fblr(scrime)
fblr()所属R语言包：scrime
Full Bayesian Logic Regression for SNP Data
全贝叶斯逻辑回归SNP数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs full Bayesian logic regression for Single Nucleotide Polymorphism (SNP) data as described in Fritsch and Ickstadt (2007).
执行完整的贝叶斯逻辑回归的单核苷酸多态性（SNP）的数据中描述的弗里奇和Ickstadt的（2007年）。
fblr.weight allows to incorporate prior pathway information by restricting search for interactions to specific groups of SNPs and/or giving them different weights. fblr.weight is only implemented for an interaction level of 2.
fblr.weight允许前将路径信息，通过限制搜索的互动，特定群体的SNP位点和/或给予不同的权重。 fblr.weight只实施了一个交互水平的2。
用法----------Usage----------
fblr(y, bin, niter, thin = 5, nburn = 10000, int.level = 2, kmax = 10,
geo = 1, delta1 = 0.001, delta2 = 0.1, predict = FALSE,
file = "fblr_mcmc.txt")
fblr.weight(y, bin, niter, thin = 5, nburn = 10000, kmax = 10, geo = 1,
delta1 = 0.001, delta2 = 0.1, predict = FALSE, group = NULL,
weight = NULL, file = "fblr_mcmc.txt")
参数----------Arguments----------
参数：y
binary vector indicating case-control status.
二进制向量表示的情况下控制状态。
参数：bin
binary matrix with number of rows equal to length(y). Usually the result of applying snp2bin to a matrix of SNP data.
二进制矩阵的行数等于length(y)。通常情况下，由于的应用snp2bin的SNP数据矩阵。
参数：niter
number of MCMC iterations after burn-in.
烧伤后的MCMC迭代中。
参数：thin
after burn-in only every thinth iteration is kept.
烧伤后每thin次迭代被保留。
参数：nburn
number of burn-in iterations.
数的老化迭代。
参数：int.level
maximum number of binaries allowed in a logic predictor. Is fixed to 2 for fblr.weight.
逻辑预测的二进制文件中允许的最大数量。固定为2 fblr.weight。
参数：kmax
maximum number of logic predictors allowed in the model.
模型中的的逻辑预测因子允许的最大数目。
参数：geo
geometric penalty parameter for the number of binaries in a predictor. Value between 0 and 1. Default is 1, meaning no penalty.
几何惩罚参数的预测数的二进制文件。 0和1之间的值。默认是的1，这意味着没有惩罚。
参数：delta1
shape parameter for hierarchical gamma prior on precision of regression parameters.
形状参数层次伽马之前回归参数的精度。
参数：delta2
rate parameter for hierarchical gamma prior on precision of regression parameters.
回归参数的精度层次伽马之前的速率参数。
参数：predict
should predicted case probabilities be returned?
预测的情况下，概率应该回来了吗？
参数：file
character string naming a file to write the MCMC output to. If fblr is called again, the file is overwritten.
字符串命名的MCMC输出到一个文件中写入。如果fblr再次被调用，该文件将被覆盖。
参数：group
list containing vectors of indices of binaries that are allowed to interact. Groups may be overlapping, but every binary has to be in at least one group. Groups have to contain at least two binaries. Defaults to NULL, meaning that all interactions are allowed.
允许进行交互的二进制文件的索引列表，其中包含向量。基团可以是重叠的，但每一个二进制文件具有在至少一个基团。组必须至少包含两个二进制文件。默认为空，这意味着所有的交互都不允许。
参数：weight
vector of length ncol(bin) containing different relative prior weights for binaries. Defaults to NULL, meaning equal weight for all binaries.
向量的长度ncol(bin)包含不同的二进制文件相对以前的权重。默认值到NULL，所有的二进制文件的意思是相同的权重。
Details
详细信息----------Details----------
The MCMC output in file can be analysed using the function analyse.models. In the help of this function it is also described how the models are stored in file.
MCMC输出file可以分析使用的功能analyse.models。在此功能的帮助下，它也描述了如何在模型存储在file。
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td>accept</td> <td> acceptance rate of MCMC algorithm.</td></tr> <tr valign="top"><td>pred</td> <td> vector of predicted case probabilities. Only given if predict = TRUE.</td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD> accept</ TD> <TD>录取率的MCMC算法。</ TD> </ TR> <TR VALIGN = “顶”> <TD>pred</ TD> <TD>预测的情况下，概率向量。只有predict = TRUE。</ TD> </ TR>
</table>
</ TABLE>
（作者）----------Author(s)----------
Arno Fritsch, <a href="mailto:arno.fritsch@uni-dortmund.de">arno.fritsch@uni-dortmund.de</a>
参考文献----------References----------
based methods for identifying SNP interactions. In Bioinformatics in Research and Development, Hochreiter, S.\ and
参见----------See Also----------
analyse.models,predictFBLR
analyse.models，predictFBLR
实例----------Examples----------
# SNP dataset with 500 persons and 20 SNPs each,[有500人，20个SNPs每个SNP数据集，]
# a two-SNP interaction influences the case probability[一个的两个SNP互动影响的情况下概率]
snp <- matrix(rbinom(500*20,2,0.3),ncol=20)
bin <- snp2bin(snp)
int <- apply(bin,1,function(x) (x[1] == 1 & x[3] == 0)*1)
case.prob <- exp(-0.5+log(5)*int)/(1+exp(-0.5+log(5)*int))
y <- rbinom(nrow(snp),1,prob=case.prob)
# normally more iterations should be used[通常迭代次数越多，应使用]
fblr(y, bin, niter=1000, nburn=0)
analyse.models("fblr_mcmc.txt")
# Prior information: SNPs 1-10 belong to genes in one pathway, [在此之前的信息：单核苷酸多态性1-10属于基因的一个途径，]
# SNPs 8-20 to another. Only interactions within a pathway are [单核苷酸多态性8-20到另一个。只有一个路径的相互作用]
# considered and the first pathway is deemed to be twice as [考虑和第一通路被认为是两倍]
# important than the second.[比第二个重要。]
fblr.weight(y,bin,niter=1000, nburn=0, group=list(1:20, 15:40),
weight=c(rep(2,20),rep(1,20)))
analyse.models("fblr_mcmc.txt")
## End(Not run)[＃（不执行）]
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 2012-9-29 23:02:12
