blocks|key|23354|text|如果你的问题是“为什么我会得到这个错误？”答案是，你正在尝试对一个矩阵进行子集，这是一个根据第三维的二维数组。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|23355|请参见：|23356|dim(lead)
#+[1]+26+2603
lead[,,6.418423]+#+yes,+that's+the+value+j+has+the+first+time+through+the+loop
#+This+will+reproduce+your+error
lead[,,1]
#+This+will+also+reproduce+your+error|code-block|syntax|javascript|23357|希望您现在能看到您有几个问题：|23358|你正在尝试根据一个三维矩阵对一个矩阵进行子集，你的i和j值分别是control和lead中的值。你可以使用它们作为它们的值，或者你可以生成索引，例如，for(i+in+seq_along(control)，如果你计划使用它来做其他事情，而不是得到相同的值。|offset|length|style|CODE|23359|将它带到下一步，不清楚你想要传递给dist函数的是什么。dist采用单个矩阵并计算其行之间的距离。您似乎正在尝试传递来自两个不同矩阵的两个值，或者两个不同矩阵的两个子集。看起来您可能需要返回并查看xtr|unordered-list-item|23360|
文档中的示例|23361|entityMap^0|0|0|0|0|P|1|R|1|W|7|14|4|23|R|0|H|4|S|4|2Q|3|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Y|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Z|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|10|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|11|8|@$M|12|N|13|O|P]|$M|14|N|15|O|P]|$M|16|N|17|O|P]|$M|18|N|19|O|P]|$M|1A|N|1B|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1C|8|@$M|1D|N|1E|O|P]|$M|1F|N|1G|O|P]|$M|1H|N|1I|O|P]]|9|@]|A|$]]|$1|T|3|U|5|6|7|1J|8|@]|9|@]|A|$]]|$1|V|3|-4|5|6|7|1K|8|@]|9|@]|A|$]]]|W|$]]

<p>If your question is "why am I getting this error?" the answer is that you're trying to subset a matrix, which is a two dimensional array, according to a 3rd dimension.</p>

<p>see:</p>

<pre><code>dim(lead)
# [1] 26 2603
lead[,,6.418423] # yes, that's the value j has the first time through the loop
# This will reproduce your error
lead[,,1]
# This will also reproduce your error
</code></pre>

<p>Hopefully you can see now that you have a few problems: </p>

<ol>
<li>You're trying to subset a matrix according to a 3rd dimension</li>
<li>Your <code>i</code> and <code>j</code> values are the values in <code>control</code> and <code>lead</code> respectively. You can use them as their values, or you can generate the index, e.g., <code>for(i in seq_along(control)</code> if you're planning to use it for something other than getting that same value out.</li>
<li>Taking it to the next step, it's unclear what you want to pass to the <code>dist</code> function. <code>dist</code> takes a single matrix and computes the distance between its rows. You seem to be trying to pass it two values from two different matrices, or perhaps two subsets of two different matrices. It looks like you might need to go back and look at the examples in the documentation for <code>xtr</code></li>
</ol>


blocks|key|21988|text|已经有类似于你的问题，但答案还不是太详细。以下是您需要了解的详细信息，在R的特定情况下。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|21989|计算交叉距离矩阵|21990|proxy软件包是专门为计算交叉距离矩阵而开发的。您应该检查它的小插曲，以了解它已经实现了哪些措施。它的用法示例如下：|offset|length|style|CODE|21991|set.seed(1L)
sample_data+<-+matrix(rnorm(50L),+nrow+=+5L,+ncol+=+10L)

