STpredictor_BLH(RCASPAR)
STpredictor_BLH()所属R语言包：RCASPAR
Predicts the survival times of the validation set based on the regression coefficients and baseline hazards determined according to the Piecewise baseline hazard Cox
预测生存时间的验证的基础上的回归系数，并根据分段基线危险考克斯确定的基线危险
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function uses the training set to estimated the best regression coefficients, and baseline hazards describing the data according to the piecewise baseline hazard Cox regression model. It then takes them and uses them to predict the survival times of the validation set, which are determined as the mean value of the p.d.f. of the survival time, as a continuous random variable, given the co-variate values of that subject.
使用此功能设置为回归系数估计最好的培训，并描述基准危害的数据，根据分段基线危险Cox回归模型。然后，它需要和使用它们来预测验证集的生存时间，这是确定的PDF平均值的存活时间，作为一个连续随机变量，鉴于这一问题的联合变量的值。
用法----------Usage----------
STpredictor_BLH(geDataS, survDataS, cut.off, file = paste(getwd(), "STpredictor_results", sep = "/"), geDataT, survDataT, groups = NULL, a = 2, b = 2, q = 1, s = 1, BLHs =
NULL, geneweights = NULL, method = "BFGS", noprior = 1, extras = list())
参数----------Arguments----------
参数：geDataS
The co-variate data of the validation 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.
由用户通过验证的共同变量的数据集。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：survDataS
The survival data of the validation 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”.
由用户通过验证的生存数据集。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：cut.off
The value of the separator around which the patients are grouped according to their predicted survival times.
周围的患者进行分组，根据其预测的存活时间分隔的值。
参数：file
The path of the file to which the log file of this session is saved.
本次会议的log文件保存的文件路径。
参数：geDataT
The co-variate data of the kth training set passed on by the user.
第k个训练的共同变量的数据集通过用户。
参数：survDataT
The survival data of the kth training set passed on by the user.
第k个训练生存数据集通过对用户。
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。
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：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：BLHs
A vector with the initial values for the baseline hazards. Should be of length groups. The default is NULL, in which case a vector of length groups with values corresponding to the maximum of the gamma distributions with the given parameters is created.
与基线危险的初始值向量。应该长度团体。默认是空的，在这种情况下，长度groups矢量值与给定参数的伽玛分布的最大创造。
参数：geneweights
A vector with the initial values of the weights(regression coefficients) for the co-variates. The default is NULL, in which case a vector of zeros the same length as ncol(geData) is created as the initial starting value.
权重的初始值（回归系数）为合作，变元的向量。默认是空的，在这种情况下的零ncol(geData)相同长度的向量作为初始值开始创建。
参数：method
The preferred optimization method. It can be one of the following:\ "Nelder-Mead": for the Nelder-Mead simplex algorithm.\ "L-BFGS-B": for the L-BFGS-B quasi-Newtonian method.\ "BFGS": for the BFGS quasi-Newtonian method.\ "CG": for the Conjugate Gradient decent method\ "SANN": for the simulated annealing algorithm.\
首选的优化方法。它可以是下列之一：\"Nelder-Mead":内尔德-Mead单纯算法\"L-BFGS-B" -bfgs-B的准牛顿法\"BFGS":BFGS拟牛顿方法。\"CG":体面的方法共轭梯度\"SANN":模拟退火算法。\
参数：noprior
An integer indicating the number of iterations to be done without assuming a prior on the regression coefficients.
一个整数，表示迭代次数进行回归系数事先假设。
参数：extras
The extra arguments to passed to the optimization function optim. For further details on them, see the documentation for the optim function.
额外的参数传递给函数的优化OPTIM。对于他们的进一步详情，请参阅为optim功能的文档。
值----------Value----------
参数：log_optimization
The result of the optimization performed on the training set as is described in the documentation for the optim function
对训练集进行优化的结果是optim函数的文档中描述
参数：short_survivors
A data frame of results for the patients living less than the cut off value; with the columns True\_STs (the observed survival times), Predicted\_STs (the predicted survival times), censored(the censoring status of the patient, absolute\_error(the sign-less difference between the predicted and observed survival times), PatientOrderValidation (The patient's number)
数据框的患者活不到切断价值的结果;列真\ _STs（观察生存时间），预计\ _STs（预测生存时间），审查（审查病人的状态，绝对\ _error（符号之间的预测和观察到的存活时间的差异），PatientOrderValidation（病人数）
参数：long_survivors
A data frame with the results for the patients living at least as long as the cut off value; with columns True\_STs (the observed survival times), Predicted\_STs (the predicted survival times), censored(the censoring status of the patient,absolute\_error(the sign-less difference between the predicted and observed survival times), PatientOrderValidation (The patient's number)
只要切断价值至少为患者的结果与数据框;列真\ _STs（观察生存时间），预计\ _STs（预测生存时间），审查（审查病人的状态绝对\ _error（符号预测和观察到的存活时间之间的差异），PatientOrderValidation（病人的数量）
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 Hierarchical 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----------
STpredictor_xvBLH
STpredictor_xvBLH
举例----------Examples----------
data(Bergamaschi)
data(survData)
result <- STpredictor_BLH(geDataS=Bergamaschi[1:27, 1:2], survDataS=survData[1:27, 9:10], geDataT=Bergamaschi[28:82, 1:2], survDataT=survData[28:82, 9:10], q = 1,
s = 1, a = 1.558, b = 0.179, cut.off=3, groups = 3, method = "CG", noprior = 1, extras = list(reltol=1))
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。
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发表于 2012-2-26 12:07:35
