new.ssa(Rssa)
new.ssa()所属R语言包：Rssa
Create a new SSA object
创建一个新的SSA对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Set up the SSA object and perform the decomposition, if necessary.
设置了SSA的对象，并且，如有必要，进行分解。
用法----------Usage----------
new.ssa(x,
L = (N + 1) %/% 2,
...,
kind = c("1d-ssa", "2d-ssa", "toeplitz-ssa"),
svd.method = c("nutrlan", "propack", "svd", "eigen"),
force.decompose = TRUE)
参数----------Arguments----------
参数：x
object to be decomposed. Vector for 1d SSA (or something which can be coerced to a vector), matrix-like object for 2d SSA.
反对被分解。 1D SSA（或可以强制转换为一个向量），矩阵对象为2D SSA的向量。
参数：L
integer, window length. Fixed to half of the series length by default. Should be vector of length 2 for 2d SSA
整数，窗口长度。固定默认情况下，该系列的长度的一半。应该是SSA的2D向量长度为2
参数：...
further arguments passed to decompose (e.g. neig).
通过进一步的论据decompose（例如neig）。
参数：kind
SSA method. This includes ordinary 1d SSA, 2d SSA and toeplitz variant of 1d SSA.
SSA的方法。这包括普通一维SSA，2D变种，维SSA SSA与Toeplitz。
参数：svd.method
singular value decomposition method. See 'Details' for more info.
奇异值分解法。的详细信息，请参阅“详细信息”。
参数：force.decompose
logical, if 'TRUE' then the decomposition is performed before return.
逻辑，如果TRUE，然后分解之前进行回报。
Details
详细信息----------Details----------
This is the main entry point to the package. This routine constructs the SSA object filling all necessary internal structures and performing the decomposition if necessary.
这是主要的入口点的包。此程序构建SSA对象，填写所有必要的内部结构进行分解，如果必要的。
The following implementations of the SSA method are supported (corresponds to different values of kind argument):
支持以下的SSA方法的实现（对应kind参数不同的值）：
Basic 1d SSA as described in Chapter 1 of (Golyandina at all, 2001).
基本1D SSA第1章（Golyandina，2001）。
2d SSA for decomposition of images and fields. See (Golyandina and Usevich, 2009) for more information.
2D SSA分解图像和领域的。更多信息，请参见的（Golyandina Usevich，2009年）。
Toeplitz variant of 1d SSA. See Section 1.7.2 in (Golyandina at all, 2001).
Toeplitz算子的变体一维SSA。请参见1.7.2节中（Golyandina在所有，2001年）。
Note that so-called M-SSA method (aka multichannel SSA) is a special version of 2d SSA with window length having one component set to one.
需要注意的是所谓的M-的SSA方法（又名多通道SSA）是一种特殊版本的2D SSA与窗口长度的一个组成部分，设置为1。
The main step of the SSA method is the singular decomposition of the so-called series trajectory matrix. Package provides several implementations of this procedure (corresponds to different values of svd.method) argument:
SSA的方法的主要步骤，是所谓的一系列轨迹矩阵的奇异值分解。软件包提供了几种实现此过程（对应于不同的值svd.method）的说法：
nutrlan Thick-restart Lanczos eigensolver which operates on cross-product matrix. This methods exploits the Hankel structure of the trajectory matrix efficiently and is really fast. The method allows the truncated SVD (only specifid amount of eigentriples to be computed) and the continuation of the decomposition. See
nutrlan厚重启的兰克泽斯eigensolver经营上跨产品矩阵。此方法利用汉克尔有效的轨迹矩阵结构，是真快。该方法允许截断奇异值分解（只有specifid eigentriples量的计算）和继续分解。看
propack SVD via implicitly restarted Lanczos bidiagonalization with partial reothogonalization. This methods exploits the Hankel structure of the trajectory matrix efficiently and is really fast. This is the 'proper' SVD implementation (the matrix of factor vectors are calculated), thus the memory requirements of the methods are higher than for nu-TRLAN. Usually the method is slightly faster that nu-TRLAN and more numerically stable. The method allows the truncated SVD (only specifid amount of eigentriples to be computed). See (Korobeynikov, 2010) for more
PROPACK奇异值分解，通过隐式重新启动的Lanczos双对角化部分reothogonalization。此方法利用汉克尔有效的轨迹矩阵结构，是真快。这是SVD实施“适当的”（系数向量矩阵的计算），因此对内存的需求的方法是高比为Nu-TRLAN的。通常采用的方法是速度稍快，Nu-TRLAN的数值上更稳定。该方法允许被截断的的（SVD计算只有specifid量eigentriples）。更多见（Korobeynikov，2010）
svd Full SVD as provided by LAPACK DGESDD routine. Neither continuation of the decomposition nor the truncated SVD is supported. The method does not assume anything
全SVD奇异值分解作为提供的LAPACK DGESDD的常规。的分解，也没有被截断的SVD对于继续被支持。该方法不作任何假设
eigen Full SVD via eigendecompsition of the cross-product matrix. In many cases faster than previous method, but still
特征全部SVD通过eigendecompsition的产品矩阵。在许多情况下，速度比以前的方法，但仍
Usually the new.ssa function tries to provide the best SVD implementation for given series length and the window size. In particular, for small series and window sizes it is better to use generic black-box routines (as provided by 'svd' and 'eigen' methods). For long series special-purpose routines are to be used.
通常情况下，new.ssa的函数试图提供最好的SVD实现给定序列的长度和窗口大小。特别地，对于小系列和窗口大小，最好是使用通用的黑箱例程（如所提供的“SVD”和“本征”方法）。对于长系列的特殊用途的例程被使用。
值----------Value----------
Object of class "ssa". The precise layout of the object is mostly meant opaque and subject to change in different version of the package.
类的SSA的对象。的精确布局的对象主要是指不透明，如有变更，在不同版本的软件包。
参考文献----------References----------
Time Series Structure: SSA and related techniques. Chapman and Hall/CRC. ISBN 1584881941
spectrum analysis: algorithm and elements of theory. In Matrix Methods: Theory, Algorithms, Applications. World Scientific Publishing, 450-474.
implementation of SSA. Statistics and Its Interface, Vol. 3, No. 3, Pp. 257-268
参见----------See Also----------
svd, decompose, ssa
svd，decompose，ssa
实例----------Examples----------
# Decompose 'co2' series with default parameters[分解的CO2系列使用默认参数]
s <- new.ssa(co2)
# Reconstruct the series, grouping elementary series 2, 3 and 4, 5.[重构系列，分组初级系列2，3和4，5。]
r <- reconstruct(s, groups = list(1, c(2,3), c(4,5)))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-28 22:20:52
