library(readxl)
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
library(ggplot2)
DF <- read_excel("C:/Users/JuanSebH2/Downloads/Archivos/Fisioo_A/DATOS PARA ANALISIS.xlsx",
sheet = "Combinado")
df= data.frame(DF)
df$Muestreo= factor(df$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"))
df$TRATAMIENTO= factor(df$TRATAMIENTO)
#Muestreo=df$Muestreo
#TRATAMIENTO=df$TRATAMIENTO
#PS_R=df$PS_R
#DR=df$DR
#CCI=df$CCI
#CRA=df$CRA
#L_PA=df$L_PA
#PF_R=df$PF_R
#PS_PA=df$PS_PA
#Pf_PA=df$Pf_PA
#Temp=df$Temp
df=data.frame(df)
# muestreo 0ddt
muestreo_0= df |>
filter(Muestreo == "0ddt");muestreo_0
## Muestreo TRATAMIENTO PS_R DR CCI CRA L_PA PF_R PS_PA Pf_PA Temp
## 1 0ddt (-DH-Si ) 0.205 20.80 15.6 89.53804 11.5 4.40 0.235 2.36 19.60
## 2 0ddt (-DH-Si ) 0.196 19.22 14.6 87.20930 10.0 4.58 0.193 2.31 20.10
## 3 0ddt (-DH-Si ) 0.298 18.66 13.7 91.26280 10.8 5.58 0.173 2.03 21.50
## 4 0ddt (-DH-Si ) 0.180 15.74 13.8 88.71834 9.5 6.39 0.054 1.75 18.70
## 5 0ddt (+DH-Si ) 0.160 19.70 13.3 66.87898 9.5 3.94 0.057 1.68 22.90
## 6 0ddt (+DH-Si ) 0.281 16.71 11.6 69.61559 10.0 3.37 0.219 2.02 23.41
## 7 0ddt (+DH-Si ) 0.217 15.49 12.9 66.93688 10.1 2.65 0.173 1.87 20.26
## 8 0ddt (+DH-Si ) 0.213 16.76 14.5 67.94872 8.4 3.15 0.116 2.13 22.00
## 9 0ddt (+DH+Si1) 0.238 17.07 14.3 79.80000 9.7 3.23 0.186 1.94 20.00
## 10 0ddt (+DH+Si1) 0.199 18.58 12.5 75.10000 9.4 3.26 0.175 1.89 19.70
## 11 0ddt (+DH+Si1) 0.087 19.09 15.4 76.60000 9.2 1.09 0.199 2.20 22.40
## 12 0ddt (+DH+Si1) 0.267 17.41 13.6 78.60000 7.9 2.89 0.242 2.18 18.70
## 13 0ddt (+DH+Si2) 0.302 17.35 14.9 74.56000 11.0 4.06 0.209 2.22 22.00
## 14 0ddt (+DH+Si2) 0.212 18.32 14.3 75.46000 10.3 3.75 0.204 2.34 22.10
## 15 0ddt (+DH+Si2) 0.099 16.90 17.7 78.21000 9.5 0.46 0.184 2.45 20.20
## 16 0ddt (+DH+Si2) 0.269 17.77 10.6 76.85000 9.4 3.54 0.166 1.03 19.10
muestreo_0=data.frame(muestreo_0)
##PS_R
PS_R= aov(PS_R~TRATAMIENTO, data= muestreo_0)
anova(PS_R)
## Analysis of Variance Table
##
## Response: PS_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.001414 0.0004712 0.0969 0.9603
## Residuals 12 0.058355 0.0048629
TukeyHSD(PS_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_R ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.00200 -0.1483963 0.1443963 0.9999746
## (+DH+Si1)-(-DH-Si ) -0.02200 -0.1683963 0.1243963 0.9691204
## (+DH+Si2)-(-DH-Si ) 0.00075 -0.1456463 0.1471463 0.9999987
## (+DH+Si1)-(+DH-Si ) -0.02000 -0.1663963 0.1263963 0.9764329
## (+DH+Si2)-(+DH-Si ) 0.00275 -0.1436463 0.1491463 0.9999340
## (+DH+Si2)-(+DH+Si1) 0.02275 -0.1236463 0.1691463 0.9660659
library(TukeyC)
## Warning: package 'TukeyC' was built under R version 4.3.2
tc=TukeyC(PS_R,'TRATAMIENTO')
plot(tc)
##2
DR= aov(DR~TRATAMIENTO, muestreo_0)
anova(DR)
## Analysis of Variance Table
##
## Response: DR
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 4.5785 1.5262 0.6823 0.5797
## Residuals 12 26.8412 2.2368
TukeyHSD(DR)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DR ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -1.4400 -4.57972 1.69972 0.5444700
## (+DH+Si1)-(-DH-Si ) -0.5675 -3.70722 2.57222 0.9483737
## (+DH+Si2)-(-DH-Si ) -1.0200 -4.15972 2.11972 0.7715040
## (+DH+Si1)-(+DH-Si ) 0.8725 -2.26722 4.01222 0.8416193
## (+DH+Si2)-(+DH-Si ) 0.4200 -2.71972 3.55972 0.9778066
## (+DH+Si2)-(+DH+Si1) -0.4525 -3.59222 2.68722 0.9725640
library(TukeyC)
tc=TukeyC(DR,'TRATAMIENTO')
plot(tc)
##3
CCI= aov(CCI~TRATAMIENTO, muestreo_0)
anova(CCI)
## Analysis of Variance Table
##
## Response: CCI
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 4.687 1.5623 0.5115 0.6819
## Residuals 12 36.653 3.0544
TukeyHSD(CCI)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CCI ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -1.350 -5.018951 2.318951 0.7005720
## (+DH+Si1)-(-DH-Si ) -0.475 -4.143951 3.193951 0.9797892
## (+DH+Si2)-(-DH-Si ) -0.050 -3.718951 3.618951 0.9999748
## (+DH+Si1)-(+DH-Si ) 0.875 -2.793951 4.543951 0.8919879
## (+DH+Si2)-(+DH-Si ) 1.300 -2.368951 4.968951 0.7234976
## (+DH+Si2)-(+DH+Si1) 0.425 -3.243951 4.093951 0.9853266
library(TukeyC)
tc=TukeyC(CCI,'TRATAMIENTO')
plot(tc)
##4
CRA= aov(CRA~TRATAMIENTO, muestreo_0)
anova(CRA)
## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 924.14 308.046 108.04 5.979e-09 ***
