我有树木生长测量数据(直径和高度)在已知的X & &;Y坐标。我想确定每棵树的最近邻居与大小相等或更大。
我见过其他SE问题询问最近邻计算(例如，见这里，这里，这里，这里等)，但没有指定要搜索的最近邻的约束。
是否存在一个函数(或其他方法)，允许我确定一个点的最近邻居的距离，假设最近的点满足一些条件(例如，必须等于或大于感兴趣的点的大小)?
[一组更复杂的约束会更有帮助…]
- 以我的例子为例:指定一棵树必须也与感兴趣的树在同一地块上，或者与感兴趣的树是同一物种
我将使用非相等连接和data.table
编辑:(供参考，这需要数据。表1.9.7(可以从github获取)
EDIT2:使用数据的副本。表，因为它似乎是通过自己的阈值连接的。我将在将来修复这个问题，但是现在可以正常工作。
library(data.table)
dtree <- data.table(id = 1:1000,
x = runif(1000),
y = runif(1000),
height = rnorm(1000,mean = 100,sd = 10),
species = sample(LETTERS[1:3],1000,replace = TRUE),
plot = sample(1:3,1000, replace = TRUE))
dtree_self <- copy(dtree)
dtree_self[,thresh1 := height + 10]
dtree_self[,thresh2 := height - 10]
# Join on a range, must be a cartesian join, since there are many candidates
test <- dtree[dtree_self, on = .(height >= thresh2,
height <= thresh1),
allow.cartesian = TRUE]
# Calculate the distance
test[, dist := (x - i.x)**2 + (y - i.y)**2]
# Exclude identical matches and
# Take the minimum distance grouped by id
final <- test[id != i.id, .SD[which.min(dist)],by = id]
最终数据集包含每对，根据给定的阈值
编辑:
附加变量:
如果你想加入额外的参数，这允许你这样做，(如果你额外加入像情节或物种这样的东西，可能会更快，因为笛卡尔连接会更小)
下面是连接两个额外的分类变量，物种和情节的例子:
library(data.table)
dtree <- data.table(id = 1:1000,
x = runif(1000),
y = runif(1000),
height = rnorm(1000,mean = 100,sd = 10),
species = sample(LETTERS[1:3],1000,replace = TRUE),
plot = sample(1:3,1000, replace = TRUE))
dtree_self <- copy(dtree)
dtree_self[,thresh1 := height + 10]
dtree_self[,thresh2 := height - 10]
# Join on a range, must be a cartesian join, since there are many candidates
test <- dtree[dtree_self, on = .(height >= thresh2,
height <= thresh1,
species == species,
plot == plot),
nomatch = NA,
allow.cartesian = TRUE]
# Calculate the distance
test[, dist := (x - i.x)**2 + (y - i.y)**2]
# Exclude identical matches and
# Take the minimum distance grouped by id
final <- test[id != i.id, .SD[which.min(dist)],by = id]
final
> final
id x y height species plot height.1 i.id i.x i.y i.height dist
1: 3 0.4837348 0.4325731 91.53387 C 2 111.53387 486 0.5549221 0.4395687 101.53387 0.005116568
2: 13 0.8267298 0.3137061 94.58949 C 2 114.58949 754 0.8408547 0.2305702 104.58949 0.007111079
3: 29 0.2905729 0.4952757 89.52128 C 2 109.52128 333 0.2536760 0.5707272 99.52128 0.007054301
4: 37 0.4534841 0.5249862 89.95493 C 2 109.95493 72 0.4807242 0.6056771 99.95493 0.007253044
5: 63 0.1678515 0.8814829 84.77450 C 2 104.77450 289 0.1151764 0.9728488 94.77450 0.011122404
---
994: 137 0.8696393 0.2226888 66.57792 C 2 86.57792 473 0.4467795 0.6881008 76.57792 0.395418724
995: 348 0.3606249 0.1245749 110.14466 A 2 130.14466 338 0.1394011 0.1200064 120.14466 0.048960849
996: 572 0.6562758 0.1387882 113.61821 A 2 133.61821 348 0.3606249 0.1245749 123.61821 0.087611511
997: 143 0.9170504 0.1171652 71.39953 C 3 91.39953 904 0.6954973 0.3690599 81.39953 0.112536771
998: 172 0.6834473 0.6221259 65.52187 A 2 85.52187 783 0.4400028 0.9526355 75.52187 0.168501816
>
注:在最终答案中，有列height和height。1，后者似乎是数据的结果。
mems -efficient解决方案
@theforestecologist的一个问题是，这需要大量的内存来完成，
(在这种情况下，有额外的42列乘以笛卡尔连接，这导致了内存问题)，
然而，我们可以通过使用。eachi(我相信)以一种更有效的内存方式来做到这一点。因为我们不会将整个表加载到内存中。解决方案如下:
library(data.table)
dtree <- data.table(id = 1:1000,
x = runif(1000),
y = runif(1000),
height = rnorm(1000,mean = 100,sd = 10),
species = sample(LETTERS[1:3],1000,replace = TRUE),
plot = sample(1:3,1000, replace = TRUE))
dtree_self <- copy(dtree)
dtree_self[,thresh1 := height + 10]
dtree_self[,thresh2 := height - 10]
# In order to navigate the sometimes unusual nature of scoping inside a
# data.table join, I set the second table to have its own uniquely named id
dtree_self[,id2 := id]
dtree_self[,id := NULL]
# for clarity inside the brackets,
# I define the squared euclid distance
eucdist <- function(x,xx,y,yy) (x - xx)**2 + (y - yy)**2
# Join on a range, must be a cartesian join, since there are many candidates
# Return a table of matches, using .EACHI to keep from loading too much into mem
test <- dtree[dtree_self, on = .(height >= thresh2,
height <= thresh1,
species,
plot),
.(id2, id[{z = eucdist(x,i.x,y,i.y); mz <- min(z[id2 != id]); mz == z}]),
by = .EACHI,
nomatch = NA,
allow.cartesian = TRUE]
# join the metadata back onto each id
test <- dtree[test, on = .(id = V2), nomatch = NA]
test <- dtree[test, on = .(id = id2), nomatch = NA]
> test
id x y height species plot i.id i.x i.y i.height i.species i.plot i.height.2 i.height.1 i.species.1 i.plot.1
1: 1 0.17622235 0.66547312 84.68450 B 2 965 0.17410840 0.63219350 93.60226 B 2 74.68450 94.68450 B 2
2: 2 0.04523011 0.33813054 89.46288 B 2 457 0.07267547 0.35725229 88.42827 B 2 79.46288 99.46288 B 2
3: 3 0.24096368 0.32649256 103.85870 C 3 202 0.20782303 0.38422814 94.35898 C 3 93.85870 113.85870 C 3
4: 4 0.53160655 0.06636979 101.50614 B 1 248 0.47382417 0.01535036 103.74101 B 1 91.50614 111.50614 B 1
5: 5 0.83426727 0.55380451 101.93408 C 3 861 0.78210747 0.52812487 96.71422 C 3 91.93408 111.93408 C 3