RCASPAR-package(RCASPAR)
RCASPAR-package()所属R语言包：RCASPAR
A package for survival time prediction based on a piecewise baseline hazard Cox regression model.
包一个生存时间的基础上分段基线危险Cox回归模型的预测。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The package is the R-version of the C-based software CASPAR (Kaderali,2006). It is meant to help predict survival times in the presence of high-dimensional explanatory co-variates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant co-variates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine.
该软件包是基于C语言的软件卡斯帕（Kaderali，2006年）的R版本。它是用来帮助预测中存在的高维解释的变元的存活时间。该模型是根据事先的LQ-范数，最重要的回归系数选择一个分段基线危险考克斯回归模型，并在打开的生存分析最相关的合作变元。它主要是试图对基因表达和aCGH数据，但可用于任何其他类型的高维数据，并在比生物学和医学等学科。
Details
详情----------Details----------
作者（S）----------Author(s)----------
Douaa Mugahid
Maintainer: Douaa Mugahid <mugahid@stud.uni-heidelberg.de>, Lars Kaderali <lars.kaderali@bioquant.uni-heidelberg.de>
参考文献----------References----------
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 Heirarchial 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).
举例----------Examples----------
## Eg.(1): A simple example performed with a training and validation set:[＃例如：（1）：一个简单的例子进行了培训和验证集：]
data(Bergamaschi)
data(survData)
## Generate prediction:[＃生成的预测：]
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=15, groups = 3, method = "CG", noprior = 1, extras = list(reltol=1))
## Plot a KM plot with both long and short survivors:[＃绘制一个术语和短期的幸存者公里的图：]
kmplt_svrl(long=result$long_survivors, short=result$short_survivors,title="KM plot of long and short survivors")
## Determine the area under the curve of AUROC curves vs. time to see the performance of the predictor given the chosen parameters and the current partitioning into training[＃面积确定AUROC均随时间变化曲线的曲线下进入训练所选择的参数和当前分区预测的性能]
## and validation sets:[＃审定集：]
survivAURC(Stime=result$predicted_STs$True_STs,status=result$predicted_STs$censored, marker=result$predicted_STs$Predicted_STs, time.max=20)
## Perform a log-rank test to see if the difference between the long and short survivors is significant:[＃执行log-rank检验，看是否术语和短期的幸存者之间的差异显着：]
logrnk(dataL=result$long_survivors, dataS=result$short_survivors)
## Eg.(2): A simple example performed with cross validation:[（2）＃例如：一个简单的例子进行交叉验证：]
data(Bergamaschi)
data(survData)
## Generate prediction:[＃生成的预测：]
STpredictor_xvBLH(geData=Bergamaschi[1:40,1:2], survData=survData[1:40,9:10], k = 10, cut.off = 10, q = 1, s = 1, a = 1.558, b = 0.179, groups = 3, method = "BFGS", noprior = 1, extras = list(reltol=1))
## Plot a KM plot with both long and short survivors:[＃绘制一个术语和短期的幸存者公里的图：]
kmplt_svrl(long=result$long_survivors, short=result$short_survivors,title="KM plot of long and short survivors")
## Determine the area under the curve of AUROC curves vs. time to see the performance of the predictor given the chosen parameters and the current partitioning into training[＃面积确定AUROC均随时间变化曲线的曲线下进入训练所选择的参数和当前分区预测的性能]
## and validation sets:[＃审定集：]
survivAURC(Stime=result$predicted_STs$True_STs,status=result$predicted_STs$censored, marker=result$predicted_STs$Predicted_STs, time.max=20)
## Perform a log-rank test to see if the difference between the long and short survivors is significant:[＃执行log-rank检验，看是否术语和短期的幸存者之间的差异显着：]
logrnk(dataL=result$long_survivors, dataS=result$short_survivors)
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 12:07:22
