wle.ar(wle)
wle.ar()所属R语言包：wle
Fit Autoregressive Models to Time Series - Preliminary Version
适合时间序列的自回归模型 - 初步版本
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This is a preliminary version of functions for the estimation of the autoregressive parameters via Weighted Likelihood Estimating Equations and a cassification algorithm. The main function is wle.ar, the remain functions are for internal use and they should not call by the users. They are not documented here.
这是一个初步的版本的功能，通过加权似然估计方程和cassification的算法的自回归参数的估计。其主要功能是wle.ar，其余的功能是供内部使用，不应要求的用户。他们不是记录在这里。
用法----------Usage----------
wle.ar(x, order=c(1, 0), seasonal=list(order = c(0, 0),
period = NA), group, group.start, group.step=group.start,
xreg=NULL, include.mean=TRUE, na.action=na.fail,
tol=10^(-6), tol.step=tol, equal=10^(-3), equal.step=equal,
raf="HD", smooth=0.0031, smooth.ao=smooth, boot=10,
boot.start=10, boot.step=boot.start, num.sol=1, x.init=0,
x.seasonal.init=0, max.iter.out=20, max.iter.in=50,
max.iter.start=200, max.iter.step=500, verbose=FALSE,
w.level=0.4, min.weights=0.5, population.size=10,
population.choose=5, elements.random=2, wle.start=FALSE,
init.values=NULL, num.max=NULL, num.sol.step=2, approx.w=TRUE)
参数----------Arguments----------
参数：x
a univariate time series.
一个单变量时间序列。
参数：order
a specification of the non-seasonal part of the ARI model: the two components (p,d) are the AR order and the degree of differencing.
一个规范的非季节性的ARI模型的两个组成部分(p,d)AR顺序和程度的差异。
参数：seasonal
a specification of the seasonal part of the ARI model, plus the period (which defaults to frequency(x)).
季节性的ARI模型的规范，再加上句点（默认为frequency(x)）。
参数：group
the dimension of the bootstap subsamples.
的维度的bootstap子样本。
参数：group.start
the dimension of the bootstap subsamples used in the starting process if wle.init=TRUE.
在起动过程中所用的维数的的bootstap子样本如果wle.init=TRUE。
参数：group.step
the dimension of the bootstap subsamples used in a step, it must be less than group.
的工序中使用的维数的的bootstap子样本，它必须小于group。
参数：xreg
optionally, a vector or matrix of external regressors, which must have the same number of rows as x.
任选地，向量或外部回归量矩阵，其中必须有相同数量的行作为x。
参数：include.mean
Should the ARI model include a mean term? The default is TRUE for undifferenced series, FALSE for differenced ones (where a mean would not affect the fit nor predictions).
ARI模型应该包括平均期限吗？默认值是TRUE非差系列，FALSE差的（平均不会影响的适合也预测）。
参数：na.action
function to be applied to remove missing values.
功能要施加到删除丢失的值。
参数：tol
the absolute accuracy to be used to achieve convergence of the algorithm.
要使用的绝对精度实现算法的收敛性。
参数：tol.step
the absolute accuracy to be used to achieve convergence in a step.
要使用的绝对精度在一个步骤中以达到收敛。
参数：equal
the absolute value for which two roots are considered the same. (This parameter must be greater than tol).
绝对的值，两个根被认为是相同的。 （此参数必须大于tol）。
参数：equal.step
the absolute value for which two roots are considered the same in a step. (This parameter must be greater than tol.step).
绝对值的两个根被认为是相同的一个步骤。 （此参数必须大于tol.step）。
参数：raf
type of Residual adjustment function to be use: raf="HD": Hellinger Distance RAF, raf="NED": Negative Exponential Disparity RAF, raf="SCHI2": Symmetric Chi-Squared Disparity RAF.
类型的残余调节功能，使用方法：raf="HD"：Hellinger距离RAF，raf="NED"负指数差异RAF，raf="SCHI2"：对称卡方差异RAF。
参数：smooth
the value of the smoothing parameter.
