multipart(vegan)
multipart()所属R语言包：vegan
Multiplicative Diversity Partitioning
乘法多样性分区
译者：生物统计家园网 机器人LoveR
描述----------Description----------
In multiplicative diversity partitioning, mean values of alpha diversity at lower levels of a sampling hierarchy are compared to the total diversity in the entire data set or the pooled samples (gamma diversity).
在分区乘多样性，α多样性水平较低的采样层次的平均值进行比较的总多样性在整个数据集或混合样品（伽玛多样性）。
用法----------Usage----------
multipart(...)
## Default S3 method:[默认方法]
multipart(y, x, index=c("renyi", "tsallis"), scales = 1,
global = FALSE, relative = FALSE, nsimul=99, ...)
## S3 method for class 'formula'[类formula的方法]
multipart(formula, data, index=c("renyi", "tsallis"), scales = 1,
global = FALSE, relative = FALSE, nsimul=99, ...)
参数----------Arguments----------
参数：y
A community matrix.
一个社区矩阵。
参数：x
A matrix with same number of rows as in y, columns coding the levels of sampling hierarchy. The number of groups within the hierarchy must decrease from left to right. If x is missing, two levels are assumed: each row is a group in the first level, and all rows are in the same group in the second level.
在yA矩阵相同的行数，列编码的水平采样层次结构。内的层次结构中的基团的数目，必须减小由左到右。如果x失踪，假设两个层次：每行的第一级是一组，在同一组中的第二个层次是和所有行。
参数：formula
A two sided model formula in the form y ~ x, where y is the community data matrix with samples as rows and species as column. Right hand side (x) must contain factors referring to levels of sampling hierarchy, terms from right to left will be treated as nested (first column is the lowest, last is the highest level). These variables must be factors in order to unambiguous handling. Interaction terms are not allowed.
一种双面模型公式的形式y ~ x，其中y是社会数据矩阵的行和列物种的样本。右手侧（x）必须包含的因素，指的是采样层次的水平，由右至左的条款将被视为嵌套（第一列是最低的，最后是最高级别）。这些变量必须以明确的处理因素。互动方面都是不允许的。
参数：data
A data frame where to look for variables defined in the right hand side of formula. If missing, variables are looked in the global environment.
一个数据框寻找定义的变量在右侧的formula。如果缺少，变量的研究在全球环境中。
参数：index
Character, the entropy index to be calculated (see Details).
字符，熵指数来计算（见详情）。
参数：relative
Logical, if TRUE then beta diversity is standardized by its maximum (see Details).
逻辑，如果TRUE然后beta多样性是其最大的标准化（见详情）。
参数：scales
Numeric, of length 1, the order of the generalized diversity index to be used.
数字，长度为1的，广义的多样性指数的顺序使用。
参数：global
Logical, indicates the calculation of beta diversity values, see Details.
逻辑，表明beta多样性价值的计算方法，请参阅详细信息。
参数：nsimul
Number of permutation to use if matr is not of class 'permat'. If nsimul = 0, only the FUN argument is evaluated. It is thus possible to reuse the statistic values without using a null model.
号码的排列使用，如果matr是不是类的permat“的。如果nsimul = 0，只有FUN参数进行评估。因此，它是可以重复使用的统计值，而无需使用一个空的模型。
参数：...
Other arguments passed to oecosimu, i.e. method, thin or burnin.
其他参数传递给oecosimu，即method，thin或burnin。
Details
详细信息----------Details----------
Multiplicative diversity partitioning is based on Whittaker's (1972) ideas, that has recently been generalised to one parametric diversity families (i.e. R茅nyi and Tsallis) by Jost (2006, 2007). Jost recommends to use the numbers equivalents (Hill numbers), instead of pure diversities, and proofs, that this satisfies the multiplicative partitioning requirements.
