R Notebook

file.choose()

[1] "C:\\Users\\HP\\Downloads\\MARIO.xlsx"

ruta_Ensamble <-"C:\\Users\\HP\\Downloads\\MARIO.xlsx"

excel_sheets(ruta_Ensamble)

[1] "VINOS POR PRUEBA FQ."

casoDBCA1<-read_excel(ruta_Ensamble)

print(casoDBCA1)

#Se crea un objeto tipo factor

VOL<-factor(casoDBCA1$VOLUMEN)

VAR<-factor(casoDBCA1$Variedad)

#Luego el vector TIEM se convierte a un vector ALT1 de tipo numérico

VOL1<-as.numeric(VOL)

#Diagrama de cajas de dispersión (Box plot)

par(mfrow=c(1,1))

boxplot(split(VOL1,VAR),xlab="variedad", ylab="Volumen")

resaov<-aov(VOL1 ~ VAR)

anova(resaov)

Analysis of Variance Table

Response: VOL1
          Df  Sum Sq Mean Sq F value Pr(>F)
VAR        6  339.71  56.619  0.7115 0.6438
Residuals 24 1909.96  79.582               

cv.model(resaov)

[1] 57.37486

euc.lm <- lm(VOL1 ~ VAR)

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

Analysis of Variance Table

Response: VOL1
          Df  Sum Sq Mean Sq F value Pr(>F)
VAR        6  339.71  56.619  0.7115 0.6438
Residuals 24 1909.96  79.582               

 shapiro.test(euc.lm$res)

    Shapiro-Wilk normality test

data:  euc.lm$res
W = 0.96325, p-value = 0.3548

fitb <- fitted(resaov)

res_stb <- rstandard(resaov)

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

#Prueba de Levene

leveneTest(VOL1 ~ VAR, center = "median")

Levene's Test for Homogeneity of Variance (center = "median")
      Df F value Pr(>F)
group  6  0.3228 0.9185
      24               

6.- Pruebas de comparación múltiple de medias #Método de la diferencia mínima significativa, Least Significant Difference (LSD)

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

Study: resaov ~ "VAR"

LSD t Test for VOL1 

Mean Square Error:  79.58183 

VAR,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 24
Critical Value of t: 2.063899 

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

Treatments with the same letter are not significantly different.

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

Study: resaov ~ "VAR"

Student Newman Keuls Test
for VOL1 

Mean Square Error:  79.58183 

VAR,  means

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

Means with the same letter are not significantly different.

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

Study: resaov ~ "VAR"

Duncan's new multiple range test
for VOL1 

Mean Square Error:  79.58183 

VAR,  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, "VAR", p.adj= "bon",console=TRUE)

Study: resaov ~ "VAR"

LSD t Test for VOL1 
P value adjustment method: bonferroni 

Mean Square Error:  79.58183 

VAR,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 24
Critical Value of t: 3.395988 

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= "VAR",  dispersion="se", sig.level=0.05)

summary(sk)

Goups of means at sig.level = 0.05 

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

print(tukey_result)

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

Fit: aov(formula = VOL1 ~ VAR)

$VAR
                                        diff        lwr      upr     p adj
Cabernet sauvignon-Cabernet Franc -6.6000000 -30.567485 17.36749 0.9715681
Carmenere-Cabernet Franc          -1.6000000 -32.980804 29.78080 0.9999980
Italia-Cabernet Franc             -4.9750000 -21.306088 11.35609 0.9540023
Malbeck-Cabernet Franc             5.0666667 -15.853870 25.98720 0.9850261
Negra Criolla-Cabernet Franc       0.1777778 -15.800546 16.15610 1.0000000
Syrah-Cabernet Franc               1.7333333 -19.187203 22.65387 0.9999639
Carmenere-Cabernet sauvignon       5.0000000 -30.084806 40.08481 0.9991607
Italia-Cabernet sauvignon          1.6250000 -21.022145 24.27214 0.9999845
Malbeck-Cabernet sauvignon        11.6666667 -14.484004 37.81734 0.7793182
Negra Criolla-Cabernet sauvignon   6.7777778 -15.616318 29.17187 0.9553747
Syrah-Cabernet sauvignon           8.3333333 -17.817337 34.48400 0.9434243
Italia-Carmenere                  -3.3750000 -33.759333 27.00933 0.9997993
Malbeck-Carmenere                  6.6666667 -26.411606 39.74494 0.9942844
Negra Criolla-Carmenere            1.7777778 -28.418415 31.97397 0.9999952
Syrah-Carmenere                    3.3333333 -29.744939 36.41161 0.9998861
Malbeck-Italia                    10.0416667  -9.352190 29.43552 0.6454740
Negra Criolla-Italia               5.1527778  -8.766979 19.07254 0.8916290
Syrah-Italia                       6.7083333 -12.685523 26.10219 0.9186204
Negra Criolla-Malbeck             -4.8888889 -23.986638 14.20886 0.9801776
Syrah-Malbeck                     -3.3333333 -26.723204 20.05654 0.9991607
Syrah-Negra Criolla                1.5555556 -17.542194 20.65330 0.9999673

plot(tukey_result)