A.mat(rrBLUP)
A.mat()所属R语言包：rrBLUP
Additive relationship matrix
添加剂关系矩阵
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculates the realized additive relationship matrix.
计算已实现的添加剂关系矩阵。
用法----------Usage----------
A.mat(G,min.MAF=NULL,max.missing=NULL,impute=TRUE,tol=0.02,n.core=1,return.G=FALSE)
参数----------Arguments----------
参数：G
Matrix (n \times m) of unphased genotypes for n lines and m biallelic markers, coded as {-1,0,1} = {aa,Aa,AA}. Fractional (imputed) and missing values (NA) are allowed.
矩阵（n \times m）n行m双等位基因位点，编码为{-1,0,1} = {AA，AA，AA} UNPHASED基因型。分数（虚拟）和缺失值（NA）是允许的。
参数：min.MAF
Minimum minor allele frequency; default removes monomorphic markers.
最小次要等位基因频率，默认删除单态的标记。
参数：max.missing
Maximum proportion of missing data; default removes completely missing markers.
最大比例;默认情况下删除丢失的数据完全丢失的标记。
参数：impute
If TRUE, missing genotypic data are imputed (see below). If FALSE, A is calculated from pairwise complete observations, which does not guarantee positive semidefiniteness (this can cause problems with mixed.solve).
如果是TRUE，缺失基因型数据估算的（见下文）。如果为false，计算成对完整的观测，这并不能保证正半定（这可能会导致问题，mixed.solve）。
参数：tol
Specifies convergence criterion for imputing missing data with an EM algorithm (see details). If tol < 0, missing data are imputed with the population mean for each marker.
指定收敛准则填充缺失数据的EM算法（见详情）。 TOL <0，如果丢失的数据是估算的人口平均每个标记。
参数：n.core
For Mac, Linux, and UNIX users, setting n.core > 1 will enable parallel execution on a machine with multiple cores. R package multicore must be installed for this to work. Do not run multicore from within the R GUI; you must use the command line.
在Mac，Linux，和UNIX用户，设置n.core> 1，使多核心的机器上并行执行。 R封装的多核必须安装这个工作。不要在R GUI运行多核，你必须使用命令行。
参数：return.G
If TRUE (and impute = TRUE), the imputed marker matrix is returned. When the EM algorithm is used, the imputed alleles can lie outside the interval [-1,1]. Polymorphic markers that do not meet the min.MAF and max.missing criteria are not imputed.
如果是TRUE（归罪于= TRUE），返回矩阵的估算标记。当使用EM算法，估算等位基因不在区间[-1,1]。多态性遗传标记，不符合min.MAF和max.missing标准估算。
Details
详细信息----------Details----------
The A matrix is calculated as W W'/c, where W_{ik} = G_{ik} + 1 - 2 p_k and p_k is the frequency of the 1 allele at marker k. The normalization constant is c = 2 ∑_k {p_k (1-p_k)}.
A矩阵的计算公式为W W'/c，其中W_{ik} = G_{ik} + 1 - 2 p_k和p_k是在标记K表1等位基因的频率。归一化常数是c = 2 ∑_k {p_k (1-p_k)}。
When marker data are missing, by default an EM algorithm is used to impute genotypes and converge on the maximum likelihood solution for A. The EM algorithm stops at iteration t when the RMS error = n^{-1} \|A_{t} - A_{t-1}\|_2 < tol. If the user passes a negative value for tol, or if the number of markers is less than the number of lines, missing alleles are imputed with the population mean for each marker.
当标记的数据丢失，默认情况下，使用EM算法归咎于基因型和EM算法收敛的最大可能的解决方案A.停在迭代吨时的均方根误差=n^{-1} \|A_{t} - A_{t-1}\|_2TOL。如果用户通过一个负值托尔，或的意思是每个标记的标记数，如果是小于的行数，缺失的等位基因与人口插补。
值----------Value----------
If return.G = FALSE, the n \times n additive relationship matrix is returned.
如果return.G = FALSE，n \times n添加剂关系矩阵，则返回。
If return.G = TRUE, a list containing
如果return.G = TRUE，一个列表，其中包含
A the A matrix
一个矩阵A
G.imp the imputed G matrix
G.imp G矩阵的估算
实例----------Examples----------
#random population of 200 lines with 1000 markers[随机人口200线1000标记]
G <- matrix(rep(0,200*1000),200,1000)
for (i in 1:200) {
G[i,] <- ifelse(runif(1000)<0.5,-1,1)
}
#Additive relationship matrix[添加剂关系矩阵]
A <- A.mat(G)
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-28 20:34:48
