mgcv-package(mgcv)
mgcv-package()所属R语言包：mgcv
GAMs with GCV/AIC/REML smoothness estimation and GAMMs by REML/PQL
GAMS与GCV的/ AIC / REML法平滑估计REML法/ PQL GAMMs
译者：生物统计家园网 机器人LoveR
描述----------Description----------
mgcv provides functions for generalized additive modelling (gam and bam) and generalized additive mixed modelling (gamm, and random.effects). The term GAM is taken to include any GLM estimated by quadratically penalized (possibly quasi-) likelihood maximization.
mgcv提供广义添加剂建模功能（gam和bam）和广义添加剂混合建模（gamm，random.effects）。长期的GAM采取包括二次处罚（准）的可能性最大化可能估计任何的GLM。
Particular features of the package are facilities for automatic smoothness selection, and the provision of a variety of smooths of more than one variable. User defined smooths can be added. A Bayesian approach to confidence/credible interval calculation is provided. Linear functionals of smooths, penalization of parametric model terms and linkage of smoothing parameters are all supported. Lower level routines for generalized ridge regression and penalized linearly constrained least squares are also available.
特别是包的特点是自动选择平滑的设施，并提供各种平滑多个变量。可以添加用户自定义平滑。信心/可信区间计算的贝叶斯方法来提供。平滑的线性泛函，都支持参数化模型和平滑参数的联动处罚。也可广义岭回归和处罚线性约束最小二乘较低水平例程。
Details
详情----------Details----------
mgcv provides generalized additive modelling functions gam, predict.gam and plot.gam, which are very similar in use to the S functions of the same name designed by Trevor Hastie (with some extensions). However the underlying representation and estimation of the models is based on a penalized regression spline approach, with automatic smoothness selection. A number of other functions such as summary.gam and anova.gam are also provided, for extracting information from a fitted gamObject.
mgcv广义添加剂建模功能gam，predict.gam和plot.gam，这是非常相似特雷弗·黑斯蒂（有一些扩展设计的名称相同的职能，在使用）。然而，底层表示和模型的估计是基于1惩罚的回归具有自动选择平滑，样条方法。一些其他功能，如summary.gam和anova.gam还提供从拟合gamObject提取信息，。
Use of gam is much like use of glm, except that within a gam model formula, isotropic smooths of any number of predictors can be specified using s terms, while scale invariant smooths of any number of predictors can be specified using te terms. smooth.terms provides an overview of the built in smooth classes, and random.effects should be refered to for an overview of random effects terms (see also mrf for Markov random fields). Estimation is by penalized likelihood or quasi-likelihood maximization, with smoothness selection by GCV, GACV, gAIC/UBRE or (RE)ML. See gam, gam.models, linear.functional.terms and gam.selection for some discussion of model specification and selection. For detailed control of fitting see gam.convergence, gam arguments method and optimizer and gam.control. For checking and visualization see gam.check, choose.k, vis.gam and plot.gam. While a number of types of smoother are built into the package, it is also extendable with user defined smooths, see smooth.construct, for example.
使用gam是很像glm使用，除了在gam模型公式，各向同性的任何预测数平滑，可以指定使用s条款，而规模不变的平滑，可以使用te条款指定任何数量的预测。 smooth.terms顺利类的建成提供了一个概述，random.effects应参考随机效应方面的概述（又见mrf马尔可夫随机场）。估计是受到惩罚的可能性或准的可能性最大化，GCV的，GACV，加伊奇/ UBRE（RE）的ML与平滑的选择。看到gam，gam.models，linear.functional.terms和gam.selection一些型号规格和选择的讨论。看到详细的装修控制gam.convergence，gam参数method和optimizer和gam.control。检查和可视化gam.check，choose.k，vis.gam和plot.gam。虽然一些类型的平滑包建，这也是与用户定义的平滑扩展，看到smooth.construct，例如。
A Bayesian approach to smooth modelling is used to derive standard errors on predictions, and hence credible intervals. The Bayesian covariance matrix for the model coefficients is returned in Vp of the gamObject. See predict.gam for examples of how this can be used to obtain credible regions for any quantity derived from the fitted model, either directly, or by direct simulation from the posterior distribution of the model coefficients. Approximate p-values can also be obtained for testing individual smooth terms for equality to the zero function, using similar ideas. Frequentist approximations can be used for hypothesis testing based model comparison. See anova.gam and summary.gam for more on hypothesis testing.
顺利建模贝叶斯方法用于产生标准预测误差，因此可信区间。贝叶斯模型系数的协方差矩阵在VpgamObject的返回。看到predict.gam这可以用来获得可靠的区域派生的任何数量的拟合模型，直接或通过直接模拟模型系数的后验分布的例子。近似P-值也可以得到平等零功能测试顺利个别条款，使用类似的想法。可用于基于假设检验的模型比较frequentist近似。看到更多的假设检验anova.gam和summary.gam。
For large datasets (that is large n) see bam which is a version of gam with a much reduced memory footprint.
对于大型数据集（即大的n）的看到bam这是一个版本的gam大幅减少内存占用。
The package also provides a generalized additive mixed modelling function, gamm, based on a PQL approach and lme from the nlme library (for an lme4 based version, see package gamm4). gamm is particularly useful for modelling correlated data (i.e. where a simple independence model for the residual variation is inappropriate). In addition, low level routine magic can fit models to data with a known correlation structure.
该软件包还提供广义添加剂混合建模功能，gamm，基于上PQL方法和lmenlme库（lme4的版本，请参阅软件包 gamm4）。 gamm是建模相关的数据（即一个简单的独立的残余变异的模型是不恰当的）特别有用。此外，低级例程magic可以容纳与已知的相关结构的数据模型。
Some underlying GAM fitting methods are available as low level fitting functions: see magic and mgcv. But there is little functionality that can not be more conventiently accessed via gam . Penalized weighted least squares with linear equality and inequality constraints is provided by pcls.
一些基本的GAM拟合方法是低水平拟合函数：看到magic和mgcv。但有一点功能，不能更conventiently通过gam访问。惩罚加权最小二乘线性等式和不等式约束最小二乘提供pcls的。
For a complete list of functions type library(help=mgcv). See also mgcv-FAQ.
有关完整列表的功能型library(help=mgcv)。还可以看mgcv-FAQ。
作者（S）----------Author(s)----------
Simon Wood <simon.wood@r-project.org>
with contributions and/or help from Thomas Kneib, Kurt Hornik, Mike Lonergan, Henric Nilsson
and Brian Ripley.
Maintainer: 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
generalized additive models. J. Amer. Statist. Ass. 99:673-686.
CRC
举例----------Examples----------
## see examples for gam and gamm[＃看到GAM和GAMM的例子]
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。
|
发表于 2012-2-16 22:11:40
