estTSFmodel(tsfa)
estTSFmodel()所属R语言包：tsfa
Estimate Time Series Factor Model
估计时间序列因子模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate a TSFmodel.
估计一个TSFmodel。
用法----------Usage----------
estTSFmodel(y, p, diff.=TRUE,
est="factanal",
estArgs=list(scores="none", control=list(opt=list(maxit=10000))),
rotation=if(p==1) "none" else "quartimin",
rotationArgs=NULL,
GPFargs=list(Tmat=diag(p),normalize=TRUE, eps=1e-5, maxit=1000),
BpermuteTarget=NULL,
factorNames=paste("Factor", seq(p)))
estTSF.ML(y, p, diff.=TRUE,
rotation=if(p==1) "none" else "quartimin",
rotationArgs=NULL,
normalize=TRUE, eps=1e-5, maxit=1000, Tmat=diag(p),
BpermuteTarget=NULL,
factorNames=paste("Factor", seq(p)))
参数----------Arguments----------
参数：y
a time series matrix.
时间序列矩阵。
参数：p
integer indication number of factors to estimate.
整数指示数估计的因素。
参数：diff.
logical indicating if model should be estimated with differenced data.
逻辑表明如果模型与差分数据进行估计。
参数：est
character vector indicating the factor estimation method (currently only factanal is supported).
字符向量表示的系数估计方法（目前支持只有factanal是）。
参数：estArgs
list passed to as arguments to the estimation function.
列表的估计函数的参数传递给。
参数：rotation
character vector indicating the factor rotation method (see GPArotation for options).
字符向量表示的的因素轮换方法（见GPArotation的选项）。
参数：rotationArgs
list passed to the rotation method, specifying arguments for the rotation criteria.
名单通过旋转方法，指定参数的旋转标准。
参数：GPFargs
list passed to GPFoblq or GPForth, possibly via the rotation method, specifying arguments for the rotation optimization. See GPFoblq and GPForth.
名单通过GPFoblq或GPForth，可能通过旋转法，指定的旋转优化的参数。见GPFoblq和GPForth。
参数：normalize
Passed to GPFoblq. TRUE means do Kaiser normalization before rotation and then undo it after completing rotatation. FALSE means do no normalization. See GPFoblq for other possibilities.
传递到GPFoblq。 TRUE是指做皇帝标准化之前旋转，然后完成后，rotatation撤消它。 FALSE是指不标准化。见GPFoblq其他的可能性。
参数：eps
passed to GPFoblq
传递GPFoblq的
参数：maxit
passed to GPFoblq
传递GPFoblq的
参数：Tmat
passed to GPFoblq
传递GPFoblq的
参数：BpermuteTarget
matrix of loadings. If supplied, this is used to permute the order of estimated factors and change signs in order to compare properly.
矩阵的负荷。如果提供，这是用来重排的顺序估计的因素和变化的迹象，以比较正常。
参数：factorNames
vector of strings indicating names to be given to factor series.
名称被给定因子序列的字符串表示的矢量。
Details
详细信息----------Details----------
The function estTSF.ML is a wrapper to estTSFmodel.
函数estTSF.ML是一个包装estTSFmodel。
The function estTSF.ML estimates parameters using standard (quasi) ML factor analysis (on the correlation matrix and then scaled back). The function factanal with no rotation is used to find the initial (orthogonal) solution. Rotation, if specified, is then done with GPFoblq. factanal always uses the correlation matrix, so standardizing does not affect the solution.
的功能estTSF.ML估计参数的标准（准）的ML因素分析（的相关性矩阵，然后缩减）。该函数factanal与没有旋转被用来寻找初始（正交）的解决方案。旋转时，如果指定，然后完成GPFoblq。 factanal总是使用的相关矩阵，所以规范不影响该溶液中。
If diff. is TRUE (the default) the indicator data is differenced before it is passed to factanal. This is necessary if the data is not stationary. The resulting Bartlett factor score coefficient matrix (rotated) is applied to the undifferenced data. See <CITE>Gilbert and Meijer (2005)</CITE> for a discussion of this approach.
