selectSETAR(tsDyn)
selectSETAR()所属R语言包：tsDyn
Automatic selection of SETAR hyper-parameters
SETAR的超参数的自动选择
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Automatic selection of SETAR hyper-parameters
SETAR的超参数的自动选择
用法----------Usage----------
selectSETAR(x, m, d=1, steps=d, series, mL, mH,mM, thDelay=0, mTh, thVar, th=MakeThSpec(), trace=TRUE, include = c("const", "trend","none", "both"), common=c("none", "include","lags", "both"), model=c("TAR", "MTAR"), ML=seq_len(mL),MH=seq_len(mH), MM=seq_len(mM),nthresh=1,trim=0.15,criterion = c("pooled-AIC", "AIC","BIC", "SSR"),thSteps = 7, plot=TRUE,max.iter=2, type=c("level", "diff", "ADF"), same.lags=FALSE, restriction=c("none","OuterSymAll","OuterSymTh"), hpc=c("none", "foreach") )
参数----------Arguments----------
参数：x
time series
时间序列
参数：m, d, steps
embedding parameters. For their meanings, see help about nlar
嵌入参数。它们各自的含义，请参阅帮助有关nlar
参数：series
time series name (optional)
时间序列的名称（可选）
参数：mL, mH,mM
autoregressive order for "low" (mL) "middle" (mM, only useful if nthresh=2) and "high" (mH)regime (default values: m). Must be <=m. Alternatively, you can specify ML
自回归阶为“低”（mL）的“中间”（MM，唯一有用的，如果nthresh = 2）和“高”（MH）制度（默认值：M）。必须<= M。或者，您也可以指定ML
参数：thDelay
Vector of possible "threshold delay" values to check for
向量的可能“阈值延迟值，以检查
参数：mTh
coefficients for the lagged time series, to obtain the threshold variable
的滞后时间序列的系数，以获得阈值变量
参数：thVar
external threshold variable
外部的阈值变量
参数：th
Different specifications of the grid search, to pre-specify a value or set the number of points to search. SeeMakeThSpec
不同规格的网格搜索，预先指定一个值或设置点的数量来搜索。见MakeThSpec
参数：trace
should additional infos be printed? (logical)
额外的相关信息进行打印呢？ （逻辑）
参数：include
Type of deterministic regressors to include
确定性回归变量的类型，包括
参数：common
Indicates which elements are common to all regimes: no, only the include variables, the lags or both
表示哪些元素是共同所有的制度：没有，只有include变量，滞后或两个
参数：model
Currently not implemented
目前尚未实现
参数：ML,MM,MH
vector of lags for order for "low" (ML) "middle" (MM, only useful if nthresh=2) and "high" (MH)regime. Max must be <=m
向量的滞后为“低”（ML）为了“中间”（MM，唯一有用的，如果nthresh = 2）和“高”（MH）制度。最大必须<=米
参数：nthresh
Number of threshold of the model
模型的阈值的数目
参数：trim
trimming parameter indicating the minimal percentage of observations in each regime. Default to 0.15
微调参数表示的最小百分比在每一个政权的意见。默认为0.15
参数：criterion
Model selection criterion
模型选择标准
参数：thSteps
Not used
未使用
参数：plot
Should a plot showing the criterion values be printed? (logical)
如果一个图，显示与标准值进行打印呢？ （逻辑）
参数：max.iter
Number of iterations for the alogorithm
的迭代的alogorithm数
参数：type
Whether the variable is taken is level, difference or a mix (diff y= y-1, diff lags) as in the ADF test
在ADF检验变量是否是水平，差异性或混合（差异Y = Y-1，比较滞后）
参数：same.lags
Logical. When AIC or pooled-AIC is used and arg m is given, should it search for same number of lags in each regime (TRUE) or allow for different (FALSE) lags in each regime. Different lags involves more computation
逻辑。池AIC AIC或使用ARG米，它的搜索在每一个政权的滞后阶数相同（TRUE）或允许不同的（FALSE）滞后于每个政权。不同的滞后涉及到更多的计算
参数：restriction
Restriction on the threshold. OuterSymAll will take a symmetric threshold and symmetric coefficients for outer regimes. OuterSymTh currently unavailable
限制在阈值上。 OuterSymAll将采取对称的阈值和外部制度的对称系数。 OuterSymTh目前不能使用。
参数：hpc
Possibility to run the bootstrap on parallel core. See details
可并行内核上运行的引导。查看详细资料
Details
详细信息----------Details----------
Routine for automatic selection of SETAR models hyper parameters.
