 LRT2 PFRT2
## 1 36 -DH-M A 13.05 38.8 112.650 12.5 3.946 0.447 7.0 10.0 20.563
## 2 36 -DH-M B 16.15 34.8 136.590 12.0 4.746 0.536 5.0 9.5 20.037
## 3 36 -DH-M C 14.70 35.8 106.610 11.5 4.112 0.409 4.0 7.0 6.065
## 4 36 -DH-M D 15.16 34.5 89.175 12.5 3.507 0.455 8.5 10.0 22.050
## 5 36 DH-M A 17.15 46.0 80.664 9.5 2.965 0.364 5.0 7.5 9.812
## 6 36 DH-M B 17.80 46.2 88.777 11.0 3.339 0.385 4.0 5.0 8.019
## 7 36 DH-M C 16.83 36.9 67.010 8.5 2.627 0.268 3.5 4.3 4.883
## 8 36 DH-M D 18.80 38.1 72.012 13.0 2.568 0.302 2.0 3.4 3.084
## 9 36 DH+150M A 27.75 46.9 65.977 9.5 2.748 0.287 5.0 5.0 11.954
## 10 36 DH+150M B 25.55 43.2 87.999 10.5 3.509 0.340 4.5 6.0 8.094
## 11 36 DH+150M C 28.90 42.4 90.618 10.5 2.825 0.340 5.5 8.5 13.723
## 12 36 DH+150M D 26.95 46.4 121.526 13.5 5.557 0.547 3.0 6.0 9.778
## 13 36 DH+250M A 20.40 40.1 94.095 11.0 3.187 0.365 6.0 7.0 9.149
## 14 36 DH+250M B 23.20 43.7 98.685 10.5 3.500 0.474 4.2 7.0 8.274
## 15 36 DH+250M C 22.05 35.7 117.813 13.0 4.774 0.557 2.0 5.5 3.210
## 16 36 DH+250M D 19.45 36.5 53.669 9.5 2.775 0.283 5.0 8.3 11.307
## PSRT2
## 1 2.328
## 2 3.631
## 3 0.448
## 4 1.761
## 5 0.787
## 6 0.626
## 7 0.394
## 8 0.239
## 9 1.085
## 10 0.699
## 11 1.830
## 12 1.048
## 13 0.834
## 14 0.988
## 15 0.395
## 16 0.684
mod2a=aov(CRC2~Tratamiento, df2)
summary(mod2a)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 168.8 56.27 4.756 0.0208 *
## Residuals 12 142.0 11.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod2b=aov(LPA2~Tratamiento, df2)
summary(mod2b)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 5.672 1.891 0.819 0.508
## Residuals 12 27.688 2.307
mod2c=aov(PFPA2~Tratamiento, df2)
summary(mod2c)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 2.986 0.9952 1.396 0.292
## Residuals 12 8.553 0.7128
mod2d=aov(PSPA2~Tratamiento, df2)
summary(mod2d)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 0.0383 0.012766 1.523 0.259
## Residuals 12 0.1006 0.008381
mod2e=aov(DRT2~Tratamiento, df2)
summary(mod2e)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 13.48 4.494 1.856 0.191
## Residuals 12 29.05 2.421
mod2f=aov(LRT2~Tratamiento, df2)
summary(mod2f)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 34.60 11.532 5.303 0.0147 *
## Residuals 12 26.09 2.175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod2g=aov(PFRT2~Tratamiento, df2)
summary(mod2g)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 269.7 89.90 4.352 0.0271 *
## Residuals 12 247.9 20.66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod2h=aov(PSRT2~Tratamiento, df2)
summary(mod2h)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 5.512 1.8372 3.514 0.0491 *
## Residuals 12 6.275 0.5229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod2i=aov(TH2~Tratamiento, df2)
summary(mod2i)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 349.8 116.6 64.67 1.12e-07 ***
## Residuals 12 21.6 1.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mod2j=aov(AF2~Tratamiento, df2)
summary(mod2j)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 2365 788.3 1.827 0.196
## Residuals 12 5177 431.4
shapiro.test(mod2a$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2a$residuals
## W = 0.94565, p-value = 0.4241
bartlett.test(mod2a$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2a$residuals and df2$Tratamiento
## Bartlett's K-squared = 2.8717, df = 3, p-value = 0.4118
shapiro.test(mod2b$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2b$residuals
## W = 0.9152, p-value = 0.1412
bartlett.test(mod2b$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2b$residuals and df2$Tratamiento
## Bartlett's K-squared = 4.2201, df = 3, p-value = 0.2387
shapiro.test(mod2c$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2c$residuals
