R语言 如何修复“组件[[i]]中的错误：下标超出范围”以使字母紧凑显示？

yyyllmsg  于 2023-01-06  发布在  其他
关注(0)|答案(1)|浏览(102)

我收到以下错误消息时遇到问题：第一个月
我想在我的分析中指出一个紧凑的字母显示，但它没有工作。我试图在互联网上找到一些解决方案，但它失败了。有人能帮助我吗？这是我的data

structure(list(Ratio = c(0.267055286, 0.235446484, 0.224992335, 
0.228212575, 0.257381176, 0.256859674, 0.243903929, 0.252712714, 
0.241461807, 0.248338451, 0.256563425, 0.26601715, 0.250073217, 
0.251969117, 0.253287549, 0.263241548, 0.269360378, 0.264825074, 
0.25672374, 0.2534554, 0.246267242, 0.246695711, 0.236139498, 
0.249491444, 0.251564819, 0.240452818, 0.254713159, 0.25147281, 
0.26201919, 0.248360746, 0.246830304, 0.266038937, 0.26905912, 
0.24791562, 0.247594584, 0.256053813, 0.251228178, 0.246707173, 
0.250456004, 0.27637359, 0.26508449, 0.262086576, 0.256718454, 
0.248851991, 0.248653789, 0.252162637, 0.257240293, 0.256834233, 
0.28264247, 0.29802879, 0.258576741, 0.277733515, 0.296467765, 
0.286141117, 0.277513708, 0.273090289, 0.278239429, 0.267859464, 
0.264483192, 0.276063591, 0.262313997, 0.246508881, 0.279584358, 
0.287600757, 0.279089811, 0.278508984, 0.255397803, 0.282189954, 
0.281931686, 0.274297023, 0.314339694, 0.190332237, 0.200487283, 
0.221774473, 0.194636823, 0.212372143, 0.191236662, 0.172644425, 
0.22595976, 0.198123319, 0.211837134, 0.215018989, 0.195312021, 
0.20158237, 0.184286731, 0.19498543, 0.196400274, 0.17994453, 
0.208702986, 0.220364396, 0.202560056, 0.202323629, 0.209563815, 
0.211821257, 0.211889051, 0.169961202, 0.165792165, 0.143280229, 
0.141520745, 0.155981145, 0.1505676, 0.169778706, 0.148619699, 
0.14276644, 0.182916256, 0.134962743, 0.162540603, 0.147899504, 
0.172803323, 0.171328653, 0.148332232, 0.17731353, 0.137293375, 
0.167809004, 0.187015484, 0.16659136, 0.143882683, 0.195064548, 
0.145268859, 0.139506029, 0.158491822, 0.161545847, 0.142343264, 
0.172845598, 0.140114282, 0.14208018, 0.147465037, 0.158342427, 
0.141087175, 0.152013369, 0.152338253, 0.147960271, 0.159925355, 
0.127860026, 0.147602983, 0.152138695, 0.169946914, 0.151562855, 
0.130802593, 0.161859989, 0.12996254, 0.155459895, 0.150915199, 
0.16102091, 0.151073748, 0.169443662, 0.138065717, 0.141765129, 
0.168697363, 0.180178444, 0.152726489, 0.132928661, 0.137527664, 
0.162030059, 0.156803768, 0.144039257, 0.177741017, 0.162964524, 
0.17659578, 0.141199988, 0.158541033, 0.156337255, 0.147436957, 
0.155102179, 0.167067911, 0.158620908, 0.15569626), Strain = c("a_ M1",  "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1",  "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1",  "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1", "a_ M1",  "a_ M1", "a_ M1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1",  "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1",  "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1", "a_N1",  "a_N1", "a_N1", "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1",  "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1",  "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1", "a_ H1",  "a_ H1", "a_ H1", "a_ H1", "a_ H1", "b_S1", "b_S1", "b_S1", "b_S1",  "b_S1", "b_S1", "b_S1", "b_S1", "b_S1", "b_S1", "b_S1", "b_S1",  "b_S1", "b_S1", "b_S1", "b_S1", "b_S1", "b_S1", "b_S1", "b_S1",  "b_S1", "b_S1", "b_S1", "b_S1", "B_H1", "B_H1", "B_H1", "B_H1",  "B_H1", "B_H1", "B_H1", "B_H1", "B_H1", "B_H1", "B_H1", "B_H1",  "B_H1", "B_H1", "B_H1", "B_H1", "B_H1", "B_H1", "B_H1", "B_H1",  "B_H1", "B_H1", "B_H1", "B_H1", "B-O1", "B-O1", "B-O1", "B-O1",  "B-O1", "B-O1", "B-O1", "B-O1", "B-O1", "B-O1", "B-O1", "B-O1",  "B-O1", "B-O1", "B-O1", "B-O1", "B-O1", "B-O1", "B-O1", "B-O1",  "B-O1", "B-O1", "B-O1", "B-O1", "b_N1", "b_N1", "b_N1", "b_N1",  "b_N1", "b_N1", "b_N1", "b_N1", "b_N1", "b_N1", "b_N1", "b_N1",  "b_N1", "b_N1", "b_N1", "b_N1", "b_N1", "b_N1", "b_N1", "b_N1",  "b_N1", "b_N1", "b_N1", "b_N1"), species = c("a", "a", "a", "a",  "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a",  "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a",  "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a",  "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a",  "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a",  "a", "a", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b",  "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b",  "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b",  "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b",  "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b",  "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b", "b",  "b", "b", "b", "b", "b", "b", "b")), class = "data.frame", row.names = c(NA, 
-167L))

