gamObject(mgcv)
gamObject()所属R语言包：mgcv
Fitted gam object
拟合GAM对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A fitted GAM object returned by function gam and of class "gam" inheriting from classes "glm" and "lm". Method functions anova, logLik, influence, plot, predict, print, residuals and summary exist for this class.
合身的自由亚齐运动对象返回由函数gam和"gam"类继承的类"glm"和"lm"。方法函数anova，logLik，influence，plot，predict，print，residuals和summary存在这个类。
All compulsory elements of "glm" and "lm" objects are present, but the fitting method for a GAM is different to a linear model or GLM, so that the elements relating to the QR decomposition of the model matrix are absent.
所有义务教育元素"glm"和"lm"对象是存在，但一个自由亚齐运动的拟合方法是不同的线性模型或方差分析，使有关模型矩阵的QR分解的元素缺席。
值----------Value----------
A gam object has the following elements:
一个gam对象有下列内容：
参数：aic
AIC of the fitted model: bear in mind that the degrees of freedom used to calculate this are the effective degrees of freedom of the model, and the likelihood is evaluated at the maximum of the penalized likelihood in most cases, not at the MLE.
拟合模型的AIC的熊：记住，用来计算自由的程度是有效度模型，自由的可能性进行评估，在大多数情况下受到惩罚的可能性最大，而不是在的MLE。
参数：assign
Array whose elements indicate which model term (listed in pterms) each parameter relates to: applies only to non-smooth terms.
数组，其元素表明该模型长期（上市pterms）每个参数涉及到：只适用于非光滑条件。
参数：boundary
did parameters end up at boundary of parameter space?
没有参数，最终在参数空间的边界呢？
参数：call
the matched call (allows update to be used with gam objects, for example).
匹配的调用（允许update用gam对象，例如）。
参数：cmX
column means of the model matrix (with elements corresponding to smooths set to zero ) — useful for componentwise CI calculation.
列意味着该模型的矩阵（对应的元素，以平滑设置为零） - 的分支CI计算有用。
参数：coefficients
the coefficients of the fitted model. Parametric coefficients are first, followed by coefficients for each spline term in turn.
拟合模型的系数。参数系数分别为第一，其次依次在每个样条长期系数。
参数：control
the gam control list used in the fit.
gam适合使用的控制列表。
参数：converged
indicates whether or not the iterative fitting method converged.
表示是否或不迭代拟合方法的融合。
参数：data
the original supplied data argument (for class "glm" compatibility). Only included if gam control argument element keepData is set to TRUE (default is FALSE).
原来提供的数据参数（类"glm"兼容性）。只包括如果gamcontrol参数元素keepData设置为TRUE（默认是FALSE）。
参数：deviance
model deviance (not penalized deviance).
模型偏差（不受惩罚越轨）。
参数：df.null
null degrees of freedom.
空的自由程度。
参数：df.residual
effective residual degrees of freedom of the model.
有效的残留度模型的自由。
参数：edf
estimated degrees of freedom for each model parameter. Penalization means that many of these are less than 1.
每个模型参数估计的自由程度。处罚意味着，其中许多都小于1。
参数：edf1
similar, but using alternative estimate of EDF. Useful for testing.
类似的，但用另一种估计EDF。用于测试。
参数：family
family object specifying distribution and link used.
家庭对象指定分配和链接。
参数：fitted.values
fitted model predictions of expected value for each datum.
每个数据的预期值的拟合模型的预测。
参数：formula
the model formula.
模型公式。
参数：full.sp
full array of smoothing parameters multiplying penalties (excluding any contribution from min.sp argument to gam). May be larger than sp if some terms share smoothing parameters, and/or some smoothing parameter values were supplied in the sp argument of gam.
平滑参数乘以惩罚（不包括任何贡献min.spgam参数）全阵列。可能比sp如果某些条款分享spgam参数提供平滑参数和/或一些平滑参数值较大。
参数：gcv.ubre
The minimized smoothing parameter selection score: GCV, UBRE(AIC), GACV, negative log marginal likelihood or negative log restricted likelihood.
