R语言 创建“三角形相关矩阵样式”图以进行种类配对比较

tjvv9vkg  于 2023-02-20  发布在  其他
关注(0)|答案(2)|浏览(146)

我已经做了一系列物种的配对卡方比较，并且有一个包含每个物种对的p值的数据框，我想使用类似于相关矩阵的ggplot创建一个可视化，这样我们就可以看到哪些物种对具有显著的p值，一种颜色的p值〉0.05，另一种颜色的p值〈或= 0.5，类似于下面的内容：（https://www.researchgate.net/publication/293654442_cooccur_Probabilistic_Species_Co-Occurrence_Analysis_in_R）

我已经尝试遵循这些指南：
http://www.sthda.com/english/wiki/ggplot2-quick-correlation-matrix-heatmap-r-software-and-data-visualization
https://www.youtube.com/watch?v=E3De2A73ako
没有太大的成功。我有麻烦1）把它变成一个三角形和2）有颜色是二元的，而不是梯度。我宁愿保持物种的秩序，因为我有他们在我的 Dataframe 。任何建议将是惊人的。我一直在驾驶自己的大手帕与这一个。
我已经附上了宽格式和长格式的 Dataframe 的数据输出!
非常感谢!
Wide.df:

structure(list(Species1 = c(NA, 8.29661485364936e-14, 0.197328035330918, 
3.73447143215061e-05, 0.0200245227839388, 0.000158518795659732, 
0.999999999999999, 0.999999999999999, 0.200661174254572, 0.0259711510883781, 
0.624415639985824, 0.999999999999995, 0.999999999999991), Species2 = c(8.29661485364936e-14, 
NA, 0.911548596172133, 0.0808672833723648, 0.00232408080140171, 
0.00257625010714883, 0.999999999999777, 0.999999999999777, 0.789829339795786, 
0.198077829941541, 0.768466049890891, 0.999999999999885, 0.999999999999994
), Species3 = c(0.197328035330918, 0.911548596172133, NA, 0.181696316252709, 
0.575238254258972, 0.939866701307512, 0.999999999999999, 0.999999999999999, 
0.999999999999999, 0.507611967012476, 0.673527222144056, 0.999999999999996, 
1), Species4 = c(3.73447143215061e-05, 0.0808672833723648, 0.181696316252709, 
NA, 1.40996305374498e-10, 0.405410680293625, 0.999999999999999, 
0.999999999999999, 1.56797538624063e-08, 8.50447159522988e-05, 
0.00644405295214749, 0.88381428087806, 0.99999999999993), Species5 = c(0.0200245227839388, 
0.00232408080140171, 0.575238254258972, 1.40996305374498e-10, 
NA, 0.622095128306733, 0.999999999999959, 0.999999999999959, 
0.000115476294641169, 1.60499061530966e-19, 4.67520836455185e-05, 
0.528580893876124, 0.795044191844885), Species6 = c(0.000158518795659732, 
0.00257625010714883, 0.939866701307512, 0.405410680293625, 0.622095128306733, 
NA, 0.999999999999999, 0.999999999999999, 0.200661174254572, 
0.410636112084533, 0.999999999999929, 0.999999999999995, 0.999999999999991
), Species7 = c(0.999999999999999, 0.999999999999777, 0.999999999999999, 
0.999999999999999, 0.999999999999959, 0.999999999999999, NA, 
0.999999999999299, 0.779426832974571, 0.999999999999933, 0.999999999999999, 
0.999999999999998, 0.999999999999687), Species8 = c(0.999999999999999, 
0.999999999999777, 0.999999999999999, 0.999999999999999, 0.999999999999959, 
0.999999999999999, 0.999999999999299, NA, 0.999999999999999, 
0.611136265859179, 0.999999999999999, 0.999999999999998, 0.999999999999687
), Species9 = c(0.200661174254572, 0.789829339795786, 0.999999999999999, 
1.56797538624063e-08, 0.000115476294641169, 0.200661174254572, 
0.779426832974571, 0.999999999999999, NA, 0.0311037604732729, 
0.0122054515551129, 0.999999999999984, 0.999999999999999), Species10 = c(0.0259711510883781, 
0.198077829941541, 0.507611967012476, 8.50447159522988e-05, 1.60499061530966e-19, 
0.410636112084533, 0.999999999999933, 0.611136265859179, 0.0311037604732729, 
NA, 0.0403275386741277, 0.508244635418544, 0.999999999999999), 
    Species11 = c(0.624415639985824, 0.768466049890891, 0.673527222144056, 
    0.00644405295214749, 4.67520836455185e-05, 0.999999999999929, 
    0.999999999999999, 0.999999999999999, 0.0122054515551129, 
    0.0403275386741277, NA, 1, 0.999999999999823), Species12 = c(0.999999999999995, 
    0.999999999999885, 0.999999999999996, 0.88381428087806, 0.528580893876124, 
    0.999999999999995, 0.999999999999998, 0.999999999999998, 
    0.999999999999984, 0.508244635418544, 1, NA, 0.999999999998991
    ), Species13 = c(0.999999999999991, 0.999999999999994, 1, 
    0.99999999999993, 0.795044191844885, 0.999999999999991, 0.999999999999687, 
    0.999999999999687, 0.999999999999999, 0.999999999999999, 
    0.999999999999823, 0.999999999998991, NA)), row.names = c("Species1", 
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7", 
"Species8", "Species9", "Species10", "Species11", "Species12", 
"Species13"), class = "data.frame")