的平滑化参数的值。
参数：smooth.ao
the value of the smoothing parameter used in the outliers classificaton, default equal to smooth.
的值的的平滑参数离群点classificaton使用，，默认情况下，等于smooth。
参数：boot
the number of starting points based on boostrap subsamples to use in the search of the roots.
基于自举子样本的起点，使用在搜索的根的数目。
参数：boot.start
the number of starting points based on boostrap subsamples to use in the search of the roots in the starting process.
基于自举子样本的起点，使用在搜索在起动过程中的根的数目。
参数：boot.step
the number of starting points based on boostrap subsamples to use in the search of the roots in a step.
基于自举子样本的出发点，在一个步骤中的根在搜索中使用的数目。
参数：num.sol
maximum number of roots to be searched.
要搜索的最大根数。
参数：num.sol.step
maximum number of roots to be searched in a step.
在一个步骤中被搜索的根的最大数目。
参数：x.init
initial values, a vector with the same length of the AR order, or a number, default is 0.
初始值，一个向量的AR的顺序具有相同的长度，或一个数字，缺省值为0。
参数：x.seasonal.init
initial values, a vector with the same length of the SAR order, or a number, default is 0.
初始值，矢量特区秩序，或具有相同的长度，默认为0。
参数：max.iter.out
maximum number of iterations in the outer loop.
在外层循环迭代的最大数量。
参数：max.iter.in
maximum number of iterations in the inner loop.
在内部循环的迭代的最大数量。
参数：max.iter.start
maximum number of iterations in the starting process.
在起动过程中的迭代的最大数量。
参数：max.iter.step
maximum number of iterations in a step.
在一个步骤中的迭代的最大数量。
参数：w.level
the threshold used to decide if an observation could be an additive outlier.
使用的阈值，以决定是否观察可能是一个加性离群值。
参数：population.size
see details.
查看详细信息。
参数：population.choose
see details.
查看详细信息。
参数：elements.random
see details.
查看详细信息。
参数：num.max
maximum number of observations can be considered as possible additive outliers.
观测值的最大数目，可以被认为是可能的加性离群值。
参数：wle.start
if TRUE a weighted likelihood estimation is used to have a starting value.
如果TRUE加权似然估计有一个初始值。
参数：init.values
a vector with initial values for the AR and seasonal AR coefficients and the innovations variance.
AR和季节性AR系数和创新方差与初始值的矢量。
参数：verbose
if TRUE warnings are printed.
如果TRUE警告被打印出来。
参数：min.weights
see details.
查看详细信息。
参数：approx.w
logical: if TRUE an approximation is used to evaluate the weights in the outlier identification procedure.
逻辑：如果TRUE一个近似使用，以评估异常值识别过程中的权重。
Details
详细信息----------Details----------
min.weight: the weighted likelihood equation could have more than one solution. These roots appear for particular situation depending on contamination level and type. We introduce the min.weight parameter in order to choose only between roots that do not down weight everything. This is not still the optimal solution, and perhaps, in the new release, this part will be change.
min.weight：加权似然方程可以有一个以上的解决方案。这些根出现特定情况下，根据污染程度和类型。我们介绍了min.weight参数，以便选择唯一的根之间不下来的重量一切。这是不是仍然是最佳的解决方案，也许，在新版本中，这部分将有所改变。
The algorithm used to classify the observations as additive outliers is made by a genetic algorithm. The population.size, population.choose and elements.random are parameters related to this algorithm.
