STpredictor_xvBLH(RCASPAR)
STpredictor_xvBLH()所属R语言包：RCASPAR
This function performs a cross validation on the full data set to help predict the survival times of the patients using the piecewise baseline hazard PH Cox model.
这个函数执行一个完整的数据设置，以帮助预测患者使用分段基线危险PH值Cox模型的生存时间的交叉验证。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Using the full data provided by the user, this function splits the data set k times, into a smaller validation set, and a much bigger training set. The regression coefficients of the model are estimated from the training set and used to predict the survival times of the validation set. The patients can then be split into patients two groups around a cut off value also specified by the user.
使用由用户提供完整的数据，这个功能拆分k次的数据，到一个较小的验证组，和一个更大的训练集。该模型的回归系数估计从设置和使用验证集的预测生存时间的培训。病人可以被分成两组患者切围绕小康值也由用户指定。
用法----------Usage----------
STpredictor_xvBLH(geData, survData, k = 10, cut.off, file = paste(getwd(), "STpredictor.xv.BLH_results", sep = "/"), q = 1, s = 1, a = 2, b = 2, groups = 3, geneweights = NULL
, BLHs = NULL, method = "BFGS", noprior = 1, extras = list())
参数----------Arguments----------
参数：geData
A matrix with the co-variate data of the full set of subjects. It is constructed with the co-variate in the columns and the subjects in the rows.Each cell corresponds to that rowth subject's column th co-variate's value.
一个科目的全套共同变量数据矩阵。它的构造与共同变量列和在rows.Each单元的科目对应，rowth主题的专栏中日联合变量的值。
参数：survData
The survival data of the entire set of subjects. 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”.
整套科目的生存数据。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：k
The number of times the cross-validation is.
交叉验证的次数。
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：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。这个数字应该是作为为基准的危害通过的“weights_baselineH”的说法开始在初始值的数目相同
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：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：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的拟牛顿方法。 "BFGS":BFGS拟牛顿方法。 "CG":体面的共轭梯度法"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 <KBD>optim</KBD> 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OPTIM的<KBD> </骨节病>函数的文档。
值----------Value----------
参数：predicted_STs
A data frame of the results for all patients, 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 signless difference between the predicted and oberved survival times), PatientOrderValidation (The patient's number)
对所有患者的结果的一个数据框，列True_STs（观察生存时间），Predicted_STs（预测生存时间）审查（审查病人的状态，absolute_error（预测和oberved的生存时间无符号之间的差异），PatientOrderValidation（病人的数量）
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 signless difference between the predicted and oberved survival times), PatientOrderValidation (The patient's number)
数据框的患者活不到切断价值的结果列True_STs（观察生存时间），Predicted_STs（预测生存时间）审查（审查病人的状态，absolute_error（无符号的区别;预测和oberved的存活时间），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)
患者的生活至少只要切断价值的结果与数据框;列True_STs（观察生存时间），Predicted_STs（预测生存时间）审查（审查病人的状态，absolute_error（标志的预测和观察到的存活时间之间的差异），PatientOrderValidation（病人数）
参数：weights
A vector with the mean value of the regression coefficients obtained from the k training sets
从K培训获得的价值回归系数平均向量集
参数：baselineHs
A vector with the mean value of the baseline hazards returned from the k training sets
从K训练集返回一个与基线危害的平均值向量
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 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----------
STpredictor_BLH
STpredictor_BLH
举例----------Examples----------
data(Bergamaschi)
data(survData)
STpredictor_xvBLH(geData=Bergamaschi[1:20, 1:2], survData=survData[1:20, 9:10], k = 10, cut.off=3, file = paste(getwd(), "STpredictor.xv.BLH_results", sep = "/"), q = 1, s = 1, a = 2, b = 2,
groups = 3, geneweights = NULL, BLHs = NULL, 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。
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发表于 2012-2-26 12:07:42