## W = 0.8998, p-value = 0.07978
bartlett.test(mod2c$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2c$residuals and df2$Tratamiento
## Bartlett's K-squared = 4.7472, df = 3, p-value = 0.1913
shapiro.test(mod2d$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2d$residuals
## W = 0.95661, p-value = 0.601
bartlett.test(mod2d$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2d$residuals and df2$Tratamiento
## Bartlett's K-squared = 2.9539, df = 3, p-value = 0.3988
shapiro.test(mod2e$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2e$residuals
## W = 0.95898, p-value = 0.6433
bartlett.test(mod2e$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2e$residuals and df2$Tratamiento
## Bartlett's K-squared = 1.2446, df = 3, p-value = 0.7423
shapiro.test(mod2f$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2f$residuals
## W = 0.97153, p-value = 0.8628
bartlett.test(mod2f$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2f$residuals and df2$Tratamiento
## Bartlett's K-squared = 0.47796, df = 3, p-value = 0.9237
shapiro.test(mod2g$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2g$residuals
## W = 0.87918, p-value = 0.03769
bartlett.test(mod2g$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2g$residuals and df2$Tratamiento
## Bartlett's K-squared = 4.2371, df = 3, p-value = 0.237
shapiro.test(mod2h$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2h$residuals
## W = 0.8875, p-value = 0.05086
bartlett.test(mod2h$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2h$residuals and df2$Tratamiento
## Bartlett's K-squared = 10.318, df = 3, p-value = 0.01605
shapiro.test(mod2i$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2i$residuals
## W = 0.95566, p-value = 0.5843
bartlett.test(mod2i$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2i$residuals and df2$Tratamiento
## Bartlett's K-squared = 1.0773, df = 3, p-value = 0.7826
shapiro.test(mod2j$residuals)
##
## Shapiro-Wilk normality test
##
## data: mod2j$residuals
## W = 0.96142, p-value = 0.6876
bartlett.test(mod2j$residuals, df2$Tratamiento)
##
## Bartlett test of homogeneity of variances
##
## data: mod2j$residuals and df2$Tratamiento
## Bartlett's K-squared = 2.4907, df = 3, p-value = 0.477
TukeyHSD(mod2a)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRC2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M 5.825 -1.395978 13.045978 0.1310560
## DH+150M--DH-M 8.750 1.529022 15.970978 0.0166830
## DH+250M--DH-M 3.025 -4.195978 10.245978 0.6128195
## DH+150M-DH-M 2.925 -4.295978 10.145978 0.6368161
## DH+250M-DH-M -2.800 -10.020978 4.420978 0.6667173
## DH+250M-DH+150M -5.725 -12.945978 1.495978 0.1400097
TukeyHSD(mod2b)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LPA2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -1.625 -4.813835 1.563835 0.4601876
## DH+150M--DH-M -1.125 -4.313835 2.063835 0.7260496
## DH+250M--DH-M -1.125 -4.313835 2.063835 0.7260496
## DH+150M-DH-M 0.500 -2.688835 3.688835 0.9652029
## DH+250M-DH-M 0.500 -2.688835 3.688835 0.9652029
## DH+250M-DH+150M 0.000 -3.188835 3.188835 1.0000000
TukeyHSD(mod2c)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PFPA2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -1.20300 -2.9753617 0.5693617 0.2358493
## DH+150M--DH-M -0.41800 -2.1903617 1.3543617 0.8950428
## DH+250M--DH-M -0.51875 -2.2911117 1.2536117 0.8205725
## DH+150M-DH-M 0.78500 -0.9873617 2.5573617 0.5713723
## DH+250M-DH-M 0.68425 -1.0881117 2.4566117 0.6696284
## DH+250M-DH+150M -0.10075 -1.8731117 1.6716117 0.9981924
TukeyHSD(mod2d)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PSPA2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -0.13200 -0.3241922 0.06019216 0.2276707