long.df:

structure(list(SpeciesA = c("Species1", "Species2", "Species3", 
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9", 
"Species10", "Species11", "Species12", "Species13", "Species1", 
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7", 
"Species8", "Species9", "Species10", "Species11", "Species12", 
"Species13", "Species1", "Species2", "Species3", "Species4", 
"Species5", "Species6", "Species7", "Species8", "Species9", "Species10", 
"Species11", "Species12", "Species13", "Species1", "Species2", 
"Species3", "Species4", "Species5", "Species6", "Species7", "Species8", 
"Species9", "Species10", "Species11", "Species12", "Species13", 
"Species1", "Species2", "Species3", "Species4", "Species5", "Species6", 
"Species7", "Species8", "Species9", "Species10", "Species11", 
"Species12", "Species13", "Species1", "Species2", "Species3", 
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9", 
"Species10", "Species11", "Species12", "Species13", "Species1", 
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7", 
"Species8", "Species9", "Species10", "Species11", "Species12", 
"Species13", "Species1", "Species2", "Species3", "Species4", 
"Species5", "Species6", "Species7", "Species8", "Species9", "Species10", 
"Species11", "Species12", "Species13", "Species1", "Species2", 
"Species3", "Species4", "Species5", "Species6", "Species7", "Species8", 
"Species9", "Species10", "Species11", "Species12", "Species13", 
"Species1", "Species2", "Species3", "Species4", "Species5", "Species6", 
"Species7", "Species8", "Species9", "Species10", "Species11", 
"Species12", "Species13", "Species1", "Species2", "Species3", 
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9", 
"Species10", "Species11", "Species12", "Species13", "Species1", 
"Species2", "Species3", "Species4", "Species5", "Species6", "Species7", 
"Species8", "Species9", "Species10", "Species11", "Species12", 
"Species13", "Species1", "Species2", "Species3", "Species4", 
"Species5", "Species6", "Species7", "Species8", "Species9", "Species10", 
"Species11", "Species12", "Species13"), SpeciesB = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L), .Label = c("Species1", "Species2", "Species3", 
"Species4", "Species5", "Species6", "Species7", "Species8", "Species9", 
"Species10", "Species11", "Species12", "Species13"), class = "factor"), 
    p_value = c(NA, 8.29661485364936e-14, 0.197328035330918, 
    3.73447143215061e-05, 0.0200245227839388, 0.000158518795659732, 
    0.999999999999999, 0.999999999999999, 0.200661174254572, 
    0.0259711510883781, 0.624415639985824, 0.999999999999995, 
    0.999999999999991, 8.29661485364936e-14, NA, 0.911548596172133, 
    0.0808672833723648, 0.00232408080140171, 0.00257625010714883, 
    0.999999999999777, 0.999999999999777, 0.789829339795786, 
    0.198077829941541, 0.768466049890891, 0.999999999999885, 
    0.999999999999994, 0.197328035330918, 0.911548596172133, 
    NA, 0.181696316252709, 0.575238254258972, 0.939866701307512, 
    0.999999999999999, 0.999999999999999, 0.999999999999999, 
