mrpp(vegan)
mrpp()所属R语言包：vegan
Multi Response Permutation Procedure and Mean Dissimilarity Matrix
多响应置换过程和平均相异度矩阵
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Multiple Response Permutation Procedure (MRPP) provides a test of whether there is a significant difference between two or more groups of sampling units. Function meandist finds the mean within
多重响应组合程序（MRPP）提供一个测试的两个或两个以上的基团的取样单位是否有显着差异。函数meandist发现意味着在
用法----------Usage----------
mrpp(dat, grouping, permutations = 999, distance = "euclidean",
weight.type = 1, strata)
meandist(dist, grouping, ...)
## S3 method for class 'meandist'
summary(object, ...)
## S3 method for class 'meandist'
plot(x, kind = c("dendrogram", "histogram"), cluster = "average",
ylim, axes = TRUE, ...)
参数----------Arguments----------
参数：dat
data matrix or data frame in which rows are samples and columns are response variable(s), or a dissimilarity object or a symmetric square matrix of dissimilarities.
矩阵数据或数据框中的行样品和列是响应变量（s），或相异对象或对称方阵异同。
参数：grouping
Factor or numeric index for grouping observations.
因子或数字进行分组观察指标。
参数：permutations
Number of permutations to assess the significance of the MRPP statistic, delta.
的排列数，以评估的意义的MRPP统计，delta。
参数：distance
Choice of distance metric that measures the dissimilarity between two observations . See vegdist for options. This will be used if dat was not a dissimilarity structure of a symmetric square matrix.
选择距离度量的测量两次观测之间的差异性。见vegdist的选项。这将用于如果dat是不是一个相异结构的对称的正方形矩阵。
参数：weight.type
choice of group weights. See Details below for options.
组权重的选择。请参阅下面的详细信息的选项。
参数：strata
An integer vector or factor specifying the strata for permutation. If supplied, observations are permuted only within the specified strata.
一个整数向量或因素确定地层的置换。如果提供，观测置换仅在指定的阶层。
参数：dist
A dist object of dissimilarities, such as produced by functions dist, vegdist or designdist. </table>
Adist对象的不同点，例如由功能dist，vegdist或designdist。 </ TABLE>
参数：object, x
A meandist result object.
Ameandist的结果对象。
参数：kind
Draw a dendrogram or a histogram; see Details.
画树状图或直方图查看详细资料。
参数：cluster
A clustering method for the hclust function for kind = "dendrogram". Any hclust method can be used, but perhaps only "average" and "single" make sense.
一个聚类方法hclust功能，kind = "dendrogram"。任何hclust方法可以使用，但也许是唯一的"average"和"single"有意义。
参数：ylim
Limits for vertical axes (optional).
垂直轴（可选）限制。
参数：axes
Draw scale for the vertical axis.
为纵轴绘制规模。
参数：...
Further arguments passed to functions.
更多参数传递给函数。
Details
详细信息----------Details----------
Multiple Response Permutation Procedure (MRPP) provides a test of whether there is a significant difference between two or more groups of sampling units. This difference may be one of location (differences in mean) or one of spread (differences in within-group distance; cf. Warton et al. 2012). Function mrpp operates on a data.frame matrix where rows are observations and responses data matrix. The response(s) may be uni- or multivariate. The method is philosophically and mathematically allied with analysis of variance, in that it compares dissimilarities within and among groups. If two groups of sampling units are really different (e.g. in their species composition), then average of the within-group compositional dissimilarities ought to be less than the average of the dissimilarities between two random collection of sampling units drawn from the entire population.
多重响应组合程序（MRPP）提供一个测试的两个或两个以上的基团的取样单位是否有显着差异。这种差异可能是一个的位置（平均差异）或传播（在组内距离的差异，比照沃顿等人，2012）。功能mrpp在data.frame意见和响应数据矩阵的矩阵的行。响应（s）可能是单向或多元。方差分析的方法在哲学和数学联盟，因为它比较异同间和组内。如果真的是不同的两组抽样单位（例如，在他们的物种组成），然后平均的组内成分的不同点应该是平均不到两个随机抽样单位收集来自整个人口之间的不同。
The mrpp statistic δ is the overall weighted mean of within-group means of the pairwise dissimilarities among sampling units. The choice of group weights is currently not clear. The mrpp function offers three choices: (1) group size (n), (2) a degrees-of-freedom analogue (n-1), and (3) a weight that is the number of unique distances calculated among n sampling units (n(n-1)/2).