## DH+150M--DH-M -0.08325 -0.2754422 0.10894216 0.5881765
## DH+250M--DH-M -0.04200 -0.2341922 0.15019216 0.9139542
## DH+150M-DH-M 0.04875 -0.1434422 0.24094216 0.8736504
## DH+250M-DH-M 0.09000 -0.1021922 0.28219216 0.5281415
## DH+250M-DH+150M 0.04125 -0.1509422 0.23344216 0.9179502
TukeyHSD(mod2e)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DRT2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -2.500 -5.766635 0.7666346 0.1594003
## DH+150M--DH-M -1.625 -4.891635 1.6416346 0.4797625
## DH+250M--DH-M -1.825 -5.091635 1.4416346 0.3851690
## DH+150M-DH-M 0.875 -2.391635 4.1416346 0.8552621
## DH+250M-DH-M 0.675 -2.591635 3.9416346 0.9258230
## DH+250M-DH+150M -0.200 -3.466635 3.0666346 0.9977463
TukeyHSD(mod2f)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LRT2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -4.075 -7.170771 -0.9792295 0.0096952
## DH+150M--DH-M -2.750 -5.845771 0.3457705 0.0879495
## DH+250M--DH-M -2.175 -5.270771 0.9207705 0.2123267
## DH+150M-DH-M 1.325 -1.770771 4.4207705 0.5970895
## DH+250M-DH-M 1.900 -1.195771 4.9957705 0.3103235
## DH+250M-DH+150M 0.575 -2.520771 3.6707705 0.9443805
TukeyHSD(mod2g)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PFRT2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -10.72925 -20.271091 -1.1874088 0.0262776
## DH+150M--DH-M -6.29150 -15.833341 3.2503412 0.2564789
## DH+250M--DH-M -9.19375 -18.735591 0.3480912 0.0602377
## DH+150M-DH-M 4.43775 -5.104091 13.9795912 0.5335418
## DH+250M-DH-M 1.53550 -8.006341 11.0773412 0.9625787
## DH+250M-DH+150M -2.90225 -12.444091 6.6395912 0.8035613
TukeyHSD(mod2h)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PSRT2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -1.53050 -3.0485502 -0.01244979 0.0479403
## DH+150M--DH-M -0.87650 -2.3945502 0.64155021 0.3585680
## DH+250M--DH-M -1.31675 -2.8348002 0.20130021 0.0975446
## DH+150M-DH-M 0.65400 -0.8640502 2.17205021 0.5922267
## DH+250M-DH-M 0.21375 -1.3043002 1.73180021 0.9743173
## DH+250M-DH+150M -0.44025 -1.9583002 1.07780021 0.8244595
TukeyHSD(mod2i)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TH2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M 2.8800 0.06108252 5.698917 0.0447346
## DH+150M--DH-M 12.5225 9.70358252 15.341417 0.0000001
## DH+250M--DH-M 6.5100 3.69108252 9.328917 0.0000899
## DH+150M-DH-M 9.6425 6.82358252 12.461417 0.0000016
## DH+250M-DH-M 3.6300 0.81108252 6.448917 0.0112418
## DH+250M-DH+150M -6.0125 -8.83141748 -3.193583 0.0001909
TukeyHSD(mod2j )
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AF2 ~ Tratamiento, data = df2)
##
## $Tratamiento
## diff lwr upr p adj
## DH-M--DH-M -34.14050 -77.74410 9.463102 0.1467042
## DH+150M--DH-M -19.72625 -63.32985 23.877352 0.5551084
## DH+250M--DH-M -20.19075 -63.79435 23.412852 0.5369794
## DH+150M-DH-M 14.41425 -29.18935 58.017852 0.7624038
## DH+250M-DH-M 13.94975 -29.65385 57.553352 0.7793075
## DH+250M-DH+150M -0.46450 -44.06810 43.139102 0.9999879
ggplot(df2, aes(x = Tratamiento, y = CRC2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = AF2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = LPA2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = PFPA2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = PSPA2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = DRT2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = LRT2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = PFRT2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = PSRT2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
ggplot(df2, aes(x = Tratamiento, y = TH2, fill = Tratamiento)) +
stat_summary(fun = mean, geom = "bar")
