这是我第一次在这里发帖，多年来一直是一个匿名读者。请善待我的情况下，格式张贴问题是错误的。
我的数据集包括存储迭代和粒子以及相关的llh和se值。

llh           se Particles Iterations     Time
1         NaN           NA       500          5    7.222
2  -2087.0886  41.53552846      1000          5   14.149
3  -1903.6823 171.30398540      1500          5   19.488
4  -2474.3789           NA      2000          5   25.336
5  -1229.1886   1.33015305      3000          5   37.858
6  -1331.1882   9.66674817      5000          5   60.994
7  -2330.5701  35.17979986      7500          5   92.654
8  -1753.6308 137.62546543       500         10   13.891
9  -1468.1730  64.58164086      1000         10   26.474
10 -2221.8960  73.11124703      1500         10   37.651
11 -2606.5620  46.51251610      2000         10   51.719
12 -1301.0474  12.59814717      3000         10   75.776
13  -927.7820   0.18559457      5000         10  125.121
14 -1180.8230  10.55185851      7500         10  151.593
15 -3109.6442  55.29536888       500         15   15.997
16 -1959.0457  44.58603179      1000         15   39.391
17 -1268.8367  24.06368751      1500         15   58.382
18 -2832.5527           NA      2000         15   76.853
19  -845.2781   0.21124844      3000         15   99.497
20  -845.4272   0.02649884      5000         15  147.611
21 -1446.8511  17.06673528      7500         15  217.608

或者如果dput（）是首选：

> dput(logliks[1:21,])
structure(list(llh = c(NaN, -2087.08855486818, -1903.6823477862, 
-2474.37893002966, -1229.18856210967, -1331.18815912831, -2330.57009669248, 
-1753.63084316259, -1468.17297841903, -2221.89596236152, -2606.56196704478, 
-1301.0473771866, -927.782003670307, -1180.82300393742, -3109.64417468708, 
-1959.04572793909, -1268.83669965093, -2832.5527445189, -845.278087151579, 
-845.427210637555, -1446.85110262111), se = c(NA, 41.5355284568715, 
171.303985396005, NA, 1.33015305002498, 9.66674817155666, 35.1797998633679, 
137.625465433877, 64.5816408601655, 73.1112470277094, 46.5125161022654, 
12.5981471672579, 0.185594570806789, 10.5518585121374, 55.2953688797359, 
44.5860317855338, 24.0636875106622, NA, 0.21124844438021, 0.0264988432776242, 
17.0667352804977), Particles = c(500, 1000, 1500, 2000, 3000, 
5000, 7500, 500, 1000, 1500, 2000, 3000, 5000, 7500, 500, 1000, 
1500, 2000, 3000, 5000, 7500), Iterations = c(5, 5, 5, 5, 5, 
5, 5, 10, 10, 10, 10, 10, 10, 10, 15, 15, 15, 15, 15, 15, 15), 
    Time = c(7.222, 14.149, 19.488, 25.336, 37.858, 60.994, 92.654, 
    13.891, 26.474, 37.651, 51.719, 75.776, 125.121, 151.593, 
    15.997, 39.391, 58.382, 76.853, 99.497, 147.611, 217.608)), row.names = c(NA, 
21L), class = "data.frame")

我试图绘制一个箱形图，它没有按预期分组。我尝试根据我在这里找到的另一篇文章离散化x轴，但是它给了我错误“错误：提供给连续刻度的离散值”。
下面是我的代码：

library(ggthemes)

g <- ggplot(logliks,aes(x=factor(Iterations), y=llh, group=Particles, fill=factor(Particles)))+
  geom_boxplot(position=position_dodge(1))+ 
  ylim(-4000,-400)+
  xlim(5,250)+
  theme(axis.text.x = element_text(angle=65, vjust=0.6))+ 
  labs(title="log Likelihoods", 
       subtitle = TeX(paste("For one guess of $\\epsilon$ and $\\kappa$ each")),
       caption="Likelihoods with respect to iterations and particles",
       x="Iterations",
       y ="log Likelihood",
       fill = paste("Particles"))+
  scale_fill_manual(values = colour_palette_parts)+
  guides(colour = guide_legend(override.aes = list(size=6,shape = 20),nrow=2))+
  theme_bw()+
  themespecs

我定义了自己的调色板 colour_palette_parts

colour_palette_parts <- c("#ffbe0b", "#ff8e09", "#ff5d07", "#ff2b05", "#ff040e", "#ff023e", "#ff006e")

也称为 library（latex 2 exp），用于字幕/字幕中的LateX符号。
我想要的是Taken from another website
下面是我使用上面的代码得到的结果，除了没有离散化x轴（即使用...aes(x=Iterations,...代替...aes(x=factor(Iterations),...）。
我甚至得到错误“position_dodge()需要非重叠的x间隔”
The box plot is not grouping each number of particles for each iteration. Ideally, I would like to have 7 smaller box-plots corresponding to each iteration.
我怎么能把它们分成小盒子呢？请帮帮我。先谢谢你了！
更新：我已经找到了如何在没有错误的情况下离散x轴：...aes(x=factor(Iterations,levels=c(5,10,15,20,30,50,100,150,200,250)), y=llh,...
现在它生成了一个图像，尽管缺少分组。This is the updated image which still lacks grouping into number of particles for each iteration.