suppressPackageStartupMessages(library(proxy))
distance_matrix+<-+proxy::dist(sample_data,+method+=+"euclidean",+
+++++++++++++++++++++++++++++++upper+=+TRUE,+diag+=+TRUE)
print(distance_matrix)
#>++++++++++1++++++++2++++++++3++++++++4++++++++5
#>+1+0.000000+2.636027+3.834764+5.943374+3.704322
#>+2+2.636027+0.000000+2.587398+4.515470+2.310364
#>+3+3.834764+2.587398+0.000000+4.008678+3.899561
#>+4+5.943374+4.515470+4.008678+0.000000+5.059321
#>+5+3.704322+2.310364+3.899561+5.059321+0.000000|code-block|syntax|javascript|21992|注意:在时间序列的上下文中，proxy将矩阵中的每个行视为一个序列，这可以从上面的sample_data是5x10矩阵以及产生的交叉距离矩阵是5x5的事实中得到证实。|BOLD|21993|使用DTW距离|21994|dtw包实现了DTW的许多变体，并且它还利用了proxy。您可以使用以下命令计算DTW距离矩阵：|21995|suppressPackageStartupMessages(library(dtw))
dtw_distmat+<-+proxy::dist(sample_data,+method+=+"dtw",+
+++++++++++++++++++++++++++upper+=+TRUE,+diag+=+TRUE)
print(distance_matrix)
#>++++++++++1++++++++2++++++++3++++++++4++++++++5
#>+1+0.000000+2.636027+3.834764+5.943374+3.704322
#>+2+2.636027+0.000000+2.587398+4.515470+2.310364
#>+3+3.834764+2.587398+0.000000+4.008678+3.899561
#>+4+5.943374+4.515470+4.008678+0.000000+5.059321
#>+5+3.704322+2.310364+3.899561+5.059321+0.000000|21996|使用自定义距离|21997|proxy的一个优点是它提供了注册自定义函数的选项。您似乎对DTW的规范化版本感兴趣，所以您可以这样做：|21998|ndtw+<-+function(x,+y+=+NULL,+...)+{
++++dtw::dtw(x,+y,+...,+distance.only+=+TRUE)$normalizedDistance
}

pr_DB$set_entry(
++FUN+=+ndtw,
++names+=+"ndtw",
++loop+=+TRUE,
++distance+=+TRUE
)

ndtw_distmat+<-+proxy::dist(sample_data,+method+=+"ndtw",
++++++++++++++++++++++++++++upper+=+TRUE,+diag+=+TRUE)
print(ndtw_distmat)
#>+++++++++++1+++++++++2+++++++++3+++++++++4+++++++++5
#>+1+0.0000000+0.4046622+0.5075772+0.6789465+0.5290478
#>+2+0.4046622+0.0000000+0.3630849+0.4866252+0.3612722
#>+3+0.5075772+0.3630849+0.0000000+0.5678698+0.3303344
#>+4+0.6789465+0.4866252+0.5678698+0.0000000+0.5078112
#>+5+0.5290478+0.3612722+0.3303344+0.5078112+0.0000000|21999|有关更多信息，请参阅pr_DB的文档。|22000|其他DTW实现|22001|dtwclust包(我制作的)实现了一个基本但速度更快的DTW版本，它可以使用多线程，还可以利用proxy|22002|suppressPackageStartupMessages(library(dtwclust))
dtw_basic_distmat+<-+proxy::dist(sample_data,+method+=+"dtw_basic",+normalize+=+TRUE)
print(dtw_basic_distmat)
#>++++++[,1]++++++[,2]++++++[,3]++++++[,4]++++++[,5]+++++
#>+[1,]+0.0000000+0.4046622+0.5075772+0.6789465+0.5290478
#>+[2,]+0.4046622+0.0000000+0.3630849+0.4866252+0.3612722
#>+[3,]+0.5075772+0.3630849+0.0000000+0.5678698+0.3303344
#>+[4,]+0.6789465+0.4866252+0.5678698+0.0000000+0.5078112
#>+[5,]+0.5290478+0.3612722+0.3303344+0.5078112+0.0000000|22003|dtw_basic实现只支持两个步骤模式和一个窗口类型，但它的速度要快得多：|22004|suppressPackageStartupMessages(library(microbenchmark))
microbenchmark(
++proxy::dist(sample_data,+method+=+"dtw",+window.type+=+"sakoechiba",+window.size+=+5L),
++proxy::dist(sample_data,+method+=+"dtw_basic",+window.size+=+5L)
)

