R语言 simba包 rin()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-9-30 02:30:11

rin(simba)
rin()所属R语言包：simba

 Calculate multiple plot resemblance measures
 计算多个图相似措施

 译者：生物统计家园网机器人LoveR

描述----------Description----------

The functions calculate several multiple plot similarity measures. In addition rin provides a wrapper that allows for the easy calculation of multiple plot (site) resemblance measures in neighboorhoods in an automated fashion including testing whether the found resemblance patterns are significantly different from random.
函数计算数多图相似的措施。此外rin提供了一个包装，它允许多个图进行简单的计算（网站） R esemblance措施我 N Ñ eighboorhoods以自动化的方式，包括测试是否发现了相似的模式显着不同的随机。

用法----------Usage----------

mpd(x, method="simpson", all=FALSE)

mps(x, method="whittaker", all=FALSE)

mps.ave(x, method="soerensen", all=FALSE, foc=NULL,
what="mean", ...)

mos.f(x, foc, d.inc=FALSE, preso=FALSE, pc = NULL)

mos.ft(x, foc = NULL, method = "soerensen", quant = FALSE, binary = TRUE, ...)

sos(x, method="mean", foc=NULL, normal.sp=TRUE, normal.pl=TRUE)

rin(veg, coord, dn, func, test = TRUE, permutations = 100,
permute = 2, sfno = TRUE, p.level = 0.05, ...)

参数----------Arguments----------

参数：x
Species composition data, a matrix-like object.
物种组成数据，矩阵状的物体。

参数：method
Method for the calculation of multiple plot resemblance. The possible choices depend on the function used and include (among others) Simpson based multiple plot dissimilarity, S酶rensen based multiple plot dissimilarity, Nestedness based multiple plot dissimilarity, Whittaker's beta, additive partitioning, Harrison multiple plot similarity, Harrison multiple plot turnover, Williams multiple plot turnover, average pairwise similarity (with a similarity measure of your choice from sim), Diserud & 脴degaard multiple plot similarity. The methods of mos.f (a new group of multiple plot similarity measures) evolve from setting the arguments accordingly. For sos the choice is mean or foc. See details.
多图相似的计算方法。可能的选择取决于所使用的功能，并包括（除其他外）辛普森基于多图相异，索伦森基于多个图相异的嵌套基于多个图相异，惠特克的测试，添加剂分区，哈里森多个图相似，哈里森多个图营业额，威廉姆斯多个图营业额，平均成对相似（与您选择的sim），DiserudØdegaard多图相似的相似性度量。 mos.f（一组新的多图相似的措施）发展的方法设置相应的参数。对于sos的选择是mean或foc。查看详细信息。

参数：all
Logical. Depending on the function this argument has a different meaning. In mps and mpd it sets whether the results of all possible methods shall be given in the result, or only the method given in the method argument. Because some of the measures are just derived from others all methods are always calculated within the function when it is called and the method argument just triggers which to give back. In mps.ave it sets whether all statistics calculated (mean and sd) shall be given back or only the one specified by the what argument.
逻辑。根据不同的功能这种说法有不同的含义。在mps和mpd将所有可能的方法的结果是否应得到的结果，或者只method参数的方法。由于一些措施只是来自其他所有的方法都始终被计算在该函数被调用时，method参数触发来回馈。在mps.ave设置是否所有的统计计算（mean和sd）应给予或只有一个指定的what参数。

参数：foc
Character vector with length one or an integer specifying which one is the focal plot. Four of the functions are/can be sensitive to the species composition in the focal plot (mos.f, mos.ft, mps.ave, sos). The automation function rin is able to automatically derive the identity of the focal plot. Just set foc = foc in the func argumemt (see example). When the functions are used stand alone either the name of the plot in parenthesis or the index of the plot within the species matrix (x) has to be given.
字符向量长度为一个或一个整数，指定哪一个是重点图。四个功能/可敏感的物种组成的焦点图（mos.f，mos.ft，mps.ave，sos）。自动化功能rin是能够自动获得身份的焦点图。只需设置foc = focfunc实辨（见示例）。的功能，当单独使用的名称在括号中的图或索引内的物种矩阵的图（x）必须。

