H(vegetarian)
H()所属R语言包：vegetarian
'Standard Diversity Indices' for Alpha, Beta, and Gamma Diversities
“标准的多样性指数为α，β和γ多样性
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Jost describes the conversion of common indices to 'true' or 'standard' diversities (see Table 1 Jost (2006)). The H function calculates the alpha, beta, and gamma 'standard diversity indices.' Specification of different arguments allows for the calculation of species richness, Shannon entropy, Simpson concentration, Gini-Simpson Index, HCDT entropy, and Renyi entropy.
约斯特介绍了常见的指标转换为“真”或“标准”的多样性（见表1约斯特（2006））。 H函数计算出α，β，γ标准的多样性指数。不同的参数规格允许的物种丰富度，HCDT指数基尼辛普森，辛普森浓度，香农熵，熵和Renyi熵的计算。
用法----------Usage----------
H(abundances, lev = "alpha", wts = FALSE, q = 1, HCDT = FALSE, gini = FALSE, boot = FALSE, boot.arg = list(s.sizes = NULL, num.iter = 100))
参数----------Arguments----------
参数：abundances
Community data as a matrix where columns are individual species and rows are sites. Matrix elements are abundance data (e.g. counts, percent cover estimates).
社区数据列是一个矩阵，其中个别品种和行的网站。矩阵元素丰度数据（如计数，百分比盖的估计）。
参数：lev
Level of diversity to be calculated. Will accept: 'alpha,' 'beta,' or 'gamma.'
多样性水平来计算的。接受：“阿尔法”，“测试”，或“γ”。
参数：wts
A vector of community weights whose length equals the number of communities being compared. See details below for examples of when community weights might be treated as equal or unequal. Defaults to wts=FALSE where all communities are equally weighted.
社区的权重，其长度等于所比较的社区的数量的向量。查看详细资料社会的权重时，可能会被视为相等或者不相等的例子。默认WTS = FALSE，所有社区都同样加权。
参数：q
Order of the diversity measure. Defaults to the Shannon case where q = 1.
订购的多样性的措施。默认的Shannon情况下，q = 1时。
参数：HCDT
Logical statement. If HCDT = TRUE, then the HCDT or 'Tsallis' entropy is calculated (Keylock 2005, Czachor and Knaudts 2002). Defaults to HCDT = FALSE.
逻辑语句。如果HCDT = TRUE，然后HCDT或，Tsallis“熵计算（键锁2005年，2002年Czachor和Knaudts）。默认为HCDT = FALSE。
参数：gini
Logical statement. If gini = TRUE and q = 2, then the function calculates the Gini- Simpson index. Defaults to gini = FALSE.
逻辑语句。如果基尼= TRUE和q = 2，则该函数计算出的基尼Simpson指数。默认= FALSE基尼。
参数：boot
Logical indicating whether to use bootstrapping to estimate uncertainty.
逻辑指示是否使用自举到估计不确定性。
参数：boot.arg
(optional) List of arguments to pass bootstrapping function: list(s.sizes = number you specify, num.iter = number you specify)
（可选）的参数列表，通过自举功能：列表（s.sizes =您指定的号码，num.iter =您指定的号码）
Details
详细信息----------Details----------
Orders equal to zero, one, or two calculate species richness, Shannon entropy, and Simpson concentration, respectively. Calculating HCDT entropy requires that q is not equal to zero, one, or two.
订单等于0，一个或两个物种丰富度，Shannon熵，辛普森浓度计算。 HCDT熵计算要求，q是零个，一个或两个不相等。
Determining how to set community weights depends on the types of comparisons about communities that a researcher wants to make. For instance, Jost (2007) suggests that if species frequencies, but not the actual sizes of the communities are the topic of interest then communities should all be weighed equally. In contrast, community weights are usually unequal when the sizes of the communities has a large influence on the comparisons being made. For instance, when comparing the alpha and beta diversities of a landscape, using unequal community weights makes more sense.
确定如何设置社区的权重取决于类型的比较，研究人员希望的社区。例如，约斯特（2007）认为，如果物种的频率，而不是实际的尺寸的社区感兴趣的主题，然后社区都应该加以权衡同样。与此相反，社区权重通常不平等的社区的大小时，有很大的影响作出比较。例如，当α和β多样性的一道风景线，不平等的社会权重，更有意义。
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td>H.Value</td> <td> A scalar that gives the 'standard diversity index' of alpha, beta, or gamma diversity for the specified combination of 'order' (q), entropy or index, and community weights.</td></tr> <tr valign="top"><td>StdErr</td> <td> (optional) Standard error of value estimated through bootstrapping.</td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD> H.Value</ TD> <td>一个标量，给出了“标准的多样性指数的α，β，γ多样性指定组合的命令（Q），熵或指数，与社会权重。</ TD> </ TR> <tr valign="top"> <TD>StdErr</ TD> <TD （可选）通过自举的估计值的标准误差。</ TD> </ TR>
</table> ...
</ TABLE> ...
（作者）----------Author(s)----------
Noah Charney, Sydne Record
参考文献----------References----------
Jost, L. 2006. Entropy and diversity. Oikos 113(2): 363-375.
Jost, L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88(10): 2427-2439.
Hill, M. 1973. Diversity and evenness: A unifying notation and its consequences. Ecology 54: 427-432.
参见----------See Also----------
d bootstrap
dbootstrap
实例----------Examples----------
data(simesants)
H(simesants[,-1]) #remove column with site names; calculates alpha diversity of entire data-set[删除列与站点名称;计算整个数据集的α多样性]
hemlock<-subset(simesants,Habitat=="Hemlock")
H(hemlock[-1])#alpha diversity of just hemlock site[α多样性的只是铁杉网站]
H(simesants[,-1],lev="gamma", q=0)#species richness for entire data set[整个数据集的物种丰富度]
H(simesants[,-1],q=2,boot=TRUE)
H(simesants[,-1],q=2,boot=TRUE,boot.arg=list(num.iter=1000))
simesants_freq<-normalize.rows(simesants[,-1])#not a necessary step, but just to show what you would do if your data were in the form of frequencies, not counts[不是一个必要的步骤，但只是为了显示你会做什么，如果你的数据在频率的形式，而不是计算]
H(simesants_freq,q=2,boot=TRUE,boot.arg=list(s.sizes=c(198,186,102,108,187)))
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。
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发表于 2012-10-1 15:18:55
