关于linear mixed model 的R包为什么都不能指定随机变量方差的值？

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R 中有很多包适用于linear mixed model，可是好像都不能想SAS中的Proc mixed 过程一样可以自设方差的值啊？
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关键词：mixed model Linear Mixed model Linea 混合模型 方差 proc mixed 统计

同感！

看看下面说的这几个吧

As for many other problems, there are several packages in R that let you deal with linear mixed models from a frequentist (REML) point of view. I will only mention nlme (Non-Linear Mixed Effects), lme4 (Linear Mixed Effects) and asreml (average spatial reml). There are also several options for Bayesian approaches, but that will be another post.

nlme is the most mature one and comes by default with any R installation. In addition to fitting hierarchical generalized linear mixed models it also allows fitting non-linear ones. Its main advantages are, in my humble opinion, the ability to fit fairly complex hierarchical models using linear or non-linear approaches, a good variety of variance and correlation structures, and access to several distributions and link functions for generalized models. In my opinion, its main drawbacks are i- fitting cross-classified random factors is a pain, ii- it can be slow and may struggle with lots of data, iii- it does not deal with pedigrees by default and iv- it does not deal with multivariate data.

lme4 is a project led by Douglas Bates (one of the co-authors of nlme), looking at modernizing the code and making room for trying new ideas. On the positive side, it seems to be a bit faster than nlme and it deals a lot better with cross-classified random factors. Drawbacks: similar to nlme’s, but dropping point i- and adding that it doesn’t deal with covariance and correlation structures yet. It is possible to fit pedigrees using the mmpedigree package, but I find the combination a bit flimsy.

ASReml-R is, unsurprisingly, an R package interface to ASReml. On the plus side it i- deals well with cross-classified random effects, ii- copes very well with pedigrees, iii- can work with fairly large datasets, iv-can run multivariate analyses and v- covers a large number of covariance and correlation structures. Main drawbacks are i- limited functionality for non-Gaussian responses, ii- it does not cover non-linear models and iii- it is non-free (as in beer an speech). The last drawback is relative; it is possible to freely use asreml for academic purposes (and there is also a version for developing countries). Besides researchers, the main users of ASReml/asreml-r are breeding companies.

All these three packages are available in Windows, Linux and OS X.

谢谢，对这些包的一个不错的介绍
但是这些介绍都未提到自设方差的问题