参数：what
For mps.ave, which statistic (mean or sd) should be given back? See details.
对于mps.ave，统计（mean或sd）应给予回来吗？查看详细信息。

参数：d.inc
Logical. Shall all species that are within veg but not within the plots that make up a neighborhood be regarded when computing mos.f. This setting dramatically changes the behaviour of mps.f because it then becomes a symmetric similarity coefficient. Defaults to FALSE so that an asymmetric multiple plot similarity coefficient is computed. Only makes sense when mos.f is applied within rin and changes nothing otherwise.
逻辑。应内veg但不会出现在了附近的图，使所有的物种，被视为计算mos.f。此设置极大地改变了行为，因为它的mps.f然后就变成了对称相似系数。默认为FALSE“这样非对称多图相似系数计算。才有意义，当mos.f内rin并没有改变什么，否则。

参数：preso
Logical. Shall a presence only version of mos.f be computed? Default is FALSE. See details.
逻辑。的存在唯一版本的mos.f计算？默认是FALSE。查看详细信息。

参数：pc
Numeric. Triggers whether pattern control is done in function mos.f. With pattern control (pc!=NULL) the similarity of the focal plot to the pooled surrounding plots is evaluated. Doing that assures that species which only occur in the focal plot are are only absent on the focal plot influence the resulting index value. With pc = 1 the binary variant is done, with pc > 1 a quantitative version is done. For details see Jurasinski et al. 2011.
数字。触发模式控制是否是在功能mos.f的。模式控制（pc！= NULL）汇集周边图的焦点图的相似性进行评估。这样做，以确保该物种的焦点图只发生在是唯一缺席的焦点图的影响力得到的索引值。 pc= 1的二进制变体，与pc> 1的定量版本完成。有关详细信息，请参阅：Jurasinski等。 2011年。

参数：quant
Logical. If TRUE use a quantitative index for calculating the similarity between the focal and the pooled surrounding plots.
逻辑。如果TRUE使用量化指标计算的焦点，合并周边图之间的相似性。

参数：binary
Logical. If TRUE pool the data for the surrounding the plots by taking the columns sums and correct the abundances on the focal plot by multiplying with the number of surrounding plots (to avoid a bias due to the area effect). If FALSE the data are pooled by taking the proportional columns sums and do no correction to the abundances of the focal plot.
逻辑。如果TRUE凝聚周边图通过采取的列款项的数据，并与邻近图（以避免由于区域效果的偏置）的数目乘以纠正的焦点的图上的丰度。如果FALSE的数据汇集通过采取比例列金额不改正的焦点图的丰度。

参数：normal.sp
In case of sos (sum of squares of species matrix, which is a measure of beta-diversity (Legendre et al. 2005)): Shall the result be normalized with respect to the number of species.
的情况下，sos（小号嗯 O F quares的物种矩阵，这是一个衡量的β-多样性（勒让德等人，2005））：相对于物种的数量的结果是否应该被归。

参数：normal.pl
In case of sos (sum of squares of species matrix, which is a measure of beta-diversity (Legendre et al. 2005)): Shall the result be normalized with respect to the number of plots.
的情况下，sos（小号嗯 O F quares的物种矩阵，这是一个衡量的β-多样性（勒让德等人，2005））的结果：应被归一相对于数目图。

参数：veg
Species composition data, a matrix-like object that is ought to be recorded in a regular array or a similar structure and that shall be divided into neighborhoods with a moving window so that each plot becomes the focal plot with a certain neighborhood of plots around for which the multiple plot resemblance measures are then calculated.
物种组成的数据，矩阵对象，它是应该被记录在一个普通的数组或类似的结构，而应分成一个移动的窗口，使每个图成为焦点的图具有一定附近的图周围的街区多图相似措施，然后计算出来的。

参数：coord
Spatial coordinates of the field plots where the data in veg comes from. The function expects a data.frame with two columns with the first column giving the x (easting) coordinate and the second giving the y (northing) coordinate in UTM or the like. These coordinates are used to calculate the neighborhoods within a moving window approach.
的田间小区中的数据蔬菜来自空间的坐标。函数需要一个data.frame两列，第一列给出的x坐标（东），第二次给予的Y（纵坐标）坐标的坐标，或等。这些坐标被用于计算内的移动窗口的方法的街区。