这是剧本

library(datasets)
library(ggplot2)
library(multcompView)
library(dplyr)
library(datasets)
library(tidyverse)
library(multcomp)

Data = read.csv("data.csv", h= TRUE)
qplot(x = species, y = Ratio, geom = "point", data = Data) +
  facet_grid(.~Strain)

# creating a variable as factor for the ANOVA
Data$Strain <- as.factor(Data$Strain)
Data$species <- as.factor(Data$species)
str(Data)

# analysis of variance
anova <- aov(Ratio ~ Strain*factor(species), data = Data)
summary(anova)

# table with factors, means and standard deviation
data_summary <- group_by(Data, Strain, species) %>%
  summarise(mean=mean(Ratio), sd=sd(Ratio)) %>%
  arrange(desc(mean))
print(data_summary)

# Tukey's test
tukey <- TukeyHSD(anova)
print(tukey)

# compact letter display
coba = multcompLetters4(anova, tukey)
print(coba)

# creating the compact letter display
tukey.cld <- multcompLetters4(anova, tukey)
print(tukey.cld)

这就是我想要得到的---〉我想要在我的数据

中得到一个字母
请帮帮我

r

1条答案

按热度按时间
ttvkxqim

ttvkxqim1#

这对你有用吗？我在下面添加了一些注解。

library(datasets)
library(ggplot2)
library(multcompView)
library(dplyr)
library(datasets)
library(tidyverse)
library(multcomp)