最小化的平滑参数选择评分：GCV的，UBRE（AIC），GACV，负边际的可能性日志或负日志限制的可能性。
参数：hat
array of elements from the leading diagonal of the "hat" (or "influence") matrix. Same length as response data vector.
从领导的“帽子”（或“影响”）矩阵对角线元素的数组。响应数据向量的长度相同。
参数：iter
number of iterations of P-IRLS taken to get convergence.
得到收敛的P-IRLS迭代的数量。
参数：linear.predictors
fitted model prediction of link function of expected value for each datum.
拟合模型的预测，预计每个基准值的链接功能。
参数：method
One of "GCV" or "UBRE", "REML", "P-REML", "ML", "P-ML", "PQL", "lme.ML" or "lme.REML", depending on the fitting criterion used.
"GCV"或"UBRE"，"REML"，"P-REML"，"ML"，"P-ML"，"PQL"，"lme.ML" "lme.REML"，取决于使用的装修标准。
参数：mgcv.conv
A list of convergence diagnostics relating to the "magic" parts of smoothing parameter estimation - this will not be very meaningful for pure "outer" estimation of smoothing parameters. The items are: full.rank, The apparent rank of the problem given the model matrix and constraints; rank, The numerical rank of the problem; fully.converged, TRUE is multiple GCV/UBRE converged by meeting convergence criteria and FALSE if method stopped with a steepest descent step failure; hess.pos.defWas the hessian of the GCV/UBRE score positive definite at smoothing parameter estimation convergence?; iter How many iterations were required to find the smoothing parameters? score.calls, and how many times did the GCV/UBRE score have to be evaluated?; rms.grad, root mean square of the gradient of the GCV/UBRE score at convergence. </table>
列表中的有关"magic"部分平滑参数估计的收敛诊断 - 这不会纯"outer"平滑参数的估计是非常有意义的。该项目是：full.rank，模型矩阵和约束的问题明显的排名; rank，数值秩的问题;fully.converged，TRUE是多个GCV的/ UBRE融合满足收敛准则和FALSE如果方法用最速下降步骤失败停止; hess.pos.def是GCV / UBRE得分积极明确的平滑参数估计的收敛麻袋; iter 需要多少次迭代平滑参数？ score.calls，多少次GCV / UBRE评分进行评估;rms.grad，GCV / UBRE得分在收敛的梯度均方根平方米。 </ TABLE>
参数：min.edf
Minimum possible degrees of freedom for whole model.
整个模型的最小可能的自由度。
参数：model
model frame containing all variables needed in original model fit.
包含在原模型拟合所需的所有变量的模型框架。
参数：na.action
The na.action used in fitting.
na.action用于装修。
参数：nsdf
number of parametric, non-smooth, model terms including the intercept.
数参数，非光滑模型方面，其中包括拦截。
参数：null.deviance
deviance for single parameter model.
单参数模型的偏差。
参数：offset
model offset.
模型偏移。
参数：optimizer
optimizer argument to gam, or "magic" if it's a pure additive model.
optimizer参数gam或"magic"如果它是一个纯粹的加法模型。
参数：outer.info
If "outer" iteration has been used to fit the model (see gam argument optimizer) then this is present and contains whatever was returned by the optimization routine used (currently nlm or optim).
如果“外”的迭代被用来拟合模型（见gam说法optimizer），那么这是目前包含任何被使用优化程序（返回当前nlm或 optim）。
参数：paraPen
If the paraPen argument to gam was used then this provides information on the parametric penalties. NULL otherwise.
如果paraPen参数gam使用，那么这个提供参数罚则的信息。 NULL不然。
参数：prior.weights
prior weights on observations.
在观测前的重量。
参数：pterms
terms object for strictly parametric part of model.
terms对象严格的参数模型的一部分。
参数：rank
apparent rank of fitted model.
