smoothSurvReg.object(smoothSurv)
smoothSurvReg.object()所属R语言包：smoothSurv
Smoothed Survival Regression Object
平滑生存回归对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This class of objects is returned by the smoothSurvReg class of functions to represent a fitted smoothed survival regression model.
这个类的对象，则返回的smoothSurvReg类代表一个的拟合平滑生存回归模型的功能。
Objects of this class have methods for the functions print, summary, plot, residuals, survfit.
这个类的对象有方法的功能print，summary，plot，residuals，survfit。
COMPONENTS我----------COMPONENTS I----------
The following components must be included in a legitimate smoothSurvReg object.
以下组件必须被包括在一个合法的smoothSurvReg对象。
Indicator of the failure of the fitting procedure. Possible values are 0 for no problems, 3 if the iteration process was stopped because of non-positive definite minus Hessian, 4 if the eiteration process was stopped because too many halving steps were performed, 5 if it was not possible to find the three reference knots (it was not then possible to perform optimization with respect to the full parameter vector), 6 if the maximal number of iterations was performed without reaching a convergence. The fail component is increased by 10 if the final minus Hessian of the penalized log-likelihood was not positive definite. The fail component is further increased by 20 if the computed effective degrees of freedom were non-positive. The fail component is further increased by 40 if there are negative estimates of standard errors for some regression parameters. The fail component is 99 or higher if the fitting procedure failed at all and there is no fit produced.
指标的嵌合过程的失败。可能的值是0没有问题，如果在迭代过程已停止，因为的非正定减黑森州，4，如果eiteration过程也停止了，因为太多减半的步骤进行，5，如果它是不可能找到的三个参考节（然后就可以进行优化的参数向量），如果没有达到收敛迭代的最大数量进行。如果最终减黑森州受到处罚的对数似然不是正定fail成分增加了10。 fail组件进一步增加20，如果所计算的有效自由度非正。 fail组件进一步增加40点，如果有一些回归参数的标准误差是负的估计。如果安装程序失败，有没有合适的生产fail分量为99或更高。
COMPONENTS II----------COMPONENTS II----------
The following components must be included in a legitimate smoothSurvReg object if fail is lower than 99.
以下组件中必须包含一个合法的smoothSurvReg对象，如果fail是低于99。
Estimates of the regression parameters alpha, beta, sigma if these have been estimated with their standard errors stored in a data frame with colnames “Value”, “Std.Error”, “Std.Error2” and rownames derived from the names of the design matrix with “(Intercept)” for the intercept, “Scale” for the scale and “Log(scale)” for the log-scale. If the log-scale depends on covariates then rows named “LScale.(Intercept)”, “LScale.cov1” etc. give estimates of regression parameters for log-scale. The two standard errors are computed using either var or var2 described below.
的回归参数的估计alpha, beta, sigma，如果这些都被存储在一个数据框与colnames“价值”中，“Std.Error”中，“Std.Error2”和它们的标准误差估计rownames来自设计矩阵“（截取）”的拦截，“规模”的规模和“log的log规模（规模）”的名称。如果的log规模取决于协变量，然后行命名为“LScale。（截距）”的“LScale.cov1，”等方面给予的回归参数估计的log规模。可以使用var或var2下面描述的两个标准误差计算。
Description of the fitted error density. A data frame with colnames “Knot”, “SD basis”, “c coef.”, “Std.Error.c”, “Std.Error2.c”, “a coef.”, “Std.Error.a” and “Std.Error2.a” and rownames knot[1], ..., knot[g] where g stands for the number of basis G-splines. The column “Knot” contains the knots in ascending order, “SD basis” the standard deviation of an appropriate basis G-spline, “c coef.” estimates of the G-spline coefficients and “Std.Error.c” and “Std.Error2.c” the estimates of their standard errors based either on var or var2. The column “a coef.” contains the estimates of transformed c coefficients where
说明拟合误差密度。一个数据框colnames“云水谣”，“SD基础”，“C系数。”，“Std.Error.c”中，“Std.Error2.c”，“系数“，”Std.Error.a“和”Std.Error2.a“和rownames结[1]，...，结[克]其中g代表该号码基G-样条曲线。 “云水谣”列中包含的结升序排列，“SD依据”一个适当的基础，G-样条曲线的标准差，“C系数。”估计的G-样条系数和“Std.Error的。 c“和”Std.Error2.c“的标准误差的估计基于var或var2。列包含“系数”。转化c系数的估计
If the error distribution is estimated, one of the a coefficients is set to zero and two other a's are expressed as a function of the remaining a coefficients (to avoid equality constraints concerning the mean and the variance of the error distribution). The standard error for these three a coefficients is then not available (it is equal to NA). Standard error is set to NaN is a diagonal element of the appropriate covariance matrix was negative.