raw <- tibble::tribble(
         ~Ratio, ~Strain, ~species,
  "267.055.286", "a_ M1",      "a",
  "235.446.484", "a_ M1",      "a",
  "224.992.335", "a_ M1",      "a",
  "228.212.575", "a_ M1",      "a",
  "257.381.176", "a_ M1",      "a",
  "256.859.674", "a_ M1",      "a",
  "243.903.929", "a_ M1",      "a",
  "252.712.714", "a_ M1",      "a",
  "241.461.807", "a_ M1",      "a",
  "248.338.451", "a_ M1",      "a",
  "256.563.425", "a_ M1",      "a",
   "26.601.715", "a_ M1",      "a",
  "250.073.217", "a_ M1",      "a",
  "251.969.117", "a_ M1",      "a",
  "253.287.549", "a_ M1",      "a",
  "263.241.548", "a_ M1",      "a",
  "269.360.378", "a_ M1",      "a",
  "264.825.074", "a_ M1",      "a",
   "25.672.374", "a_ M1",      "a",
    "2.534.554", "a_ M1",      "a",
  "246.267.242", "a_ M1",      "a",
  "246.695.711", "a_ M1",      "a",
  "236.139.498", "a_ M1",      "a",
  "249.491.444", "a_ M1",      "a",
  "251.564.819",  "a_N1",      "a",
  "240.452.818",  "a_N1",      "a",
  "254.713.159",  "a_N1",      "a",
   "25.147.281",  "a_N1",      "a",
   "26.201.919",  "a_N1",      "a",
  "248.360.746",  "a_N1",      "a",
  "246.830.304",  "a_N1",      "a",
  "266.038.937",  "a_N1",      "a",
   "26.905.912",  "a_N1",      "a",
   "24.791.562",  "a_N1",      "a",
  "247.594.584",  "a_N1",      "a",
  "256.053.813",  "a_N1",      "a",
  "251.228.178",  "a_N1",      "a",
  "246.707.173",  "a_N1",      "a",
  "250.456.004",  "a_N1",      "a",
   "27.637.359",  "a_N1",      "a",
   "26.508.449",  "a_N1",      "a",
  "262.086.576",  "a_N1",      "a",
  "256.718.454",  "a_N1",      "a",
  "248.851.991",  "a_N1",      "a",
  "248.653.789",  "a_N1",      "a",
  "252.162.637",  "a_N1",      "a",
  "257.240.293",  "a_N1",      "a",
  "256.834.233",  "a_N1",      "a",
   "28.264.247", "a_ H1",      "a",
   "29.802.879", "a_ H1",      "a",
  "258.576.741", "a_ H1",      "a",
  "277.733.515", "a_ H1",      "a",
  "296.467.765", "a_ H1",      "a",
  "286.141.117", "a_ H1",      "a",
  "277.513.708", "a_ H1",      "a",
  "273.090.289", "a_ H1",      "a",
  "278.239.429", "a_ H1",      "a",
  "267.859.464", "a_ H1",      "a",
  "264.483.192", "a_ H1",      "a",
  "276.063.591", "a_ H1",      "a",
  "262.313.997", "a_ H1",      "a",
  "246.508.881", "a_ H1",      "a",
  "279.584.358", "a_ H1",      "a",
  "287.600.757", "a_ H1",      "a",
  "279.089.811", "a_ H1",      "a",
  "278.508.984", "a_ H1",      "a",
  "255.397.803", "a_ H1",      "a",
  "282.189.954", "a_ H1",      "a",
  "281.931.686", "a_ H1",      "a",
  "274.297.023", "a_ H1",      "a",
  "314.339.694", "a_ H1",      "a",
  "190.332.237",  "b_S1",      "b",
  "200.487.283",  "b_S1",      "b",
  "221.774.473",  "b_S1",      "b",
  "194.636.823",  "b_S1",      "b",
  "212.372.143",  "b_S1",      "b",
  "191.236.662",  "b_S1",      "b",
  "172.644.425",  "b_S1",      "b",
   "22.595.976",  "b_S1",      "b",
  "198.123.319",  "b_S1",      "b",
  "211.837.134",  "b_S1",      "b",
  "215.018.989",  "b_S1",      "b",
  "195.312.021",  "b_S1",      "b",
   "20.158.237",  "b_S1",      "b",
  "184.286.731",  "b_S1",      "b",
   "19.498.543",  "b_S1",      "b",
  "196.400.274",  "b_S1",      "b",
   "17.994.453",  "b_S1",      "b",
  "208.702.986",  "b_S1",      "b",
  "220.364.396",  "b_S1",      "b",
  "202.560.056",  "b_S1",      "b",
  "202.323.629",  "b_S1",      "b",
  "209.563.815",  "b_S1",      "b",
  "211.821.257",  "b_S1",      "b",
  "211.889.051",  "b_S1",      "b",
  "169.961.202",  "B_H1",      "b",
  "165.792.165",  "B_H1",      "b",
  "143.280.229",  "B_H1",      "b",
  "141.520.745",  "B_H1",      "b",
  "155.981.145",  "B_H1",      "b",
    "1.505.676",  "B_H1",      "b",
  "169.778.706",  "B_H1",      "b",
  "148.619.699",  "B_H1",      "b",
   "14.276.644",  "B_H1",      "b",
  "182.916.256",  "B_H1",      "b",
  "134.962.743",  "B_H1",      "b",
  "162.540.603",  "B_H1",      "b",
  "147.899.504",  "B_H1",      "b",
  "172.803.323",  "B_H1",      "b",
  "171.328.653",  "B_H1",      "b",
  "148.332.232",  "B_H1",      "b",
   "17.731.353",  "B_H1",      "b",
  "137.293.375",  "B_H1",      "b",
  "167.809.004",  "B_H1",      "b",
  "187.015.484",  "B_H1",      "b",
   "16.659.136",  "B_H1",      "b",
  "143.882.683",  "B_H1",      "b",
  "195.064.548",  "B_H1",      "b",
  "145.268.859",  "B_H1",      "b",
  "139.506.029",  "B-O1",      "b",
  "158.491.822",  "B-O1",      "b",
  "161.545.847",  "B-O1",      "b",
  "142.343.264",  "B-O1",      "b",
  "172.845.598",  "B-O1",      "b",
  "140.114.282",  "B-O1",      "b",
   "14.208.018",  "B-O1",      "b",
  "147.465.037",  "B-O1",      "b",
  "158.342.427",  "B-O1",      "b",
  "141.087.175",  "B-O1",      "b",
  "152.013.369",  "B-O1",      "b",
  "152.338.253",  "B-O1",      "b",
  "147.960.271",  "B-O1",      "b",
  "159.925.355",  "B-O1",      "b",
  "127.860.026",  "B-O1",      "b",
  "147.602.983",  "B-O1",      "b",
  "152.138.695",  "B-O1",      "b",
  "169.946.914",  "B-O1",      "b",
  "151.562.855",  "B-O1",      "b",
  "130.802.593",  "B-O1",      "b",
  "161.859.989",  "B-O1",      "b",
   "12.996.254",  "B-O1",      "b",
  "155.459.895",  "B-O1",      "b",
  "150.915.199",  "B-O1",      "b",
   "16.102.091",  "b_N1",      "b",
  "151.073.748",  "b_N1",      "b",
  "169.443.662",  "b_N1",      "b",
  "138.065.717",  "b_N1",      "b",
  "141.765.129",  "b_N1",      "b",
  "168.697.363",  "b_N1",      "b",
  "180.178.444",  "b_N1",      "b",
  "152.726.489",  "b_N1",      "b",
  "132.928.661",  "b_N1",      "b",
  "137.527.664",  "b_N1",      "b",
  "162.030.059",  "b_N1",      "b",
  "156.803.768",  "b_N1",      "b",
  "144.039.257",  "b_N1",      "b",
  "177.741.017",  "b_N1",      "b",
  "162.964.524",  "b_N1",      "b",
   "17.659.578",  "b_N1",      "b",
  "141.199.988",  "b_N1",      "b",
  "158.541.033",  "b_N1",      "b",
  "156.337.255",  "b_N1",      "b",
  "147.436.957",  "b_N1",      "b",
  "155.102.179",  "b_N1",      "b",
  "167.067.911",  "b_N1",      "b",
  "158.620.908",  "b_N1",      "b",
   "15.569.626",  "b_N1",      "b"
  ) 