    0.507611967012476, 0.673527222144056, 0.999999999999996, 
    1, 3.73447143215061e-05, 0.0808672833723648, 0.181696316252709, 
    NA, 1.40996305374498e-10, 0.405410680293625, 0.999999999999999, 
    0.999999999999999, 1.56797538624063e-08, 8.50447159522988e-05, 
    0.00644405295214749, 0.88381428087806, 0.99999999999993, 
    0.0200245227839388, 0.00232408080140171, 0.575238254258972, 
    1.40996305374498e-10, NA, 0.622095128306733, 0.999999999999959, 
    0.999999999999959, 0.000115476294641169, 1.60499061530966e-19, 
    4.67520836455185e-05, 0.528580893876124, 0.795044191844885, 
    0.000158518795659732, 0.00257625010714883, 0.939866701307512, 
    0.405410680293625, 0.622095128306733, NA, 0.999999999999999, 
    0.999999999999999, 0.200661174254572, 0.410636112084533, 
    0.999999999999929, 0.999999999999995, 0.999999999999991, 
    0.999999999999999, 0.999999999999777, 0.999999999999999, 
    0.999999999999999, 0.999999999999959, 0.999999999999999, 
    NA, 0.999999999999299, 0.779426832974571, 0.999999999999933, 
    0.999999999999999, 0.999999999999998, 0.999999999999687, 
    0.999999999999999, 0.999999999999777, 0.999999999999999, 
    0.999999999999999, 0.999999999999959, 0.999999999999999, 
    0.999999999999299, NA, 0.999999999999999, 0.611136265859179, 
    0.999999999999999, 0.999999999999998, 0.999999999999687, 
    0.200661174254572, 0.789829339795786, 0.999999999999999, 
    1.56797538624063e-08, 0.000115476294641169, 0.200661174254572, 
    0.779426832974571, 0.999999999999999, NA, 0.0311037604732729, 
    0.0122054515551129, 0.999999999999984, 0.999999999999999, 
    0.0259711510883781, 0.198077829941541, 0.507611967012476, 
    8.50447159522988e-05, 1.60499061530966e-19, 0.410636112084533, 
    0.999999999999933, 0.611136265859179, 0.0311037604732729, 
    NA, 0.0403275386741277, 0.508244635418544, 0.999999999999999, 
    0.624415639985824, 0.768466049890891, 0.673527222144056, 
    0.00644405295214749, 4.67520836455185e-05, 0.999999999999929, 
    0.999999999999999, 0.999999999999999, 0.0122054515551129, 
    0.0403275386741277, NA, 1, 0.999999999999823, 0.999999999999995, 
    0.999999999999885, 0.999999999999996, 0.88381428087806, 0.528580893876124, 
    0.999999999999995, 0.999999999999998, 0.999999999999998, 
    0.999999999999984, 0.508244635418544, 1, NA, 0.999999999998991, 
    0.999999999999991, 0.999999999999994, 1, 0.99999999999993, 
    0.795044191844885, 0.999999999999991, 0.999999999999687, 
    0.999999999999687, 0.999999999999999, 0.999999999999999, 
    0.999999999999823, 0.999999999998991, NA)), row.names = c(NA, 
-169L), class = "data.frame")
r

2条答案

按热度按时间
h79rfbju

h79rfbju1#

这里有一个粗略的开始。基本的图是一个简单的分箱热图，但有几个阶段我们需要经历：

  • 用cut对数据进行二进制;我随意地把它们分成0、0. 25、0. 5和1的小桶，你可以根据自己的需要随意改变数字;
  • 为了保持基于类数字排序的顺序，我们需要将它们转换为factor s，并且为了正确地排序它们，我们需要解析出数字，因此先解析出as.integer(gsub(..))，然后解析出factor。
  • 为了得到一个半三角形，我确保x轴的值num*大于y轴的值;这是任意的，但符合你的样本图。
  • 然而，为了在对角线上显示标签，我们在进入ggplot(.)的数据中保留numA == numB，子集化出瓦片的对角线，然后 * 仅 * 保留标签的对角线（geom_text）。
  • 为了稍微扩展画布以允许对角线的标签，我们需要expand=两个轴;实际数量可以取决于画布（PDF、HTML等）。

