deriv_weight_estimator_BLH_noprior(RCASPAR)
deriv_weight_estimator_BLH_noprior()所属R语言包：RCASPAR
A function that gives the derivative of the objective function of the model for gradient-based optimization algorithms without including the prior on the regression coefficients.
一个功能，使基于梯度的优化算法模型的目标函数的导数，不包括前回归系数。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given the necessary data, this function calculates the derivative of the objective function without a prior on the regression coefficients w.r.t. the baseline hazards and weights(regression coefficients) in the model to be used in gradient-based optimization algorithms. This is sometimes necessary to get the optimization algorithm out of a peaked origin where it could start.
给予必要的数据，这个函数计算的目标函数的导数不上的回归系数WRT事先基线的危害，并在该模型重量（回归系数）用于在基于梯度的优化算法。这有时是必要的，得到一个见顶的原产地，在那里它可以开始优化算法。
用法----------Usage----------
deriv_weight_estimator_BLH_noprior(survDataT, geDataT, weights_baselineH, a, b, groups)
参数----------Arguments----------
参数：survDataT
The survival data of the patient set passed on by the user. It takes on the form of a data frame with at least have the following columns “True\_STs” and “censored”, corresponding to the observed survival times and the censoring status of the subjects consecutively. Censored patients are assigned a “1” while patients who experience an event are assigned “1”.
患者组的生存数据传给用户。它需要一个数据框的形式，至少有以下几列“真\ _STs”和“审查”，相应的观测到的生存时间和审查的受试者连续状态。截患者被分配了一个“1”，而谁遇到事件的患者被指定为“1”。
参数：geDataT
The co-variate data (gene expression or aCGH, etc...) of the patient set passed on by the user. It is a matrix with the co-variates in the columns and the subjects in the rows. Each cell corresponds to that rowth subject's columnth co-variate's value.
病人组（基因表达或aCGH等）的共同变量的数据传给用户。这是一个矩阵与合作中的列和行的科目的变元。每个单元格对应，该rowth题目的columnth的共同变量的值。
参数：weights_baselineH
A single vector with the initial values of the baseline hazards followed by the weights(regression coefficients) for the co-variates.
单矢量与共同变元的权重（回归系数）危害基线的初始值。
参数：a
The shape parameter for the gamma distribution used as a prior on the baseline hazards.
作为基线的危害之前使用伽玛分布的形状参数。
参数：b
The scale parameter for the gamma distribution used as a prior on the baseline hazards.
作为基线的危害之前使用伽玛分布的尺度参数。
参数：groups
The number of partitions along the time axis for which a different baseline hazard is to be assigned. This number should be the same as the number of initial values passed for the baseline hazards in the beginning of the “weights\_baselineH” argument.
被分配不同的基线危险是沿时间轴的分区数。这个数字应该是通过的“权重\ _baselineH”的说法开始在基线危害的初始值相同。
值----------Value----------
A vector of the same length as the “weights\_baselineH” argument corresponding to the calculated derivatives of the objective with respect to every component of “weights\_baselineH”.
作为“重\ _baselineH”的说法与尊重每一个组件的“砝码\ _baselineH”的目标计算的衍生工具的长度相同的向量。
注意----------Note----------
This function is in itself not very useful to the user, but is used within the function weights\_BLH.
这个功能本身并不是非常有用的用户，但在功能weights\_BLH使用。
作者（S）----------Author(s)----------
Douaa Mugahid
参考文献----------References----------
Society of Statistics, 34(2), 187-220. The extension of the Cox model to its stepwise form was adapted from: Ibrahim, J.G, Chen, M.-H. & Sinha, D. (2005). Bayesian Survival Analysis (second ed.). NY: Springer. as well as Kaderali, Lars.(2006) A Hierarchial Bayesian Approach to Regression and its Application to Predicting Survival Times in Cancer Patients. Aachen: Shaker
参见----------See Also----------
weight_estimator_BLH_noprior, deriv_weight_estimator_BLH
weight_estimator_BLH_noprior，deriv_weight_estimator_BLH
举例----------Examples----------
data(Bergamaschi)
data(survData)
deriv_weight_estimator_BLH_noprior(survDataT=survData[1:10, 9:10], geDataT=Bergamaschi[1:10, 1:2], weights_baselineH=c(0.1,0.2,0.3,rep(0,2)), a=1.5, b=0.3, groups=3)
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。
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发表于 2012-2-26 12:06:49