Data <- raw %>% 
  mutate(Ratio = as.integer(str_remove_all(Ratio, "\\."))) %>% 
  mutate(across(where(is.character), as.factor))

# set up model
mod <- lm(Ratio ~ Strain*species, data = Data)

library(emmeans)
emmeans(object = mod, specs = ~ species) %>% cld(Letters = letters)
#> NOTE: A nesting structure was detected in the fitted model:
#>     Strain %in% species
#> NOTE: Results may be misleading due to involvement in interactions
#>  species   emmean      SE  df lower.CL upper.CL .group
#>  b       1.46e+08 7097529 160 1.32e+08  1.6e+08  a    
#>  a       2.24e+08 8254695 160 2.07e+08  2.4e+08   b   
#> 
#> Results are averaged over the levels of: Strain 
#> Confidence level used: 0.95 
#> 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.
emmeans(object = mod, specs = ~ species:Strain) %>% cld(Letters = letters)
#> NOTE: A nesting structure was detected in the fitted model:
#>     Strain %in% species
#>  Strain species   emmean       SE  df lower.CL upper.CL .group
#>  B_H1   b       1.35e+08 14195058 160 1.07e+08 1.63e+08  a    
#>  b_N1   b       1.38e+08 14195058 160 1.10e+08 1.66e+08  ab   
#>  B-O1   b       1.40e+08 14195058 160 1.12e+08 1.68e+08  ab   
#>  b_S1   b       1.72e+08 14195058 160 1.44e+08 2.00e+08  abc  
#>  a_N1   a       1.96e+08 14195058 160 1.68e+08 2.24e+08   bcd 
#>  a_ M1  a       2.21e+08 14195058 160 1.93e+08 2.49e+08    cd 
#>  a_ H1  a       2.55e+08 14500363 160 2.26e+08 2.83e+08     d 
#> 
#> Confidence level used: 0.95 
#> P value adjustment: tukey method for comparing a family of 7 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.

创建于2022年9月5日，使用reprex v2.0.2

  • 您所显示的屏幕截图包含（i）第一个因子、（ii）第二个因子和（iii）其交互作用的紧凑字母平均值比较。这对于您的数据是不可能的，因为菌株完全嵌套在种内。因此，我仅生成species和species:Strain的紧凑字母平均值比较。
  • 请参阅this chapter on compact letter displays了解有关紧凑型字母显示屏的优点和缺点的更多详细信息。
  • 当你有两个因素，特别是它们之间的相互作用时，紧凑字母显示的主题会有点复杂。查看this answer了解你的选择的细节。
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