## Residuals 12 34.21 2.851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CRA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRA ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -21.337079 -24.881897 -17.792261 0.0000000
## (+DH+Si1)-(-DH-Si ) -11.657122 -15.201940 -8.112303 0.0000024
## (+DH+Si2)-(-DH-Si ) -12.912122 -16.456940 -9.367303 0.0000008
## (+DH+Si1)-(+DH-Si ) 9.679957 6.135139 13.224776 0.0000170
## (+DH+Si2)-(+DH-Si ) 8.424957 4.880139 11.969776 0.0000680
## (+DH+Si2)-(+DH+Si1) -1.255000 -4.799818 2.289818 0.7239761
library(TukeyC)
tc=TukeyC(CRA,'TRATAMIENTO')
plot(tc)
##5
L_PA= aov(L_PA~TRATAMIENTO, muestreo_0)
anova(L_PA)
## Analysis of Variance Table
##
## Response: L_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 4.5275 1.50917 2.3428 0.1246
## Residuals 12 7.7300 0.64417
TukeyHSD(L_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_PA ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.95 -2.6349222 0.7349222 0.3777360
## (+DH+Si1)-(-DH-Si ) -1.40 -3.0849222 0.2849222 0.1166115
## (+DH+Si2)-(-DH-Si ) -0.40 -2.0849222 1.2849222 0.8932502
## (+DH+Si1)-(+DH-Si ) -0.45 -2.1349222 1.2349222 0.8563074
## (+DH+Si2)-(+DH-Si ) 0.55 -1.1349222 2.2349222 0.7690345
## (+DH+Si2)-(+DH+Si1) 1.00 -0.6849222 2.6849222 0.3366104
library(TukeyC)
tc=TukeyC(L_PA,'TRATAMIENTO')
plot(tc)
##6
PF_R= aov(PF_R~TRATAMIENTO, muestreo_0)
anova(PF_R)
## Analysis of Variance Table
##
## Response: PF_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 16.581 5.5269 4.4061 0.02617 *
## Residuals 12 15.053 1.2544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PF_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PF_R ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -1.960 -4.311226 0.3912263 0.1150884
## (+DH+Si1)-(-DH-Si ) -2.620 -4.971226 -0.2687737 0.0276982
## (+DH+Si2)-(-DH-Si ) -2.285 -4.636226 0.0662263 0.0577416
## (+DH+Si1)-(+DH-Si ) -0.660 -3.011226 1.6912263 0.8376998
## (+DH+Si2)-(+DH-Si ) -0.325 -2.676226 2.0262263 0.9756330
## (+DH+Si2)-(+DH+Si1) 0.335 -2.016226 2.6862263 0.9734412
library(TukeyC)
tc=TukeyC(PF_R,'TRATAMIENTO')
plot(tc)
##7
PS_PA= aov(PS_PA~TRATAMIENTO, muestreo_0)
anova(PS_PA)
## Analysis of Variance Table
##
## Response: PS_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.008642 0.0028806 0.9444 0.4498
## Residuals 12 0.036603 0.0030503
TukeyHSD(PS_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_PA ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.02250 -0.13844444 0.09344444 0.9373442
## (+DH+Si1)-(-DH-Si ) 0.03675 -0.07919444 0.15269444 0.7839341
## (+DH+Si2)-(-DH-Si ) 0.02700 -0.08894444 0.14294444 0.8984260
## (+DH+Si1)-(+DH-Si ) 0.05925 -0.05669444 0.17519444 0.4579054
## (+DH+Si2)-(+DH-Si ) 0.04950 -0.06644444 0.16544444 0.5989487
## (+DH+Si2)-(+DH+Si1) -0.00975 -0.12569444 0.10619444 0.9942395
library(TukeyC)
tc=TukeyC(PS_PA,'TRATAMIENTO')
plot(tc)
##8
Pf_PA= aov(Pf_PA~TRATAMIENTO, muestreo_0)
anova(Pf_PA)
## Analysis of Variance Table
##
## Response: Pf_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.07455 0.02485 0.1717 0.9134
## Residuals 12 1.73665 0.14472
TukeyHSD(Pf_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Pf_PA ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.1875 -0.9861312 0.6111312 0.8962630
## (+DH+Si1)-(-DH-Si ) -0.0600 -0.8586312 0.7386312 0.9958681
## (+DH+Si2)-(-DH-Si ) -0.1025 -0.9011312 0.6961312 0.9802864
## (+DH+Si1)-(+DH-Si ) 0.1275 -0.6711312 0.9261312 0.9634015
## (+DH+Si2)-(+DH-Si ) 0.0850 -0.7136312 0.8836312 0.9885215
## (+DH+Si2)-(+DH+Si1) -0.0425 -0.8411312 0.7561312 0.9985145
library(TukeyC)
tc=TukeyC(Pf_PA,'TRATAMIENTO')
plot(tc)
##9
Temp= aov(Temp~TRATAMIENTO, muestreo_0)
anova(Temp)
## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 11.381 3.7936 1.9288 0.1788
## Residuals 12 23.602 1.9668
TukeyHSD(Temp)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Temp ~ TRATAMIENTO, data = muestreo_0)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) 2.1675 -0.7766795 5.11168 0.1823882
## (+DH+Si1)-(-DH-Si ) 0.2250 -2.7191795 3.16918 0.9956545
## (+DH+Si2)-(-DH-Si ) 0.8750 -2.0691795 3.81918 0.8139565
## (+DH+Si1)-(+DH-Si ) -1.9425 -4.8866795 1.00168 0.2560211
## (+DH+Si2)-(+DH-Si ) -1.2925 -4.2366795 1.65168 0.5781011
## (+DH+Si2)-(+DH+Si1) 0.6500 -2.2941795 3.59418 0.9116121
library(TukeyC)
tc=TukeyC(Temp,'TRATAMIENTO')
plot(tc)
#Muestreo 7ddt
muestreo_7= df |>
filter(Muestreo == "7ddt");muestreo_7
