adipart(vegan)
adipart()所属R语言包：vegan
Additive Diversity Partitioning and Hierarchical Null Model Testing
添加剂多样性分区和分层空模型试验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
In additive 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 (gamma diversity). In hierarchical null model testing, a statistic returned by a function is evaluated according to a nested hierarchical sampling design (hiersimu).
添加剂多样性分区的α多样性水平较低的采样层次，平均值进行比较整个数据集的（伽玛多样性）的总多样性。在分层空模型试验，评估的统计函数的返回一个嵌套的分层抽样设计（hiersimu）。
用法----------Usage----------
adipart(...)
## Default S3 method:[默认方法]
adipart(y, x, index=c("richness", "shannon", "simpson"),
weights=c("unif", "prop"), relative = FALSE, nsimul=99, ...)
## S3 method for class 'formula'[类formula的方法]
adipart(formula, data, index=c("richness", "shannon", "simpson"),
weights=c("unif", "prop"), relative = FALSE, nsimul=99, ...)
hiersimu(...)
## Default S3 method:[默认方法]
hiersimu(y, x, FUN, location = c("mean", "median"),
relative = FALSE, drop.highest = FALSE, nsimul=99, ...)
## S3 method for class 'formula'[类formula的方法]
hiersimu(formula, data, FUN, location = c("mean", "median"),
relative = FALSE, drop.highest = 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 diversity index to be calculated (see Details).
字符，多样性指数来计算（见详情）。
参数：weights
Character, "unif" for uniform weights, "prop" for weighting proportional to sample abundances to use in weighted averaging of individual alpha values within strata of a given level of the sampling hierarchy.
字符，"unif"统一的重量，"prop"样品的丰度，地层中的一个给定的采样层次使用单独的alpha值加权平均的权重比例。
参数：relative
Logical, if TRUE then alpha and beta diversity values are given relative to the value of gamma for function adipart.
逻辑，如果TRUE然后α和β多样性相对于γ值为功能adipart。
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：FUN
A function to be used by hiersimu. This must be fully specified, because currently other arguments cannot be passed to this function via ....
一个要使用的功能hiersimu。这必须被完全指定，因为目前其他参数不能传递给该功能通过...。
参数：location
Character, identifies which function (mean or median) is to be used to calculate location of the samples.
字符，识别功能（平均值或中位数）是被用于计算样品的位置。
参数：drop.highest
Logical, to drop the highest level or not. When FUN evaluates only arrays with at least 2 dimensions, highest level should be dropped, or not selected at all.
逻辑，下降的最高水平或没有。当FUN只计算阵列，至少有2个维度，水平最高的应该被丢弃，或不选择的。
参数：...
Other arguments passed to functions, e.g. base of logarithm for Shannon diversity, or method, thin or burnin arguments for oecosimu.
其他的功能，例如参数传递碱基的多样性，或method，thin或burnin参数为oecosimu为底的对数。
Details
详细信息----------Details----------
Additive diversity partitioning means that mean alpha and beta diversity adds up to gamma diversity, thus beta diversity is measured in the same dimensions as alpha and gamma (Lande 1996). This additive procedure is than extended across multiple scales in a hierarchical sampling design with i = 1, 2, 3, …, m levels of sampling (Crist et al. 2003). Samples in lower hierarchical levels are nested within higher level units, thus from i=1 to i=m grain size is increasing under constant survey extent. At each level i, α_i denotes average diversity found within samples.
添加剂多样性分区表示α和β多样性的装置添加到γ多样性，从而beta多样性alpha和gamma（兰德1996）中的相同的尺寸测量。此添加剂程序是比第二端延伸跨过在一个分层抽样设计的多尺度i = 1, 2, 3, …, m水平采样（克里斯特等人，2003）。在较低的层次嵌套在上级单位的样品，因此，从i=1i=m粒度调查程度不断增加。在每个级别上i，α_i表示内发现样品的平均多样性。
At the highest sampling level, the diversity components are calculated as
在最高的采样电平，计算的多样性组件
For each lower sampling level as
对于每个较低的采样电平作为
Then, the additive partition of diversity is
然后，该添加剂的多样性是分区
Average alpha components can be weighted uniformly (weight="unif") to calculate it as simple average, or proportionally to sample abundances (weight="prop") to calculate it as weighted average as follows
可以加权平均alpha分量均匀（weight="unif"）简单平均计算，或按比例样品的丰度（weight="prop"）的加权平均来计算的话，如下
where D_{ij} is the diversity index and w_{ij} is the weight calculated for the jth sample at the ith sampling level.
D_{ij}是多样性指数和w_{ij}在j次采样的i个样本的权重计算的。
The implementation of additive diversity partitioning in adipart follows Crist et al. 2003. It is based on species richness (S, not S-1), Shannon's and Simpson's diversity indices stated as the index argument.
adipart如下克里斯特等添加剂多样性的实施分区。 2003年。它是基于物种丰富度（S不S-1），香农和辛普森多样性指数表示为index参数。
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。
hiersimu works almost the same as adipart, but without comparing the actual statistic values returned by FUN to the highest possible value (cf. gamma diversity). This is so, because in most of the cases, it is difficult to ensure additive properties of the mean statistic values along the hierarchy.
hiersimu的工作原理几乎相同adipart，但没有比较实际的统计值返回FUN可能的最高值（参见伽玛多样性）。这是如此，因为在大多数情况下，这是困难的，以确保添加剂沿层次结构中的平均统计值的属性。
值----------Value----------
An object of class 'adipart' or 'hiersimu' with same structure as 'oecosimu' objects.
对象类的adipart或hiersimu“的具有相同结构为”oecosimu的对象。
（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----------
K.S. (2003). Partitioning species diversity across landscapes and regions: a hierarchical analysis of <code>α</code>, <code>β</code>, and <code>γ</code>-diversity. Am. Nat., 162, 734–743.
diversity, and similarity among multiple communities. Oikos, 76, 5–13.
参见----------See Also----------
See oecosimu for permutation settings and calculating p-values.
见oecosimu排列设置，并计算p值的。
实例----------Examples----------
## NOTE: 'nsimul' argument usually needs to be >= 99[＃注意：“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$y, cut = seq(0, 10, by = 10)))
## Let's see in a map[＃让我们来看看图]
par(mfrow=c(1,3))
plot(mite.xy, main="l1", col=as.numeric(levsm$l1)+1)
plot(mite.xy, main="l2", col=as.numeric(levsm$l2)+1)
plot(mite.xy, main="l3", col=as.numeric(levsm$l3)+1)
par(mfrow=c(1,1))
## Additive diversity partitioning[＃添加剂多样性分区]
adipart(mite, index="richness", nsimul=19)
adipart(mite ~ ., levsm, index="richness", nsimul=19)
## Hierarchical null model testing[＃分层空模型试验]
## diversity analysis (similar to adipart)[＃多样性分析（类似adipart）]
hiersimu(mite, FUN=diversity, relative=TRUE, nsimul=19)
hiersimu(mite ~., levsm, FUN=diversity, relative=TRUE, nsimul=19)
## Hierarchical testing with the Morisita index[＃分层测试，Morisita指数]
morfun <- function(x) dispindmorisita(x)$imst
hiersimu(mite ~., levsm, morfun, drop.highest=TRUE, nsimul=19)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-10-1 15:02:25
