R Notebook

file.choose()

[1] "C:\\Users\\LENOVO\\Downloads\\rSTUDIO 2022.xlsx"

ruta_Ensamble<-"C:\\Users\\LENOVO\\Downloads\\rSTUDIO 2022.xlsx"

excel_sheets(ruta_Ensamble)

[1] "Hoja1"

casoDBCA1<- read_excel(ruta_Ensamble)

print(casoDBCA1)

DIST<-factor(casoDBCA1$Distrito)

TEM<-factor(casoDBCA1$`Temperatura    (°C)`)

pH<- as.vector(casoDBCA1$`Potencial de H (PH)`)

pH1<-as.numeric(pH)

par(mfrow=c(20,20))

boxplot(split(pH1,DIST), xlab="Distrito",ylab="Potencial de H (PH)") 

resaov<-aov(pH1~TEM+DIST)

anova(resaov)

Analysis of Variance Table

Response: pH1
           Df  Sum Sq Mean Sq F value   Pr(>F)   
TEM         4  3.6858 0.92144  4.1185 0.003653 **
DIST       12  4.6324 0.38603  1.7254 0.069298 . 
Residuals 121 27.0716 0.22373                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

cv.model(resaov)

[1] 6.395446

euc.lm<-lm(pH1~TEM+DIST)

anova(euc.lm,test="f")

Analysis of Variance Table

Response: pH1
           Df  Sum Sq Mean Sq F value   Pr(>F)   
TEM         4  3.6858 0.92144  4.1185 0.003653 **
DIST       12  4.6324 0.38603  1.7254 0.069298 . 
Residuals 121 27.0716 0.22373                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Prueba de normalidad Shapiro-Wilk

Hipótesis

Ho: El pH siguen la distribución normal Ha: El pH no siguen la distribución normal

Prueba de normalidad Shapiro-wilk para los residuos

shapiro.test(euc.lm$res)

    Shapiro-Wilk normality test

data:  euc.lm$res
W = 0.98745, p-value = 0.2438

fitb <- fitted(resaov)

res_stb <- rstandard(resaov)

plot(fitb,res_stb,xlab="Valores predichos", ylab="Residuos estandarizados",abline(h=0))

leveneTest(pH1~DIST, center = "median")

Levene's Test for Homogeneity of Variance (center = "median")
       Df F value   Pr(>F)   
group  12  2.3437 0.009555 **
      125                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

outLSD <-LSD.test(resaov, "DIST",console=TRUE)

Study: resaov ~ "DIST"

LSD t Test for pH1 

Mean Square Error:  0.2237323 

DIST,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 121
Critical Value of t: 1.979764 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.

outHSD<-HSD.test(resaov, "DIST",console=TRUE)

Study: resaov ~ "DIST"

HSD Test for pH1 

Mean Square Error:  0.2237323 

DIST,  means

Alpha: 0.05 ; DF Error: 121 
Critical Value of Studentized Range: 4.780138 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.

SNK.test(resaov, "DIST",console=TRUE)

Study: resaov ~ "DIST"

Student Newman Keuls Test
for pH1 

Mean Square Error:  0.2237323 

DIST,  means

Groups according to probability of means differences and alpha level( 0.05 )

Means with the same letter are not significantly different.

scheffe.test(resaov, "DIST",console=TRUE)

Study: resaov ~ "DIST"

Scheffe Test for pH1 

Mean Square Error  : 0.2237323 

DIST,  means

Alpha: 0.05 ; DF Error: 121 
Critical Value of F: 1.833013 

Groups according to probability of means differences and alpha level( 0.05 )

Means with the same letter are not significantly different.

duncan.test(resaov, "DIST",console=TRUE)

Study: resaov ~ "DIST"

Duncan's new multiple range test
for pH1 

Mean Square Error:  0.2237323 

DIST,  means

Groups according to probability of means differences and alpha level( 0.05 )

Means with the same letter are not significantly different.