的的MRPP统计δ是组内的整体加权平均的方式抽样单位之间的两两相异。组权重的选择，目前尚不清楚。 mrpp功能提供了三种选择：（1）组的大小（n），（2）（n-1），和（3）的重量，是一个度的自由模拟数之间n的抽样单位计算的独特的距离（n(n-1)/2）。
The mrpp algorithm first calculates all pairwise distances in the entire dataset, then calculates δ. It then permutes the sampling units and their associated pairwise distances, and recalculates δ based on the permuted data. It repeats the permutation step permutations times. The significance test is the fraction of permuted deltas that are less than the observed delta, with a small sample correction. The function also calculates the change-corrected within-group agreement A = 1 -δ/E(δ), where E(δ) is the expected δ assessed as the average of dissimilarities.
mrpp算法先计算整个数据集的所有成对距离，然后计算δ。然后，它置换采样单元和其相关联的成对距离，并重新计算δ根据置换后的数据。它重复置换步骤permutations倍。显着性检验的比例置换Delta小于观测到的Delta，一个小样本校正。功能的变化也计算校正组内的协议A = 1 -δ/E(δ)，其中E(δ)是预期的δ评估的平均相异。
If the first argument dat can be interpreted as dissimilarities, they will be used directly. In other cases the function treats dat as observations, and uses vegdist to find the dissimilarities. The default distance is Euclidean as in the traditional use of the method, but other dissimilarities in vegdist also are available.
如果第一个参数dat可以解释为相异性，他们将被直接使用。在其他情况下的功能将dat的观察，并使用vegdist找到的异同。默认distance是欧氏在传统的使用方法，但其他vegdist的异同也可提供。
Function meandist calculates a matrix of mean within-cluster dissimilarities (diagonal) and between-cluster dissimilarities (off-diagonal elements), and an attribute n of grouping counts. Function summary finds the within-class, between-class and overall means of these dissimilarities, and the MRPP statistics with all weight.type options and the Classification Strength, CS (Van Sickle and Hughes, 2000). CS is defined for dissimiliraties as Bbar-Wbar, where Bbar is the mean between cluster dissimilarity and Wbar is the mean within cluster dissimilarity with weight.type = 1. The function does not perform significance tests for these statistics, but you must use mrpp with appropriate weight.type. There is currently no significance test for CS, but mrpp with weight.type = 1 gives the correct test for Wbar and a good approximation for CS. Function plot draws a dendrogram or a histogram of the result matrix based on the within-group and between group dissimilarities. The dendrogram is found with the method given in the cluster argument using function hclust. The terminal segments hang to within-cluster dissimilarity. If some of the clusters are more heterogeneous than the combined class, the leaf segment are reversed. The histograms are based on dissimilarites, but ore otherwise similar to those of Van Sickle and Hughes (2000): horizontal line is drawn at the level of mean between-cluster dissimilarity and vertical lines
函数meandist聚类内的不同点（对角线）和聚类之间的不同点（非对角（off-diagonal）元素）的平均值，计算一个矩阵和一个属性ngrouping计数。功能summary发现的类内类间和整体的这些差异，MRPP所有weight.type选项和分类强度，CS“（范镰刀和休斯，2000年）的统计数据。 CS被定义为dissimiliraties Bbar-Wbar，其中Bbar之间的平均聚类差异性和Wbar的平均聚类内差异性与weight.type = 1。该函数不执行这些统计的显着性检验，但你必须用mrpp适当weight.type的。目前还没有显着性检验CS，但mrppweight.type = 1给出了正确的测试Wbar和一个很好的近似CS。函数plot画树状图或直方图的结果矩阵组内和组之间异同的基础上。聚类分析发现在cluster使用功能hclust参数的方法。终端分部挂聚类内的差异性。如果一些聚类的综合类更多的异构，叶段是相反的。直方图基于的dissimilarites，但矿石否则范镰刀和Hughes（2000）相似：平均聚类间的水平差异性和垂直线被画在水平行
值----------Value----------
The function returns a list of class mrpp with following items:
该函数返回一个列表类MRPP与下列项目：
参数：call
Function call.