参数：dn
Distance to neighbors or neighbor definition. A positive numeric, a two value vector (also positive numeric), or a character string. In the first case it gives the distance from each sampling unit in m until which other sampling units should be seen as neighbours. In the second the two values define a ring around each plot. Plots that fall into the ring are considered as neighbors. In the third case, the character string defines the number of k nearest neighbors that should be regarded as the neighborhood. This being a character just triggers a different way to calculate the neighbors. See details.
距离邻居或邻居的定义。一个正数，两个值向量（正数），或一个字符串。在第一种情况下，它给了米，直到它从每个抽样单位在，其他抽样单位应被看作是邻居的距离。第二，这两个值定义每个小区周围环。陷入环的图，被认为是邻居。在第三种情况下，字符串定义k个最近邻的数目，应被视为邻近。这是一个字符只是触发不同的方式来计算的邻居。查看详细信息。

参数：func
A character string that defines the formula which shall be applied to calculate a multiple plot resemblance measure for all possible neighborhoods within an array. For instance "mpd(x)" to compute the Simpson multiple plot dissimilarity coefficient sensu Baselga (2010). See details.
一个字符串，它定义了应适用的公式计算多图相似的措施在阵列中所有可能的区域。例如"mpd(x)"计算辛普森多图相异系数意义上的巴塞尔加（2010年）。查看详细信息。

参数：test
Logical. Shall the significance of the calculated values of multiple plot resemblance be tested regarding its deviation from random expectations. Defaults to TRUE. See details.
逻辑。应进行测试的多个图的相似性的计算值的意义，就其从随机的期望偏差。默认为TRUE的。查看详细信息。

参数：permutations
The number of permutations run for testing the significance. Defaults to 100. And it is already slow. So test before you give much higher number of runs here.
的排列数运行测试的意义。默认为100。而现在已经是缓慢的。因此，测试之前，你给高得多的运行。

参数：permute
When testing with rin, how should the permutation of species to reflect random expectations be done: An integer of either 1, 2, or 3. With 1 the species matrix (veg) is permuted across rows. With 2 the species matrix (veg) is permuted across columns. With 3 the species in the focal plot are permuted (They are randomly drawn from the species pool).
测试时，rin，应该如何排列的品种，以反映随机期望做到：1，2或3的整数。 1种矩阵（veg）之间的置换行。 2的物种矩阵（veg）置换在列。 3的焦点图的物种被置换（从种库中随机抽取）。

参数：sfno
Species from neigborhood only? Logical, that is only be set in combination with permute = 3. If TRUE, than the species are only drawn at random from the neighboorhod species sub matrix. If set to FALSE, the species are drawn at random from the whole species matrix veg.
邻域内唯一的物种吗？逻辑，只设置结合permute= 3。如果TRUE，比种在从种子矩阵neighboorhod随机仅绘制。如果设置为FALSE，品种随机抽出品种全矩阵veg。

参数：p.level
Significance level below which the resemblance patterns shall be considered as significantly different from random expectations. Defaults to 0.05. Enables to give asteriks and stars in the results.
显着性水平以下的相似模式应被视为从随机的期望显着不同的。默认值为0.05。能够给星号和星星的结果。

参数：...
Further arguments to the workhorse functions mpd, mps, mps.ave, mos.f can be passed via ....
进一步的论据的主力功能mpd，mps，mps.ave，mos.f可以通过通过......