LSD.test(resaov, "DIST", p.adj= "bon",console=TRUE)

Study: resaov ~ "DIST"

LSD t Test for pH1 
P value adjustment method: bonferroni 

Mean Square Error:  0.2237323 

DIST,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 121
Critical Value of t: 3.505022 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.

sk <- SK(resaov, which= "DIST",  dispersion="se", sig.level=0.05)

summary(sk)

Goups of means at sig.level = 0.05 

tukey_result <- TukeyHSD(resaov, "DIST", conf.level = 0.95)

print(tukey_result)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = PH1 ~ DIST + TEM)

$DIST
                          diff        lwr        upr     p adj
Ayaviri-Ananea     0.227857143 -1.3898479 1.84556220 0.9999995
Azangaro-Ananea    0.177187500 -1.4463852 1.80076020 1.0000000
Caracoto-Ananea    0.770000000 -1.4910218 3.03102184 0.9947097
Coata-Ananea       0.655000000 -1.3031024 2.61310235 0.9955020
Cuyuraya-Ananea    0.264000000 -1.4873800 2.01537999 0.9999989
Huancane-Ananea    0.258571429 -1.3963271 1.91346996 0.9999984
Juliaca-Ananea    -0.120588235 -1.7657233 1.52454680 1.0000000
pajcha-Ananea     -0.202000000 -1.9533800 1.54937999 1.0000000
Pekosani-Ananea   -0.205000000 -1.9924947 1.58249471 1.0000000
Pokopaka-Ananea    0.161666667 -1.5652173 1.88855062 1.0000000
Puno-Ananea       -0.378750000 -2.0745164 1.31701638 0.9999192
Putina-Ananea     -0.320000000 -2.5810218 1.94102184 0.9999995
Azangaro-Ayaviri  -0.050669643 -0.4258205 0.32448125 0.9999997
Caracoto-Ayaviri   0.542142857 -1.0755622 2.15984791 0.9954252
Coata-Ayaviri      0.427142857 -0.7299720 1.58425767 0.9892564
Cuyuraya-Ayaviri   0.036142857 -0.7202180 0.79250371 1.0000000
Huancane-Ayaviri   0.030714286 -0.4626811 0.52410970 1.0000000
Juliaca-Ayaviri   -0.348445378 -0.8080312 0.11114048 0.3445252
pajcha-Ayaviri    -0.429857143 -1.1862180 0.32650371 0.7798442
Pekosani-Ayaviri  -0.432857143 -1.2694498 0.40373549 0.8700611
Pokopaka-Ayaviri  -0.066190476 -0.7639570 0.63157601 1.0000000
Puno-Ayaviri      -0.606607143 -1.2233514 0.01013713 0.0585680
Putina-Ayaviri    -0.547857143 -2.1655622 1.06984791 0.9949690
Caracoto-Azangaro  0.592812500 -1.0307602 2.21638520 0.9902200
Coata-Azangaro     0.477812500 -0.6874915 1.64311648 0.9745027
Cuyuraya-Azangaro  0.086812500 -0.6820180 0.85564305 1.0000000
Huancane-Azangaro  0.081383929 -0.4309223 0.59369020 0.9999981
Juliaca-Azangaro  -0.297775735 -0.7776068 0.18205532 0.6653602
pajcha-Azangaro   -0.379187500 -1.1480180 0.38964305 0.9036994
Pekosani-Azangaro -0.382187500 -1.2300707 0.46569569 0.9479203
Pokopaka-Azangaro -0.015520833 -0.7267850 0.69574336 1.0000000
Puno-Azangaro     -0.555937500 -1.1879123 0.07603732 0.1460482
Putina-Azangaro   -0.497187500 -2.1207602 1.12638520 0.9980254
Coata-Caracoto    -0.115000000 -2.0731024 1.84310235 1.0000000