函数调用。
参数：delta
The overall weighted mean of group mean distances.
组的整体加权平均的平均距离。
参数：E.delta
expected delta, under the null hypothesis of no group structure. This is the mean of original dissimilarities.
预计Delta，没有组结构的零假设下。这是原始异同的平均值。
参数：CS
Classification strength (Van Sickle and Hughes, 2000). Currently not implemented and always NA.
分类实力“（范镰刀和休斯，2000年）。目前没有实施和总是NA的。
参数：n
Number of observations in each class.
在每个类的观测数。
参数：classdelta
Mean dissimilarities within classes. The overall δ is the weighted average of these values with given weight.type </table>
平均类的相异之内。的整体δ这些值的加权平均给定weight.type</ TABLE>
.
。
参数：Pvalue
Significance of the test.
测试的意义。
参数：A
A chance-corrected estimate of the proportion of the distances explained by group identity; a value analogous to a coefficient of determination in a linear model.
一个偶然的机会校正估计的距离的比例来解释群体认同的价值类似的线性模型系数的确定。
参数：distance
Choice of distance metric used; the "method" entry of the dist object.
选择使用的距离度量的“方法”项目的dist对象。
参数：weight.type
The choice of group weights used.
组权重的选择使用。
参数：boot.deltas
The vector of "permuted deltas," the deltas calculated from each of the permuted datasets.
的向量“置换Delta，”从每个置换后的数据集的计算差值。
参数：permutations
The number of permutations used.
所使用的排列的数量。
注意----------Note----------
This difference may be one of location (differences in mean) or one of spread (differences in within-group distance). That is, it may find a significant difference between two groups simply because one of those groups has a greater dissimilarities among its sampling units. Most mrpp models can be analysed with adonis which seems not suffer from the same problems as mrpp and is a more robust alternative.
这种差别可能是一个位置（均值的差异）或传播之一（在组内的距离的差异）。也就是说，它可能会发现一个显着的仅仅是因为其中一个组，两组间差异有较大的不同点在其抽样单位。大多数mrpp模型可以分析adonis似乎并没有受到同样的问题，mrpp是一个更强大的替代。
（作者）----------Author(s)----------
M. Henry H. Stevens <a href="mailto:HStevens@muohio.edu">HStevens@muohio.edu</a> and Jari Oksanen.
参考文献----------References----------
Communities. MjM Software Design, Gleneden Beach, Oregon, USA.
Distance Function Approach. Springer Series in Statistics. Springer.
ecoregions, catchments, and geographic clusters of aquatic vertebrates in Oregon. J. N. Am. Benthol. Soc. 19:370–384.
analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89–101
参见----------See Also----------
anosim for a similar test based on ranks, and mantel for comparing dissimilarities against continuous variables, and vegdist for obtaining dissimilarities, adonis is a more robust alternative in most cases.
anosim队伍的基础上，进行类似的测试和mantel比较异同，对连续变量，vegdist为获得不同点，adonis在大多数情况下，是一个更强大的替代品。
实例----------Examples----------
data(dune)
data(dune.env)
dune.mrpp <- mrpp(dune, dune.env$Management)
dune.mrpp
# Save and change plotting parameters[保存和改变绘图参数]
def.par <- par(no.readonly = TRUE)
layout(matrix(1:2,nr=1))
plot(dune.ord <- metaMDS(dune), type="text", display="sites" )
ordihull(dune.ord, dune.env$Management)
with(dune.mrpp, {
fig.dist <- hist(boot.deltas, xlim=range(c(delta,boot.deltas)),
main="Test of Differences Among Groups")
abline(v=delta);
text(delta, 2*mean(fig.dist$counts), adj = -0.5,
expression(bold(delta)), cex=1.5 ) }
)
par(def.par)
## meandist[＃meandist]
dune.md <- with(dune.env, meandist(vegdist(dune), Management))
dune.md
summary(dune.md)
plot(dune.md)
plot(dune.md, kind="histogram")
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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。
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发表于 2012-10-1 15:08:30