如果估计的误差分布，其中一个a系数被设置为零，并且其他两个a的表示作为剩余的a系数的函数（以避免等式约束有关的均值和方差的误差分布）。标准误差这三个a：系数，然后提供（它等于NA）。标准的错误信息被设置成NaN是一个适当的协方差矩阵对角线元素为负。
Maximized penalized log-likelihood, log-likelihood and the penalty term. A data frame with one row and three columns named “Log Likelihood”, &ldquo enalty” and &ldquoenalized Log Likelihood”.
最大化对数似然处罚，对数似然和惩罚项。一个数据框与一个一行三列名为“对数似然”，“处罚决定书”和“惩罚对数似然”。
Akaike's information criterion of the fitted model computed as a maximized value of the penalized log-likelihood minus the effective degrees of freedom.
赤池信息准则的拟合模型计算作为价值最大化对数似然受到处罚减去有效自由度。
Effective degrees of freedom, number of parameters and related information. A data frame with one row and columns named “Lambda”, “Log(Lambda)”, “df”, “Number of parameters”, “Mean param.”, “Scale param.”, “Spline param.” where “Lambda” gives the value of the tunning parameter used in the final (optimal) fit, “df” the effective degrees of freedom, “Number of parameters” the real number of parameters and “Mean param.”, “Scale param.” and “Spline param.” its decomposition. Note that if G-spline coefficients are estimated “Spline param.” is equal to the number of basis G-spline with non-zero coefficients minus three.
有效程度的自由，参数的数量和相关信息。一个数据框的行和列名为“拉姆达”，“log（λ）”，“DF”，“参数的数量”，“平均参数。”，“缩放参数。” “样条参数”，“拉姆达”在最后的（最佳）适合使用的整定参数，用“df”的有效自由度，“参数”的实数参数所给出的值和“平均参数”，“缩放参数。”和“样条参数”。其分解。需要注意的是，当G-样条系数估计“样条曲线的参数”是相等的数量的基础上与非零系数减去3 G-样条。
The estimate of the covariance matrix of the estimates based on the Bayesian approximation. It is equal to the inverse of the converged minus Hessian of the penalized log-likelihood. Note that there are no columns and rows corresponding to the three transformed G-spline coefficients since these are functions of the remaining transformed G-spline coefficients (to avoid equality constraints).
基于贝叶斯近似估计的协方差矩阵的估计。它等于惩罚项的对数似然的融合减号Hessian矩阵的逆。请注意，有没有对应于三个转化G-样条系数的列和行，因为这些功能的剩余的变换G-样条系数（以避免等式约束）。
The estimate of the covariance matrix of the estimates based on the asymptotic theory for penalized models. It is equal to H^{-1} I H^{-1} where H is converged minus Hessian of the penalized log-likelihood and I is converged minus Hessian of the log-likelihood component of the penalized log-likelihood.
惩罚模型的渐近理论的基础上估计的协方差矩阵的估计。这是等于H^{-1} I H^{-1}其中H收敛减去黑森州受到处罚的对数似然和我融合减去黑森州的惩罚对数似然对数似然组成部分。
A matrix with derivatives of c spline coefficients with respect to d spline coefficients (these are a coefficients with three of them omitted). This matrix can be used later to compute estimates and standard errors of functions of original parameters using a Delta method. For closer definition of d coefficients see an enclosed document.
衍生工具的cd样条系数（这些是a系数，其中有三名省略）的样条系数的矩阵。以后可以使用矩阵计算的原始参数的功能，使用Delta法的估计和标准错误的。定义d系数看一个封闭的文档。
Used number of iterations to fit the model with the optimal lambda.
使用迭代次数的最佳lambda拟合模型。
Indicator of what has really been estimated and not fixed. A four-component vector with component names “(Intercept)”, “Scale”, “ccoef”, “common.logscale”. The first component is TRUE if the intercept was included in the regression model. The second component is TRUE if the scale parameter was not fixed, the third component is TRUE is the G-spline coefficients were not fixed. The fourth component is TRUE if the log-scale does not depend on covariates.
真的估计，而不是固定的指标。四分量的向量与组件名称“（截取）”，“缩放”中，“ccoef”中，“common.logscale”。第一部分是TRUE，如果被列入回归模型的截距。第二个组件是TRUE，如果尺度参数是不固定的，第三成分为TRUE是不固定的G-样条系数。第四部分是TRUE，如果log规模不依赖于协变量。
A data frame with one column called “warnings” and three rows called “Convergence”, “Final minus Hessian” and “df” containing a string information corresponding to the value of the fail component of the object. It contains a string “OK” if there are no problems with the appropriate part of the fitting process.