Unit:+microseconds
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++expr++++++min+++++++lq+++++mean
+proxy::dist(sample_data,+method+=+"dtw",+window.type+=+"sakoechiba",++++++window.size+=+5L)+5279.124+5621.742+6070.069
++++++++++++++++++++++++++++proxy::dist(sample_data,+method+=+"dtw_basic",+window.size+=+5L)++657.966++710.418++776.474
+++median+++++++uq+++++++max+neval+cld
+5802.354+6348.199+10411.000+++100+++b
++752.282++814.037++1161.626+++100++a|22005|另一个多线程实现包含在parallelDist包中，尽管我没有亲自测试过它。|22006|多变量或多维时间序列|22007|单个多变量序列通常是一个矩阵，其中时间跨越行，多个变量跨越列。DTW也适用于它们：|22008|mv_series1+<-+matrix(rnorm(15L),+nrow+=+5L,+ncol+=+3L)
mv_series2+<-+matrix(rnorm(15L),+nrow+=+5L,+ncol+=+3L)
print(dtw_distance+<-+dtw_basic(mv_series1,+mv_series2))
#>+[1]+22.80421|22009|proxy的好处是，它还可以计算列表中包含的对象之间的距离，因此您可以在矩阵列表中放置几个多元序列：|22010|mv_series+<-+lapply(1L:5L,+function(dummy)+{
++matrix(rnorm(15L),+nrow+=+5L,+ncol+=+3L)
})

mv_distmat_dtwclust+<-+proxy::dist(mv_series,+method+=+"dtw_basic")
print(mv_distmat_dtwclust)
#>++++++[,1]+++++[,2]+++++[,3]+++++[,4]+++++[,5]++++
#>+[1,]++0.00000+27.43599+32.14207+36.42211+31.19279
#>+[2,]+27.43599++0.00000+20.88470+23.88436+29.73219
#>+[3,]+32.14207+20.88470++0.00000+22.14376+29.99899
#>+[4,]+36.42211+23.88436+22.14376++0.00000+28.81111
#>+[5,]+31.19279+29.73219+29.99899+28.81111++0.00000|22011|你的案例|22012|不管您选择什么，您都可以使用proxy获得结果，但是由于您没有提供全部数据，所以我不能给您提供更具体的示例。我假设dtwclust::dtw_basic(control[,+1:4],+lead[,+1:4],+normalize+=+TRUE)会给出一对序列之间的距离，假设你把每一对序列都看作是一个有4个变量的多变量序列。|22013|entityMap|0|LINK|mutability|MUTABLE|url|https://cran.r-project.org/package=proxy|1|https://cran.r-project.org/package=dtw|2|https://cran.r-project.org/package=dtwclust|3|https://cran.r-project.org/package=parallelDist^0|0|0|0|5|0|5|0|0|0|E|5|15|B|1H|4|1Z|3|Q|1|0|0|0|3|N|5|0|3|1|0|0|0|0|5|0|0|A|5|0|0|0|8|1C|5|0|8|2|0|0|0|9|0|0|B|C|B|C|3|0|0|0|0|0|5|0|0|0|E|5|1L|1U|0^^$0|@$1|2|3|4|5|6|7|29|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|2A|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|2B|8|@$F|2C|G|2D|H|I]]|9|@$F|2E|G|2F|1|2G]]|A|$]]|$1|J|3|K|5|L|7|2H|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|2I|8|@$F|2J|G|2K|H|I]|$F|2L|G|2M|H|I]|$F|2N|G|2O|H|I]|$F|2P|G|2Q|H|I]|$F|2R|G|2S|H|Q]]|9|@]|A|$]]|$1|R|3|S|5|6|7|2T|8|@]|9|@]|A|$]]|$1|T|3|U|5|6|7|2U|8|@$F|2V|G|2W|H|I]|$F|2X|G|2Y|H|I]]|9|@$F|2Z|G|30|1|31]]|A|$]]|$1|V|3|W|5|L|7|32|8|@]|9|@]|A|$M|N]]|$1|X|3|Y|5|6|7|33|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|34|8|@$F|35|G|36|H|I]]|9|@]|A|$]]|$1|11|3|12|5|L|7|37|8|@]|9|@]|A|$M|N]]|$1|13|3|14|5|6|7|38|8|@$F|39|G|3A|H|I]]|9|@]|A|$]]|$1|15|3|16|5|6|7|3B|8|@]|9|@]|A|$]]|$1|17|3|18|5|6|7|3C|8|@$F|3D|G|3E|H|I]|$F|3F|G|3G|H|I]]|9|@$F|3H|G|3I|1|3J]]|A|$]]|$1|19|3|1A|5|L|7|3K|8|@]|9|@]|A|$M|N]]|$1|1B|3|1C|5|6|7|3L|8|@$F|3M|G|3N|H|I]]|9|@]|A|$]]|$1|1D|3|1E|5|L|7|3O|8|@]|9|@]|A|$M|N]]|$1|1F|3|1G|5|6|7|3P|8|@$F|3Q|G|3R|H|I]]|9|@$F|3S|G|3T|1|3U]]|A|$]]|$1|1H|3|1I|5|6|7|3V|8|@]|9|@]|A|$]]|$1|1J|3|1K|5|6|7|3W|8|@]|9|@]|A|$]]|$1|1L|3|1M|5|L|7|3X|8|@]|9|@]|A|$M|N]]|$1|1N|3|1O|5|6|7|3Y|8|@$F|3Z|G|40|H|I]]|9|@]|A|$]]|$1|1P|3|1Q|5|L|7|41|8|@]|9|@]|A|$M|N]]|$1|1R|3|1S|5|6|7|42|8|@]|9|@]|A|$]]|$1|1T|3|1U|5|6|7|43|8|@$F|44|G|45|H|I]|$F|46|G|47|H|I]]|9|@]|A|$]]|$1|1V|3|-4|5|6|7|48|8|@]|9|@]|A|$]]]|1W|$1X|$5|1Y|1Z|20|A|$21|22]]|23|$5|1Y|1Z|20|A|$21|24]]|25|$5|1Y|1Z|20|A|$21|26]]|27|$5|1Y|1Z|20|A|$21|28]]]]

