multistagegain.each(selectiongain)
multistagegain.each()所属R语言包：selectiongain
Function for calculating the selection gain in each stage
计算在每个阶段的选择增益的功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
In some situations, the user wants to know the increase of Δ G(y) for each stage so that it is possible to determine the stage which contributes most to Δ G(y). This function calculates Δ G(y) stepwise for each stage.
在某些情况下，用户希望知道的增加Δ G(y)每个阶段，以便它是可能的，以确定的阶段，这有助于最Δ G(y)的。此函数计算Δ G(y)每个阶段逐步。
用法----------Usage----------
multistagegain.each(Q, corr, alg, lim.y)
参数----------Arguments----------
参数：Q
(length n) refers to the coordinates of the truncation points Q, which is the output of the next function (multistagetp) that we are going to introduced.
（长度为n）是指截断点Q，这是下一个函数的输出的坐标（multistagetp），我们将要介绍。
参数：corr
(n+1-dimensional matrix) is the correlation matrix of y and X. The correlation matrix must be symmetric and positive-definite. Before starting the calculations, the user is recommended to check the correlation matrix, which is usually obtained by analysis of one or several experiments designed for estimating covariance components as well as phenotypic and genotypic correlations.
（n +1次的二维矩阵）是y和X的相关矩阵的相关矩阵必须是对称的正定。开始计算之前，用户将被建议来检查的相关矩阵，这通常是通过以下方式获得的一个或几个实验设计用于估计协方差分量以及表型和基因型的相关性分析。
参数：alg
is used to switch between two algorithms. If alg = GenzBretz(), which is by default, the quasi-Monte Carlo algorithm from Genz(1999) will be used. If alg = Miwa(), the program will use the Miwa algorithm (Mi2009), which an analytical solution of the MVN integral (Miwa2003). Miwa's algorithm has higher accuracy (7 digits) than quasi-Monte Carlo algorithm (5 digits), however the computational speed is also slower. Therefore, we recommend the user to use the default value of this parameter.
用于两种算法之间切换。如果ALG：= GenzBretz（），这是在默认情况下，准蒙特卡洛算法GENZ（1999）将被使用。如果ALG =三轮（）时，程序将使用的三轮的算法（Mi2009），它的解析解的的MVN积分（Miwa2003的）。三轮的算法具有更高的精度（7位）比准蒙特卡洛算法（5位），但计算速度也比较慢。因此，我们建议用户使用此参数的默认值。
参数：lim.y
is the lower limit of y and is set to -200 as default, which is on the safe side.
是y和下限被设置为-200作为缺省情况下，这是在安全方面。
Details
详细信息----------Details----------
This function calculates the well-known selection gain Δ G(y), which is described by Cochran (1951). For one-stage selection the gain is defined as Δ G (y) = i ρ_{y} ρ_{1}, where i is the selection intensity, ρ_{1} is the correlation between the true breeding value and the selection index y (Utz 1969).
此函数计算选择增益著名的Δ G(y)，这是所描述的科克伦（1951）。对于一个阶段的选择的增益被定义为Δ G (y) = i ρ_{y} ρ_{1}，其中i是选择强度，ρ_{1}是真实育种值和选择指数之间的相关性的y（伍兹1969年）。
More details are in the JSS paper section 3.2.
更多细节JSS本文3.2节。
值----------Value----------
The output is given as (Δ G_1(y), Δ G_2(y)-Δ G_1(y), Δ G_3(y)-Δ G_2(y), ...)$ where $Δ G_i(y) refers to the total selection gain after the first i stages of selection.
输出(Δ G_1(y), Δ G_2(y)-Δ G_1(y), Δ G_3(y)-Δ G_2(y), ...)$ where $Δ G_i(y)指的是总选择增益后的第i个阶段的选择。
注意----------Note----------
No further notes
没有进一步的说明
（作者）----------Author(s)----------
Xuefei Mi
参考文献----------References----------
参见----------See Also----------
selectiongain
selectiongain
实例----------Examples----------
k=c(-200,0.4308,0.9804,1.8603)
corr=matrix( c(1, 0.3508,0.3508,0.4979,
0.3508 ,1, 0.3016,0.5630,
0.3508, 0.3016,1 ,0.5630,
0.4979, 0.5630,0.5630,1),
nrow=4
)
multistagegain.each(Q=c(0.4308,0.9804,1.8603),corr=corr)
# further examples 3 for the JSS paper[更多的例子3 JSS纸]
alpha1<- 1/24
alpha2<- 1
Q=multistagetp(alpha=c(alpha1,alpha2),corx=corr[2:3,2:3])
k=c(-200,Q)
corr=matrix( c(1, 0.7071068, 0.9354143,
0.7071068, 1, 0.7559289,
0.9354143, 0.7559289, 1
),
nrow=3
)
alphaofx=pmvnorm(lower=k,corr=corr)
multistagegain(Q=Q,corr=corr,)
multistagegain(Q=Q,corr=corr,stages=TRUE)
multistagegain.each(Q=Q,corr=corr)
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 2012-9-30 00:26:28