Cuyuraya-Caracoto -0.506000000 -2.2573800 1.24537999 0.9988743
Huancane-Caracoto -0.511428571 -2.1663271 1.14346996 0.9978463
Juliaca-Caracoto  -0.890588235 -2.5357233 0.75454680 0.8307695
pajcha-Caracoto   -0.972000000 -2.7233800 0.77937999 0.8058410
Pekosani-Caracoto -0.975000000 -2.7624947 0.81249471 0.8234476
Pokopaka-Caracoto -0.608333333 -2.3352173 1.11855062 0.9928464
Puno-Caracoto     -1.148750000 -2.8445164 0.54701638 0.5288957
Putina-Caracoto   -1.090000000 -3.3510218 1.17102184 0.9170692
Cuyuraya-Coata    -0.391000000 -1.7286386 0.94663856 0.9987386
Huancane-Coata    -0.396428571 -1.6049956 0.81213844 0.9962407
Juliaca-Coata     -0.775588235 -1.9707511 0.41957466 0.5978411
pajcha-Coata      -0.857000000 -2.1946386 0.48063856 0.6177154
Pekosani-Coata    -0.860000000 -2.2445875 0.52458745 0.6641001
Pokopaka-Coata    -0.493333333 -1.7987349 0.81206824 0.9868871
Puno-Coata        -1.033750000 -2.2976996 0.23019963 0.2329833
Putina-Coata      -0.975000000 -2.9331024 0.98310235 0.8976139
Huancane-Cuyuraya -0.005428571 -0.8383759 0.82751876 1.0000000
Juliaca-Cuyuraya  -0.384588235 -1.1979648 0.42878830 0.9272124
pajcha-Cuyuraya   -0.466000000 -1.4771597 0.54515971 0.9390397
Pekosani-Cuyuraya -0.469000000 -1.5414968 0.60349683 0.9582654
Pokopaka-Cuyuraya -0.102333333 -1.0704450 0.86577838 1.0000000
Puno-Cuyuraya     -0.642750000 -1.5541970 0.26869704 0.4625703
Putina-Cuyuraya   -0.584000000 -2.3353800 1.16737999 0.9956298
Juliaca-Huancane  -0.379159664 -0.9561681 0.19784873 0.5780772
pajcha-Huancane   -0.460571429 -1.2935188 0.37237590 0.8096964
Pekosani-Huancane -0.463571429 -1.3699967 0.44285383 0.8789445
Pokopaka-Huancane -0.096904762 -0.8770314 0.68322189 0.9999999
Puno-Huancane     -0.637321429 -1.3459066 0.07126379 0.1242413
Putina-Huancane   -0.578571429 -2.2334700 1.07632711 0.9933106
pajcha-Juliaca    -0.081411765 -0.8947883 0.73196477 1.0000000
Pekosani-Juliaca  -0.084411765 -0.9728862 0.80406270 1.0000000
Pokopaka-Juliaca   0.282254902 -0.4769405 1.04145034 0.9885835
Puno-Juliaca      -0.258161765 -0.9436347 0.42731117 0.9872618
Putina-Juliaca    -0.199411765 -1.8445468 1.44572327 0.9999999
Pekosani-pajcha   -0.003000000 -1.0754968 1.06949683 1.0000000
Pokopaka-pajcha    0.363666667 -0.6044450 1.33177838 0.9875371
Puno-pajcha       -0.176750000 -1.0881970 0.73469704 0.9999824
Putina-pajcha     -0.118000000 -1.8693800 1.63337999 1.0000000
Pokopaka-Pekosani  0.366666667 -0.6653439 1.39867722 0.9922880
Puno-Pekosani     -0.173750000 -1.1528012 0.80530118 0.9999934
Putina-Pekosani   -0.115000000 -1.9024947 1.67249471 1.0000000
Puno-Pokopaka     -0.540416667 -1.4038586 0.32302531 0.6529181
Putina-Pokopaka   -0.481666667 -2.2085506 1.24521729 0.9992040
Putina-Puno        0.058750000 -1.6370164 1.75451638 1.0000000