<p>There have already been questions similar to yours,
but the answers haven't been too detailed.
Here's a breakdown of what you need to know,
in the specific case of R.</p>

<h1>Calculating cross-distance matrices</h1>

<p>The <a href="https://cran.r-project.org/package=proxy" rel="noreferrer"><code>proxy</code></a> package is made specifically for the calculation of cross-distance matrices.
You should check its vignette to know which measures are already implemented by it.
An example of its use:</p>

<pre><code>set.seed(1L)
sample_data &lt;- matrix(rnorm(50L), nrow = 5L, ncol = 10L)

suppressPackageStartupMessages(library(proxy))
distance_matrix &lt;- proxy::dist(sample_data, method = "euclidean", 
                               upper = TRUE, diag = TRUE)
print(distance_matrix)
#&gt;          1        2        3        4        5
#&gt; 1 0.000000 2.636027 3.834764 5.943374 3.704322
#&gt; 2 2.636027 0.000000 2.587398 4.515470 2.310364
#&gt; 3 3.834764 2.587398 0.000000 4.008678 3.899561
#&gt; 4 5.943374 4.515470 4.008678 0.000000 5.059321
#&gt; 5 3.704322 2.310364 3.899561 5.059321 0.000000
</code></pre>

<p><em>Note</em>: in the context of time series,
<code>proxy</code> treats each <strong>row</strong> in a matrix as a series,
which can be confirmed by the fact that <code>sample_data</code> above is a <code>5x10</code> matrix and the resulting cross-distance matrix is <code>5x5</code>.</p>

<h1>Using the DTW distance</h1>

<p>The <a href="https://cran.r-project.org/package=dtw" rel="noreferrer"><code>dtw</code></a> package implements many variations of DTW,
and it also leverages <code>proxy</code>.
You could calculate a DTW distance matrix with:</p>