一个数据框，其中一列名为“警告”和三排，被称为“融合”，“最终减黑森州”和“df”fail组件的对象的值包含一个字符串对应的信息。它包含的字符串“OK”（确定）与适当的嵌合过程的一部分，如果不存在任何问题。
Converged minus Hessian of the penalized log-likelihood.
融合减黑森州受到处罚的对数似然。
Converged minus Hessian of the log-likelihood component of the penalized log-likelihood. I = H - G.
融合减黑森州的惩罚对数似然对数似然组成部分。 I = H - G。
Converged minus Hessian of the penalty term of the penalty term of the penalized log-likelihood. G = H - I.
融合减去黑森州的惩罚的惩罚，惩罚的对数似然。 G = H - I。
Converged score vector based on the penalized log-likelihood.
根据处罚的对数似然融合得分向量。
The na.action attribute, if any, that was returned by the na.action routine.
na.action属性，如果有的话，返回na.action常规。
The terms object used.
terms对象。
A symbolic description of the model to be fit.
一个象征性的模型来描述是合适的。
The matched call.
匹配的呼叫。
A string indicating the error distribution of the untransformed response to find the initial values. Possible values are “lognormal”, “loglogistic”, “weibull”.
一个字符串，指示未转换的响应初始值的误差分布。可能的值是“对数正态分布”，“loglogistic”，“威布尔”的。
If requested, the model frame used.
如果有要求，使用的模型框架。
The model matrix used.
所使用的模型矩阵。
The response matrix used (two columns if there were no interval censored observations, three columns if there were some interval censored observations). The last column indicates the death status.
响应矩阵（两列，如果有不区间的观察，三列，如果有一些区间观察）。最后一列表示的死亡状态。
The model matrix used for the expression of log-scale.
的模型矩阵用于log规模的表达。
A data frame describing the initial error density. It has columns named “Knot”, “SD basis”, “c coef.” and rows named “knot[1]”, ..., “knot[g]”.
一个数据框描述初始误差密度。它列命名为“云水谣”，“SD基础”，“C系数。”，并命名为“结[1]”的行，...，“结[G]。
Initial estimates of the regression parameters. A data frame with one column named “Value” and rows named as in the regres component of the smoothSurvReg object.
初步估计回归参数。一个数据框，其中一列名为“值”，并命名为regres组成部分smoothSurvReg对象的行。
Adjusted intercept and scale. A data frame with a column named “Value” and rows named “(Intercept)” and “Scale”. “(Intercept)” gives the overall intercept taking into account the mean of the fitted error distribution, “Scale” gives the overall scale taking into account the variance of the fitted error distribution. If the error distribution is standardized (always when G-spline coefficients are estimated) then the “(Intercept)” is equal to the “(Intercept)” from the regres component and “Scale” is equal to the “Scale” of either regres or init.regres component. NA's appeare in this data.frame in the case that log-scale depends on covariates.
调整后的拦截和规模。一个数据框与一列名为“价值”指定行“（截取）”和“规模”。 “（截取）”给人的整体拦截考虑到的拟合误差分布的均值，“规模”给人的整体规模，考虑到方差的拟合误差分布。如果误差分布是标准化的（总是当G-样条系数估计），则“（截取）”等于“（截取）”从regres分量和“缩放”是等于“比例”是regres或init.regres组成部分。 NA的出现了在此data.frame的情况下，对数规模取决于协变量。
A data frame with columns named “Mean”, “Var” and “SD” and a row named “Error distribution: ” giving the mean, variance and the standard deviation of the fitted error distribution. These are equal to 0, 1 and 1 if the G-spline coefficients were estimated.
一个数据框的列命名为“中庸”，“无功”和“SD”和一排命名为“错误分配：”给人的拟合误差分布的均值，方差和标准差。这些是等于0，1和1，如果G-样条系数估计。
Information concerning the searched values of the tunning paramater lambda when looking for the best AIC. A data frame with columns named “Lambda”, “Log(Lambda)”, “AIC”, “df”, &ldquoenalLogLik”, “LogLik”, “nOfParm”, “fail”.
有关的搜索值的整定paramater lambda寻找最好的AIC。一个数据框列名为“拉姆达”，“log（λ）”，“AIC”，“DF”，“PenalLogLik”中，“LogLik”中，“nOfParm”，“失败”。
（作者）----------Author(s)----------
Arno拧t Kom谩rek <a href="mailto:arnost.komarek[AT]mff.cuni.cz">arnost.komarek[AT]mff.cuni.cz</a>
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。
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发表于 2012-9-30 10:32:52