明显的拟合模型的排名。
参数：reml.scale
The scale (RE)ML scale parameter estimate, if (P-)(RE)ML used for smoothness estimation.
（RE）的ML尺度参数估计的规模，如果（P）（RE）的ML平滑估计。
参数：residuals
the working residuals for the fitted model.
拟合模型的残差工作。
参数：rV
If present, rV%*%t(rV)*sig2 gives the estimated Bayesian covariance matrix.
如果存在，rV%*%t(rV)*sig2给出估计贝叶斯协方差矩阵。
参数：scale
when present, the scale (as sig2)
目前，规模（sig2）
参数：scale.estimated
TRUE if the scale parameter was estimated, FALSE otherwise.
TRUE如果尺度参数估计，FALSE否则。
参数：sig2
estimated or supplied variance/scale parameter.
方差/尺度参数估计或提供。
参数：smooth
list of smooth objects, containing the basis information for each term in the model formula in the order in which they appear. These smooth objects are what gets returned by the smooth.construct objects.
光滑对象的名单，每学期的基础信息包含在模型公式在它们出现的顺序。这些光滑的对象是被smooth.construct对象返回。
参数：sp
estimated smoothing parameters for the model. These are the underlying smoothing parameters, subject to optimization. For the full set of smoothing parameters multiplying the penalties see full.sp. Divide the scale parameter by the smoothing parameters to get, variance components, but note that this is not valid for smooths that have used rescaling to improve conditioning.
平滑模型参数估计。这些都是底层的平滑参数，优化。为平滑参数的全套乘以处罚看到full.sp。分得到平滑参数的尺度参数，方差分量，但要注意，这不是有效的平滑，已重新调整，以提高空调使用。
参数：terms
terms object of model model frame.
termsmodel模型框架的对象。
参数：var.summary
A named list of summary information on the predictor variables. If a parametric variable is a matrix, then the summary is a one row matrix, containing the observed data value closest to the column median, for each matrix column. If the variable is a factor the then summary is the modal factor level, returned as a factor, with levels corresponding to those of the data. For numerics and matrix arguments of smooths, the summary is the mean, nearest observed value to median and maximum, as a numeric vector. Used by vis.gam, in particular.
命名的预测变量的摘要信息列表。如果一个参变量是一个矩阵，然后总结是一个行矩阵，包含值最接近的观测数据列中位数为每个矩阵列。如果变量是一个因素，然后总结是模态因子水平的一个因素，返回相应的数据与水平。对于数字和平滑的矩阵参数，总结是平均值，最近观测到的中位数和最大的价值，作为一个数字的向量。 vis.gam使用，尤其如此。
参数：Ve
frequentist estimated covariance matrix for the parameter estimators. Particularly useful for testing whether terms are zero. Not so useful for CI's as smooths are usually biased.
frequentist估计参数估计的协方差矩阵。特别有用的测试条款是否是零。不那么有用CI的作为平滑通常是失之偏颇。
参数：Vp
estimated covariance matrix for the parameters. This is a Bayesian posterior covariance matrix that results from adopting a particular Bayesian model of the smoothing process. Paricularly useful for creating credible/confidence intervals.
参数估计的协方差矩阵。这是一个贝叶斯后验协方差矩阵，采用平滑的过程中特别是贝叶斯模型的结果。创建可信/可信区间paricularly有用。
参数：weights
final weights used in IRLS iteration.
IRLS迭代中使用的决赛权。
参数：y
response data.
响应数据。
警告----------WARNINGS ----------
This model object is different to that described in Chambers and Hastie (1993) in order to allow smoothing parameter estimation etc.
这个模型对象是不同的钱伯斯和哈斯蒂（1993），以便允许平滑参数估计等。
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----------
& Hall/ CRC, Boca Raton, Florida
and Hall.
参见----------See Also----------
gam
gam
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 20:47:08