## Muestreo TRATAMIENTO PS_R DR CCI CRA L_PA PF_R PS_PA Pf_PA
## 1 7ddt (-DH-Si ) 0.6491 19.17 15.20 87.98340 11.9 18.340 0.6351 6.08
## 2 7ddt (-DH-Si ) 0.9384 29.54 13.80 86.14610 10.5 20.450 0.5606 4.46
## 3 7ddt (-DH-Si ) 0.7043 18.17 18.50 85.18079 11.9 17.520 0.4165 4.55
## 4 7ddt (-DH-Si ) 0.8099 25.38 15.40 89.22764 11.8 14.560 0.4660 4.48
## 5 7ddt (+DH-Si ) 0.2791 12.59 9.50 52.19512 10.4 2.240 0.1741 1.34
## 6 7ddt (+DH-Si ) 0.1762 9.75 9.75 51.23989 9.2 4.310 0.3101 2.03
## 7 7ddt (+DH-Si ) 0.3652 13.62 12.80 51.99214 9.3 3.750 0.2952 1.98
## 8 7ddt (+DH-Si ) 0.3256 14.12 12.20 54.73210 11.0 5.350 0.2937 1.79
## 9 7ddt (+DH+Si1) 0.5908 14.21 16.60 65.31587 10.2 10.840 0.3467 1.87
## 10 7ddt (+DH+Si1) 0.3407 18.00 14.30 68.05161 11.3 6.580 0.3480 2.52
## 11 7ddt (+DH+Si1) 0.4457 17.36 13.30 62.56576 10.1 8.980 0.3645 2.79
## 12 7ddt (+DH+Si1) 0.4303 19.81 22.30 64.73210 9.2 5.530 0.2808 1.97
## 13 7ddt (+DH+Si2) 0.3491 19.23 19.20 66.40535 10.7 11.210 0.2579 2.95
## 14 7ddt (+DH+Si2) 0.7384 17.49 22.50 66.19946 11.2 7.622 0.2231 1.92
## 15 7ddt (+DH+Si2) 0.3043 19.09 21.30 65.71048 10.5 5.390 0.1998 1.49
## 16 7ddt (+DH+Si2) 0.8099 16.14 16.14 62.56576 9.1 8.310 0.2233 1.74
## Temp
## 1 19.10
## 2 21.20
## 3 20.36
## 4 19.40
## 5 26.40
## 6 29.20
## 7 30.10
## 8 30.70
## 9 19.50
## 10 20.50
## 11 22.30
## 12 23.70
## 13 22.60
## 14 19.70
## 15 19.50
## 16 20.90
muestreo_7=data.frame(muestreo_7)
##PS_R
PS_R= aov(PS_R~TRATAMIENTO, muestreo_7)
anova(PS_R)
## Analysis of Variance Table
##
## Response: PS_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.50103 0.167010 6.5785 0.007043 **
## Residuals 12 0.30464 0.025387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PS_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_R ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.48890 -0.82339269 -0.15440731 0.0046015
## (+DH+Si1)-(-DH-Si ) -0.32355 -0.65804269 0.01094269 0.0590944
## (+DH+Si2)-(-DH-Si ) -0.22500 -0.55949269 0.10949269 0.2421879
## (+DH+Si1)-(+DH-Si ) 0.16535 -0.16914269 0.49984269 0.4848567
## (+DH+Si2)-(+DH-Si ) 0.26390 -0.07059269 0.59839269 0.1426081
## (+DH+Si2)-(+DH+Si1) 0.09855 -0.23594269 0.43304269 0.8177414
library(TukeyC)
tc=TukeyC(PS_R,'TRATAMIENTO')
plot(tc)
##2
DR= aov(DR~TRATAMIENTO, muestreo_7)
anova(DR)
## Analysis of Variance Table
##
## Response: DR
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 223.28 74.428 7.4046 0.004563 **
## Residuals 12 120.62 10.052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(DR)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DR ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -10.5450 -17.20075 -3.8892496 0.0024831
## (+DH+Si1)-(-DH-Si ) -5.7200 -12.37575 0.9357504 0.1014569
## (+DH+Si2)-(-DH-Si ) -5.0775 -11.73325 1.5782504 0.1612264
## (+DH+Si1)-(+DH-Si ) 4.8250 -1.83075 11.4807504 0.1919985
## (+DH+Si2)-(+DH-Si ) 5.4675 -1.18825 12.1232504 0.1220559
## (+DH+Si2)-(+DH+Si1) 0.6425 -6.01325 7.2982504 0.9913654
library(TukeyC)
tc=TukeyC(DR,'TRATAMIENTO')
plot(tc)
##3
CCI= aov(CCI~TRATAMIENTO, muestreo_7)
anova(CCI)
## Analysis of Variance Table
##
## Response: CCI
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 156.042 52.014 6.7677 0.006359 **
## Residuals 12 92.227 7.686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CCI)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CCI ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -4.6625 -10.4824353 1.157435 0.1345828
## (+DH+Si1)-(-DH-Si ) 0.9000 -4.9199353 6.719935 0.9665295
## (+DH+Si2)-(-DH-Si ) 4.0600 -1.7599353 9.879935 0.2170726
## (+DH+Si1)-(+DH-Si ) 5.5625 -0.2574353 11.382435 0.0626577
## (+DH+Si2)-(+DH-Si ) 8.7225 2.9025647 14.542435 0.0038132
## (+DH+Si2)-(+DH+Si1) 3.1600 -2.6599353 8.979935 0.4084033
library(TukeyC)
tc=TukeyC(CCI,'TRATAMIENTO')
plot(tc)
##4
CRA= aov(CRA~TRATAMIENTO, muestreo_7)
anova(CRA)
## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 2479.85 826.62 237.54 6.006e-11 ***
## Residuals 12 41.76 3.48
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CRA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRA ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -34.5946671 -38.510875 -30.678459 0.0000000
## (+DH+Si1)-(-DH-Si ) -21.9681464 -25.884355 -18.051938 0.0000000
## (+DH+Si2)-(-DH-Si ) -21.9142185 -25.830427 -17.998010 0.0000000
## (+DH+Si1)-(+DH-Si ) 12.6265207 8.710312 16.542729 0.0000030
## (+DH+Si2)-(+DH-Si ) 12.6804486 8.764240 16.596657 0.0000029
## (+DH+Si2)-(+DH+Si1) 0.0539279 -3.862280 3.970136 0.9999740
library(TukeyC)
tc=TukeyC(CRA,'TRATAMIENTO')
plot(tc)
##5
L_PA= aov(L_PA~TRATAMIENTO, muestreo_7)
anova(L_PA)