<pre><code>suppressPackageStartupMessages(library(dtw))
dtw_distmat &lt;- proxy::dist(sample_data, method = "dtw", 
                           upper = TRUE, diag = TRUE)
print(distance_matrix)
#&gt;          1        2        3        4        5
#&gt; 1 0.000000 2.636027 3.834764 5.943374 3.704322
#&gt; 2 2.636027 0.000000 2.587398 4.515470 2.310364
#&gt; 3 3.834764 2.587398 0.000000 4.008678 3.899561
#&gt; 4 5.943374 4.515470 4.008678 0.000000 5.059321
#&gt; 5 3.704322 2.310364 3.899561 5.059321 0.000000
</code></pre>

<h1>Using custom distances</h1>

<p>One nice thing about <code>proxy</code> is that it gives you the option to register custom functions.
You seem to be interested in the normalized version of DTW,
so you could do something like this:</p>

<pre><code>ndtw &lt;- function(x, y = NULL, ...) {
    dtw::dtw(x, y, ..., distance.only = TRUE)$normalizedDistance
}

pr_DB$set_entry(
  FUN = ndtw,
  names = "ndtw",
  loop = TRUE,
  distance = TRUE
)

ndtw_distmat &lt;- proxy::dist(sample_data, method = "ndtw",
                            upper = TRUE, diag = TRUE)
print(ndtw_distmat)
#&gt;           1         2         3         4         5
#&gt; 1 0.0000000 0.4046622 0.5075772 0.6789465 0.5290478
#&gt; 2 0.4046622 0.0000000 0.3630849 0.4866252 0.3612722
#&gt; 3 0.5075772 0.3630849 0.0000000 0.5678698 0.3303344
#&gt; 4 0.6789465 0.4866252 0.5678698 0.0000000 0.5078112
#&gt; 5 0.5290478 0.3612722 0.3303344 0.5078112 0.0000000
</code></pre>

<p>See the documentation of <code>pr_DB</code> for more information.</p>

<h1>Other DTW implementations</h1>

<p>The <a href="https://cran.r-project.org/package=dtwclust" rel="noreferrer"><code>dtwclust</code></a> package
(which I made)
implements a basic but faster version of DTW which can use multi-threading and also leverages <code>proxy</code>:</p>

<pre><code>suppressPackageStartupMessages(library(dtwclust))
dtw_basic_distmat &lt;- proxy::dist(sample_data, method = "dtw_basic", normalize = TRUE)
print(dtw_basic_distmat)
#&gt;      [,1]      [,2]      [,3]      [,4]      [,5]     
#&gt; [1,] 0.0000000 0.4046622 0.5075772 0.6789465 0.5290478
#&gt; [2,] 0.4046622 0.0000000 0.3630849 0.4866252 0.3612722
#&gt; [3,] 0.5075772 0.3630849 0.0000000 0.5678698 0.3303344
#&gt; [4,] 0.6789465 0.4866252 0.5678698 0.0000000 0.5078112
#&gt; [5,] 0.5290478 0.3612722 0.3303344 0.5078112 0.0000000
</code></pre>

<p>The <code>dtw_basic</code> implementation only supports two step patterns and one window type,
but it is considerably faster:</p>

<pre><code>suppressPackageStartupMessages(library(microbenchmark))
microbenchmark(
  proxy::dist(sample_data, method = "dtw", window.type = "sakoechiba", window.size = 5L),
  proxy::dist(sample_data, method = "dtw_basic", window.size = 5L)
)

Unit: microseconds
                                                                                        expr      min       lq     mean
 proxy::dist(sample_data, method = "dtw", window.type = "sakoechiba",      window.size = 5L) 5279.124 5621.742 6070.069
                            proxy::dist(sample_data, method = "dtw_basic", window.size = 5L)  657.966  710.418  776.474
   median       uq       max neval cld
 5802.354 6348.199 10411.000   100   b
  752.282  814.037  1161.626   100  a
</code></pre>

