gam.fit3(mgcv)
gam.fit3()所属R语言包：mgcv
P-IRLS GAM estimation with GCV \& UBRE/AIC or RE/ML derivative calculation
P-IRLS的GAM估计与GCV \＆UBRE / AIC或RE / ML衍生的计算
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimation of GAM smoothing parameters is most stable if optimization of the UBRE/AIC, GCV, GACV, REML or ML score is outer to the penalized iteratively re-weighted least squares scheme used to estimate the model given smoothing parameters.
GAM的平滑参数的估计的UBRE / AIC，GCV，GACV，REML或ML评分的优化是最稳定的，如果是外受到处罚的迭代重加权最小二乘计划，用于平滑参数估计模型。
This routine estimates a GAM (any quadratically penalized GLM) given log smoothing paramaters, and evaluates derivatives of the smoothness selection scores of the model with respect to the log smoothing parameters. Calculation of exact derivatives is generally faster than approximating them by finite differencing, as well as generally improving the reliability of GCV/UBRE/AIC/REML score minimization.
此例程估计一个GAM（任何二次惩罚GLM）给出log平滑paramaters，相对于平滑化参数的log和评估的平滑模型选择分数的衍生物。计算确切的衍生工具通常比接近他们用有限差分，以及普遍提高的可靠性GCV / UBRE的/ AIC / REML得分最小化的。
The approach is to run the P-IRLS to convergence, and only then to iterate for first and second derivatives.
方法是运行收敛，P-IRLS，然后重复第一和第二的衍生物。
Not normally called directly, but rather service routines for gam.
通常不直接调用，而是服务程序gam。
用法----------Usage----------
gam.fit3(x, y, sp, Eb ,UrS=list(),
weights = rep(1, nobs), start = NULL, etastart = NULL,
mustart = NULL, offset = rep(0, nobs), U1 = diag(ncol(x)),
Mp = -1, family = gaussian(), control = gam.control(),
intercept = TRUE,deriv=2,gamma=1,scale=1,
printWarn=TRUE,scoreType="REML",null.coef=rep(0,ncol(x)),
pearson.extra=0,dev.extra=0,n.true=-1,...)
参数----------Arguments----------
参数：x
The model matrix for the GAM (or any penalized GLM).
为GAM模型矩阵（或任何处罚GLM）。
参数：y
The response variable.
响应变量。
参数：sp
The log smoothing parameters.
该log平滑参数。
参数：Eb
A balanced version of the total penalty matrix: usd for numerical rank determination.
一个平衡的版本：美元的罚款总额矩阵的数值等级确定。
参数：UrS
List of square root penalties premultiplied by transpose of orthogonal basis for the total penalty.
预乘转正交基的罚款总额的平方根处罚名单。
参数：weights
prior weights for fitting.
以前的权重装修。
参数：start
optional starting parameter guesses.
可选的起始参数的猜测。
参数：etastart
optional starting values for the linear predictor.
可选的起始值的线性预测。
参数：mustart
optional starting values for the mean.
可选的初始值的意思。
参数：offset
the model offset
模型偏移量
参数：U1
An orthogonal basis for the range space of the penalty — required for ML smoothness estimation only.
的罚款的范围空间的正交基 - 所需的仅ML平滑估计。
参数：Mp
The dimension of the total penalty null space — required for ML smoothness estimation only.
维度的的罚款总额空空间 - 所需的仅ML平滑估计。
参数：family
the family - actually this routine would never be called with gaussian()
家庭 - 实际上这个程序将永远不会被调用gaussian()
参数：control
control list as returned from glm.control
控制列表返回glm.control
参数：intercept
does the model have and intercept, TRUE or FALSE
模型和拦截，TRUE或FALSE
参数：deriv
Should derivatives of the GCV and UBRE/AIC scores be calculated? 0, 1 or 2, indicating the maximum order of differentiation to apply.
