llar(tsDyn)
llar()所属R语言包：tsDyn
Locally linear model
局部线性模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Casdagli test of nonlinearity via locally linear forecasts
通过局部线性预测的非线性Casdagli测试
用法----------Usage----------
llar(x, m, d = 1, steps = d, series, eps.min = sd(x)/2,
eps.max = diff(range(x)), neps = 30, trace = 0)
llar.predict(x, m, d=1, steps=d, series, n.ahead=1,
eps=stop("you must specify a window value"),
onvoid=c("fail","enlarge"), r = 20, trace=1)
llar.fitted(x, m, d=1, steps=d, series, eps, trace=0)
参数----------Arguments----------
参数：x
time series
时间序列
参数：m, d, steps
embedding dimension, time delay, forecasting steps
嵌入维，时间延迟，预测的步骤
参数：series
time series name (optional)
时间序列的名称（可选）
参数：n.ahead
n. of steps ahead to forecast
N。提前预测的步骤
参数：eps.min, eps.max
min and max neighbourhood size
最小和最大邻域大小
参数：neps
number of neighbourhood levels along which iterate
附近水平，以及会遍历数
参数：eps
neighbourhood size
邻域大小
参数：onvoid
what to do in case of an isolated point: stop or enlarge neighbourhood size by an r%
在一个孤立点的情况下该怎么做：停止或扩大邻域大小由R％
参数：r
if an isolated point is found, enlarge neighbourhood window by r%
如果发现一个孤立的点，扩大邻域窗口R％
参数：trace
tracing level: 0, 1 or more than 1 for llar, 0 or 1 for llar.forecast
跟踪级别：0，1或大于1llar，0或1llar.forecast
Details
详细信息----------Details----------
llar does the Casdagli test of non-linearity. Given the embedding state-space (of dimension m and time delay d) obtained from time series series, for a sequence of distance values eps, the relative error made by forecasting time series values with a linear autoregressive model estimated on points closer than eps is computed. If minimum error is reached at relatively small length scales, a global linear model may be inappropriate (using current embedding parameters). This was suggested by Casdagli(1991) as a test for non-linearity.
llar做Casdagli的非线性度测试。由于嵌入状态空间（维m和时间延迟d）得到的距离值的序列的时间序列series，eps，相对误差作出的时间序列预测值的线性回归模型估计比eps计算。如果达到最小误差为一个全球性的线性模型可以在相对较小的尺度，是不恰当的（使用电流嵌入参数）。这建议者Casdagli（1991）作为试验为非线性。
llar.predict tries to extend the given time series by n.ahead points by iteratively fitting locally (in the embedding space of dimension m and time delay d) a linear model. If the spatial neighbourhood window is too small, your time series last point would be probably isolated. You can ask to automatically enlarge the window eps by a factor of r% sequentially, until enough neighbours are found for fitting the linear model.
llar.predictn.ahead点尝试延长给定的时间序列，通过迭代地嵌合在本地（在嵌入空间的维数m和时间延迟d）一个线性模型。如果空间邻域窗口太小，时间序列的最后一点可能会分离出来。你可以要求自动放大窗口eps的r％，比上一季度的一个因素，直到有足够的邻居发现拟合的线性模型。
llar.fitted gives out-of-sample fitted values from locally linear models.
llar.fitted给出了样本的局部线性模型的拟合值。
值----------Value----------
llar gives an object of class 'llar'. I.e., a list of components:
llar给出了的对象类的llar“。即，组件的列表：
参数：RMSE
vector of relative errors
相对误差向量
参数：eps
vector of neighbourhood sizes (in the same order of RMSE)
矢量附近的大小（以相同的顺序，RMSE）
参数：frac
vector of fractions of the time series used for RMSE computation
用零碎的时间序列向量计算RMSE
参数：avfound
vector of average number of neighbours for each point in the time series which can be plotted using the plot method, and transformed to a regular data.frame with the as.data.frame function.
矢量的时间序列中的可以使用plot方法绘制，并转化到定期data.frameas.data.frame函数平均数为每个点的邻居。
Function llar.forecast gives the vector of n steps ahead locally linear iterated forecasts.
函数llar.forecast给出的向量n步未来局部线性迭代预测。
Function llar.fitted gives out-of-sample fitted values from locally linear models.
功能llar.fitted给出了样本局部线性模型的拟合值。
警告----------Warning----------
For long time series, this can be slow, especially for relatively big neighbourhood sizes.
长的时间序列，这可能是缓慢的，尤其是对于比较大的区域的大小。
注意----------Note----------
The C implementation was re-adapted from that in the TISEAN package ("ll-ar" routine, see references). However, here the euclidean norm is used, in place of the max-norm.
C实现重新改编自，在TISEAN包（“LL-ar”的常规，请参阅参考资料）。然而，这里的欧几里德范数被使用，代替的最大范数。
（作者）----------Author(s)----------
Antonio, Fabio Di Narzo
参考文献----------References----------
实例----------Examples----------
res <- llar(log(lynx), m=3, neps=7)
plot(res)
x.new <- llar.predict(log(lynx),n.ahead=20, m=3, eps=1, onvoid="enlarge", r=5)
lag.plot(x.new, labels=FALSE)
x.fitted <- llar.fitted(log(lynx), m=3, eps=1)
lag.plot(x.fitted, labels=FALSE)
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 2012-10-1 12:32:53