## Analysis of Variance Table
##
## Response: L_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 5.7219 1.90729 2.7435 0.08933 .
## Residuals 12 8.3425 0.69521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_PA ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -1.550 -3.300404 0.2004037 0.0891810
## (+DH+Si1)-(-DH-Si ) -1.325 -3.075404 0.4254037 0.1657219
## (+DH+Si2)-(-DH-Si ) -1.150 -2.900404 0.6004037 0.2590892
## (+DH+Si1)-(+DH-Si ) 0.225 -1.525404 1.9754037 0.9801994
## (+DH+Si2)-(+DH-Si ) 0.400 -1.350404 2.1504037 0.9032771
## (+DH+Si2)-(+DH+Si1) 0.175 -1.575404 1.9254037 0.9904342
library(TukeyC)
tc=TukeyC(L_PA,'TRATAMIENTO')
plot(tc)
##6
PF_R= aov(PF_R~TRATAMIENTO, muestreo_7)
anova(PF_R)
## Analysis of Variance Table
##
## Response: PF_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 411.61 137.204 28.715 9.27e-06 ***
## Residuals 12 57.34 4.778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PF_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PF_R ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -13.8050 -18.3939076 -9.216092 0.0000062
## (+DH+Si1)-(-DH-Si ) -9.7350 -14.3239076 -5.146092 0.0002008
## (+DH+Si2)-(-DH-Si ) -9.5845 -14.1734076 -4.995592 0.0002319
## (+DH+Si1)-(+DH-Si ) 4.0700 -0.5189076 8.658908 0.0885580
## (+DH+Si2)-(+DH-Si ) 4.2205 -0.3684076 8.809408 0.0751710
## (+DH+Si2)-(+DH+Si1) 0.1505 -4.4384076 4.739408 0.9996498
library(TukeyC)
tc=TukeyC(PF_R,'TRATAMIENTO')
plot(tc)
##7
PS_PA= aov(PS_PA~TRATAMIENTO, muestreo_7)
anova(PS_PA)
## Analysis of Variance Table
##
## Response: PS_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.201468 0.067156 17.388 0.000115 ***
## Residuals 12 0.046347 0.003862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PS_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_PA ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.251275 -0.38174205 -0.12080795 0.0004831
## (+DH+Si1)-(-DH-Si ) -0.184550 -0.31501705 -0.05408295 0.0058493
## (+DH+Si2)-(-DH-Si ) -0.293525 -0.42399205 -0.16305795 0.0001155
## (+DH+Si1)-(+DH-Si ) 0.066725 -0.06374205 0.19719205 0.4572502
## (+DH+Si2)-(+DH-Si ) -0.042250 -0.17271705 0.08821705 0.7731405
## (+DH+Si2)-(+DH+Si1) -0.108975 -0.23944205 0.02149205 0.1141633
library(TukeyC)
tc=TukeyC(PS_PA,'TRATAMIENTO')
plot(tc)
##8
Pf_PA= aov(Pf_PA~TRATAMIENTO, muestreo_7)
anova(Pf_PA)
## Analysis of Variance Table
##
## Response: Pf_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 25.0441 8.348 25.066 1.871e-05 ***
## Residuals 12 3.9965 0.333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Pf_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Pf_PA ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -3.1075 -4.319026 -1.895974 0.0000320
## (+DH+Si1)-(-DH-Si ) -2.6050 -3.816526 -1.393474 0.0001771
## (+DH+Si2)-(-DH-Si ) -2.8675 -4.079026 -1.655974 0.0000708
## (+DH+Si1)-(+DH-Si ) 0.5025 -0.709026 1.714026 0.6200149
## (+DH+Si2)-(+DH-Si ) 0.2400 -0.971526 1.451526 0.9337601
## (+DH+Si2)-(+DH+Si1) -0.2625 -1.474026 0.949026 0.9158738
library(TukeyC)
tc=TukeyC(Pf_PA,'TRATAMIENTO')
plot(tc)
##9
Temp= aov(Temp~TRATAMIENTO, muestreo_7)
anova(Temp)
## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 214.599 71.533 28.456 9.719e-06 ***
## Residuals 12 30.166 2.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Temp)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Temp ~ TRATAMIENTO, data = muestreo_7)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) 9.085 5.756486 12.413514 0.0000171
## (+DH+Si1)-(-DH-Si ) 1.485 -1.843514 4.813514 0.5658192
## (+DH+Si2)-(-DH-Si ) 0.660 -2.668514 3.988514 0.9335894
## (+DH+Si1)-(+DH-Si ) -7.600 -10.928514 -4.271486 0.0001002
## (+DH+Si2)-(+DH-Si ) -8.425 -11.753514 -5.096486 0.0000366
## (+DH+Si2)-(+DH+Si1) -0.825 -4.153514 2.503514 0.8808187
library(TukeyC)
tc=TukeyC(Temp,'TRATAMIENTO')
plot(tc)
#Muestreo 14ddt
muestreo_14= df |>
filter(Muestreo == "14ddt");muestreo_14
## Muestreo TRATAMIENTO PS_R DR CCI CRA L_PA PF_R PS_PA Pf_PA
## 1 14ddt (-DH-Si ) 1.1627 29.27 15.60 87.74788 12.5 11.20 1.0152 7.97
## 2 14ddt (-DH-Si ) 1.0666 29.32 16.50 93.92009 11.8 19.88 0.8008 5.83
## 3 14ddt (-DH-Si ) 0.8275 28.93 18.70 88.95184 12.8 23.75 0.6969 6.97
## 4 14ddt (-DH-Si ) 1.0576 30.67 21.30 85.77465 14.5 29.21 0.8028 8.41
## 5 14ddt (+DH-Si ) 0.3804 16.14 26.50 45.43651 9.9 3.56 0.3580 1.33
## 6 14ddt (+DH-Si ) 0.4514 15.46 22.60 41.19346 10.1 4.68 0.3202 1.14
## 7 14ddt (+DH-Si ) 0.5500 16.24 27.70 40.84231 9.6 2.50 0.4063 1.35
## 8 14ddt (+DH-Si ) 0.1654 8.44 28.44 44.08740 9.0 5.81 0.5103 2.54
## 9 14ddt (+DH+Si1) 0.5833 19.04 22.00 63.34688 9.8 12.20 0.5732 4.46
## 10 14ddt (+DH+Si1) 0.8173 23.12 21.00 63.94928 9.5 12.99 0.4987 5.78
## 11 14ddt (+DH+Si1) 0.7770 21.11 20.80 61.31265 9.3 10.30 0.4785 3.57
## 12 14ddt (+DH+Si1) 0.6618 24.87 21.60 62.56983 9.0 11.10 0.4972 3.81
## 13 14ddt (+DH+Si2) 0.5515 21.15 19.30 58.68803 9.4 10.45 0.5504 2.94
## 14 14ddt (+DH+Si2) 0.7309 25.54 24.70 63.71308 9.9 12.20 0.4482 3.14
## 15 14ddt (+DH+Si2) 0.8997 16.03 20.50 59.06863 8.5 12.25 0.4844 2.83
## 16 14ddt (+DH+Si2) 0.6706 25.48 18.70 62.65823 9.5 10.67 0.4307 4.10
## Temp
## 1 20.42
## 2 17.47
## 3 18.45
## 4 17.84
## 5 26.27
## 6 24.56
## 7 23.45
## 8 21.60
## 9 21.08
## 10 25.35
## 11 21.38
## 12 20.82
## 13 19.98
## 14 21.22
## 15 21.26
## 16 20.68
muestreo_14=data.frame(muestreo_14)
