spe(spe)
spe()所属R语言包：spe
Implements the stochastic proximity embedding algorithm
实现随机接近嵌入算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Embeds an N dimensional dataset in M dimensions, such that distances (or similarities) in the original N dimensions are maintained (as close as possible) in the final M dimensions
在M维嵌入了一个N维的数据集，这样，在原始的N维的距离（或相似）保持（尽可能接近）在最终的M维
用法----------Usage----------
spe( coord, rcutpercent = 1, maxdist = 0,
nobs = 0, ndim = 0, edim,
lambda0 = 2.0, lambda1 = 0.01,
nstep = 1e6, ncycle = 100,
evalstress=FALSE, sampledist=TRUE, samplesize = 1e6)
参数----------Arguments----------
参数：coord
This should be a matrix with number of rows equal to the number of observations and number of columns equal to the input dimension. A data.frame may also be supplied and it will be converted to a matrix (so all names will be lost)
这应该是一个矩阵与数的观测值和输入维数等于列数等于行数。也可以提供，它会被转换成一个矩阵（因此，所有的名称将会丢失数据框）
参数：rcutpercent
This is the percentage of the maximum distance (as determined by probability sampling) that will be used as the neighborhood radius. Setting rcutpercent to a value greater than 1 effectively sets it to infinity.
这是，将被用作周边的半径的最大距离（所确定的概率抽样）的百分比。将rcutpercent设置到一个大于1的值，有效地将其设置为无穷大。
参数：maxdist
If you have alread calculated a mxaimum distance then you can supply it and probability sampling will not be carried out to obtain a maximum distance. The default is to carry out sampling. By setting maxdist to a non zero value sampling will not be carried out (even if sampledist=TRUE)
如果你已经alread计算一个mxaimum的距离，然后你可以提供它并不能进行概率抽样，以获得最大的距离。默认情况下是进行取样。通过设置maxdist到一个非零值采样将不能进行（即使sampledist = TRUE）
参数：nobs
The number of observations. If it is not specified nobs will be taken as nrow(coord)
的若干意见。如果是未指定诺布斯将被视为NROW（经纬度）
参数：ndim
The number of input dimensions. If not specified it will be taken as ncol(coord)
输入尺寸的数量。如果没有指定，将被视为NCOL（经纬度）
参数：edim
The number of dimensions to embed in
的维数，以嵌入
参数：lambda0
The starting value of the learning parameter
学习参数的初始值
参数：lambda1
The ending value of the learning parameter
结束值的学习参数
参数：nstep
The number of refinement steps
细化步骤的数量
参数：ncycle
The number of cycles to carry out refinement for
的周期数进行细化为
参数：evalstress
If TRUE the function will evaluate the Sammon stress on the final embedding
如果为true，函数将计算扩展，且优于塞曼压力的最终嵌入
参数：sampledist
If TRUE an approximation to the maximum distance in the input dimensions will be obtained via probability sampling
如果为TRUE在输入尺寸的最大距离的近似值，将通过以下方式获得概率抽样
参数：samplesize
The number of iterations for probability sampling. For a dataset of 6070 observations there will be 6070x6069/2 pairwise distances. The default value gives a close approximation and runs fast. If you want a bettr approximation 1e7 is a good value. YMMV
迭代概率抽样的数量。对于6070的观测数据集会有6070x6069 / 2成对距离。从预设值非常接近，运行速度快。如果，你想bettr近似1E7是一个很好的价值。 YMMV
Details
详细信息----------Details----------
Efficient determination of rcut is yet to be implemented (using the connected component method). As a result you will have to determine a value of rcutpercent by trail and error. The pivot SPE method (J. Mol. Graph. Model., 2003, 22, 133-140) is not yet implemented
RCUT尚未实施（使用连接的组件的方法）的有效测定。因此，你必须确定一个值rcutpercent的线索和错误。尚未实现枢轴的SPE的方法（J.-走势。型号。，2003，22，133-140）
值----------Value----------
If evalstress is TRUE it will be a list with two components named x and stress. x is the matrix of the final embedding and stress is the final stress
如果是TRUE evalstress，这将是一个列表命名为x和压力两部分组成。 x是最终的嵌入和应力的矩阵是最后的应力
（作者）----------Author(s)----------
Rajarshi Guha <a href="mailto:rajarshi@presidency.com">rajarshi@presidency.com</a>
参考文献----------References----------
Stochastic Proximity Embedding, J. Comput. Chem., 2003, 24, 1215-1221 A Modified Rule for Stochastic Proximity Embedding, J. Mol. Graph. Model., 2003, 22, 133-140 A Geodesic Framework for Analyzing Molecular Similarities, J. Chem. Inf. Comput. Sci., 2003, 43, 475-484
参见----------See Also----------
eval.stress, sample.max.distance
eval.stress，sample.max.distance
实例----------Examples----------
## load the phone dataset[＃加载手机的数据集]
data(phone)
## run SPE, embed$stress should be 0 or very close to it[＃运行SPE，嵌入压力应为0或非常接近]
## You can plot the embedding using the scatterplot3d package[＃您可以绘制嵌入使用scatterplot3d包]
## (This will take a few minutes to run)[＃（这将需要几分钟的时间来运行）]
embed <- spe(phone, edim=3, evalstress=TRUE)
## evaluate the Sammon stress[评估的扩展，且优于塞曼压力]
stress <- eval.stress(embed$x, phone)
## embed the Swiss Roll dataset in 2D[＃嵌入的瑞士卷中的数据集的二维]
data(swissroll)
embed <- spe(swissroll, edim=2, evalstress=TRUE)
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 15:06:05