Details

详细信息----------Details----------

Several multiple plot similarity indices have been presented that cure some of the problems associated with the approaches for the calculation of compositional similarity for groups of plots by averaging pairwise similarities (Diserud and 脴degaard 2007, Baselga 2010). These indices calculate the similarity between more than two plots whilst considering the species composition on all compared plots. The resulting similarity value is true for the whole group of plots considered (called neighborhood in the following). Further, there are multiple plot similarity coefficients that are determined by the species composition on a reference plot (named focal plot in the following). All of these, can be calculated with the functions described in this help file. See vignette for an overview table. Further, the function rin takes all of them and provides a framework for applying the measures to an array of plots to calculate multiple plot resemblance in neighborhoods (Jurasinski et al. submitted).
已提出了几个多图相似性指数，治愈某些组图的平均成对相似（2007年Diserud和Ødegaard2010年，巴塞尔加）成分相似的计算方法相关的问题。这些指数的计算相比，图上所有的物种组成，同时考虑以上两个图之间的相似性。由此产生的相似性值是true为全团考虑的图（在下面的邻里关系）。此外，还有多个绘图参考图（名为图在下面的联络人）的物种组成上的相似系数所决定的。所有这些，可以计算出此帮助文件中描述的功能。概览表见暗角。此外，函数rin把所有的人都提供了一个框架的各项措施，到一个数组中的图来计算多个绘图 R esemblance 我 N N eighborhoods（Jurasinski等。提交）。

mps stands for multiple plot similarity, whereas mpd stands for multiple plot dissimilarity and mos stands for measure of singularity; the letters behind the "." further specifiy the class of measures that can be calculated with the respective function.
mps代表 ultiple 很多 imilarity的，而mpd代表为米米> ultiple 很多 D issimilarity和mos代表米 easure O F 小号 ingularity的字母后面的“。”，进一步指定类的措施，可以计算出相应的功能。

mps.ave calculates average multiple plot (dis-)similarities from pairwise (dis-)similarity calculations between the plots in the dataset or in the specified neighborhood. It has several options. With setting the foc argument different from NULL, only the pairwise (dis-)similarities between the specified focal plot and all others in the dataset (neighborhood) are taken to calculate the mean and sd from. When the specified focal plot is not existing, the function will issue a warning and stop. When run with defaults (foc = NULL), all pairwise similarities between the plots in the neighborhood (dataset) are considered. Any resemblance measure available via sim or sim.yo can be taken as base for calculating the average (dis-)similarity and its spread.
mps.ave计算 AVE 愤怒多个绘图（DIS）从成对相似（DIS）相似度计算中的数据集之间的图或在指定的区域。它有几个选项。随着foc不同NULL的参数，只有成对（DIS）指定的焦点图和所有其他数据集（街道）之间的相似性来计算mean和sd设置。当指定的焦点图是不存在的，该函数将发出警告，并停止。运行时的默认值（foc= NULL），在附近的图两两之间的相似性（数据集）。任何相似的措施可以通过sim或sim.yo的可以采取为基础计算的平均（DIS）的相似性和它的传播。

mps calculates multiple plot (dis-)similarities that are either derived from other approaches to beta-diversity calculation (Whittaker's beta, additive partitioning), or have been around for quite a while (Harrison multiple plot dissimilarity, Harrison multiple plot turnover, Williams multiple plot turnover). None of these considers the actual species composition on each of the compared plots. The following methods are available (n = number of plots, S = number of species, γ = gamma diversity (S_n), α = alpha diversity (S_i)):
mps计算 M ultiple 很多（DIS） imilarities是来自其他β-多样性计算方法（惠特克的测试，添加剂分区），或已经存在了相当长的一段时间（哈里森多个图不同，哈里森多个绘图营业额，威廉姆斯多个绘图营业额）。没有这些参考实际的物种在每个组合物的比较图。下面的方法是（N =号图，S =物种数量，γ = gamma diversity (S_n)，α = alpha diversity (S_i)）：

whittaker: Calculates Whittaker's beta (multiplicative partitioning, Whittaker 1960) β = γ/mean(α).
whittaker：计算Whittaker的β（乘分区1960年，惠特克）β = γ/mean(α)。

inverse.whittaker: Inverse Whittaker's beta (multiplicative partitioning). Scales between 1/n (when the considered plots do not share any species at all) and 1 (when all plots share the same species)
inverse.whittaker：逆惠特克的的测试（乘分区）。秤之间的1 / N（时所考虑的图不共享任何物种）和1（当所有图共享同一物种）

