deriv_weight_estimator_BLH(RCASPAR)
deriv_weight_estimator_BLH()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.
一个功能，使基于梯度的优化算法模型的目标函数的导数。
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 w.r.t. the baseline hazards and weights(regression coefficients) in the model to be used in gradient-based optimization algorithms.
给予必要的数据，这个函数计算目标函数的导数，没有一个WRT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。
用法----------Usage----------
deriv_weight_estimator_BLH(geDataT, survDataT, weights_baselineH, q, s, a, b, groups)
参数----------Arguments----------
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：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，至少有下列列“True_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：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：q
One of the two parameters on the prior distribution used on the weights (regression coefficients) in the model.
模型中的权重（回归系数）上使用的先验分布的两个参数之一。
参数：s
The second of the two parameters on the prior distribution used on the weights (regression coefficients) in the model.
第二先验分布的两个参数用于模型中的权重（回归系数）。
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 ver useful to the user, but is used within the function weights\_BLH
此功能本身是没有版本对用户有用的，但在功能weights\_BLH
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 The prior on the regression coefficients was adopted from: Mazur, J., Ritter,D.,Reinelt, G. & Kaderali, L. (2009). Reconstructing Non-Linear dynamic Models of Gene Regulation using Stochastic Sampling. BMC Bioinformatics, 10(448).
参见----------See Also----------
weight_estimator_BLH, codederiv_weight_estimator_BLH_noprior
weight_estimator_BLH，codederiv_weight_estimator_BLH_noprior
举例----------Examples----------
data(Bergamaschi)
data(survData)
deriv_weight_estimator_BLH(survDataT=survData[1:10, 9:10], geDataT=Bergamaschi[1:10, 1:2], weights_baselineH=c(0.1,0.2,0.3,rep(0,2)), q=1, s=1, 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。
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发表于 2012-2-26 12:06:43
