nestedtemp(vegan)
nestedtemp()所属R语言包：vegan
Nestedness Indices for Communities of Islands or Patches
社区群岛或修补程序的嵌套指数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Patches or local communities are regarded as nested if they all could be subsets of the same community. In general, species poor communities should be subsets of species rich communities, and rare species should only occur in species rich communities.
修补程序或当地社区被视为嵌套，如果他们都可能是同一社区的子集。在一般情况下，应该是种贫困社区的物种丰富社区的子集，应该只发生在物种丰富的群落和珍稀物种。
用法----------Usage----------
nestedchecker(comm)
nestedn0(comm)
nesteddisc(comm, niter = 200)
nestedtemp(comm, ...)
nestednodf(comm, order = TRUE, weighted = FALSE)
nestedbetasor(comm)
nestedbetajac(comm)
## S3 method for class 'nestedtemp'
plot(x, kind = c("temperature", "incidence"),
col=rev(heat.colors(100)), names = FALSE, ...)
参数----------Arguments----------
参数：comm
Community data.
社区数据。
参数：niter
Number of iterations to reorder tied columns.
绑列重新排序的迭代数。
参数：x
Result object for a plot.
结果对象的plot。
参数：col
Colour scheme for matrix temperatures.
基质温度的配色方案。
参数：kind
The kind of plot produced.
那种产生的图形。
参数：names
Label columns and rows in the plot using names in comm. If it is a logical vector of length 2, row and column labels are returned accordingly.
标签列和行中的图在comm使用的名称。如果它是一个逻辑的矢量长度为2，行和列标签返回相应。
参数：order
Order rows and columns by frequencies.
订购的行和列的频率。
参数：weighted
Use species abundances as weights of interactions.
物种丰度作为权重的相互作用。
参数：...
Other arguments to functions.
其他函数的参数。
Details
详细信息----------Details----------
The nestedness functions evaluate alternative indices of nestedness. The functions are intended to be used together with Null model communities and used as an argument in oecosimu to analyse the non-randomness of results.
的嵌套函数的嵌套性的替代指标进行评估。该功能的目的是零示范社区一起使用oecosimu分析非随机性的结果作为参数使用。
Function nestedchecker gives the number of checkerboard units, or 2x2 submatrices where both species occur once but on different sites (Stone & Roberts 1990).
函数nestedchecker给人的的数量的棋盘单位，或2×2子矩阵，这两个物种出现一次，但在不同的地点（石罗伯茨1990年）。
Function nestedn0 implements nestedness measure N0 which is the number of absences from the sites which are richer than the most pauperate site species occurs (Patterson & Atmar 1986).
函数nestedn0实现的嵌套性措施N0的缺席更丰富的比最pauperate的网站种（1986年帕特森和阿特马尔）的网站是多少。
Function nesteddisc implements discrepancy index which is the number of ones that should be shifted to fill a row with ones in a table arranged by species frequencies (Brualdi & Sanderson 1999). The original definition arranges species (columns) by their frequencies, but did not have any method of handling tied frequencies. The nesteddisc function tries to order tied columns to minimize the discrepancy statistic but this is rather slow, and with a large number of tied columns there is no guarantee that the best ordering was found (argument niter gives the maximum number of tried orders). In that case a warning of tied columns will be issued.
函数nesteddisc实现差异指数是多少，应转移到填充的种的频率（Brualdi桑德森1999年）安排表中的一个行。安排物种的原始定义（列），其频率，但没有任何方法处理并列的频率。 nesteddisc函数试图订购绑列，以尽量减少差异的统计这是相当缓慢的，并有大量的绑列有没有保证，最好的顺序被发现（参数niter给出了最大的尝试的订单数）。在这种情况下，会发出警告绑列。
Function nestedtemp finds the matrix temperature which is defined as the sum of “surprises” in arranged matrix. In arranged unsurprising matrix all species within proportion given by matrix fill are in the upper left corner of the matrix, and the surprise of the absence or presences is the diagonal distance from the fill line (Atmar & Patterson 1993). Function tries to pack species and sites to a low temperature (Rodr铆guez-Giron茅s & Santamaria 2006), but this is an iterative procedure, and the temperatures usually vary among runs. Function nestedtemp also has a plot method which can display either incidences or temperatures of the surprises. Matrix temperature was rather vaguely described (Atmar & Patterson 1993), but Rodr铆guez-Giron茅s & Santamaria (2006) are more explicit and their description is used here. However, the results probably differ from other implementations, and users should be cautious in interpreting the results. The details of calculations are explained in the vignette Design decisions and implementation that you can read using functions vignette or vegandocs. Function nestedness in the bipartite package is a direct port of the BINMATNEST programme of Rodr铆guez-Giron茅s & Santamaria (2006).
