extract.lme.cov(mgcv)
extract.lme.cov()所属R语言包：mgcv
Extract the data covariance matrix from an lme object
提取从LME对象的数据的协方差矩阵
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This is a service routine for gamm. Extracts the estimated covariance matrix of the data from an lme object, allowing the user control about which levels of random effects to include in this calculation. extract.lme.cov forms the full matrix explicitly: extract.lme.cov2 tries to be more economical than this.
这是一个gamm的服务程序。提取的从lme对象的数据的估计协方差矩阵，允许用户控制哪些随机效应的水平，包括在这个计算。 extract.lme.cov形成了完整的矩阵明确：extract.lme.cov2尝试比这更经济。
用法----------Usage----------
extract.lme.cov(b,data,start.level=1)
extract.lme.cov2(b,data,start.level=1)
参数----------Arguments----------
参数：b
A fitted model object returned by a call to lme </table>
拟合模型对象调用返回一个lme</ TABLE>
参数：data
The data frame/ model frame that was supplied to lme.
数据框/模型框架，是提供给lme。
参数：start.level
The level of nesting at which to start including random effects in the calculation. This is used to allow smooth terms to be estimated as random effects, but treated like fixed effects for variance calculations.
其中包括在计算中的随机效应的嵌套级别。这是用来允许顺利条款随机效应的估计，但像固定效应方差的计算处理。
Details
详情----------Details----------
The random effects, correlation structure and variance structure used for a linear mixed model combine to imply a covariance matrix for the response data being modelled. These routines extracts that covariance matrix. The process is slightly complicated, because different components of the fitted model object are stored in different orders (see function code for details!).
随机效应，相关的结构和线性混合模型方差结构相结合，意味着被建模为响应数据的协方差矩阵。这些例程中提取，协方差矩阵。稍微复杂的过程，因为拟合模型对象的不同组成部分储存在不同的订单（详见功能代码！）。
The extract.lme.cov calculation is not optimally efficient, since it forms the full matrix, which may in fact be sparse. extract.lme.cov2 is more efficient. If the covariance matrix is diagonal, then only the leading diagonal is returned; if it can be written as a block diagonal matrix (under some permutation of the original data) then a list of matrices defining the non-zero blocks is returned along with an index indicating which row of the original data each row/column of the block diagonal matrix relates to. The block sizes are defined by the coarsest level of grouping in the random effect structure.
extract.lme.cov计算效率是不是最佳的，因为它构成了完整的矩阵，这实际上可能是稀疏。 extract.lme.cov2更有效。如果协方差矩阵是对角，然后只领先对角线返回，如果它可以作为一个块对角矩阵（下一些原始数据排列）书面然后返回沿定义的非零块矩阵列表指标块对角矩阵的每一行/列涉及到的原始数据的哪一行。在随机效应结构分组，粗级别定义的块大小。
gamm uses extract.lme.cov2.
gamm使用extract.lme.cov2的。
extract.lme.cov does not currently deal with the situation in which the grouping factors for a correlation structure are finer than those for the random effects. extract.lme.cov2 does deal with this situation.
extract.lme.cov不目前的情况，在相关结构的分组因素比随机效应的精细处理。 extract.lme.cov2处理这种情况。
值----------Value----------
For extract.lme.cov an estimated covariance matrix.
extract.lme.cov估计协方差矩阵。
For extract.lme.cov2 a list containing the estimated covariance matrix and an indexing array. The covariance matrix is stored as the elements on the leading diagonal, a list of the matrices defining a block diagonal matrix, or a full matrix if the previous two options are not possible.
extract.lme.cov2包含估计的协方差矩阵和一个索引数组列表。协方差矩阵存储作为领先的对角线，定义一个块对角矩阵，或者是全矩阵的矩阵列表中的元素，如果前面的两个选项是不可能的。
作者（S）----------Author(s)----------
Simon N. Wood <a href="mailto:simon.wood@r-project.org">simon.wood@r-project.org</a>
参考文献----------References----------
Generalized Additive Mixed Models. Biometrics 62(4):1025-1036
and Hall/CRC Press.
参见----------See Also----------
gamm, formXtViX
gamm，formXtViX
举例----------Examples----------
library(nlme)
data(Rail)
b <- lme(travel~1,Rail,~1|Rail)
extract.lme.cov(b,Rail)
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 2012-2-16 19:34:22