additive: Additive partitioning. Following Lande (1996) and keeping it with α = species number, the additive beta-component of the neighborhood (in the rin-case or the complete dataset in the mps-case) is calculated.
additive：添加剂分区。兰德（1996），并保持它。“α =物种数，添加剂β-组分的邻居（在rin的情况下，或在mps情况下完整的数据）的计算方法。

harrison: Harrison (1992) multiple plot dissimilarity. A transformation of Whittaker's beta to be bounded between 0 and 1 ((β_W-1)/(n-1).
harrison“：哈里森（1992年）的多个图相异。（(β_W-1)/(n-1)0和1之间是有界的一个转型，惠特克的beta。

diserud: Diserud & 脴degaard (2007) derived this from the pairwise S酶rensen similarity measure. However, as Baselga highlights, this can also be derived from Whittaker's beta (n - β_W)/(n-1) and is basically the same as Harrisons multiple plot dissimilarity but expressed as a similarity.
diserud：DiserudØdegaard（2007）推导出成对Sorensen相似性措施。然而，作为巴塞尔加的亮点，这也可以被来自惠特克的beta (n - β_W)/(n-1)基本上是相同的Harrisons多个绘图不同，但表示的相似。

harrison.turnover: ((γ/max(α))-1)/(n-1) (Harrison et al. 1992).
harrison.turnover：((γ/max(α))-1)/(n-1)（Harrison等人，1992）。

williams: 1 - max(α)/γ (Williams 1996).
williams：1 - max(α)/γ 1996年（威廉姆斯）。

mpd calculates multiple plot dissimilarity indices that have been suggested by Baselga (2010). The following methods are available (The implementation differs slightly from the one offered by Baselga in the electronic appendix of his paper and is computationally more efficient):
mpd计算 M ultiple 很多 D issimilarity由巴塞尔加（2010）已建议的指数。可用以下方法（执行略有不同，提供由巴塞尔加在他的论文的电子附件一和的计算更高效）：

simpson: mps.Sim in the following. Baselga et al. (2007) derive this multiple plot dissimilarity coefficient directly from the pairwise Simpson dissimilarity index by applying it to a group of plots/sites. The authors emphasize, that this coefficient is independent of patterns of richness and peforms better than the Diserud & 脴degaard cofficient in cases of unequal species numbers between plots, because it discriminates between situations in which shared species are distributed evenly among plots or concentrated in a few pairs of sites.
simpson：mps.Sim在下面。巴塞尔加等。（2007年）中获得多个小区相异系数直接从两两的辛普森相异指数的把它应用到一组图/网站。作者强调的是，这个系数是独立的模式，丰富和运行程比系数的DiserudØdegaard的图之间的不平等的物种数量的情况下，因为它区分的情况下共享物种均匀分布之间的图或集中在对网站。

sorensen: mps.Sor in the following. By building multiple site equivalents of the matching components (a, b, c) Baselga (2010) derives a S酶rensen based measure of multiple plot dissimilarity.
sorensen：mps.Sor在下面。通过建立多个站点当量的匹配元件（A，B，C）巴塞尔加（2010）推导出的索伦森基于指标的多个图相异。

nestedness: mps.nes in the following. Because the S酶rensen based multiple plot dissimilarity coefficient accounts for both spatial turnover and nestedness whilst the Simpson based multiple plot dissimilarity coefficient accounts only for spatial turnover, it is possible to calculate the multiple plot similarity that is completely due to nestedness by calculating mps.Sor - mps.Sim.
nestedness：mps.nes在下面。由于索伦森基于多个绘图相异系数占空间的营业额及嵌套性，而辛普森的多个图相异系数占营业额的空间，它是可以计算的，完全是因为嵌套性通过计算mps.Sor，多图相似 - mps.Sim。

mos.f calculates a focal measure of singularity. In contrast to the other functions the different outcomes can be triggered by setting the further arguments accordingly.
mos.f计算一个 F OCAL M easure O F ingularity。相反的其他功能，可以触发不同的结果，通过设置相应的进一步的论据。