##PS_R
PS_R= aov(PS_R~TRATAMIENTO, muestreo_14)
anova(PS_R)
## Analysis of Variance Table
##
## Response: PS_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.82389 0.274632 13.846 0.0003341 ***
## Residuals 12 0.23801 0.019834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PS_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_R ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.641800 -0.93745723 -0.34614277 0.0001620
## (+DH+Si1)-(-DH-Si ) -0.318750 -0.61440723 -0.02309277 0.0334234
## (+DH+Si2)-(-DH-Si ) -0.315425 -0.61108223 -0.01976777 0.0354278
## (+DH+Si1)-(+DH-Si ) 0.323050 0.02739277 0.61870723 0.0309956
## (+DH+Si2)-(+DH-Si ) 0.326375 0.03071777 0.62203223 0.0292380
## (+DH+Si2)-(+DH+Si1) 0.003325 -0.29233223 0.29898223 0.9999858
library(TukeyC)
tc=TukeyC(PS_R,'TRATAMIENTO')
plot(tc)
##2
DR= aov(DR~TRATAMIENTO, muestreo_14)
anova(DR)
## Analysis of Variance Table
##
## Response: DR
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 479.33 159.775 15.409 0.0002039 ***
## Residuals 12 124.43 10.369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(DR)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DR ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -15.4775 -22.237549 -8.7174513 0.0000976
## (+DH+Si1)-(-DH-Si ) -7.5125 -14.272549 -0.7524513 0.0281339
## (+DH+Si2)-(-DH-Si ) -7.4975 -14.257549 -0.7374513 0.0284600
## (+DH+Si1)-(+DH-Si ) 7.9650 1.204951 14.7250487 0.0198611
## (+DH+Si2)-(+DH-Si ) 7.9800 1.219951 14.7400487 0.0196330
## (+DH+Si2)-(+DH+Si1) 0.0150 -6.745049 6.7750487 0.9999999
library(TukeyC)
tc=TukeyC(DR,'TRATAMIENTO')
plot(tc)
##3
CCI= aov(CCI~TRATAMIENTO, muestreo_14)
anova(CCI)
## Analysis of Variance Table
##
## Response: CCI
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 142.662 47.554 9.1264 0.002018 **
## Residuals 12 62.527 5.211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CCI)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CCI ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) 8.285 3.492933 13.0770668 0.0012232
## (+DH+Si1)-(-DH-Si ) 3.325 -1.467067 8.1170668 0.2207076
## (+DH+Si2)-(-DH-Si ) 2.775 -2.017067 7.5670668 0.3562137
## (+DH+Si1)-(+DH-Si ) -4.960 -9.752067 -0.1679332 0.0417568
## (+DH+Si2)-(+DH-Si ) -5.510 -10.302067 -0.7179332 0.0230285
## (+DH+Si2)-(+DH+Si1) -0.550 -5.342067 4.2420668 0.9857131
library(TukeyC)
tc=TukeyC(CCI,'TRATAMIENTO')
plot(tc)
##4
CRA= aov(CRA~TRATAMIENTO, muestreo_14)
anova(CRA)
## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 4343.3 1447.77 234.15 6.537e-11 ***
## Residuals 12 74.2 6.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CRA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRA ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -46.208695 -51.428885 -40.988505 0.0000000
## (+DH+Si1)-(-DH-Si ) -26.303954 -31.524144 -21.083764 0.0000000
## (+DH+Si2)-(-DH-Si ) -28.066622 -33.286812 -22.846433 0.0000000
## (+DH+Si1)-(+DH-Si ) 19.904741 14.684551 25.124930 0.0000005
## (+DH+Si2)-(+DH-Si ) 18.142072 12.921882 23.362262 0.0000013
## (+DH+Si2)-(+DH+Si1) -1.762668 -6.982858 3.457522 0.7509539
library(TukeyC)
tc=TukeyC(CRA,'TRATAMIENTO')
plot(tc)
##5
L_PA= aov(L_PA~TRATAMIENTO, muestreo_14)
anova(L_PA)
## Analysis of Variance Table
##
## Response: L_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 35.767 11.9223 23.775 2.449e-05 ***
## Residuals 12 6.017 0.5015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_PA ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -3.250 -4.736614 -1.763386 0.0001515
## (+DH+Si1)-(-DH-Si ) -3.500 -4.986614 -2.013386 0.0000746
## (+DH+Si2)-(-DH-Si ) -3.575 -5.061614 -2.088386 0.0000607
## (+DH+Si1)-(+DH-Si ) -0.250 -1.736614 1.236614 0.9576953
## (+DH+Si2)-(+DH-Si ) -0.325 -1.811614 1.161614 0.9138647
## (+DH+Si2)-(+DH+Si1) -0.075 -1.561614 1.411614 0.9987328
library(TukeyC)
tc=TukeyC(L_PA,'TRATAMIENTO')
plot(tc)
##6
PF_R= aov(PF_R~TRATAMIENTO, muestreo_14)
anova(PF_R)
## Analysis of Variance Table
##
## Response: PF_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 573.93 191.311 12.383 0.0005522 ***
## Residuals 12 185.39 15.449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PF_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PF_R ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -16.8725 -25.1239585 -8.621042 0.0002818
## (+DH+Si1)-(-DH-Si ) -9.3625 -17.6139585 -1.111042 0.0249194
## (+DH+Si2)-(-DH-Si ) -9.6175 -17.8689585 -1.366042 0.0212178
## (+DH+Si1)-(+DH-Si ) 7.5100 -0.7414585 15.761458 0.0788703
## (+DH+Si2)-(+DH-Si ) 7.2550 -0.9964585 15.506458 0.0919987
## (+DH+Si2)-(+DH+Si1) -0.2550 -8.5064585 7.996458 0.9997068
library(TukeyC)
tc=TukeyC(PF_R,'TRATAMIENTO')
plot(tc)
##7
PS_PA= aov(PS_PA~TRATAMIENTO, muestreo_14)
anova(PS_PA)
## Analysis of Variance Table
##
## Response: PS_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 0.42874 0.14291 19.576 6.466e-05 ***
## Residuals 12 0.08760 0.00730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PS_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_PA ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.430225 -0.60959643 -0.2508536 0.0000622
## (+DH+Si1)-(-DH-Si ) -0.317025 -0.49639643 -0.1376536 0.0010166
## (+DH+Si2)-(-DH-Si ) -0.350500 -0.52987143 -0.1711286 0.0004247
## (+DH+Si1)-(+DH-Si ) 0.113200 -0.06617143 0.2925714 0.2889576
## (+DH+Si2)-(+DH-Si ) 0.079725 -0.09964643 0.2590964 0.5686969
## (+DH+Si2)-(+DH+Si1) -0.033475 -0.21284643 0.1458964 0.9436531
library(TukeyC)
