Question

我想知道是否有办法将比较的逻辑矩阵转换为多重比较测试中使用的字母符号。与multcomp::cld中一样。

我的数据如下：

test_data <- data.frame(mean=c(1.48, 1.59, 1.81,1.94),CI_lower=c(1.29,1.38,1.54, 1.62),CI_upper=c(1.56,1.84, 2.3, 2.59))

  mean CI_lower CI_upper
1 1.48     1.29     1.56
2 1.59     1.38     1.84
3 1.81     1.54     2.30
4 1.94     1.62     2.59

我感兴趣的是一种表示哪些条目具有重叠CI以获得最终结果的符号：

final <- data.frame(mean=c(1.48, 1.59, 1.81,1.94),CI_lower=c(1.29, 1.38,1.54, 1.62),CI_upper=c(1.56,1.84, 2.3, 2.59),letters = c("a","ab","ab","b"))

  mean CI_lower CI_upper letters
1 1.48     1.29     1.56       a
2 1.59     1.38     1.84      ab
3 1.81     1.54     2.30      ab
4 1.94     1.62     2.59       b

我做了一个可怜的尝试，就像这样：

same <- outer(test_data$CI_lower, test_data$CI_upper,"-")
same <- same<0
same <- lower.tri(same, diag = FALSE) & same

same_ind <- which(same,arr.ind = T)

groups <- as.list(as.numeric(rep(NA,nrow(test_data))))

for(i in 1:nrow(same_ind)){
  group_pos <- as.numeric(same_ind[i,])
  for(i2 in group_pos){
    groups[[i2]] <- c(groups[[i2]],i)
  }
}

letters_notation <- sapply(groups,function(x){
  x <- x[!is.na(x)]
  x <- letters[x]
  x <- paste0(x,collapse="")
  return(x)
}
)

会给出这个：

  mean CI_lower CI_upper letters
1 1.48     1.29     1.56      ab
2 1.59     1.38     1.84     acd
3 1.81     1.54     2.30     bce
4 1.94     1.62     2.59      de

有关如何执行此操作的任何想法？

Answer 1

以下是使用data.table非常高效的foverlaps功能的可能解决方案。这不完全是您想要的输出（因为我并不完全理解它），但您可以从中识别出重叠点

library(data.table)
setkey(setDT(test_data), CI_lower, CI_upper)
Overlaps <- foverlaps(test_data, test_data, type = "any", which = TRUE) ## returns overlap indices
test_data[ , overlaps := Overlaps[, paste(letters[yid], collapse = ""), xid]$V1][]
#    mean CI_lower CI_upper overlaps
# 1: 1.48     1.29     1.56      abc <~~ not overlapping with d
# 2: 1.59     1.38     1.84     abcd
# 3: 1.81     1.54     2.30     abcd
# 4: 1.94     1.62     2.59      bcd <~~ not overlapping with a

Answer 2

根据David Arenburg的建议和http://menugget.blogspot.it/2014/05/automated-determination-of-distribution.html这篇很好的文章我发现了一个解决方案。

library(igraph)

test_data <- data.frame(mean=c(1.48, 1.59, 1.81,1.94),CI_lower=c(1.29,1.38,1.54, 1.62),CI_upper=c(1.56,1.84, 2.3, 2.59))

n <- nrow(test_data)


g <- outer(test_data$CI_lower, test_data$CI_upper,"-")
g <- !(g<0)
g <- g + t(g) # not necessary, but make matrix symmetric
g <- g!=1
rownames(g) <- 1:n # change row names
colnames(g) <- 1:n # change column names


# Re-arrange data into an "edge list" for use in igraph (i.e. which groups are "connected") - Solution from "David Eisenstat" ()
same <- which(g==1)
g2 <- data.frame(N1=((same-1) %% n) + 1, N2=((same-1) %/% n) + 1)
g2 <- g2[order(g2[[1]]),] # Get rid of loops and ensure right naming of vertices
g3 <- simplify(graph.data.frame(g2,directed = FALSE))



# Calcuate the maximal cliques - these are groupings where every node is connected to all others
cliq <- maximal.cliques(g3) # Solution from "majom" ()
cliq2 <- lapply(cliq, as.numeric)

# Reorder by level order - Solution from "MrFlick" ()
ml<-max(sapply(cliq, length))
reord <- do.call(order, data.frame(
  do.call(rbind, 
          lapply(cliq2, function(x) c(sort(x), rep.int(0, ml-length(x))))
  )
))
cliq <- cliq[reord]
cliq

# Generate labels to  factor levels
lab.txt <- vector(mode="list", n) # empty list
lab <- letters[seq(cliq)] # clique labels
for(i in seq(cliq)){ # loop to concatenate clique labels
  for(j in cliq[[i]]){
    lab.txt[[j]] <- paste0(lab.txt[[j]], lab[i])
  }
}

unlist(lab.txt)
[1] "a"  "ab" "ab" "b"

来自逻辑矩阵的紧凑字母显示

2 个答案: