com.sim(simba)
com.sim()所属R语言包：simba
Compare mean similarity between subsets of data
比较平均的数据子集之间的相似性
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Related to mrpp. Are the differences in mean similarity between data subsets significant? Function takes the whole data-set (species matrix) and a subsetting vector and computes a specified similarity between all sampling units (rows). Then subsets are compared regarding their mean similarity. Statistical inference is obtained through permutation.
相关mrpp。平均的数据子集之间的相似性显著的差异？功能需要的整个数据集（物种矩阵）和一个子集的矢量和计算出一个指定的所有采样单元（行）之间的相似性。然后对他们的平均相似子集进行比较。统计推断是通过排列。
用法----------Usage----------
com.sim(veg, subs, simil = "soerensen", binary = TRUE,
permutations = 1000, alpha = 0.05, bonfc = TRUE, ...)
参数----------Arguments----------
参数：veg
Species matrix with columns = sites, rows = species. Deliver presence/absence data or abundance data. However, binary has to be set accordingly.
物种矩阵列的网站，行数=种。提供存在/不存在数据或丰度数据。然而，binary进行相应的设置。
参数：subs
Vector containing the subset definition. Same entries are understood to indicate belonging to the same subset (can be characters, factors or numerics). For each subset similarities/distances are calculated. Then all subsets are compared regarding mean and variance of the similarities/distances.
Vector，其中包含的子集定义。所理解的相同的条目，以指示属于相同的子集（可以是字符，因素或数值计算）。对于每个子集相似性/距离的计算。然后，所有的子集进行比较的相似/距离的均值和方差。
参数：simil
Sets the coefficient to be used for calculating similarities/distances. If binary = TRUE, see sim, otherwise see vegdist for possible choices.
设置系数要用于计算相似性/距离。如果binary= TRUE，看到sim，否则看到的vegdist可能的选择。
参数：binary
Changes the function used for the calculation of similarity/distance. If binary species data ist provided in veg keep the default (binary = TRUE). In this case sim is used to calculate the similarities. Set to FALSE when abundance or frequency data is provided. This calls vegdist to calculate the distances between sites in species similarity space.
更改的功能，用于计算的相似度/距离。如果二进制的物种数据IST提供veg保持默认值（binary= TRUE）。在这种情况下sim被用于计算的相似性。设置为false时，丰度或频率提供数据。这要求vegdist计算网站的物种相似性空间之间的距离。
参数：permutations
Number of permutations performed to obtain the statistical inference. See Details.
获得的统计推断的排列数。查看详细信息。
参数：alpha
Initial alpha level to test against. Defaults to 0.05.
初始α水平进行测试。默认值为0.05。
参数：bonfc
Shall Bonferroni correction be applied? Defaults to true.
应Bonferroni校正吗？默认为true。
参数：...
Further arguments to functions.
进一步函数的参数。
Details
详细信息----------Details----------
Entries of similarity/distance matrices are not independent. Therefore normal statistics might fail. One possibility is the application of permutation procedures. This means that the statistical distribution against which significance is tested is derived from the data.
作品相似度/距离矩阵不是独立的。因此，正常的统计可能会失败。一种可能性是在置换过程中的应用。这意味着，意义测试的统计分布对从数据中提取。
Here it is implemented as follows: For each subset the similarities/distances between all sites (plots) are calculated with the specified coefficient. Then the resulting similarity/distance matrices are compared with diffmean. This is done for the comparison of each subset with each other subset. If specified (defaults to TRUE), Bonferroni correction is applied (to correct for multiple testing).
它的实现如下：对于每个子集的相似网站（图）/之间的距离是指定的系数计算。你得到的相似度/距离矩阵进行比较diffmean。这样做是为了与每个其它子集的每个子集的比较。如果指定（默认为true），Bonferroni校正（纠正多个测试）。
Depending on the number of subsets and the number of sites per subset it may take some seconds to be computed.
根据子集的数量，并在每个子集的站点的数目，它可能需要几秒钟，以进行计算。
值----------Value----------
Returns an object of class cslist containing the call to the function, the used method for similarity/distance calculation, a comparison matrix showing the connections between data-subsets (rows and columns connected with "*" are significantly different), the number of subsets involved, the number of permutations and a matrix giving information about the following components for each comparison between subsets:
返回类的一个对象cslist包含调用该函数时，所使用的方法，比较矩阵计算相似度/距离显示数据的子集（带“*”的行和列之间的连接是显着不同的所涉及的子集），则数的排列数的矩阵给出有关下列内容的信息为每个子集之间的比较的以下组件：
参数：X
Subset identifier for one of the compared subsets
一个的比较的子集的子集标识符
参数：Y
Subset identifier for the other compared subset
相比的另一子集的子集标识符
参数：mean.x
Average distance/similarity for subset X.
平均距离/相似的子集十
参数：mean.y
Average distance/similarity for subset Y.
平均距离/相似的子集Y.
参数：diff
Difference in average distance/similarity for this comparison
进行这种比较的平均距离/相似性差异
参数：sig
Significance of the difference in mean of the similarities.
意义的差异在平均的相似之处。
参数：sigs
Significance flag for the comparison ("*" means significant differences, "ns" means that the differences are not significant).
显着性标志的比较（“*”是指显着的差异，以“ns”表示的差异不显着）。
参数：Fval
F-value for the Comparison.
的F值的比较。
参数：sigF
Is F significant?
是F显著吗？
参数：sigsF
Significance flag for F.
显着性标志，F.
（作者）----------Author(s)----------
Gerald Jurasinski
参见----------See Also----------
mrpp for an anova like approach for comparing the differences of species data subsets.
mrpp像方法方差分析比较不同物种的数据子集。
实例----------Examples----------
data(abis)
## see environmental data (see documentation on data for details)[＃环境数据（对数据的详细信息，请参阅文档）]
abis.env
## calculate the difference in similarities for the three major [＃计算相似的三个主要的区别在]
## vegetation types[＃植被类型]
## therefore create a vector from the data expressing belonging[＃因此，创建一个矢量数据表达属于]
## to the vegetation types:[＃植被类型：]
tcs.sub <- rep(0, 61)
tcs.sub[abis.env[,29]==1] <- 1
tcs.sub[abis.env[,30]==1] <- 2
tcs.sub[abis.env[,31]==1] <- 3
## calulate differences with Bray-Curtis as the distance measure[＃calulate布雷柯蒂斯距离测量的差异]
com.sim(abis.spec, tcs.sub, simil="bray", binary=FALSE)
## calculate differences with Soerensen as the similarity measure[＃计算差异的相似性度量与瑟伦森]
com.sim(abis.spec, tcs.sub)
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。
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发表于 2012-9-30 02:28:42