我使用了base-R（R-4）变换/子集，这很容易转换为使用dplyr的mutate/filter。

long.df |>
  transform(
    bin = cut(p_value, c(0, 0.25, 0.5, 1), labels = c("low", "moderate", "high")),
    numA = as.integer(gsub("\\D", "", SpeciesA)),
    numB = as.integer(gsub("\\D", "", SpeciesB))
  ) |> transform(
    SpeciesA = factor(SpeciesA, levels = unique(SpeciesA[order(numA)])),
    SpeciesB = factor(SpeciesB, levels = unique(SpeciesB[order(numB)]))
  ) |>
  subset(numA >= numB) |>
  ggplot(aes(SpeciesA, SpeciesB)) +
  geom_tile(aes(fill = bin), data = ~ subset(., numA > numB)) +
  geom_text(aes(label = SpeciesA), data = ~ subset(., numA == numB),
            hjust = 1.1, angle = -30) +
  scale_x_discrete(name = NULL, expand = expansion(0, c(2.5, 0)), drop = FALSE) +
  scale_y_discrete(name = NULL, expand = expansion(0, c(0, 2)), drop = FALSE) +
  theme(
    axis.text = element_blank(),
    axis.line = element_blank(),
    axis.ticks = element_blank()
  )

赞(0）分享  回复(0）举报  2023-02-20
7vux5j2d

7vux5j2d2#

在r2 evans的帮助下解决了！我最终使用的代码如下！

test.plot1 <- long.df1 %>%
  mutate(
    color = # get colors of graphs
      ifelse(test = p_value > 0.05,
             yes = "#440154FF",
             no = (ifelse(test = p_value <= 0.05,
                          yes = "#FDE725FF",
                          no = NA)
          )
      )
  ) %>% 
  mutate(
    Species1 = factor(Species1, levels = unique(Species1[order(order_species1)])),
    Species2 = factor(Species2 , levels = unique(Species2 [order(order_species2)]))
  ) %>% 
  subset(order_species1>=order_species2) %>% 
  ggplot(aes(Species1, Species2)) +
  geom_tile(aes(fill = color), data = ~ subset(., order_species1> order_species2), color = "white") +
  geom_text(aes(label = Species1), data = ~ subset(., order_species1 == order_species2),
            hjust = 1.1, angle = -30) +
  scale_x_discrete(name = NULL, expand = expansion(0, c(2.5, 0)), drop = FALSE) +
  scale_y_discrete(name = NULL, expand = expansion(0, c(0, 2)), drop = FALSE) + 
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.line = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank()
  ) +   scale_fill_identity() 

