makead(simba)
makead()所属R语言包：simba
Create artificial data set (species matrix).
创建人工数据集（物种矩阵）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The functions allow for the automated creation of artificial data (species matrix). The user can choose between random organization or a gradient. The gradient can be defined via a gradient vector which allows for fine tuning of the gradient. ads has a different implementation and produces better results for gradients.
该功能允许自动创建的的人工数据（物种矩阵）。用户可以选择，随机组织或一个梯度。的梯度可以通过定义允许微调的梯度的梯度向量。 ads有不同的实现，并为梯度产生更好的结果。
用法----------Usage----------
makead(nspec, nplots, avSR = NULL, anc = NULL, grad.v = NULL,
cf = 0.2, puq = 0.01)
ads(nspec, nplots, avSR = NULL, anc = NULL, grad.v = NULL,
reord = TRUE, cf = 0.2, puq = 0.01)
ads.hot(nspec, nplots, avSR = NULL, anc = NULL, grad.v = NULL,
frac=0.5, reord=TRUE, cf=0.2, puq=0.01)
ads.fbg(nspec, nplots, grad.v, n.iter = 100, method = "ads", ...)
参数----------Arguments----------
参数：nspec
Numbers of species you want to be in the data-set. Meaningless if anc != NULL.
物种数量你想成为在数据集。无意义的，如果anc！= NULL。
参数：nplots
Numbers of plots you want to be in the data-set. Meaningless if anc != NULL.
图中的数字，你想成为的数据集。无意义的，如果anc！= NULL。
参数：avSR
Average species richness. If anc is given, it is calculated from the data when the default is not changed. If avSR != NULL, the given value is taken instead. In the actual version not implemented in ads.
平均物种丰富度。如果anc，它的计算方法时，默认是不改变的数据。如果avSR！= NULL，给定的值，而不是。在实际的版本中没有实现ads。
参数：anc
If a model species matrix is available (either a real data-set, or another artificial data-set) on which creation should be based, give it here. Rows must be plots and columns be species. The first three parameters are then obtained from this set. However average species richness (avSR) can still be given by the user.
如果一个模型的物种矩阵是（一个真实的数据集，或其他人工数据集），应根据上创造的，在这里给它。行必须是图和列的物种。前三个参数，然后从这个组中获得。然而，平均物种丰富度（avSR）仍然可以由用户指定。
参数：grad.v
A numeric vector describing the gradient, or - in case of ads.hot - the hotspot. Must have the same length as nplots (or nrow(anc) respectively). See details.
描述一个数值向量的梯度，或者 - 在ads.hot - 的热点。 nplots（nrow(anc)分别）必须具有相同的长度。查看详细信息。
参数：cf
Determines the probability of the species to occur on the plots. In other words, it changes the shape of the species accumulation curve. Set to NULL if no natural species accumulation should be applied (may sometimes increase the visibility of the gradient)
确定发生物种的概率上的图。换句话说，它的变化的物种累积曲线的形状。设置为NULL，如果没有天然的物种积累应适用（有时可能会提高知名度的梯度）
参数：puq
Percentage of ubiquitous species. Set to NULL if the produced gradients seem to be unclear or if you don't want ubiquitous species to be in the data-set. Only used if a gradient vector is given (which is then not applied to the given percentage of species).
无处不在的物种百分比。如果生成的梯度似乎是不清楚的，或者如果你不想无处不在的物种是在数据集，设置为NULL。如果只使用给出的梯度矢量（然后不施加到的给定百分比的物种）。
参数：reord
Triggers reordering of the columns in the produced gradient matrix (see details). May considerably change the resulting matrix. Defaults to TRUE.
触发产生的梯度矩阵中的列重新排序（见详情）。可能大大改变产生的矩阵。默认为true。
参数：frac
Numeric between 0 and 1 giving the percentage of species which should occur on the hotspot-gradient only (see details).
数字0和1之间给予物种的百分比应该发生在仅的热点梯度（见详情）。
参数：n.iter
Number of iterations when ads.fbg is used for finding the species matrix representing best the prescribed gradient (see details).
迭代次数时ads.fbg用于发现的物种矩阵表示最好在规定的梯度（见详情）。
参数：method
Which method of makead, ads, ads.hot should be used?
makead，ads，ads.hot应该使用哪种方法？
参数：...
Further arguments to the function specified in method
进一步的论据到指定的method功能
Details
详细信息----------Details----------
There are three different implementations to create an artificial species matrix and a fourth function ads.fbg that allows to use either of the three possibilities to find a "best" gradient.
有三种不同的实现，以创建一个人造物种矩阵的第四个的功能ads.fbg会允许使用三种可能性，找到一个“最好”的梯度。
makead first applies the natural species accumulation curve, the gradient for each species is represented by a vector containing numerics between 0 and 1. Both matrices are added so that values between 0 and 2 result. Through an iteration procedure a breakvalue is defined above which all entries are converted to 1. Values below are converted to 0 resulting in a presence/absence matrix. However the random element seems to be too strong to get evident gradient representations.
makead任意适用于天然的物种累积曲线，为每个物种的梯度表示的一个向量，包含0和1之间的数值计算。这两个矩阵添加，以便在0和2的结果之间的值。一个breakvalue通过一个迭代过程，上述定义的所有条目转换为1。以下的值被转换为0，从而在存在/不存在矩阵。然而，似乎是太强大了明显的梯度表示的随机因素。
Therefore ads is implemented. It works different. First, a gradient is applied. As with makead the gradient is always applied in two directions so that half of the species are more likely to occur on plots on one side of the gradient, whereas the others are more likely to occur on the other side of the gradient. Subsequently, species occurrence for all species will oscillate around nplots/2.