乘法多样性分区是基于惠特克（1972）的想法，最近被推广到一个参数多样性的家庭（即仁义和Tsallis）约斯特（2006年，2007年）。约斯特建议使用的数字当量（希尔号码），而不是纯粹的多样性，和证明，这满足的乘法的分区要求。
The current implementation of multipart calculates Hill numbers based on the functions renyi and tsallis (provided as index argument). If values for more than one scales are desired, it should be done in separate runs, because it adds extra dimensionality to the implementation, which has not been resolved efficiently.
目前实施的multipart计算山号的功能的基础上，renyi和tsallis（提供作为index参数）。如果值超过一scales需要，应在单独运行，因为它的实施，一直没有得到解决，有效地增加了额外的维数。
Alpha diversities are then the averages of these Hill numbers for each hierarchy levels, the global gamma diversity is the alpha value calculated for the highest hierarchy level. When global = TRUE, beta is calculated relative to the global gamma value:
阿尔法多样性是这些山号的平均值为每一个层次的水平，全球伽玛多样性是最高层级的alpha值计算。当global = TRUE，β是相对于全球伽玛值计算：
when global = FALSE, beta is calculated relative to local gamma values (local gamma means the diversity calculated for a particular cluster based on the pooled abundance vector):
global = FALSE，β是相对于当地的伽玛值（当地的伽玛是指一个特定的聚类计算的基础上汇集丰富向量的多样性）计算：
where j is a particular cluster at hierarchy level i. Then beta diversity value for level i is the mean of the beta values of the clusters at that level, β_{i} = mean(β_{ij}).
j是一种特殊的聚类层次水平i。 beta多样性的价值水平i是的平均聚类的β值在这个水平，β_{i} = mean(β_{ij})。
If relative = TRUE, the respective beta diversity values are standardized by their maximum possible values (mean(β_{ij}) / β_{max,ij}) given as β_{max,ij} = n_{j} (the number of lower level units in a given cluster j).
如果relative = TRUE，各自的的beta多样性值是由它们的最大的可能的值（标准化mean(β_{ij}) / β_{max,ij}）给定为β_{max,ij} = n_{j}（较低级别的单元的数目，在一个给定的聚类j） 。
The expected diversity components are calculated nsimul times by individual based randomisation of the community data matrix. This is done by the "r2dtable" method in oecosimu by default.
个别的社会的随机数据矩阵的预期多样性组成部分的计算nsimul倍。这是通过"r2dtable"方法oecosimu默认情况下。
值----------Value----------
An object of class 'multipart' with same structure as 'oecosimu' objects.
对象的类具有相同结构为“oecosimu”对象“多重”。
（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----------
Oikos, 113, 363–375.
Ecology, 88, 2427–2439.
Taxon, 21, 213–251.
参见----------See Also----------
See adipart for additive diversity partitioning, hiersimu for hierarchical null model testing
见adipart添加剂多样性分区，hiersimu分层空模型试验
实例----------Examples----------
## NOTE: 'nsimul' argument usually needs to be >= 99[＃注：“nsimul”的说法，通常需要> = 99]
## here much lower value is used for demonstration[＃这里要低得多值用于演示]
data(mite)
data(mite.xy)
data(mite.env)
## Function to get equal area partitions of the mite data[＃函数来获得对螨数据的面积相等的分区]
cutter <- function (x, cut = seq(0, 10, by = 2.5)) {
out <- rep(1, length(x))
for (i in 2 length(cut) - 1))
out[which(x > cut[i] & x <= cut[(i + 1)])] <- i
return(as.factor(out))}
## The hierarchy of sample aggregation[＃样品汇聚的层次结构]
levsm <- data.frame(
l1=as.factor(1:nrow(mite)),
l2=cutter(mite.xy$y, cut = seq(0, 10, by = 2.5)),
l3=cutter(mite.xy$y, cut = seq(0, 10, by = 5)),
l4=cutter(mite.xy$y, cut = seq(0, 10, by = 10)))
## Multiplicative diversity partitioning[＃乘法多样性分区]
multipart(mite, levsm, index="renyi", scales=1, nsimul=19)
multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19)
multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, relative=TRUE)
multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, global=TRUE)
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 15:08:45
