R语言 vegan包 dispindmorisita()函数中文帮助文档(中英文对照)

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dispindmorisita(vegan)
dispindmorisita()所属R语言包：vegan

                                        Morisita index of intraspecific aggregation
                                         种内聚集Morisita指数

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Calculates the Morisita index of dispersion, standardized index values, and the so called clumpedness and uniform indices.
计算Morisita指数的分散性，标准化的索引值，和所谓的clumpedness和均匀指数。


用法----------Usage----------


dispindmorisita(x, unique.rm = FALSE, crit = 0.05)



参数----------Arguments----------

参数：x
community data matrix, with sites (samples) as rows and species as columns.
社区数据矩阵的行和列的物种的网站（样本）。


参数：unique.rm
logical, if TRUE, unique species (occurring in only one sample) are removed from the result.
逻辑，TRUE如果，独特的物种发生在只有一个样本是从结果中删除。


参数：crit
two-sided p-value used to calculate critical Chi-squared values.
双面p值来计算关键的卡方值。


Details

详细信息----------Details----------

The Morisita index of dispersion is defined as (Morisita 1959, 1962):
Morisita分散指数被定义为（Morisita 1959年，1962年）：

Imor = n * (sum(xi^2) - sum(xi)) / (sum(xi)^2 - sum(xi))
Imor = n * (sum(xi^2) - sum(xi)) / (sum(xi)^2 - sum(xi))

where xi is the count of individuals in sample i, and n is the number of samples (i = 1, 2, …, n). Imor has values from 0 to n. In uniform (hyperdispersed) patterns its value falls between 0 and 1, in clumped patterns it falls between 1 and n. For increasing sample sizes (i.e. joining neighbouring quadrats), Imor goes to n as the quadrat size approaches clump size. For random patterns, Imor = 1 and counts in the samples follow Poisson frequency distribution.
xi是个人的计数样品中i，n是样本数（i = 1, 2, …, n）的。 Imor值从0到n。在制服（hyperdispersed）模式，其值介于0和1，在成群模式中，它介于1和n。为了增加样本大小（即加入邻近的样方），Imor去n样方大小的方法丛大小。 Imor = 1和计数的样本中随机模式，服从泊松频率分布。

The deviation from random expectation can be tested using critical values of the Chi-squared distribution with n-1 degrees of freedom. Confidence interval around 1 can be calculated by the clumped Mclu and uniform Muni indices (Hairston et al. 1971, Krebs 1999) (Chi2Lower and Chi2Upper refers to e.g. 0.025 and 0.975 quantile values of the Chi-squared distribution with n-1 degrees of freedom, respectively, for alpha = 0.05):
随机期望的偏离，可以使用n-1自由度的卡方分布的临界值进行测试。约1可以计算出的成群Mclu和均匀的Muni指数（1999年海尔斯顿等。1971年，克雷布斯）（Chi2Lower和Chi2Upper是指，例如位数的卡方值0.025和0.975的置信区间n-1自由度，分别分布，alpha = 0.05）：

Mclu = (Chi2Lower - n + sum(xi)) / (sum(xi) - 1)
Mclu = (Chi2Lower - n + sum(xi)) / (sum(xi) - 1)

Muni = (Chi2Upper - n + sum(xi)) / (sum(xi) - 1)
Muni = (Chi2Upper - n + sum(xi)) / (sum(xi) - 1)

Smith-Gill (1975) proposed scaling of Morisita index from [0, n] interval into [-1, 1], and setting up -0.5 and 0.5 values as confidence limits around random distribution with rescaled value 0. To rescale the Morisita index, one of the following four equations apply to calculate the standardized index Imst:
史密斯 - 吉尔（1975）提出缩放Morisita指数从[0，N]区间为[1,1]，-0.5和0.5周围随机分布的重新调整的值0值的置信区间。要重新定标的Morisita指标，适用于以下四个公式计算的标准化指数Imst：

(a) Imor >= Mclu > 1: Imst = 0.5 + 0.5 (Imor - Mclu) / (n - Mclu),
（一）Imor >= Mclu > 1的：Imst = 0.5 + 0.5 (Imor - Mclu) / (n - Mclu)，

(b) Mclu > Imor >= 1: Imst = 0.5 (Imor - 1) / (Mclu - 1),
（二）Mclu > Imor >= 1的：Imst = 0.5 (Imor - 1) / (Mclu - 1)，

(c) 1 > Imor > Muni: Imst = -0.5 (Imor - 1) / (Muni - 1),
（三）1 > Imor > Muni的：Imst = -0.5 (Imor - 1) / (Muni - 1)，

(d) 1 > Muni > Imor: Imst = -0.5 + 0.5 (Imor - Muni) / Muni.
（四）1 > Muni > Imor的：Imst = -0.5 + 0.5 (Imor - Muni) / Muni。


值----------Value----------

Returns a data frame with as many rows as the number of columns in the input data, and with four columns. Columns are: imor unstandardized Morisita index, mclu the clumpedness index, muni the uniform index, imst standardized Morisita index.
与输入数据中的尽可能多的行的列数，则返回一个数据框，并有四个列。栏目有：imor的非标准化Morisita指数，mclu clumpedness指数，muni的统一指数，imst标准化Morisita指数。


注意----------Note----------

A common error found in several papers is that when standardizing as in the case (b), the denominator is given as Muni - 1. This results in a hiatus in the [0, 0.5] interval of the standardized index. The root of this typo is the book of Krebs (1999), see the Errata for the book (Page 217, http://www.zoology.ubc.ca/~krebs/downloads/errors_2nd_printing.pdf).
在一些文件中发现一个常见的错误是，在规范的情况下（二），分母为Muni - 1。这样的结果在[0，0.5]标准化指数间隔间断。这根错字是克雷布斯（1999年）的书，这本书的勘误表，http://www.zoology.ubc.ca/（第217页~krebs/downloads/errors_2nd_printing.pdf）。


（作者）----------Author(s)----------


P茅ter S贸lymos, <a href="mailto:solymos@ualberta.ca">solymos@ualberta.ca</a>



参考文献----------References----------

analysis of the distributional patterns.  Mem. Fac. Sci. Kyushu Univ. Ser. E 2, 215&ndash;235.
Res. Popul. Ecol. 4, 1&ndash;7.
patterns in the leopard frog, Rana pipiens. II.  Wild type and mutant cell specific patterns. J. Morphol. 146, 35&ndash;54.
aggregation patterns. In: Patil, G. P., Pileou, E. C. and Waters, W. E. eds. Statistical Ecology 1: Spatial Patterns and Statistical Distributions. Penn. State Univ. Press, University Park.


实例----------Examples----------


data(dune)
x <- dispindmorisita(dune)
x
y <- dispindmorisita(dune, unique.rm = TRUE)
y
dim(x) ## with unique species[＃具有独特的物种]
dim(y) ## unique species removed[＃独特的物种删除]

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


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