The indices of mos.f change depending on the vegetation composition of the focal plot. The value is therefore true and valid only for the comparison of the focal plot with the surrounding plots. Not the similarity in the neighborhood, but the similarity of the focal plot to all others in the neighborhood is calculated. The calculation is based on the occurrences and non-occurrences of species on the compared plots with the species composition on the focal plot determining which of the two is to be used for which species: For all species that occur on the focal plot the proportional frequencies of occurrence in the neighborhood are summed up. For species that do not occur on the focal plot the proportional frequencies of non-occurrence in the neighborhood are summed up.
该指数的mos.f的不同而改变对植被组成的焦点图。因此，该值是真实，有效，与周边图的焦点图的比较。在附近的相似，但相似的焦点在附近其他所有的图计算。该计算是基于的焦点图，确定要使用其中的两个是与物种组成的比较图的事件和非物种出现的种：比例的频率上发生的所有物种的焦点图发生在附近的总结。对于物种的焦点图不会发生在比例不发生的频率在附近的总结。

with f_oi = proportional frequency of occurrences of the ith species on the compared plots, only carried out for species that do occur on the focal plot, f_nj = frequency of non-occurrences of the jth species on the compared plots, only carried out for species that do not occur on the focal plot). The frequencies are calculated against the total numbers of cells in the species matrix and are therefore 'proportional frequencies' (in analogy to 'proportional abundances' as in diversity indices like Shannon or Simpson). Thus, if all compared plots have an identical species composition, the resulting value of the multi-plot similarity coefficient is 1. In this rather hypothetical case the species presence absence matrix would be filled with ones only. This is the null model against which the 'proportional frequencies' are calculated. Therefore, the coefficient can be interpreted as a measure of deviation from complete uniformity. There are three versions.
与f_oi =比例出现的频率的第i个物种对与被比较的图中，仅进行了确实发生的焦点的图上的物种，f_nj =频率非出现的第j种上的比较图，只进行物种不发生对焦点的图）。的频率对种基质中的单元总数的计算，因此成正比的频率“（比喻比例丰”像香农·辛普森多样性指数）。因此，如果所有的比较图有一个相同的物种的组合物，所得到的值的多曲线的相似性系数是1。在这一点，而假设的情况下的物种存在的情况下将充满了矩阵的唯一。这是空的计算模型“比例的频率。因此，系数可以被解释作为衡量从完全的均匀性的偏差。有三个版本。

preso=TRUE: In this case a presence only version is calculated (mos.fpo). Therefore the second term is skipped and the formula simplifies to sum(f_oi). This very much glorifies the species composition on the focal plot and evaluates whether the surrounding plots in the neighborhood feature the same species.
preso=TRUE：在这种情况下，存在唯一的版本计算（mos.fpo）。因此，第二个任期将被跳过，公式可以简化为sum(f_oi)。这非常荣耀的焦点图的物种组成，并评估是否在邻居功能，同一品种的周边图。

d.inc=FALSE: When the d.inc argument is set to FALSE, only the species in the neighborhood build the basis against which the 'proportional frequencies' are calculated. This is the default index mos.f.
d.inc=FALSE：当d.inc参数被设置成FALSE，只有种在附近建设的基础上，对“比例的频率计算。这是默认的索引mos.f。

When run with defaults (preso = FALSE) and (d.inc = TRUE), a symmetric focal measure of siingularity (mos.fs) results. It is definetely meant for use in the context of rin. The 'proportional frequencies' are calculated against the whole species matrix. Thus, the index is a symmetric similarity coefficient sensu Legendre & Legendre 1998 that considers species that do not occur on the compared plots but in the whole data set. Therefore, it is more appropriate for biodiversity or conservation studies and not so much for the investigation of ecological relationships. However, it can be interpreted as an 'ordination on the spot': By calculating mos.fs for a focal plot against its surrounding plots its position along the main gradient according to its species composition is estimated immediately because the species composition in the rest of the data set is incorporated in the construction of the proportional frequencies of the species. Because of this, mos.fs can be interpreted as a measure of deviation from complete unity in species composition. When the neighborhood is increased to the full data set, mos.f and mos.fs converge.
与默认值（preso = FALSE）（d.inc = TRUE）， ymmetric F OCAL M easure强的运行时> O F iingularity（mos.fs）结果。它definetely的使用意味着在上下文中rin。对整个物种矩阵成正比的频率计算。因此，该指数是一个对称的相似性系数意义上的勒让德和勒让德1998年，认为不会发生在比较的图，但在整个数据集的物种。因此，它更适合于生物多样性保护研究并没有这么多的生态关系的调查。然而，它可以解释为一个协调当场：通过计算mos.fs的焦点及其周边图暗算估计其位置沿主梯度，根据其种类组成，因为立即物种组合物中数据集的其余部分结合在建设的物种的比例频率。正因为如此，mos.fs可以被解释作为衡量从完全统一物种组成的偏差。当附近上升到完整的数据集，mos.f和mos.fs收敛。