因此ads实施。它的工作原理不同。首先，一个梯度被施加。正如带makead的梯度总是施加在两个方向上，使一半的物种是更可能发生图上的渐变的一侧上，而其他的是更可能发生在另一侧的梯度。随后，所有物种的物种出现振荡周围nplots / 2。
If puq is specified the given percentage of species is divided from the whole matrix before the gradient is applied. With the parameter cf a vector is produced representing quasi-natural occurrence of the species on the plots: Most species are rare and few species are very common. This is described by a power function y = 1/x^cf with x starting at 2 and gives a vector of length nspec representing the number of times each species is occuring.
如果puq被指定的给定百分比的物种从整个矩阵被划分前的梯度被施加。参数cf一个向量是代表半自然发生的物种的图：大多数物种是罕见的，很少有物种是很常见的。这是通过描述一个幂函数y = 1/x^cf与x从2开始，并给出了一个向量，长度为nspec每个物种是发生的次数表示的数目。
These numbers are applied to the gradient matrix and from the species occurrences only as many as specified by the respective number are randomly sampled. In cases were the occurrence number given by the vector exceeds the occurrences resulting from the gradient matrix, the species in the gradient matrix is replaced by a new one for which occurrence is not following the gradient and represents the number of occurrences given by the vector. The idea behind this is, that also in nature a species occuring on more than about half of the plots will likely be independent from a specific gradient.
这些号码被施加到的梯度矩阵和从种出现仅作为许多各自的数所指定的随机取样。在例，由该矢量超过由梯度矩阵产生的发生给定的发生数，在梯度矩阵的物种被替换发生之后的梯度由一个新的，并代表由矢量给出的发生数目。这背后的想法是，在自然界也超过约一半的图上发生一个物种很可能会从一个特定的梯度是独立。
In both cases (makead and ads) a totally random species matrix (under consideration of natural species occurrence, see cf) is obtained by randomly shuffling these occurrences on the columns (species) of the "natural species occurrence" matrix.
在这两种情况下（makead和ads）完全随机的物种矩阵（下考虑自然物种发生，看到cf）是通过随机洗牌这些事件的列（种） “天然物种出现”矩阵。
Contrarily to the other two functions, ads.hot allows for the creation of an artficial data-set including a hotspot of species richness and composition. In this case, frac can be used to specify which proportion of the total number of species should only occur on the hotspot gradient. All other species occur randomly on the plots. However, with the hotspot-gradient (grad.v) you can influence the explicitness of the hotspot.
相反的另两个功能，ads.hot允许创建的artficial数据集，包括物种丰富度和组成的一个热点。在这种情况下，frac可以用来指定总种数的比例应该只发生在热点梯度。所有其他物种上随机出现的图。然而，随着热点梯度（grad.v），你可以影响的明确性的热点。
The function ads.fbg allows for finding the best gradient representation with one of the above functions. A gradient is considered to be represented best, when the correlation between the first axis scores of a DCA (which is calculated with decorana of package vegan and the gradient positions as described by the gradient vector grad.v are maximized. ads.fbg just runs the specified makead function n.iter times and gives out the best result matrix and the r2.adjust value that has been obtained.
功能ads.fbg允许用于寻找最佳的梯度表示与上述功能之一。的梯度被认为是最好的表示，当相关性之间的第一轴线的DCA分数（这是计算与decorana软件包vegan和所描述的梯度向量的梯度位置<X >最大化。grad.v只运行指定的ads.fbg函数makead倍，并给出了最好的结果矩阵和n.iter值已获得的。
值----------Value----------
The three functions for creating an artificial species matrix each return a presence/absence species matrix with rows representing plots/sampling units and columns representing species. ads.fbg returns a list with
用于创建一个人工物种矩阵每个返回的存在/不存在的物种的矩阵的行代表图/抽样单位和列代表物种的三个功能。 ads.fbg返回一个列表
参数：mat
The species matrix as for the three artificial data set functions.
的物种矩阵的三个人工数据集的功能。
参数：r2.adj
The adjusted r2 value for the regression of the first axis DCA scores of the resulting species matrix against the position on the prescribed gradient as described by the gradient vector grad.v.
经调整后的回归所得的物种，如所描述的梯度向量grad.v对的位置上的规定的梯度矩阵的第一轴线DCA分数r2值。
（作者）----------Author(s)----------
Gerald Jurasinski, Vroni Retzer
实例----------Examples----------
## create a random data-set with 200 species on 60 plots[＃创建一个随机的数据集有200余种的60幅图]
artda <- makead(200, 60, avSR=25)
## create a gradient running from North to South (therefore you [＃建立一个从南到北的梯度运行（因此你]
## need a spatially explicit model of your data which is obtained [＃您的数据而得到需要的空间显式模型]
## with hexgrid())[＃hexgrid（））]
coor <- hexgrid(0, 4000, 200)
coor <- coor[order(coor$ROW),] #causes coordinates to be in order.[原因坐标是为了。]
## then the gradient vektor can easily be generated from the ROW names[＃然后的梯度VEKTOR可以很容易地从该行的名称产生]
gradvek <- as.numeric(coor$ROW)
## check how many plots your array has[＃检查数组有多少个图]
nrow(coor)
## create a data-set with 200 species[＃创建一个数据集有200种]
artda <- ads(200, 100, grad.v=gradvek)
## see the species frequency distribution curve[＃见的品种频率分布曲线]
plot(sort(colSums(artda)))
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。
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发表于 2012-9-30 02:29:39