如果diff.是TRUE（默认值）指标数据差分，之前它传递给factanal。如果数据是不固定的，这是必要的。将所得巴特利特因子得分系数矩阵（旋转）被施加到非差数据。 <CITE>吉尔伯特和梅耶尔（2005年）</ CITE>的讨论这种方法。
If rotation is "none" the result of the factanal estimation is not rotated. In this case, to avoid confusion with a rotated solution, the factor covariance matrix Phi is returned as NULL. Another possibility for its value would be the identity matrix, but this is not calculated so NULL avoids confusion.
如果rotation是"none" factanal估计的结果是不旋转的。在这种情况下，与一个旋转的解决方案，以避免混淆，因子协方差矩阵Phi被返回作为NULL。另一种可能性，其值将是单位矩阵，但是这还没有计算所以NULL避免混乱。
The arguments rotation, methodArgs, normalize, eps, maxit, and Tmat are passed to GPFoblq.
的参数rotation，methodArgs，normalize，eps，maxit和Tmat传递给GPFoblq。
The estimated loadings, Bartlett factor score coefficient matrix and predicted factor scores are put in a TSFmodel which is part of the returned object. The Bartlett factor score coefficient matrix can be calculated as
在TSFmodel返回的对象是估计的负荷，巴特利特因子得分系数矩阵和预测因子得分。巴特利特因子得分系数矩阵可以计算为
or equivalently as
或等价为
The first is simpler because Omega is diagonal, but breaks down with a Heywood case, because Omega is then singular (one or more of its diagonal elements are zero). The second only requires nonsingularity of Sigma. Typically, Sigma is not singular even if Omega is singular. Sigma is calculated from B Phi B' + Omega, where B, Phi, and Omega are the estimated values returned from factanal and rotated. The data covariance could also be used for Sigma. (It returns the same result with this estimation method.)
首先是简单的，这是因为Omega是对角线，但分解具有海沃德壳体，因为Omega然后奇异的（一个或多个它的对角线元素均为零）。第二只需要非奇异的Sigma。通常情况下，Sigma是不是奇异即使Omega是奇异的。 Sigma计算从B Phi B' + Omega，其中B, Phi,和Omega返回factanal和旋转的估计值的。的数据的协方差也可用于Sigma。 （这个估计方法返回相同的结果。）
The returned TSFestModel object is a list containing
返回TSFestModel对象是一个列表，其中包含
model the estimated TSFmodel.
模型的估计TSFmodel。
data the indicator data used in the estimation.
数据的估计中使用的指标数据。
estimates a list of
估计的列表
estimation a character string indicating the name of the
估计的字符表示的名称的字符串
diff. the setting of the argument diff.
差异。的设置参数diff。
rotation the setting of the argument rotation.
旋转的设置参数rotation。
uniquenesses the estimated uniquenesses.
独特性估计的独特性。
BpermuteTarget the setting of the argument BpermuteTarget.
BpermuteTarget的设置参数BpermuteTarget。
值----------Value----------
A TSFestModel object which is a list containing TSFmodel,
ATSFestModel对象，这是一个列表，其中包含TSFmodel
（作者）----------Author(s)----------
Paul Gilbert and Erik Meijer
参考文献----------References----------
Time Series Factor Analaysis with an Application to Measuring Money. Research Report 05F10, University of Groningen, SOM Research School. Available from http://som.eldoc.ub.rug.nl/reports/themeF/2005/05F10/.
参见----------See Also----------
TSFmodel, GPFoblq, rotations, factanal
TSFmodel，GPFoblq，rotations，factanal
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)
tfplot(ytoypc(factors(c4withML)),
Title="Factors from 4 factor model (year-to-year growth rate)")
tfplot(c4withML, graphs.per.page=3)
summary(c4withML)
summary(TSFmodel(c4withML))
}
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 12:41:53