mos.ft calculates the singularity of a focal plot with respect to the pooled species composition on surrounding plots. Many binary or quantitave similarity indices can be used (all those that are available via sim and vegdist).
mos.ft计算就汇集周边图的物种组成一个焦点图的奇异性。许多二进制或quantitave的相似性指数可用于（所有这些可通过sim和vegdist）的。

sos calculates the sum of squares of a species matrix. Legendre et al. (2005) show, that this is a measure of beta-diversity. However, when you don't normalize against the number of species and/or plots the obtained values can hardly be compared across data sets (or neighborhoods). Therefore, its advisable to run this with defaults (normal.sp = TRUE and normal.pl = TRUE). For experiments, method can be set to "foc". Then, not the deviation from the mean of the species occurence across plots builds the basis, but the deviation from the situation on a focal plot. This makes it somewhat related to the mos.f-stuff.
sos计算 S UM O F quares的一个物种矩阵。勒等。（2005）显示，β-多样性，这是一个措施。然而，当你不标准化，对物种的数量和/或图所得到的值也难以进行比较整个数据集（或社区）。因此，建议在运行此默认值（normal.sp = TRUE和normal.pl = TRUE）。实验，method可以设置为"foc"。然后，整个图的平均物种发生“偏离建立的基础，但偏离的情况在焦点图。这使有点关系mos.f的东西。

rin applies the other functions to an array of plots. For each plot a neighborhood is constructed via the dn argument and the specified index is calculated for all plots and neighborhoods. The function to be calculated is specified simply by the func argument. For instance, with func = "mpd(x, method='sorensen')" the function rin calculates the S酶rensen multiple plot dissimilarity for each plot and its neighborhood in an array. The functions that need the identity of a focal plot (mps.ave, mos.f, and mos.ft) automatically derive the focal plots. However, to trigger this it has to be specified within the func argument: func = "mos.f(x, foc = foc)".
rin的其他功能应用到一个数组中的图。对于每一个图，一个社区的构建通过dn参数指定的指数的计算方法为所有的图和邻里。简单func参数指定的函数来计算。例如，func = "mpd(x, method='sorensen')"的功能rin计算索伦森阵列中的每个图和其附近的多个图不同。该功能需要身份的焦点图（mps.ave，mos.f和mos.ft）自动获得焦点图。然而，触发此内指定func参数：func = "mos.f(x, foc = foc)"。

值----------Value----------

The functions mpd, mps, mps.ave, mos.f, and mos.ft return a single value with the calculated index (according to the method argument, or to the other arguments). When all is set to TRUE, mps.ave returns two values (the average and the standard deviation of the pairwise similarities in the neighborhood), whereas mpd and mps return a named numerical vector with the values for all indices that can be calculated with the respective function.
的功能mpd，mps，mps.ave，mos.f和mos.ft返回单值的计算指数（根据method参数，或其他参数）。当all设置为TRUE，mps.ave返回两个值（平均值和标准偏差在附近的成对相似性），而mpd和mps 返回一个命名的数值向量的值，可以计算与各自的功能的所有索引。

rin gives back a table (data.frames), that reports several values for each plot in the dataset per row. The first three columns are always returned. In case test = TRUE, three more columns with information on the significance test are returned.
rin会返回一个表（data.frame），报告每一个图，每行的数据集的多个值。总是返回的第一个三列。的情况下test = TRUE，三个列上的显着性检验的信息返回。

