checkResiduals.TSFmodel(tsfa)
checkResiduals.TSFmodel()所属R语言包：tsfa
Check Time Series Idiosyncratic Component
检查时间系列富有特色的组件
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The data is subtracted from the explained data (after differencing if diff is TRUE, the default) and the result is treated as a residual. Its covariance, the sum of the diagonal elements of the covariance, and the sum of the off-diagonal elements of the covariance are printed. The residual is then passed to the default method for checkResiduals which produces several diagonistic plots and (invisibly) returns statistics. See checkResiduals for more details. Calculation of partial autocorrelations can be problematic.
的解释的数据（差分，如果之后的数据中减去diff是TRUE，默认值），结果被视为剩余。它的协方差，协方差的对角线元素的总和，和的协方差的非对角（off-diagonal）的元素的总和被打印出来。剩余的，然后通过默认的方法为checkResiduals，这几个diagonistic图（不可见）返回统计。见checkResiduals更多详情。偏自相关函数的计算可能会出现问题。
Some care should be taken interpreting the results. Factor estimation does not minimize residuals, it extracts common factors.
应采取一些护理，解释结果。系数估计不减少残留物，提取常见的因素。
用法----------Usage----------
## S3 method for class 'TSFmodel'
checkResiduals(obj, data=obj$data, diff.=TRUE, ...)
参数----------Arguments----------
参数：obj
TSFmodel object for which the idiosyncratic component should be examined (as if it were a residual).
TSFmodel对象的特异反应性成分应检查（好像它是一个残余）。
参数：data
data from which the idiosyncratic component should be calculated.
从应计算的特异反应性成分的数据。
参数：diff.
logical indicating if data and explained should be differenced.
逻辑表明，如果数据和解释应差。
参数：...
arguments to be passed to checkResiduals default methods.
参数被传递到checkResiduals默认的方法。
（作者）----------Author(s)----------
Paul Gilbert
参见----------See Also----------
checkResiduals, TSFmodel, estTSF.ML
checkResiduals，TSFmodel，estTSF.ML
实例----------Examples----------
if (require("CDNmoney")){
data("CanadianMoneyData.asof.28Jan2005", package="CDNmoney")
data("CanadianCreditData.asof.28Jan2005", package="CDNmoney")
z <- tframed(tbind(
MB2001,
MB486 + MB452 + MB453 ,
NonbankCheq,
MB472 + MB473 + MB487p,
MB475,
NonbankNonCheq + MB454 + NonbankTerm + MB2046 + MB2047 + MB2048 +
MB2057 + MB2058 + MB482),
names=c("currency", "personal cheq.", "NonbankCheq",
"N-P demand & notice", "N-P term", "Investment" )
)
z <- tfwindow(tbind (z, ConsumerCredit, ResidentialMortgage,
ShortTermBusinessCredit, OtherBusinessCredit),
start=c(1981,11), end=c(2004,11))
cpi <- 100 * M1total / M1real
popm <- M1total / M1PerCapita
scale <- tfwindow(1e8 /(popm * cpi), tf=tframe(z))
MBandCredit <- sweep(z, 1, scale, "*")
c4withML <- estTSF.ML(MBandCredit, 4)
checkResiduals(c4withML, pac=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 12:41:33
