sim.tmp(simba)
sim.tmp()所属R语言包：simba
Calculate binary similarity in time
计算二进制相似
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function applies one of 56 similarity measures for binary data to calculate compositional similarity of plots between time steps.
该功能适用于56二进制数据的相似性度量的计算的时间步长之间的成分相似的图。
用法----------Usage----------
sim.tmp(x, y, method = "soer", normalize = FALSE, adjust = TRUE, ...)
参数----------Arguments----------
参数：x
Vegetation data, either as matrix with rows = plots and columns = species, or as data.frame with first three columns representing plots, species and occurence information respectively. All further columns are dumped before calculation. Occurence is only considered as binary. If your list or matrix contains abundances or frequencies they are transformed automatically.
植被数据，无论是作为矩阵的行=图和列=种，或data.frame前三列分别代表的图，种类和发生的信息。所有其他的列倾倒之前计算。发生只考虑为二进制。如果您的列表或矩阵中的丰度或频率的自动转换。
参数：y
Same as x for time-step two.
同x时间步长2。
参数：method
One of 42 similarity measures for binary data. The function uses the same indices as sim. See details there. Per default soerensen similarity is calculated.
其中的42个二进制数据的相似性度量的。该函数使用相同的指数，sim。查看详细资料。每默认soerensen相似的计算方法。
参数：normalize
Logical value indicating whether the values for a, b and c which are calculated in the process should be normalized to 100% (per row, which means per plot comparison). If normalize = TRUE an asymmetric index must be chosen (for details see sim).
逻辑值，该值指示是否值a，b和c计算的过程中，应该被标准化为100％（每行，这意味着每图比较）。如果标准化= TRUE，必须选择不对称指数（有关详细信息，请参阅sim）。
参数：adjust
Do not change the default behaviour (TRUE) unless you know what you do. Would spare some calculation time if set to FALSE, when your species data do not need adjustment, which means that in both or all time steps, there are exactly the same species and the same plots. However in most cases it will be more convenient to rely on the function (see details).
，除非你知道你做什么，不要改变默认的行为（TRUE）。将腾出一些计算时间，如果设置为FALSE，当你种的数据并不需要调整，这意味着在两个或全部的时间步长，是完全一样的物种和相同图。然而，在大多数情况下，这将是更加方便依赖的功能（见详情）。
参数：...
Other arguments to sim
其他参数sim
Details
详细信息----------Details----------
If you compare species data among time steps there will be most likely different numbers of species (and often also different numbers of plots for which information is available). The function takes care of this and you can give any species matrices you want. If one plot is the same, it will calculate what changed on this plot. There will be an error message if no plot is shared. The function relies on plot and species names!! As in a database - they must be unique!!
如果你比较物种之间的数据的时间步长将最有可能不同的物种数量（往往也不同图的信息是可用的）。该功能需要照顾，你可以给你想要的任何物种矩阵。如果有一个图是一样的，它会计算是什么改变了这个图。共享将是一个错误消息，如果没有图。的功能依赖于图和种名！在数据库中 - 他们必须是唯一的！
值----------Value----------
Returns a named vector with the similarities for each site between time steps for each plot.
返回一个名为矢量的时间步骤，为每个小区每个站点之间的相似性。
（作者）----------Author(s)----------
Gerald Jurasinski <a href="mailto:gerald.jurasinski@uni-rostock.de">gerald.jurasinski@uni-rostock.de</a>
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
data(bernina)
## load included data[＃负载数据]
data(bernina)
## how species changed occurrence between first recording[＃物种如何改变发生之间的第一次记录]
## and last recording?[＃和最后的录音？]
# construct a species matrix that only contains the species[构建一个只包含的物种的物种矩阵]
# that occurred on the summits at the first recording[在第一次记录发生的首脑会议]
first <- veg[summits$year=="1907",]
first <- first[,colSums(first)>0]
# make right summit names[做出正确的首脑会议名称]
row.names(first) <- as.character(summits$summit[summits$year=="1907"])
# construct a species matrix that only contains the species[构建一个只包含的物种的物种矩阵]
# that occurred on the summits at the last recording[在最后的记录上发生的高峰]
last <- veg[summits$year=="2003",]
last <- last[,colSums(last)>0]
# make right summit names[做出正确的首脑会议名称]
row.names(last) <- as.character(summits$summit[summits$year=="2003"])
# calculate similarity between time steps[计算的时间步长之间的相似性]
sim.tmp(first, last)
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 2012-9-30 02:30:18