参数：n.plots
Number of plots that make up the neighborhood.
图附近数。

参数：n.spec
Number of species that occur in the neighborhood.
在附近发生的物种数量。

参数：dis
Value of the calculated (dis)similarity index per plot.
价值计算（DIS）的相似性指数每块地。

参数：p.val
p value of the permutation test. According to the permute argument the data set is shuffled. The random data is subjected to the same calculations permutations times. The original value of multiple plot similarity is compared to the distribution of random values to obtain this p.
p值的置换试验。据permute参数的数据集被混洗。随机数据进行相同的计算permutations倍。多个绘图相似的原始值比的分布的随机值，得到该p。

参数：sig
Significance flag. Just a translation of the p value into a significance flag. There are only two possibilities: "*" value is significantly different from random, "ns" value is not significantly different from random.
显着性标志。就在翻译的P值的显着性标志。有只有两种可能性：“*”从随机的值是显着不同的，以“ns”值是没有显着不同的从随机。

参数：sig.sign
The sign of the significance value. The tail which is tested is determined by the relation of the multiple plot similarity value to the average multiple plot similarity value of the random test distribution. Thus, the sign shows whether the multiple plot similarity is significantly higher than can be expected from random expectations (+) of lower (-).
的重要性值的符号。尾巴测试由多个绘图相似度值的随机测试分布的平均多个绘图相似度值的关系决定的。因此，该标志示出是否多图的相似性是显着高于预期可以从随机的期望（+）的低级（-）。

注意----------Note----------

rin is not optimized and could perhaps profit from some C code. So when test = TRUE it takes a while because of the permutations.
rin是不是最优化的，也许从一些C代码中获利。因此，当test = TRUE还需要一段时间，因为排列。

（作者）----------Author(s)----------

Gerald Jurasinski <a href="mailto:gerald.jurasinski@uni-rostock.de">gerald.jurasinski@uni-rostock.de</a>, with contributions by Vroni Retzer <a href="mailto:vroni.retzer@gmx.de">vroni.retzer@gmx.de</a>

参考文献----------References----------

参见----------See Also----------

sim, vegdist, dsvdis for pairwise similarity measures.
sim，vegdist，dsvdis成对相似性措施。

实例----------Examples----------

## Not run: [＃不运行：]
# load the data that comes with the package[数据加载自带软件包]
data(abis)

# calculate a multiple plot similarity index [计算一个多图相似性指数]
# (S酶rensen sensu Baselga) for whole dataset[（索伦森意义上的巴塞尔加）对整个数据集]
mpd(abis.spec, method="sorensen")

# calculate a multiple plot similarity index[计算一个多图相似性指数]
# (S酶rensen sensu Baselga) for each plot and [（索伦森意义上的巴塞尔加），每个小区和]
# its neighborhood[其附近]
abis.mpd.so <- rin(abis.spec, coord=abis.env[,1:2],
dn=100, func="mpd(x, method='sorensen')")

# plot the grid of plots and show the calculated [绘制网格图显示计算出的]
# multiple plot dissimilarity value through the [通过多个图不同值]
# size of the symbol and the sign of the value[的符号和大小的值的符号]
# with a superimposed "+" or "-"[用叠加的“+”或“ - ”]
with(abis.mpd.so , {
plot(abis.env[,1:2], cex=symbol.size(dis), pch=c(21,1)[sig],
bg="grey50", xlab="", ylab="")
subs <- sig == "*"
points(abis.env[subs,1:2], pch=c("-", "+")[sig.prefix[subs]])
})

# calculate a multiple plot similarity index[计算一个多图相似性指数]
# that takes care of the species composition[需要照顾的物种组成]
# on the focal plot[的焦点图]
rin(abis.spec, coord=abis.env[,1:2], test=FALSE,
dn=100, func="mos.f(x, foc=foc)")

## End(Not run)[＃（不执行）]

转载请注明:出自生物统计家园网(http://www.biostatistic.net)。

注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。

账号		自动登录	找回密码
密码			注册

R语言 simba包 rin()函数中文帮助文档(中英文对照)

浏览过的版块