R 中的双向方差分析 Tukey 检验和箱线图

Question

我正在按照这个 tutorial and this , question 2 绘制一个 双向箱形图，其中包含小平面 和字母表示处理之间的静态差异。有谁知道这种图表怎么做？

关于我尝试的一些代码：

    library(multcompView)
    library(ggplot2)
    #sample dataframe
    data <- structure(list(nozzle = c("XR", "XR", "XR", "XR", "XR", "XR", "XR", "XR", 
                                      "XR", "XR", "XR", "XR", "XR", "XR", "XR", "XR", 
                                      "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", 
                                      "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", 
                                      "AIXR", "AIXR", "AIXR", "AIXR"), 
                           trat = c("Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 12.8", 
                                    "Cle 34", "Cle 34", "Cle 34", "Cle 34", "Cle 12.8", 
                                    "Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 34", "Cle 34", 
                                    "Cle 34", "Cle 34", "Cle 12.8", "Cle 12.8", "Cle 12.8", 
                                    "Cle 12.8", "Cle 34", "Cle 34", "Cle 34", "Cle 34", 
                                    "Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 34", 
                                    "Cle 34", "Cle 34", "Cle 34"), 
                           adj = c("Without", "Without", "Without", "Without", "Without", 
                                   "Without", "Without", "Without", "With", "With", "With", 
                                   "With", "With", "With", "With", "With", "Without", "Without", 
                                   "Without", "Without", "Without", "Without", "Without", "Without", 
                                   "With", "With", "With", "With", "With", "With", "With", "With"), 
                           dw1 = c(3.71, 5.87, 6.74, 1.65, 0.27, 0.4, 0.37, 0.34, 0.24, 0.28, 0.32, 
                                   0.38, 0.39, 0.36, 0.32, 0.28, 8.24, 10.18, 11.59, 6.18, 0.2, 0.23, 
                                   0.2, 0.31, 0.28, 0.25, 0.36, 0.27, 0.36, 0.37, 0.34, 0.19)), row.names = c(NA, -32L), class = c("tbl_df", "tbl", "data.frame"))    

    #function 
    generate_label_df <- function(TUKEY, variable){

      # Extract labels and factor levels from Tukey post-hoc 
      Tukey.levels <- variable[,4]
      Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])

      #I need to put the labels in the same order as in the boxplot :
      Tukey.labels$treatment=rownames(Tukey.labels)
      Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
      return(Tukey.labels)
    }

    # What is the effect of the treatment on the value ?
model=lm( data$dw1~ data$trat:data$adj )
ANOVA=aov(model)

# Tukey test to study each pair of treatment :
TUKEY <- TukeyHSD(x=ANOVA, 'data$trat', conf.level=0.95)

# Tuckey test representation :
plot(TUKEY , las=1 , col="brown")

p <- ggplot(data=data, aes(x=trat , y=dw1, fill=adj)) +  
      geom_boxplot(outlier.shape=NA) +
      facet_grid(~nozzle) +
      scale_fill_brewer(palette="Reds") +
      theme_minimal() +
      theme(legend.position="none") +
      theme(axis.text.x=element_text(angle=45, hjust=1)) 

    for (facetk in as.character(unique(data$nozzle))) {   
      subdf <- subset(data, as.array(nozzle==facetk))
      model=lm(dw1 ~ trat:adj, data=subdf)
      ANOVA=aov(model)
      TUKEY <- TukeyHSD(ANOVA)

      labels <- generate_label_df(TUKEY , TUKEY$`trat:adj`)
      names(labels) <- c('Letters','trat')
      yvalue <- aggregate(.~nozzle, data=subdf, quantile, probs=0.75)  
      final <- merge(labels, yvalue)
      final$nozzle <-  facetk

      p <- p + geom_text(data = final,  aes(x=trat, y=value_y, label=Letters), 
                         vjust=-1.5, hjust=-.5)
    }
    p