函数nestedtemp发现的基体温度，它被定义为在配置矩阵的“意外”的总和。在安排令人吃惊矩阵内的所有物种比例矩阵填充的是在左上角的矩阵，存在或存在的令人惊讶的是，从填充线（阿特马尔帕特森1993年）的对角距离的。尝试收拾低温（·罗德里格斯·2006年Gironés和圣玛丽亚）的种类和网站功能，但是这是一个反复的过程，和不同的运行温度。功能nestedtemp也有一个plot方法，可以显示发病率或温度的惊喜。矩阵温度相当含糊地描述（阿塔玛帕特森1993年），但罗德里格斯Gironés和圣玛丽亚（2006）更明确的描述在这里使用。然而，结果可能不同于其他的实现，用户应谨慎解释结果。计算的细节的解释，你可以阅读使用的功能vignette或vignettevegandocs的设计决策和实施。功能nestednessbipartite包是直接端口的BINMATNEST方案·罗德里格斯·Gironés和圣玛丽亚（2006）。
Function nestednodf implements a nestedness metric based on overlap and decreasing fill (Almeida-Neto et al., 2008). Two basic properties are required for a matrix to have the maximum degree of nestedness according to this metric: (1) complete overlap of 1's from right to left columns and from down to up rows, and (2) decreasing marginal totals between all pairs of columns and all pairs of rows. The nestedness statistic is evaluated separately for columns (N columns) for rows (N rows) and combined for the whole matrix (NODF). If you set order = FALSE, the statistic is evaluated with the current matrix ordering allowing tests of other meaningful hypothesis of matrix structure than default ordering by row and column totals (breaking ties by total abundances when weighted = TRUE) (see Almeida-Neto et al. 2008). With weighted = TRUE, the function finds the weighted version of the index (Almeida-Neto & Ulrich, 2011). However, this requires quantitative null models for adequate testing.
函数nestednodf实现一个嵌套性指标的基础上重叠和降低填充（阿尔梅达-内托等人，2008）。一个矩阵来具有的最大程度的嵌套性根据这个度量需要两个基本性质：（1）完整的重叠1的自右至左的列和从下降到最多的行，和（2）减少边际总数之间的所有成对的列和所有行对。的嵌套统计的行的列（N columns）（N rows）分别评估，并结合整个矩阵（NODF）的。如果你设置order = FALSE，统计评价与当前矩阵订购允许其他有意义的假设测试的矩阵结构，而不是默认的排序方式由行和列的总数（打破由总丰度的关系时，weighted = TRUE）（见阿尔梅达托等，2008）。随着weighted = TRUE，函数发现的加权指数（阿尔梅达内托和乌尔里希，2011）版本的。然而，这需要足够的测试定量空模型。
Functions nestedbetasor and nestedbetajac find multiple-site dissimilarities and decompose these into components of turnover and nestedness following Baselga (2010). This can be seen as a decomposition of beta diversity (see betadiver). Function nestedbetasor uses S酶rensen dissimilarity and the turnover component is Simpson dissimilarity (Baselga 2010), and nestedbetajac uses analogous methods with the Jaccard index. The functions return a vector of three items: turnover, nestedness and their sum which is the multiple S酶rensen or Jaccard dissimilarity. The last one is the total beta diversity (Baselga 2010). The functions will treat data as presence/absence (binary) and they can be used with binary null models (see commsimulator). The overall dissimilarity is constant in all null models that fix species (column) frequencies ("c0"), and all components are constant if row columns are also fixed (e.g., model "quasiswap"), and the functions are not meaningful with these null models.
功能nestedbetasor和nestedbetajac发现多站点的不同点和分解这些成分的营业额和嵌套性巴塞尔加（2010）。这可以看出，作为分解的β多样性（见betadiver）。函数nestedbetasor使用索伦森相异的营业额的组成部分是“辛普森相异（巴塞尔加2010），和nestedbetajac使用类似的方法，Jaccard指数。函数返回一个矢量的三个项目：营业额，嵌套性，它们的和是多索伦森的Jaccard相异。最后一个是的beta多样性（巴塞尔加2010）。处理数据的功能，存在/不存在（二进制），他们可以使用二进制零模型（见commsimulator“）。在所有空的模型，修复品种（列）频率（"c0"），和所有的组件是恒定的，如果行的列也是固定的（例如，模型"quasiswap"），和功能的整体差异性是不变的这些零模型没有意义的。
值----------Value----------
The result returned by a nestedness function contains an item called statistic, but the other components differ among functions. The functions are constructed so that they can be handled by oecosimu.
由嵌套性函数返回的结果包含一个资料名为statistic，但其他组件的不同而有差异的功能。的功能构成，使他们能够通过以下来处理oecosimu。
（作者）----------Author(s)----------
Jari Oksanen and Gustavo Carvalho (<code>nestednodf</code>).
参考文献----------References----------
Gumar茫es, P.R., Loyola, R.D. & Ulrich, W. (2008). A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239.
computational approach for measuring nestedness using quantitative matrices. Env. Mod. Software 26, 173–178.
disorder in the distribution of species in fragmented habitat. Oecologia 96, 373–382.
components of beta diversity. Global Ecol. Biogeog. 19, 134–143.
and discrepancy. Oecologia 119, 256–264.
of insular mammalian faunas and archipelagos. Biol. J. Linnean Soc. 28, 65–82.
(2006). A new algorithm to calculate the nestedness temperature of presence-absence matrices. J. Biogeogr. 33, 924–935.
distributions. Oecologia 85, 74–79.
W. (1998). A comparative analysis of nested subset patterns of species composition. Oecologia 113, 1–20.
参见----------See Also----------
In general, the functions should be used with oecosimu which generates Null model communities to assess the non-randomness of nestedness patterns.
在一般情况下，应使用功能与oecosimu而产生空的模型社区评估非随机性嵌套性图案。
实例----------Examples----------
data(sipoo)
## Matrix temperature[＃矩阵温度]
out <- nestedtemp(sipoo)
out
plot(out)
plot(out, kind="incid")
## Use oecosimu to assess the non-randomness of checker board units[使用oecosimu评估的非随机性的棋盘单位]
nestedchecker(sipoo)
oecosimu(sipoo, nestedchecker, "quasiswap")
## Another Null model and standardized checkerboard score[＃另一个空模型和标准化的棋盘得分]
oecosimu(sipoo, nestedchecker, "r00", statistic = "C.score")
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注：
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发表于 2012-10-1 15:08:52