是由一个遗传算法进行分类观察，加性离群值所使用的算法。 population.size，population.choose和elements.random是这个算法的相关参数。
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td>coef</td> <td> a vector of AR and regression coefficients.</td></tr> <tr valign="top"><td>sigma2.coef</td> <td> the estimated variance matrix of the coefficients coef.</td></tr> <tr valign="top"><td>sigma2</td> <td> the WLE of the innovations variance.</td></tr> <tr valign="top"><td>arma</td> <td> a compact form of the specification, as a vector giving the number of AR, MA=0, seasonal AR and seasonal MA=0 coefficients, plus the period and the number of non-seasonal and seasonal differences.</td></tr> <tr valign="top"><td>resid</td> <td> the residuals.</td></tr> <tr valign="top"><td>resid.with.ao</td> <td> the residuals with the additive outliers effects.</td></tr> <tr valign="top"><td>resid.without.ao</td> <td> the residuals without the additive outliers effects.</td></tr> <tr valign="top"><td>x.ao</td> <td> the time series without the additive outliers effects.</td></tr> <tr valign="top"><td>call</td> <td> the matched call.</td></tr> <tr valign="top"><td>series</td> <td> the name of the series x.</td></tr> <tr valign="top"><td>weights</td> <td> the weights.</td></tr> <tr valign="top"><td>weights.with.ao</td> <td> the weights with the additive outliers effects.</td></tr> <tr valign="top"><td>weights.without.ao</td> <td> the weights without the additive outliers effects</td></tr> <tr valign="top"><td>tot.sol</td> <td> the number of solutions found.</td></tr> <tr valign="top"><td>not.conv</td> <td> the number of starting points that does not converge after the max.iter.out iteration are reached.</td></tr> <tr valign="top"><td>ao.position</td> <td> the position of the additive outliers.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> coef</ TD> <td>一个向量的AR和回归系数。</ TD> </ TR> <TR VALIGN =“”> <TD>sigma2.coef </ TD> <TD>的系数，系数的估计方差矩阵。</ TD> </ TR> <tr valign="top"> <TD> sigma2 </ TD> <TD> WLE的创新方差。</ TD> </ TR> <tr valign="top"> <TD> arma</ TD> <td>一个紧凑的形式规范，作为向量的数量AR，MA = 0，季节性AR和季节性MA = 0系数，再加上期和非季节和季节的差异。</ TD> </ TR> <tr valign="top"> <TD> resid </ TD> <TD>的残差。</ TD> </ TR> <tr valign="top"> <TD>resid.with.ao </ TD> <TD>的残差与加性离群值的影响。</ TD> </ TR> <tr valign="top"> <TD>resid.without.ao </ TD> <TD>的残差没有加性离群值的影响。</ TD> </ TR> <tr valign="top"> <TD>x.ao </ TD> <TD>的时间序列，而不加性离群值的影响。</ TD> </ TR> <tr valign="top"> <TD> call </ TD> <TD>匹配的呼叫。</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>的名称系列series。</ TD> </ TR> <tr valign="top"> <TD> x</ TD> < TD>的权重。</ TD> </ TR> <tr valign="top"> <TD> weights </ TD> <TD>权重的加性离群值的影响。</ TD> </ TR> <tr valign="top"> <TD> weights.with.ao </ TD> <TD>没有添加剂的异常值的影响权重</ TD> </ TR> <tr valign="top"> <TD > weights.without.ao </ TD> <TD>找到解决方案的数量。</ TD> </ TR> <tr valign="top"> <TD> tot.sol</ TD> <TD>数not.conv迭代不收敛后达到的出发点。</ TD> </ TR> <tr valign="top"> <TD>max.iter.out </ TD> <TD加性离群值的位置。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Claudio Agostinelli
参考文献----------References----------
Agostinelli C., (2001) Robust time series estimation via weighted likelihood: some preliminary results, Working Paper n. 2001.3 Department of Statistics, University of Padova.
Agostinelli C., (2003) Robust time series estimation via weighted likelihood, in: Development in Robust Statistics. International Conference on Robust Statistics 2001, Eds. Dutter, R. and Filzmoser, P. and Rousseeuw, P. and Gather, U., Physica Verlag.
实例----------Examples----------
data(lh)
wle.ar(x=lh, order=c(3,0), group=30)
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 2012-10-1 22:38:38
