random.effects(mgcv)
random.effects()所属R语言包：mgcv
Random effects in GAMs
GAMS随机效应
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The smooth components of GAMs can be viewed as random effects for estimation purposes. This means that more conventional random effects terms can be incorporated into GAMs in two ways. The first method converts all the smooths into fixed and random components suitable for estimation by standard mixed modelling software. Once the GAM is in this form then conventional random effects are easily added, and the whole model is estimated as a general mixed model. gamm and gamm4 from the gamm4 package operate in this way.
GAMS的顺利组成部分，可以看作随机效应估计目的。这意味着，更传统的随机效应方面可以纳入到GAMS在两种方式。第一种方法转换分为固定和随机元件适用于标准混合建模软件估计平滑。一旦自由亚齐运动这种形式是传统的随机效应是很容易的添加，并作为一般的混合模型估计整个模型。 gamm和gamm4gamm4包以这种方式运作。
The second method represents the conventional random effects in a GAM in the same way that the smooths are represented — as penalized regression terms. This method can be used with gam by making use of s(...,"re") terms in a model: see smooth.construct.re.smooth.spec. Alternatively, but less straightforwardly, the paraPen argument to gam can be used: see gam.models. If smoothing parameter estimation is by ML or REML (e.g. gam(...,method="REML")) then this approach is a completely conventional likelihood based treatment of random effects.
第二种方法表示在以同样的方式，平滑是代表自由亚齐运动传统的随机效应 - 作为惩罚的回归条款。用gams(...,"re")模型使用这种方法可用于：看到smooth.construct.re.smooth.spec。另外，但不直截了当的paraPengam参数可以使用：看到gam.models。如果平滑参数估计是ML或REML法（例如gam(...,method="REML")），那么这种做法是完全传统的随机效应的可能性为基础的治疗。
gam can be slow for fitting models with large numbers of random effects, because it does not exploit the sparcity that is often a feature of parametric random effects. It can not be used for models with more coefficients than data. However gam is often faster and more relaiable than gamm or gamm4, when the number of random effects is modest.
gam可以拟合模型与随机效应的大量缓慢，因为它不利用sparcity，往往是一个功能参数的随机效应。它不能被用于更系数比数据模型。然而gam往往是更快，更relaiable比gamm或gamm4时，随机效应的数量是适度的。
To facilitate the use of random effects with gam, gam.vcomp is a utility routine for converting smoothing parameters to variance components. It also provides confidence intervals, if smoothness estimation is by ML or REML.
为了方便使用随机效应与gam，gam.vcomp是转换为平滑参数方差分量的实用程序。它还提供了置信区间，如果平滑ML或REML法估计。
作者（S）----------Author(s)----------
Simon Wood <simon.wood@r-project.org>
参考文献----------References----------
and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36
selection for generalized additive models. Journal of the Royal Statistical Society (B) 70(3):495-518
generalized additive mixed models. Biometrics 62(4):1025-1036
参见----------See Also----------
gam.vcomp, gam.models, smooth.terms, smooth.construct.re.smooth.spec,
gam.vcomp，gam.models，smooth.terms，smooth.construct.re.smooth.spec
举例----------Examples----------
## see also examples for gam.models, gam.vcomp and gamm[＃又见gam.models例子，gam.vcomp和GAMM]
## simple comparison of lme and gam[＃LME和GAM的简单比较]
require(nlme)
b0 <- lme(travel~1,data=Rail,~1|Rail,method="REML")
b <- gam(travel~s(Rail,bs="re"),data=Rail,method="REML")
intervals(b0)
gam.vcomp(b)
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注：
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发表于 2012-2-16 21:16:47