tc=TukeyC(PS_PA,'TRATAMIENTO')
plot(tc)
##8
Pf_PA= aov(Pf_PA~TRATAMIENTO, muestreo_14)
anova(Pf_PA)
## Analysis of Variance Table
##
## Response: Pf_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 69.257 23.0858 30.333 6.958e-06 ***
## Residuals 12 9.133 0.7611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Pf_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Pf_PA ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -5.7050 -7.536445 -3.873555 0.0000043
## (+DH+Si1)-(-DH-Si ) -2.8900 -4.721445 -1.058555 0.0025629
## (+DH+Si2)-(-DH-Si ) -4.0425 -5.873945 -2.211055 0.0001384
## (+DH+Si1)-(+DH-Si ) 2.8150 0.983555 4.646445 0.0031447
## (+DH+Si2)-(+DH-Si ) 1.6625 -0.168945 3.493945 0.0798194
## (+DH+Si2)-(+DH+Si1) -1.1525 -2.983945 0.678945 0.2911333
library(TukeyC)
tc=TukeyC(Pf_PA,'TRATAMIENTO')
plot(tc)
##9
Temp= aov(Temp~TRATAMIENTO, muestreo_14)
anova(Temp)
## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 62.812 20.9372 7.9702 0.003448 **
## Residuals 12 31.523 2.6269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Temp)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Temp ~ TRATAMIENTO, data = muestreo_14)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) 5.4250 2.0224509 8.8275491 0.0023622
## (+DH+Si1)-(-DH-Si ) 3.6125 0.2099509 7.0150491 0.0363858
## (+DH+Si2)-(-DH-Si ) 2.2400 -1.1625491 5.6425491 0.2576110
## (+DH+Si1)-(+DH-Si ) -1.8125 -5.2150491 1.5900491 0.4240027
## (+DH+Si2)-(+DH-Si ) -3.1850 -6.5875491 0.2175491 0.0692303
## (+DH+Si2)-(+DH+Si1) -1.3725 -4.7750491 2.0300491 0.6397522
library(TukeyC)
tc=TukeyC(Temp,'TRATAMIENTO')
plot(tc)
#Muestreo 21ddt
muestreo_21= df |>
filter(Muestreo == "21ddt");muestreo_21
## Muestreo TRATAMIENTO PS_R DR CCI CRA L_PA PF_R PS_PA
## 1 21ddt (-DH-Si ) 1.0680 34.36000 23.50 93.28757 14.5 15.640 1.0617
## 2 21ddt (-DH-Si ) 1.3061 39.64000 23.70 90.97816 15.2 21.240 0.9705
## 3 21ddt (-DH-Si ) 1.1875 37.91000 20.90 90.23764 13.9 23.640 0.9393
## 4 21ddt (-DH-Si ) 1.2023 36.50000 22.40 91.50112 14.2 19.210 0.9075
## 5 21ddt (+DH-Si ) 0.3046 16.55000 6.20 39.33034 8.7 2.520 0.3306
## 6 21ddt (+DH-Si ) 0.2205 12.20000 14.90 42.05714 9.2 1.330 0.1525
## 7 21ddt (+DH-Si ) 0.3231 15.66000 11.50 37.07120 8.5 2.980 0.2855
## 8 21ddt (+DH-Si ) 0.2965 14.80333 14.20 39.48623 8.3 2.965 0.3473
## 9 21ddt (+DH+Si1) 0.5896 25.08000 25.30 58.11321 11.4 13.300 0.4177
## 10 21ddt (+DH+Si1) 0.5952 26.54000 26.54 56.68966 9.9 9.710 0.5823
## 11 21ddt (+DH+Si1) 0.6136 27.62000 23.40 69.84127 9.7 12.760 0.4699
## 12 21ddt (+DH+Si1) 0.6874 23.85000 29.00 61.54804 9.6 6.874 0.3853
## 13 21ddt (+DH+Si2) 0.6896 29.58000 25.20 58.65385 10.5 12.210 0.5440
## 14 21ddt (+DH+Si2) 0.5052 19.54000 20.70 65.82405 9.8 11.930 0.4525
## 15 21ddt (+DH+Si2) 0.6138 25.39000 26.20 62.48012 11.1 10.540 0.4413
## 16 21ddt (+DH+Si2) 0.6785 24.83000 20.90 58.98601 8.7 9.610 0.3896
## Pf_PA Temp
## 1 10.460 17.24
## 2 8.320 18.56
## 3 8.010 20.10
## 4 9.520 16.08
## 5 1.710 28.12
## 6 1.250 26.12
## 7 0.750 26.97
## 8 1.570 26.72
## 9 2.990 24.12
## 10 4.960 22.82
## 11 2.460 22.41
## 12 3.050 23.10
## 13 3.050 22.47
## 14 2.469 22.77
## 15 4.870 21.27
## 16 4.440 22.21
muestreo_21=data.frame(muestreo_21)
##PS_R
PS_R= aov(PS_R~TRATAMIENTO, muestreo_21)
anova(PS_R)
## Analysis of Variance Table
##
## Response: PS_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 1.69205 0.56402 108.77 5.751e-09 ***
## Residuals 12 0.06223 0.00519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PS_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_R ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.904800 -1.055973 -0.753627 0.0000000
## (+DH+Si1)-(-DH-Si ) -0.569525 -0.720698 -0.418352 0.0000006
## (+DH+Si2)-(-DH-Si ) -0.569200 -0.720373 -0.418027 0.0000006
## (+DH+Si1)-(+DH-Si ) 0.335275 0.184102 0.486448 0.0001323
## (+DH+Si2)-(+DH-Si ) 0.335600 0.184427 0.486773 0.0001311
## (+DH+Si2)-(+DH+Si1) 0.000325 -0.150848 0.151498 0.9999999
library(TukeyC)
tc=TukeyC(PS_R,'TRATAMIENTO')
plot(tc)
##2
DR= aov(DR~TRATAMIENTO, muestreo_21)
anova(DR)
## Analysis of Variance Table
##
## Response: DR
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 997.95 332.65 47.198 6.442e-07 ***
## Residuals 12 84.58 7.05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(DR)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DR ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -22.29917 -27.872460 -16.725873 0.0000003
## (+DH+Si1)-(-DH-Si ) -11.33000 -16.903294 -5.756706 0.0002972
## (+DH+Si2)-(-DH-Si ) -12.26750 -17.840794 -6.694206 0.0001421
## (+DH+Si1)-(+DH-Si ) 10.96917 5.395873 16.542460 0.0003982
## (+DH+Si2)-(+DH-Si ) 10.03167 4.458373 15.604960 0.0008707
## (+DH+Si2)-(+DH+Si1) -0.93750 -6.510794 4.635794 0.9576636
library(TukeyC)
tc=TukeyC(DR,'TRATAMIENTO')
plot(tc)
##3
CCI= aov(CCI~TRATAMIENTO, muestreo_21)
anova(CCI)
## Analysis of Variance Table
##
## Response: CCI
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 479.05 159.685 20.652 4.965e-05 ***