我遇到了这个错误

Error in quantile.default(X[[i]], ...) : factors are not allowed

我知道我必须在分位数中输入数字，而不是因子，但我不知道如果我修复该错误它会出现在我的图表中的字母。

Answer 1

data <- structure(list(nozzle = c("XR", "XR", "XR", "XR", "XR", "XR", "XR", "XR", 
                                  "XR", "XR", "XR", "XR", "XR", "XR", "XR", "XR", 
                                  "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", 
                                  "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", "AIXR", 
                                  "AIXR", "AIXR", "AIXR", "AIXR"), 
                       trat = c("Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 12.8", 
                                "Cle 34", "Cle 34", "Cle 34", "Cle 34", "Cle 12.8", 
                                "Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 34", "Cle 34", 
                                "Cle 34", "Cle 34", "Cle 12.8", "Cle 12.8", "Cle 12.8", 
                                "Cle 12.8", "Cle 34", "Cle 34", "Cle 34", "Cle 34", 
                                "Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 12.8", "Cle 34", 
                                "Cle 34", "Cle 34", "Cle 34"), 
                       adj = c("Without", "Without", "Without", "Without", "Without", 
                               "Without", "Without", "Without", "With", "With", "With", 
                               "With", "With", "With", "With", "With", "Without", "Without", 
                               "Without", "Without", "Without", "Without", "Without", "Without", 
                               "With", "With", "With", "With", "With", "With", "With", "With"), 
                       dw1 = c(3.71, 5.87, 6.74, 1.65, 0.27, 0.4, 0.37, 0.34, 0.24, 0.28, 0.32, 
                               0.38, 0.39, 0.36, 0.32, 0.28, 8.24, 10.18, 11.59, 6.18, 0.2, 0.23, 
                               0.2, 0.31, 0.28, 0.25, 0.36, 0.27, 0.36, 0.37, 0.34, 0.19)), row.names = c(NA, -32L), class = c("tbl_df", "tbl", "data.frame"))    

data$trat_adj<-paste(data$trat,data$adj,sep = "_")
data<-as.data.frame(data[-c(2,3)])
str(data)
#function 
generate_label_df <- function(TUKEY, variable){

  # Extract labels and factor levels from Tukey post-hoc 
  Tukey.levels <- variable[,4]
  Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])

  #I need to put the labels in the same order as in the boxplot :
  Tukey.labels$treatment=rownames(Tukey.labels)
  Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
  return(Tukey.labels)
}

p <- ggplot(data=data, aes(x=data$trat_adj, y=data$dw1, fill=data$trat_adj)) +  
  geom_boxplot(outlier.shape=NA) +
  facet_grid(~nozzle) +
  scale_fill_brewer(palette="Reds") +
  theme_minimal() +
  theme(legend.position="none") +
  theme(axis.text.x=element_text(angle=45, hjust=1)) 
p

for (facetk in as.character(unique(data$nozzle))) {   
  subdf <- subset(data, nozzle==facetk)
  model=lm(dw1 ~ trat_adj, data=subdf)
  ANOVA=aov(model)
  TUKEY <- TukeyHSD(ANOVA)
  labels <- generate_label_df(TUKEY, TUKEY$trat_adj)

  names(labels) <- c('Letters','data$trat_adj')
  yvalue <- aggregate(subdf$dw1, list(subdf$trat_adj), data=subdf, quantile, probs=.75)  

  final <- data.frame(labels, yvalue[,2])

  names(final)<-c("letters","trat_adj","dw1")
  final$nozzle <-  facetk

  p <- p + geom_text(data = final,  aes(x=trat_adj, y=dw1, fill=trat_adj,label=letters),
                     vjust=-1.5, hjust=-.5)
}
p

R 中的双向方差分析 Tukey 检验和箱线图

Two-way ANOVA Tukey Test and boxplot in R

statistics

r

ggplot2

multcompview