衍生工具的的GCV和UBRE / AIC分数如何计算？ 0，1或2，分化申请指示的最大阶数。
参数：gamma
The weight given to each degree of freedom in the GCV and UBRE scores can be varied (usually increased) using this parameter.
给每个在GCV和UBRE的分数的自由程度的重量，可以改变（通常增加）使用此参数。
参数：scale
The scale parameter - needed for the UBRE/AIC score.
尺度参数 - 需要UBRE / AIC得分。
参数：printWarn
Set to FALSE to suppress some warnings. Useful in order to ensure that some warnings are only printed if they apply to the final fitted model, rather than an intermediate used in optimization.
设置为FALSE地抑制一些警告。以确保只印一些警告，如果他们申请的最终拟合模型，而不是在优化的中间体，可用于非常有用。
参数：scoreType
specifies smoothing parameter selection criterion to use.
指定使用平滑参数的选择标准。
参数：null.coef
coefficients for a model which gives some sort of upper bound on deviance. This allows immediate divergence problems to be controlled.
系数为一个模型，给出了某种上对越轨行为的约束。这允许，立即发散要被控制的问题。
参数：pearson.extra
Extra component to add to numerator of pearson statistic in P-REML/P-ML smoothness selection criteria.
额外组件添加到分子皮尔逊统计P-REML/P-ML平滑的选择标准。
参数：dev.extra
Extra component to add to deviance for REML/ML type smoothness selection criteria.
参数：n.true
Number of data to assume in smoothness selection criteria. <=0 indicates that it should be the number of rows of X.
假设在平滑的选择标准的数据的数量。 <= 0表示，它应该是X的行的数目。
参数：...
Other arguments: ignored.
其他参数：忽略不计。
Details
详细信息----------Details----------
This routine is basically glm.fit with some modifications to allow (i) for quadratic penalties on the log likelihood; (ii) derivatives of the model coefficients with respect to log smoothing parameters to be obtained by use of the implicit function theorem and (iii) derivatives of the GAM GCV, UBRE/AIC, REML or ML scores to be evaluated at convergence.
这个程序基本上是glm.fit一些修改，以允许（i）为对数似然的二次处罚;（ii）衍生工具的模型系数就登录平滑参数，以获得使用隐函数存在定理（iii）衍生工具进行评估的GAM GCV，UBRE / AIC，REML或ML分数的收敛。
In addition the routines apply step halving to any step that increases the penalized deviance substantially.
此外，该的例程申请步骤减半的任何步骤，惩罚越轨行为大幅增加。
The most costly parts of the calculations are performed by calls to compiled C code (which in turn calls LAPACK routines) in place of the compiled code that would usually perform least squares estimation on the working model in the IRLS iteration.
通过调用编译的C代码（这反过来又调用LAPACK例程）的地方编译后的代码，通常会在IRLS迭代的工作模式进行最小二乘估计的计算是最昂贵的部分。
Estimation of smoothing parameters by optimizing GCV scores obtained at convergence of the P-IRLS iteration was proposed by O'Sullivan et al. (1986), and is here termed "outer" iteration.
奥沙利文等人提出了通过优化获得的P-IRLS迭代的收敛性的GCV的分数的平滑化参数的估计。 （1986），并在这里被称为“外部”的迭代。
Note that use of non-standard families with this routine requires modification of the families as described in fix.family.link.
请注意，使用这个程序不标准的家庭需要修改的家庭中描述的fix.family.link。
（作者）----------Author(s)----------
Simon N. Wood <a href="mailto:simon.wood@r-project.org">simon.wood@r-project.org</a>
The routine has been modified from <code>glm.fit</code> in R 2.0.1, written
by the R core (see <code><a href="../../stats/html/glm.html">glm.fit</a></code> for further credits).
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
functions in generalized linear models. J. Amer. Statist. Assoc. 81:96-103.
参见----------See Also----------
gam.fit, gam, magic
gam.fit，gam，magic
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 2012-9-23 10:24:50