常规SETAR模型超参数的自动选择。
An exhaustive search over all possible combinations of values of specified hyper-parameters is performed. Thus the threshold delay, the number of lags in each regime and the threshold value are computed.
在指定的Hyper-参数值的所有可能组合进行穷举搜索。因此，阈值延迟，在每个制度的滞后和阈值的数目被计算。
Embedding parameters d,steps are kept fixed.
嵌入参数d,steps保持固定。
Possible criteria are the usual SSR, AIC and a pooled AIC formula: AIC(low regime model) + AIC(high regime model). The default criterion is the pooled AIC formula. SSR criterion can't be used to compare models with different lags.
可能的标准是一般的SSR，AIC和汇集AIC公式：AIC(low regime model) + AIC(high regime model)的。的预设标准是汇集AIC式。 SSR标准不能用于比较模型具有不同的滞后。
When two thresholds(nthresh=2) have to be computed, the search for the second is made conditional on results for first threshold as suggested in Gonzalo and Pittarakis (2002). Refinements can be obtained by using max.iter (first threshold being re-estimated based on the second one). If SSR is used, the number of lags in the inner regime is either the same if only arg m was given, otherwise it has to be pre-specified. Criterion AIC can be used to determine the number of lags in the nner regime, whereas pooled-aic is currently not implemented for nthresh=2.
两个阈值（nthresh= 2）计算时，搜索结果的第一道阈值的建议，在贡萨洛和Pittarakis的（2002年）的第二个条件。精炼可以通过以下方式获得使用max.iter（第一阈值被重新估计基于第二个）。如果使用SSR的，滞后的内政权数目是相同的，如果只有arg的米，否则它必须是预先指定的。可以使用标准AIC的数量来确定的新新人类制度的滞后，而池AIC目前尚未实现，nthresh = 2。
By default, all threshold values excluding the upper and lower trim of the threshold values are taken as potential threshold. restriction can be made with arg th. See function MakeThSpec.
缺省情况下，所有的阈值不包括上部和下部trim的阈值被作为潜在阈值。限制可以作出使用arg的th。请参阅功能MakeThSpec。
With the argument hpc, the heavy grid search can be run on parallel cores, thus alleviating the time of computation. Preliminary results indicate however that the length of the series must be very considerable in order that the parallel code becomes advantageous. To use it, the user needs simply to choose a package (among doMC, doMPI, doSNOW or doRedis) and register the backend. See the vignette for more details.
参数hpc，沉重的网格搜索可以并行内核上运行，从而减轻计算时间。初步结果显示，然而，该系列的长度必须是非常可观的，以便并行代码变得有利。要使用它，用户需要简单地选择一个包（中doMC，doMPI，doSNOW或doRedis）和注册的后端。有关详细信息，请参阅的小插曲。
值----------Value----------
An object of class selectSETAR (print and plot methods) with:
对象的类selectSETAR（打印和绘图方法）与
参数：res
A data-frame, with columns giving hyper-parameter values and the computed AIC for each row (only the best 10/5s are returned)
一个数据框，与给超参数值和所计算的AIC的每一行（只有最好10/5s返回的列）
参数：res2
Same as res, returned if nthresh=2 otherwise set to NULL
同水库，返回nthresh = 2，否则设置为NULL
参数：bests
estimated hyper-parameters
估计超参数
参数：th, firstBests, bests2th, ML, MM, MH
estimated parameters, from first and conditional search
估计参数，从第一和条件搜索
参数：criterion, nthresh,same.lags
returns args given by user
由用户给定的回报ARGS
参数：allTh
all threshold values and correspoinding criterion from first search
从第一次搜索所有阈值和correspoinding标准
（作者）----------Author(s)----------
Antonio, Fabio Di Narzo and Stigler, Matthieu
参考文献----------References----------
<h3>See Also</h3> <code>selectLSTAR</code>, <code>selectNNET</code>, MakeThSpec
实例----------Examples----------
llynx <- log10(lynx)
selectSETAR(llynx, m=2)
#Suggested model is the following:[提出的模型如下：]
setar(llynx, m=2, thDelay=1, th=3.4)
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。
|
发表于 2012-10-1 12:33:53
