Dados
#SIMULAÇÃO
dados = data.frame("Silagens" = rep(c("Milho","Cana", "Sorgo", "Tithonia"), each=6),"Blocos" = rep(c("I", "II", "III", "IV", "V", "VI"), times=4))
set.seed(3459)
Milho=rnorm(6, mean = 480, sd = 40)
Cana=rnorm(6, mean = 450, sd = 40)
Sorgo=rnorm(6, mean = 460, sd = 40)
Tithonia=rnorm(6, mean = 490, sd = 40)
dados$peso=c(Milho,Cana, Sorgo, Tithonia)
dados
## Silagens Blocos peso
## 1 Milho I 494.8560
## 2 Milho II 475.2913
## 3 Milho III 436.3242
## 4 Milho IV 376.7605
## 5 Milho V 474.5485
## 6 Milho VI 429.9814
## 7 Cana I 460.3909
## 8 Cana II 364.0620
## 9 Cana III 426.9106
## 10 Cana IV 486.9001
## 11 Cana V 444.0223
## 12 Cana VI 351.6571
## 13 Sorgo I 419.0773
## 14 Sorgo II 455.7393
## 15 Sorgo III 441.5352
## 16 Sorgo IV 430.8615
## 17 Sorgo V 418.8376
## 18 Sorgo VI 376.7051
## 19 Tithonia I 597.3355
## 20 Tithonia II 442.0970
## 21 Tithonia III 535.3202
## 22 Tithonia IV 424.4159
## 23 Tithonia V 517.3090
## 24 Tithonia VI 554.0816
dados$Silagens=as.factor(dados$Silagens)
dados$Blocos=as.factor(dados$Blocos)
View(dados)
Análise exploratória
psych::describe(dados)
## vars n mean sd median trimmed mad min max range skew
## Silagens* 1 24 2.50 1.14 2.50 2.50 1.48 1.00 4.00 3.00 0.00
## Blocos* 2 24 3.50 1.74 3.50 3.50 2.22 1.00 6.00 5.00 0.00
## peso 3 24 451.46 59.38 441.82 448.39 41.30 351.66 597.34 245.68 0.52
## kurtosis se
## Silagens* -1.49 0.23
## Blocos* -1.41 0.36
## peso -0.11 12.12
library(ggplot2)
ggplot(dados, aes(x=Silagens, y=peso))+
geom_boxplot()+theme_classic()
hist(dados$peso)
Avaliação das pressuposições
mod <- aov(peso~Silagens, data = dados)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Silagens 3 31576 10525 4.252 0.0178 *
## Residuals 20 49512 2476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist(rstandard(mod))
car::qqp(mod)
## [1] 19 22
shapiro.test(rstandard(mod))#normalidade
##
## Shapiro-Wilk normality test
##
## data: rstandard(mod)
## W = 0.94844, p-value = 0.2506
car::leveneTest(rstandard(mod)~Silagens, data=dados)#Homocedasticidade
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.2176 0.3292
## 20
Anova e tukey
#Outlier
boxplot(dados$peso)
#Medias
library(emmeans)
medias=emmeans(mod,~ Silagens)
summary(mod)
## Df Sum Sq Mean Sq F value Pr(>F)
## Silagens 3 31576 10525 4.252 0.0178 *
## Residuals 20 49512 2476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(medias)
## Silagens emmean SE df lower.CL upper.CL
## Cana 422 20.3 20 380 465
## Milho 448 20.3 20 406 490
## Sorgo 424 20.3 20 381 466
## Tithonia 512 20.3 20 469 554
##
## Confidence level used: 0.95
#Tukey
library(multcompView)
tukey = TukeyHSD(mod)
print(tukey)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = peso ~ Silagens, data = dados)
##
## $Silagens
## diff lwr upr p adj
## Milho-Cana 25.636485 -54.766533 106.03950 0.8088071
## Sorgo-Cana 1.468836 -78.934181 81.87185 0.9999503
## Tithonia-Cana 89.436013 9.032995 169.83903 0.0258831
## Sorgo-Milho -24.167648 -104.570666 56.23537 0.8341988
## Tithonia-Milho 63.799528 -16.603489 144.20255 0.1516705
## Tithonia-Sorgo 87.967176 7.564159 168.37019 0.0288652
tukey.cld= multcompLetters4(mod, tukey)
print(tukey.cld)
## $Silagens
## Tithonia Milho Sorgo Cana
## "a" "ab" "b" "b"