Anova em DBC

library(readxl)
library(tidyverse)

fungicidas <- 
read_excel("dados-diversos.xlsx", "fungicida_campo")

modelo com anova com bloco severidade em função do tratamento + bloco mesmo quando o bloco não é significativo você apresenta ele

aov_fung <- aov(sev ~ trat + rep, data = fungicidas)
summary(aov_fung)

            Df Sum Sq Mean Sq F value Pr(>F)    
trat         7   7135  1019.3 287.661 <2e-16 ***
rep          1     19    18.6   5.239 0.0316 *  
Residuals   23     81     3.5                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Verificar premissas

A média estimada é diferente da aritmética, de acordo com o modelo.

library(performance)
library(DHARMa)
check_normality(aov_fung)

OK: residuals appear as normally distributed (p = 0.230).

check_heteroscedasticity(aov_fung)

OK: Error variance appears to be homoscedastic (p = 0.484).

plot(simulateResiduals(aov_fung))

library(emmeans)

means_fung <- emmeans(aov_fung, ~trat)
library(multcomp)
library(multcompView)

cld(means_fung)

 trat       emmean    SE df lower.CL upper.CL .group
 G            29.2 0.941 23     27.3     31.2  1    
 B            29.5 0.941 23     27.6     31.4  1    
 E            30.1 0.941 23     28.2     32.1  1    
 C            30.4 0.941 23     28.4     32.3  1    
 A            30.4 0.941 23     28.4     32.3  1    
 D            31.5 0.941 23     29.6     33.4  12   
 F            35.5 0.941 23     33.6     37.4   2   
 testemunha   75.8 0.941 23     73.8     77.7    3  

Confidence level used: 0.95 
P value adjustment: tukey method for comparing a family of 8 estimates 
significance level used: alpha = 0.05 
NOTE: If two or more means share the same grouping letter,
      then we cannot show them to be different.
      But we also did not show them to be the same.

plot(means_fung)+
  coord_flip()