## Residuals 12 92.78 7.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CCI)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CCI ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -10.925 -16.762519 -5.087481 0.0006218
## (+DH+Si1)-(-DH-Si ) 3.435 -2.402519 9.272519 0.3434140
## (+DH+Si2)-(-DH-Si ) 0.625 -5.212519 6.462519 0.9883216
## (+DH+Si1)-(+DH-Si ) 14.360 8.522481 20.197519 0.0000485
## (+DH+Si2)-(+DH-Si ) 11.550 5.712481 17.387519 0.0003798
## (+DH+Si2)-(+DH+Si1) -2.810 -8.647519 3.027519 0.5062273
library(TukeyC)
tc=TukeyC(CCI,'TRATAMIENTO')
plot(tc)
##4
CRA= aov(CRA~TRATAMIENTO, muestreo_21)
anova(CRA)
## Analysis of Variance Table
##
## Response: CRA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 5474.4 1824.79 140.56 1.3e-09 ***
## Residuals 12 155.8 12.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(CRA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRA ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -52.01489339 -59.578924 -44.450863 0.0000000
## (+DH+Si1)-(-DH-Si ) -29.95307610 -37.517106 -22.389046 0.0000003
## (+DH+Si2)-(-DH-Si ) -30.01511519 -37.579145 -22.451085 0.0000003
## (+DH+Si1)-(+DH-Si ) 22.06181728 14.497787 29.625847 0.0000086
## (+DH+Si2)-(+DH-Si ) 21.99977820 14.435748 29.563808 0.0000089
## (+DH+Si2)-(+DH+Si1) -0.06203909 -7.626069 7.501991 0.9999945
library(TukeyC)
tc=TukeyC(CRA,'TRATAMIENTO')
plot(tc)
##5
L_PA= aov(L_PA~TRATAMIENTO, muestreo_21)
anova(L_PA)
## Analysis of Variance Table
##
## Response: L_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 75.435 25.1450 45.069 8.299e-07 ***
## Residuals 12 6.695 0.5579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_PA ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -5.775 -7.34306985 -4.20693 0.0000007
## (+DH+Si1)-(-DH-Si ) -4.300 -5.86806985 -2.73193 0.0000163
## (+DH+Si2)-(-DH-Si ) -4.425 -5.99306985 -2.85693 0.0000121
## (+DH+Si1)-(+DH-Si ) 1.475 -0.09306985 3.04307 0.0676454
## (+DH+Si2)-(+DH-Si ) 1.350 -0.21806985 2.91807 0.1006997
## (+DH+Si2)-(+DH+Si1) -0.125 -1.69306985 1.44307 0.9950784
library(TukeyC)
tc=TukeyC(L_PA,'TRATAMIENTO')
plot(tc)
##6
PF_R= aov(PF_R~TRATAMIENTO, muestreo_21)
anova(PF_R)
## Analysis of Variance Table
##
## Response: PF_R
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 612.12 204.040 36.395 2.645e-06 ***
## Residuals 12 67.28 5.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PF_R)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PF_R ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -17.48375 -22.454468 -12.513032 0.0000012
## (+DH+Si1)-(-DH-Si ) -9.27150 -14.242218 -4.300782 0.0006403
## (+DH+Si2)-(-DH-Si ) -8.86000 -13.830718 -3.889282 0.0009463
## (+DH+Si1)-(+DH-Si ) 8.21225 3.241532 13.182968 0.0017769
## (+DH+Si2)-(+DH-Si ) 8.62375 3.653032 13.594468 0.0011883
## (+DH+Si2)-(+DH+Si1) 0.41150 -4.559218 5.382218 0.9944991
library(TukeyC)
tc=TukeyC(PF_R,'TRATAMIENTO')
plot(tc)
##7
PS_PA= aov(PS_PA~TRATAMIENTO, muestreo_21)
anova(PS_PA)
## Analysis of Variance Table
##
## Response: PS_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 1.06207 0.35402 59.521 1.779e-07 ***
## Residuals 12 0.07137 0.00595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PS_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PS_PA ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -0.690775 -0.85268098 -0.528869 0.0000001
## (+DH+Si1)-(-DH-Si ) -0.505950 -0.66785598 -0.344044 0.0000042
## (+DH+Si2)-(-DH-Si ) -0.512900 -0.67480598 -0.350994 0.0000036
## (+DH+Si1)-(+DH-Si ) 0.184825 0.02291902 0.346731 0.0240397
## (+DH+Si2)-(+DH-Si ) 0.177875 0.01596902 0.339781 0.0300483
## (+DH+Si2)-(+DH+Si1) -0.006950 -0.16885598 0.154956 0.9992171
library(TukeyC)
tc=TukeyC(PS_PA,'TRATAMIENTO')
plot(tc)
##8
Pf_PA= aov(Pf_PA~TRATAMIENTO, muestreo_21)
anova(Pf_PA)
## Analysis of Variance Table
##
## Response: Pf_PA
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 131.649 43.883 44.544 8.849e-07 ***
## Residuals 12 11.822 0.985
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Pf_PA)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Pf_PA ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) -7.75750 -9.84119537 -5.673805 0.0000006
## (+DH+Si1)-(-DH-Si ) -5.71250 -7.79619537 -3.628805 0.0000163
## (+DH+Si2)-(-DH-Si ) -5.37025 -7.45394537 -3.286555 0.0000305
## (+DH+Si1)-(+DH-Si ) 2.04500 -0.03869537 4.128695 0.0549833
## (+DH+Si2)-(+DH-Si ) 2.38725 0.30355463 4.470945 0.0235295
## (+DH+Si2)-(+DH+Si1) 0.34225 -1.74144537 2.425945 0.9603787
library(TukeyC)
tc=TukeyC(Pf_PA,'TRATAMIENTO')
plot(tc)
##9
Temp= aov(Temp~TRATAMIENTO, muestreo_21)
anova(Temp)
## Analysis of Variance Table
##
## Response: Temp
## Df Sum Sq Mean Sq F value Pr(>F)
## TRATAMIENTO 3 163.39 54.463 46.85 6.71e-07 ***
## Residuals 12 13.95 1.162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(Temp)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Temp ~ TRATAMIENTO, data = muestreo_21)
##
## $TRATAMIENTO
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) 8.9875 6.724029 11.250971 0.0000003
## (+DH+Si1)-(-DH-Si ) 5.1175 2.854029 7.380971 0.0001101
## (+DH+Si2)-(-DH-Si ) 4.1850 1.921529 6.448471 0.0006910
## (+DH+Si1)-(+DH-Si ) -3.8700 -6.133471 -1.606529 0.0013417
## (+DH+Si2)-(+DH-Si ) -4.8025 -7.065971 -2.539029 0.0002005
## (+DH+Si2)-(+DH+Si1) -0.9325 -3.195971 1.330971 0.6248472
library(TukeyC)
tc=TukeyC(Temp,'TRATAMIENTO')
plot(tc)
##Graficos
library(dplyr)
###PS_r
