frailty(survival)
frailty()所属R语言包：survival
Random effects terms
按随机效应
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The frailty function allows one to add a simple random effects term to a Cox or survreg model.
的脆弱性功能允许一个添加一个简单的长期到考克斯或survreg的模型的随机效应。
用法----------Usage----------
frailty(x, distribution="gamma", ...)
frailty.gamma(x, sparse = (nclass > 5), theta, df, eps = 1e-05, method = c("em","aic", "df", "fixed"), ...)
frailty.gaussian(x, sparse = (nclass > 5), theta, df, method =c("reml","aic", "df", "fixed"), ...)
frailty.t(x, sparse = (nclass > 5), theta, df, eps = 1e-05, tdf = 5,method = c("aic", "df", "fixed"), ...)
参数----------Arguments----------
参数：x
the variable to be entered as a random effect. It is always treated as a factor.
要输入变量为随机效应。它始终被视为一个因素。
参数：distribution
either the gamma, gaussian or t distribution may be specified. The routines frailty.gamma, frailty.gaussian and frailty.t do the actual work.
无论是gamma，gaussian或t分布可以指定。例程frailty.gamma，frailty.gaussian和frailty.t做实际工作。
参数：...
Arguments for specific distribution, including (but not limited to)
具体分布的参数，包括（但不限于）
参数：sparse
cutoff for using a sparse coding of the data matrix. If the total number of levels of x is larger than this value, then a sparse matrix approximation is used. The correct cutoff is still a matter of exploration: if the number of levels is very large (thousands) then the non-sparse calculation may not be feasable in terms of both memory and compute time. Likewise, the accuracy of the sparse approximation appears to be related to the maximum proportion of subjects in any one class, being best when no one class has a large membership.
截止使用的数据矩阵的稀疏编码。总数如果x水平的是大于这个值，然后使用稀疏矩阵近似。正确的截止仍然是一个探索的问题：如果是非常大的（千）非稀疏计算未必是feasable两个内存和计算时间的水平。同样，出现稀疏近似的准确性有关的任何一类科目的最高比例，最好时，没有一类有一个大的成员。
参数：theta
if specified, this fixes the variance of the random effect. If not, the variance is a parameter, and a best solution is sought. Specifying this implies method='fixed'.
如果指定的话，这个修复的随机效应的方差。如果没有，方差是一个参数，并寻求最佳的解决方案。指定这意味着method='fixed'。
参数：df
if specified, this fixes the degrees of freedom for the random effect. Specifying this implies method='df'. Only one of theta or df should be specified.
如果指定的话，这个修复的随机效应的自由度。指定这意味着method='df'。只有一个theta或df应指定。
参数：method
the method used to select a solution for theta, the variance of the random effect. The fixed corresponds to a user-specified value, and no iteration is done. The df selects the variance such that the degrees of freedom for the random effect matches a user specified value. The aic method seeks to maximize Akiake's information criteria 2*(partial likelihood - df). The em and reml methods are specific to Cox models with gamma and gaussian random effects, respectively. Please see further discussion below.
方法采用随机效应的方差θ，选择一个解决方案。 fixed对应一个用户指定的值，并没有迭代完成。 df选择方差等随机效应的自由度，用户指定的值相匹配。 aic方法，旨在最大限度地Akiake的信息标准，2 *（部分的可能性 - DF）。 em和reml方法是具体考克斯与γ和高斯随机效应模型，分别。请参阅下面进一步讨论。
参数：tdf
the degrees of freedom for the t-distribution.
t分布的自由程度。
参数：eps
convergence critera for the iteration on theta.
THETA迭代收敛判据。
Details
详情----------Details----------
The frailty plugs into the general penalized modeling framework provided by the coxph and survreg routines. This framework deals with likelihood, penalties, and degrees of freedom; these aspects work well with either parent routine.
frailty插入到一般的惩罚coxph和survreg例程提供的建模框架。这个框架内处理的可能性，罚款，和自由度;这些方面的工作以及与父母任何一方例程。
Therneau, Grambsch, and Pankratz show how maximum likelihood estimation for the Cox model with a gamma frailty can be accomplished using a general penalized routine, and Ripatti and Palmgren work through a similar argument for the Cox model with a gaussian frailty. Both of these are specific to the Cox model. Use of gamma/ml or gaussian/reml with survreg does not lead to valid results.
therneau，Grambsch，Pankratz显示伽玛脆弱的Cox模型的最大似然估计可以通过类似的说法为与高斯脆弱的Cox模型采用一般处罚程序，并Ripatti和Palmgren工作完成。这些都是具体的Cox模型。伽马/毫升或高斯/ REML使用survreg不会导致有效结果。
The extensible structure of the penalized methods is such that the penalty function, such as frailty or pspine, is completely separate from the modeling routine. The strength of this is that a user can plug in any penalization routine they choose. A weakness is that it is very difficult for the modeling routine to know whether a sensible penalty routine has been supplied.
处罚方法的可扩展的结构是这样，刑罚的功能，如frailty或pspine，从建模程序是完全独立的。本的优势在于，用户可以在他们选择的任何处罚程序堵塞。弱点是，它是非常困难的建模例行知道是否已经提供了一个明智的罚款程序。
For Cox models the coxme package has replaced superseded this method.
Cox模型已经取代coxme包取代这种方法。
值----------Value----------
this function is used in the model statment of either coxph or survreg. It's results are used internally.
此功能用于在模型要么coxph或survreg结单上。它的结果是内部使用。
参考文献----------References----------
models using penalized partial likelihood, Biometrics, 56:1016-1022, 2000.
Penalized survival models and frailty, J Computational and Graphical Statistics, 12:156-175, 2003.
参见----------See Also----------
coxph, survreg
coxph，survreg
举例----------Examples----------
# Random institutional effect[随机体制的影响]
coxph(Surv(time, status) ~ age + frailty(inst, df=4), lung)
# Litter effects for the rats data[凋落物对大鼠数据的影响]
rfit2a <- survreg(Surv(time, status) ~ rx +
frailty.gaussian(litter, df=13, sparse=FALSE), rats )
rfit2b <- survreg(Surv(time, status) ~ rx +
frailty.gaussian(litter, df=13, sparse=TRUE), rats )
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 20:23:10
