wle.aic.ar(wle)
wle.aic.ar()所属R语言包：wle
Weighted Akaike Information Criterion for AR models
加权赤池信息准则的AR模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function evaluate the Weighted Akaike Information Criterion for AutoRegressive Models. This is a robust model selection method to choose the order of an AutoRegressive model.
功能评估的加权赤池信息准则自回归模型。这是一个强大的模型选择方法，选择的顺序自回归模型。
用法----------Usage----------
wle.aic.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", var.full = 0, 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, min.weights.aic = 0.5, approx.w = TRUE, ask = TRUE, alpha = 2, method = "WLS")
参数----------Arguments----------
参数：x
a univariate time series.
一个单变量时间序列。
参数：order
maximum order to investigate. 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.
最大的订单进行调查。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：var.full
An estimate of the residual variance for the full model.
估计的剩余方差为完整的模型。
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：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：verbose
if TRUE warnings are printed.
如果TRUE警告被打印出来。
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：min.weights
see details.
查看详细信息。
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：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：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：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：min.weights.aic
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。
参数：ask
logical. If TRUE, in the case of multiple roots in the full model, the users is asked for selecting the root.
逻辑。如果TRUE，多根完整的模型的情况下，用户的要求选择根。
参数：alpha
penalty value.
惩罚值。
参数：method
if "WLE" the parameters are estimated using weighted likelihood estimating equations in the reduced models, otherwise if "WLS" a weighted least squares approach is used with weights based on the full model.
如果使用加权似然估计方程模型在减少，否则，如果“WLS”加权最小二乘方法用于配重块的完整模型的基础上，“WLE”的参数估计。
Details
详细信息----------Details----------
min.weights: 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.weights：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。
min.weights.aic is used as min.weights but in the full model. 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.
min.weights.aic作为min.weights但在完整的模型。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。
The function wle.ar.wls is used to estimate the parameter of an autoregressive model by weighted least squares where the weights are those from the weighted likelihood estimating equation of the full model (the model with the hightest order).
功能wle.ar.wls是用来估计自回归模型的参数的加权最小二乘法的权重是那些从完整的模型（模型与在后的顺序）的加权似然估计方程。
值----------Value----------
A list of class wle.aic.ar with the following components: <table summary="R valueblock"> <tr valign="top"><td>full.model</td> <td> the results for the full model, that is an object of class wle.arima see wle.ar help for further details.</td></tr> <tr valign="top"><td>waic</td> <td> Weighted Akaike Information Criterion for each submodels.</td></tr> <tr valign="top"><td>call</td> <td> match.call result.</td></tr> </table>
列表类wle.aic.ar以下组件：<table summary="R valueblock"> <tr valign="top"> <TD>full.model </ TD> <TD>的业绩完整的模型，这是一个类的对象wle.arimawle.ar有助于进一步的细节。</ TD> </ TR> <tr valign="top"> <TD>waic< <TD>加权赤池信息准则对每个子模型/ TD> </ TD> </ TR> <tr valign="top"> <TD>call </ TD> <TD>match.call结果。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Claudio Agostinelli
参考文献----------References----------
Agostinelli C, (2004) Robust Akaike Information Criterion for ARMA models, Rendiconti per gli Studi Economici Quantitativi, 1-14, isbn: 88-88037-10-1.
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.
参见----------See Also----------
wle.ar
wle.ar
实例----------Examples----------
data(rocky)
res <- wle.aic.ar(x=rocky, order=c(6,0), group=50, group.start=30, method="WLS")
res
plot(res$full.model$weights)
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。
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发表于 2012-10-1 22:38:09