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(PS_R),
desviacion=sd(PS_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Peso seco de la raiz (g)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
## DR
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(DR),
desviacion=sd(DR), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Diametro de la raiz (cm)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
###CCI
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(CCI),
desviacion=sd(CCI), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Contenido de clorofilas") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
####CRA
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(CRA),
desviacion=sd(CRA), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Contenido relativo de agua (%)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#####LPA
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(L_PA),
desviacion=sd(L_PA), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Longitud parte aerea (cm)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
######PF_R
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(PF_R),
desviacion=sd(PF_R), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Peso fresco de la raiz (g)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#######PS_PA
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(PS_PA),
desviacion=sd(PS_PA), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Peso seco parte aerea (g)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#######PF_PA
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(Pf_PA),
desviacion=sd(Pf_PA), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Peso fresco parte aerea (g)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
#######Temp
datos_resumen= df |>
group_by(Muestreo, TRATAMIENTO) |>
summarise(media= mean(Temp),
desviacion=sd(Temp), n=n()) |>
mutate(error=1.96*desviacion/sqrt(n));#datos_resumen
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
datos_resumen$Muestreo= factor(datos_resumen$Muestreo, labels = c("0ddt", "7ddt", "14ddt", "21ddt"));#datos_resumen$Muestreo
datos_resumen$TRATAMIENTO= factor(datos_resumen$TRATAMIENTO); #datos_resumen$TRATAMIENTO
ggplot(datos_resumen)+
aes(x=TRATAMIENTO, y=media, fill=TRATAMIENTO)+
geom_col(strat= 'identity', position = 'dodge', color = 'black')+
geom_errorbar(aes(ymin = media - error, ymax = media + error),
width = 0.2, position = position_dodge(0.9))+
facet_grid(~Muestreo)+
labs(x = "Muestreos", y = "Temperatura (°c)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels)+ theme_bw()
## Warning in geom_col(strat = "identity", position = "dodge", color = "black"):
## Ignoring unknown parameters: `strat`
library(ggplot2)
library(dplyr)
PE<- read_excel("C:/Users/JuanSebH2/Downloads/Archivos/Fisioo_A/PE .xlsx")
PE=data.frame(PE);PE
## TRATAMIENTO PE...
## 1 (-DH-Si ) 10.08024
## 2 (+DH-Si ) 30.90909
## 3 (+DH+Si1) 16.03704
## 4 (+DH+Si2) 13.06122
## 5 (-DH-Si ) 10.08024
## 6 (+DH-Si ) 30.90909
## 7 (+DH+Si1) 16.03704
## 8 (+DH+Si2) 13.06122
## 9 (-DH-Si ) 10.08024
## 10 (+DH-Si ) 30.90909
## 11 (+DH+Si1) 16.03704
## 12 (+DH+Si2) 13.06122
## 13 (-DH-Si ) 10.08024
## 14 (+DH-Si ) 30.90909
## 15 (+DH+Si1) 16.03704
## 16 (+DH+Si2) 13.06122
names(PE)= c("Trat", "PE");PE
## Trat PE
## 1 (-DH-Si ) 10.08024
## 2 (+DH-Si ) 30.90909
## 3 (+DH+Si1) 16.03704
## 4 (+DH+Si2) 13.06122
## 5 (-DH-Si ) 10.08024
## 6 (+DH-Si ) 30.90909
## 7 (+DH+Si1) 16.03704
## 8 (+DH+Si2) 13.06122
## 9 (-DH-Si ) 10.08024
## 10 (+DH-Si ) 30.90909
## 11 (+DH+Si1) 16.03704
## 12 (+DH+Si2) 13.06122
## 13 (-DH-Si ) 10.08024
## 14 (+DH-Si ) 30.90909
## 15 (+DH+Si1) 16.03704
## 16 (+DH+Si2) 13.06122
PE$Trat= factor(PE$Trat)
PE= PE |>
group_by(Trat) ;PE
## # A tibble: 16 × 2
## # Groups: Trat [4]
## Trat PE
## <fct> <dbl>
## 1 (-DH-Si ) 10.1
## 2 (+DH-Si ) 30.9
## 3 (+DH+Si1) 16.0
## 4 (+DH+Si2) 13.1
## 5 (-DH-Si ) 10.1
## 6 (+DH-Si ) 30.9
## 7 (+DH+Si1) 16.0
## 8 (+DH+Si2) 13.1
## 9 (-DH-Si ) 10.1
## 10 (+DH-Si ) 30.9
## 11 (+DH+Si1) 16.0
## 12 (+DH+Si2) 13.1
## 13 (-DH-Si ) 10.1
## 14 (+DH-Si ) 30.9
## 15 (+DH+Si1) 16.0
## 16 (+DH+Si2) 13.1
PE_A = aov(data= PE, PE~Trat)
Tabla_PE= anova(PE_A); Tabla_PE
## Warning in anova.lm(PE_A): ANOVA F-tests on an essentially perfect fit are
## unreliable
## Analysis of Variance Table
##
## Response: PE
## Df Sum Sq Mean Sq F value Pr(>F)
## Trat 3 1026.8 342.26 6.0707e+31 < 2.2e-16 ***
## Residuals 12 0.0 0.00
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(PE_A)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PE ~ Trat, data = PE)
##
## $Trat
## diff lwr upr p adj
## (+DH-Si )-(-DH-Si ) 20.828850 20.828850 20.828850 0
## (+DH+Si1)-(-DH-Si ) 5.956796 5.956796 5.956796 0
## (+DH+Si2)-(-DH-Si ) 2.980984 2.980984 2.980984 0
## (+DH+Si1)-(+DH-Si ) -14.872054 -14.872054 -14.872054 0
## (+DH+Si2)-(+DH-Si ) -17.847866 -17.847866 -17.847866 0
## (+DH+Si2)-(+DH+Si1) -2.975813 -2.975813 -2.975813 0
library(TukeyC)
tc=TukeyC(PE_A,'Trat')
plot(tc)
ggplot(data= PE, aes(x=Trat, y=PE, fill=Trat))+
geom_col()+
ylim(0,140)+
labs(x = "Tratamientos", y = "Perdida de electrolitos (%)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))+
scale_fill_manual(values = c("#F5C35C", "#2F2F2F", "#418E4D", "#9E3D22"))+
scale_x_discrete(labels = labels) + theme_bw()