<p>Another multi-threaded implementation is included in the <a href="https://cran.r-project.org/package=parallelDist" rel="noreferrer"><code>parallelDist</code></a> package,
although I haven't personally tested it.</p>

<h1>Multivariate or multi-dimensional time series</h1>

<p>A single multivariate series is commonly a matrix where time spans the rows and the multiple variables span the columns.
DTW also works for them:</p>

<pre><code>mv_series1 &lt;- matrix(rnorm(15L), nrow = 5L, ncol = 3L)
mv_series2 &lt;- matrix(rnorm(15L), nrow = 5L, ncol = 3L)
print(dtw_distance &lt;- dtw_basic(mv_series1, mv_series2))
#&gt; [1] 22.80421
</code></pre>

<p>The nice thing about <code>proxy</code> is that it can calculate distances between objects contained in lists too,
so you can put several multivariate series in lists of matrices:</p>

<pre><code>mv_series &lt;- lapply(1L:5L, function(dummy) {
  matrix(rnorm(15L), nrow = 5L, ncol = 3L)
})

mv_distmat_dtwclust &lt;- proxy::dist(mv_series, method = "dtw_basic")
print(mv_distmat_dtwclust)
#&gt;      [,1]     [,2]     [,3]     [,4]     [,5]    
#&gt; [1,]  0.00000 27.43599 32.14207 36.42211 31.19279
#&gt; [2,] 27.43599  0.00000 20.88470 23.88436 29.73219
#&gt; [3,] 32.14207 20.88470  0.00000 22.14376 29.99899
#&gt; [4,] 36.42211 23.88436 22.14376  0.00000 28.81111
#&gt; [5,] 31.19279 29.73219 29.99899 28.81111  0.00000
</code></pre>

<h1>Your case</h1>

<p>Regardless of what you choose,
you can probably use <code>proxy</code> to get your result,
but since you haven't provided your whole data,
I can't give you a more specific example.
I presume that <code>dtwclust::dtw_basic(control[, 1:4], lead[, 1:4], normalize = TRUE)</code> would give you the distance between one pair of series,
assuming you're treating each one as a multivariate series with 4 variables.</p>


<p>I have two matrices of normalized read counts for control and treatment in a time series day1 to day26. I want to calculate distance matrix by Dynamic Time Wrapping afterward use that for clustering but seems too complicated. I did so; who can help for more clarification please? Thanks a lot</p>

<pre><code>&gt; head(control[,1:4])
               MAST2     WWC2  PHYHIPL   R3HDM2
Control_D1  6.591024 5.695156 3.388652 5.756384
Control_D1 8.043454 5.365221 6.859768 6.936970
Control_D3 7.731590 4.868267 6.919972 6.931073
Control_D4 8.129948 5.105528 6.627016 7.090268
Control_D5 7.690863 4.729501 6.824746 6.904610
Control_D6 8.101723 5.334501 6.868990 7.115883
&gt; 

&gt; head(lead[,1:4])
              MAST2     WWC2  PHYHIPL   R3HDM2
Lead30_D1  6.418423 5.610699 3.734425 5.778046
Lead30_D2 7.918360 4.295191 6.559294 6.780952
Lead30_D3 7.807142 4.294722 6.599187 6.716040
Lead30_D4 7.856720 4.432136 6.572337 6.848483
Lead30_D5 7.827311 4.204738 6.607107 6.784094
Lead30_D6 7.848760 4.458451 6.581216 6.943003
&gt;
&gt; dim(control)
[1]   26 2603
&gt; dim(lead)
[1]   26 2603
library(dtw)

for (i in control) { 
  for (j in lead) { 
    result[i,j] &lt;- dtw( dist(control[,,i],lead[,,j]), distance.only=T )$normalizedDistance 
  }
}
</code></pre>

<p>Says that </p>

<pre><code>Error in lead[, , j] : incorrect number of dimensions
</code></pre>


Calculating a distance matrix by dtw

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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