test.plot1

这就产生了下面的图：

谢谢大家！
Dataframe 的Dput如下所示：

structure(list(Species1 = c("SpeciesA", "SpeciesB", "SpeciesC", 
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", 
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", 
"SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", 
"SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", 
"SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", 
"SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", 
"SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", 
"SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", 
"SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", 
"SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", 
"SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", 
"SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", 
"SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", 
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", 
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", 
"SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", 
"SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", 
"SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", 
"SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM", 
"SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", "SpeciesF", 
"SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", "SpeciesL", 
"SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", "SpeciesE", 
"SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", "SpeciesK", 
"SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", "SpeciesD", 
"SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", "SpeciesJ", 
"SpeciesK", "SpeciesL", "SpeciesM", "SpeciesA", "SpeciesB", "SpeciesC", 
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", 
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM"), Species2 = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L), .Label = c("SpeciesA", "SpeciesB", "SpeciesC", 
"SpeciesD", "SpeciesE", "SpeciesF", "SpeciesG", "SpeciesH", "SpeciesI", 
"SpeciesJ", "SpeciesK", "SpeciesL", "SpeciesM"), class = "factor"), 
    p_value = c(NA, 8.29661485364936e-14, 0.197328035330918, 
    3.73447143215061e-05, 0.0200245227839388, 0.000158518795659732, 
    0.999999999999999, 0.999999999999999, 0.200661174254572, 
    0.0259711510883781, 0.624415639985824, 0.999999999999995, 
    0.999999999999991, 8.29661485364936e-14, NA, 0.911548596172133, 
    0.0808672833723648, 0.00232408080140171, 0.00257625010714883, 
    0.999999999999777, 0.999999999999777, 0.789829339795786, 
    0.198077829941541, 0.768466049890891, 0.999999999999885, 
    0.999999999999994, 0.197328035330918, 0.911548596172133, 
    NA, 0.181696316252709, 0.575238254258972, 0.939866701307512, 
    0.999999999999999, 0.999999999999999, 0.999999999999999, 
    0.507611967012476, 0.673527222144056, 0.999999999999996, 
    1, 3.73447143215061e-05, 0.0808672833723648, 0.181696316252709, 
    NA, 1.40996305374498e-10, 0.405410680293625, 0.999999999999999, 
    0.999999999999999, 1.56797538624063e-08, 8.50447159522988e-05, 
    0.00644405295214749, 0.88381428087806, 0.99999999999993, 
    0.0200245227839388, 0.00232408080140171, 0.575238254258972, 
    1.40996305374498e-10, NA, 0.622095128306733, 0.999999999999959, 
    0.999999999999959, 0.000115476294641169, 1.60499061530966e-19, 
    4.67520836455185e-05, 0.528580893876124, 0.795044191844885, 
    0.000158518795659732, 0.00257625010714883, 0.939866701307512, 
    0.405410680293625, 0.622095128306733, NA, 0.999999999999999, 
    0.999999999999999, 0.200661174254572, 0.410636112084533, 
    0.999999999999929, 0.999999999999995, 0.999999999999991, 
    0.999999999999999, 0.999999999999777, 0.999999999999999, 
    0.999999999999999, 0.999999999999959, 0.999999999999999, 
    NA, 0.999999999999299, 0.779426832974571, 0.999999999999933, 
    0.999999999999999, 0.999999999999998, 0.999999999999687, 
    0.999999999999999, 0.999999999999777, 0.999999999999999, 
    0.999999999999999, 0.999999999999959, 0.999999999999999, 
    0.999999999999299, NA, 0.999999999999999, 0.611136265859179, 
    0.999999999999999, 0.999999999999998, 0.999999999999687, 
    0.200661174254572, 0.789829339795786, 0.999999999999999, 
    1.56797538624063e-08, 0.000115476294641169, 0.200661174254572, 
    0.779426832974571, 0.999999999999999, NA, 0.0311037604732729, 
    0.0122054515551129, 0.999999999999984, 0.999999999999999, 
    0.0259711510883781, 0.198077829941541, 0.507611967012476, 
    8.50447159522988e-05, 1.60499061530966e-19, 0.410636112084533, 
    0.999999999999933, 0.611136265859179, 0.0311037604732729, 
    NA, 0.0403275386741277, 0.508244635418544, 0.999999999999999, 
    0.624415639985824, 0.768466049890891, 0.673527222144056, 
    0.00644405295214749, 4.67520836455185e-05, 0.999999999999929, 
    0.999999999999999, 0.999999999999999, 0.0122054515551129, 
    0.0403275386741277, NA, 1, 0.999999999999823, 0.999999999999995, 
    0.999999999999885, 0.999999999999996, 0.88381428087806, 0.528580893876124, 
    0.999999999999995, 0.999999999999998, 0.999999999999998, 
    0.999999999999984, 0.508244635418544, 1, NA, 0.999999999998991, 
    0.999999999999991, 0.999999999999994, 1, 0.99999999999993, 
    0.795044191844885, 0.999999999999991, 0.999999999999687, 
    0.999999999999687, 0.999999999999999, 0.999999999999999, 
    0.999999999999823, 0.999999999998991, NA), order_species1 = c(1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 
    3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 
    4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 
    5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 
    7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
    8L, 9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
    9L, 10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
    10L, 11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
    12L, 13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
    13L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L
    ), order_species2 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 
    13L, 13L, 13L, 13L, 13L, 13L, 13L)), row.names = c(NA, -169L
), class = "data.frame")
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