clamtest(vegan)
clamtest()所属R语言包：vegan
Multinomial Species Classification Method (CLAM)
多项物种分类方法（CLAM）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The CLAM statistical approach for classifying generalists and specialists in two distinct habitats is described in Chazdon et al. (2011).
分类通才和专才在两个不同的栖息地的的CLAM统计方法描述在Chazdon等。 （2011年）。
用法----------Usage----------
clamtest(comm, groups, coverage.limit = 10, specialization = 2/3,
npoints = 20, alpha = 0.05/20)
## S3 method for class 'clamtest'
summary(object, ...)
## S3 method for class 'clamtest'
plot(x, xlab, ylab, main, pch = 21:24, col.points = 1:4,
col.lines = 2:4, lty = 1:3, position = "bottomright", ...)
参数----------Arguments----------
参数：comm
Community matrix, consisting of counts.
社区矩阵，由计数。
参数：groups
A vector identifying the two habitats. Must have exactly two unique values or levels.
一个向量，确定了两个栖息地。必须刚好有两个独特的价值或水平。
参数：coverage.limit
Integer, below this limit the sample coverage based correction is applied to rare species. Sample coverage is calculated separately for the two habitats. Sample relative abundances are used for species with higher than or equal to coverage.limit total counts per habitat.
整数，低于此限制，样品覆盖的校正适用于稀有物种。采样覆盖范围，分别计算两种不同生境。用于样品的相对丰度物种的高于或等于coverage.limit总计数每栖息地的。
参数：specialization
Numeric, specialization threshold value between 0 and 1. The value of 2/3 represents "supermajority" rule, while a value of 1/2 represents a "simple majority" rule to assign shared species as habitat specialists.
数字化，专业化的阈值在0和1之间。 2/3代表“绝对多数”规则，而1/2的值代表一个“简单多数”规则来分配共享的物种栖息地的专家。
参数：npoints
Integer, number of points used to determine the boundary lines in the plots.
整数，用于确定在图的边界线的点的数目。
参数：alpha
Numeric, nominal significance level for individual tests. The default value reduces the conventional limit of 0.05 to account for overdispersion and multiple testing for several species simultaneously. However, the is no firm reason for exactly this limit.
数字，名义显着性水平为单独的测试。的默认值的0.05差的多测试几个品种同时考虑到降低了传统的极限。然而，正是这种限制的原因是没有坚定的。
参数：x, object
Fitted model object of class "clamtest".
拟合模型对象的类"clamtest"。
参数：xlab, ylab
Labels for the plot axes.
图轴的标签。
参数：main
Main title of the plot.
主标题的图。
参数：pch, col.points
Symbols and colors used in plotting species groups.
绘制物种群所用的符号和颜色。
参数：lty, col.lines
Line types and colors for boundary lines in plot to separate species groups.
图独立的物种群体的边界线线类型和颜色。
参数：position
Position of figure legend, see legend for specification details. Legend not shown if position = NULL.
图例的位置，请参阅legend规范的详细信息。联想不显示，如果position = NULL。
参数：...
Additional arguments passed to methods.
额外的参数传递给方法。
Details
详细信息----------Details----------
The method uses a multinomial model based on estimated species relative abundance in two habitats (A, B). It minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori (Chazdon et al. 2011). Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalists; (2) habitat A specialists; (3) habitat B specialists; and (4) too rare to classify with confidence.
该方法使用一个基于多项式模型的估计两种生境的物种的相对丰度（A，B）。它最大限度地减少偏差，由于取样强度的差异在两个栖息地类型，以及在每个栖息地采样不足造成偏差。该方法允许一个强大的统计分类专家和通才的栖息地，珍稀物种，但不排除先验（Chazdon等2011）。基于用户定义的specialization的阈值，模型的分类，品种分为四组：（1）通才（2）生境A专家;（3）生境B的专家，以及（4）太罕见了分类有信心。
值----------Value----------
A data frame (with class attribute "clamtest"), with columns:
一个数据框（类属性"clamtest"），列：
Species: species name (column names from comm),
Species：种名（comm）列名，
Total_*A*: total count in habitat A,
Total_*A*：生境A中的总数，
Total_*B*: total count in habitat B,
Total_*B*：生境B中的总数，
Classes: species classification, a factor with levels Generalist, Specialist_*A*, Specialist_*B*, and Too_rare.
Classes：品种分类的一个因素与水平Generalist，Specialist_*A*，Specialist_*B*和Too_rare。
*A* and *B* are placeholders for habitat names/labels found in the data.
*A*和*B*是在数据中发现的栖息地名称/标签的占位符。
The summary method returns descriptive statistics of the results. The plot method returns values invisibly and produces a bivariate scatterplot of species total abundances in the two habitats. Symbols and boundary lines are shown for species groups.
summary方法返回的结果的描述性统计。 plot方法返回值不可见的，并产生两种不同生境的物种总丰度在双变量散点图。符号和边界线的物种群体。
注意----------Note----------
The code was tested against standalone CLAM software provided on the website of Anne Chao (http://chao.stat.nthu.edu.tw/softwarece.html); minor inconsistencies were found, especially for finding the threshold for 'too rare' species. These inconsistencies are probably due to numerical differences between the two implementation. The current R implementation uses root finding for iso-lines instead of iterative search.
反对独立的CLAM的安超（http://chao.stat.nthu.edu.tw/softwarece.html）在网站上提供的软件代码进行了测试，发现轻微不一致，特别是对发现的阈值“太罕见了”种。这些不一致可能是由于两种实现数值之间的差异。当前R实现使用寻根异行，而不是迭代搜索。
The original method (Chazdon et al. 2011) has two major problems:
原始的方法Chazdon等。（2011年）有两个主要问题：
It assumes that the error distribution is multinomial. This is a justified choice if individuals are freely distributed, and there is no over-dispersion or clustering of individuals. In most ecological data, the variance is much higher than multinomial assumption, and therefore test statistic are too optimistic.
它假定误差的分布是多元的。这是一个合理的选择，如果个人自由发布，有没有过度分散或聚类的个人。在大多数生态数据的方差是远高于多项式假设，因此检验统计量是过于乐观。
The original authors suggest that multiple testing adjustment for multiple testing should be based on the number of points (npoints) used to draw the critical lines on the plot, whereas the adjustment should be based on the number tests (i.e, tested species). The function uses the same numerical values as the original paper, but there is no automatic connection between npoints and alpha arguments, but you must work out the adjustment yourself.
原来的作者认为，多次测试调整，应根据多个测试点的数量（npoints）用于绘制在图上的临界线，而应根据调整数测试（即测试种）。该函数使用的原始文件相同的数值，但有没有npoints和alpha参数之间的自动连接，但你必须努力调整自己。
（作者）----------Author(s)----------
Peter Solymos <a href="mailto:solymos@ualberta.ca">solymos@ualberta.ca</a>
参考文献----------References----------
Letcher, S. G., Clark, D. B., Finegan, B. and Arroyo J. P.(2011). A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332–1343.
实例----------Examples----------
data(mite)
data(mite.env)
x <- clamtest(mite, mite.env$Shrub=="None", alpha=0.005)
summary(x)
head(x)
plot(x)
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。
|
发表于 2012-10-1 15:04:52
