这是来自的示例survminer
library(survminer)
library(multcomp)
library(tidyr)
data(myeloma)
res <- pairwise_survdiff(Surv(time, event) ~ molecular_group,
data = myeloma)
glht.summary
从包中查看方法的内部结构multcomp
,我们创建了一个lvl_order
向量,该向量标识了x
从最小到最大的级别顺序。
x <- myeloma$molecular_group
levs <- levels(x)
y <- Surv(myeloma$time, myeloma$event)
lvl_order <- levels(x)[order(tapply(as.numeric(y)[1:length(x)],
x, mean))]
然后我们可以将对象中的 p 值重新排列res
到矩阵中。 mycomps
是成对比较两侧的矩阵。该signif
向量是逻辑的,指示差异是否显着。
comps <- as_tibble(res$p.value, rownames="row") %>%
pivot_longer(-row, names_to="col", values_to="p") %>%
na.omit()
mycomps <- as.matrix(comps[,1:2])
signif <- comps$p < .05
然后,您可以使用insert_absorb
内部函数来创建字母:
multcomp:::insert_absorb(signif,
decreasing=FALSE,
comps=mycomps,
lvl_order=lvl_order)
# $Letters
# MAF Proliferation Cyclin D-2 MMSET Hyperdiploid
# "ab" "a" "b" "ab" "b"
# Low bone disease Cyclin D-1
# "ab" "ab"
#
# $monospacedLetters
# MAF Proliferation Cyclin D-2 MMSET Hyperdiploid
# "ab" "a " " b" "ab" " b"
# Low bone disease Cyclin D-1
# "ab" "ab"
#
# $LetterMatrix
# a b
# MAF TRUE TRUE
# Proliferation TRUE FALSE
# Cyclin D-2 FALSE TRUE
# MMSET TRUE TRUE
# Hyperdiploid FALSE TRUE
# Low bone disease TRUE TRUE
# Cyclin D-1 TRUE TRUE
#
# $aLetters
# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v"
# [23] "w" "x" "y" "z" "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R"
# [45] "S" "T" "U" "V" "W" "X" "Y" "Z"
#
# $aseparator
# [1] "."
#
# attr(,"class")
# [1] "multcompLetters"