seqBoundariesGrid(gaga)
seqBoundariesGrid()所属R语言包：gaga
Evaluate expected utility for parametric sequential stopping boundaries.
预计参数的顺序停止边界的效用评估。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate the expected utility for sequential boundaries parameterized by (b0,b1). Expected utility is estimated on a grid of (b0,b1) values based on a forward simulation output such as that generated by the function forwsimDiffExpr.
估计为连续边界参数（B0，B1）的预期效用。期望效用估计上的网格（B0，B1）价值观基础上的，如功能forwsimDiffExpr正向模拟输出。
用法----------Usage----------
seqBoundariesGrid(b0, b1, forwsim, samplingCost, powmin = 0, f = "linear", ineq = "less")
参数----------Arguments----------
参数：b0
Vector with b0 values. Expected utility is evaluated for a grid defined by all combinations of (b0,b1) values.
向量B0价值。预期效用评估（B0，B1）的所有组合值定义一个网格。
参数：b1
Vector with b1 values.
B1值向量。
参数：forwsim
data.frame with forward simulation output, such as that returned by the function forwsimDiffExpr. It must have columns named simid, time, u, fdr, fnr, power and summary. See forwsimDiffExpr for details on the meaning of each column.
data.frame正向模拟输出，如功能forwsimDiffExpr返回。它必须有列名为simid，time，u，fdr，fnr，power和summary。看到forwsimDiffExpr每列的含义的详细信息。
参数：samplingCost
Cost of obtaining one more data batch, in terms of the number of new truly differentially expressed discoveries that would make it worthwhile to obtain one more data batch.
获得一次一批数据的成本，在真正差异表达的新的发现，将使值得获得一个一批数据的数量。
参数：powmin
Constraint on power. Optimization chooses the optimal b0, b1 satisfying power>=powermin (if such b0,b1 exists).
约束权力。优化选择最佳b0，b1满意的权力> = powermin如b0，b1存在。
参数：f
Parametric form for the stopping boundary. Currently only 'linear' and 'invsqrt' are implemented. For 'linear', the boundary is b0+b1*time. For 'invsqrt', the boundary is b0+b1/sqrt(time), where time is the sample size measured as number of batches.
停止边界的参数形式。目前只有“线性”和“invsqrt”的贯彻落实。对于“线性”，边界是b0+b1*time。为invsqrt，边界b0+b1/sqrt(time)，时间是样本大小，批次的数量来衡量。
参数：ineq
For ineq=='less' the trial stops when summary is below the stopping boundary. This is appropriate whenever summary measures the potential benefit of obtaining one more data batch. For ineq=='greater' the trial stops when summary is above the stopping boundary. This is approapriate whenever summary measures the potential costs of obtaining one more data batch.
ineq=='less'审判停止时summary下面是停止边界。这是适当的，每当summary措施取得一一批数据的潜在好处。 ineq=='greater'审判停止时summary停止边界以上。这是approapriate时summary措施取得一一批数据的潜在成本。
Details
详情----------Details----------
Intuitively, the goal is to stop collecting new data when the expected benefit of obtaining one more data batch is small, i.e. below a certain boundary. We consider two simple parametric forms for such a boundary (linear and inverse square root), which allows to easily evaluate the expected utility for each boundary within a grid of parameter values. The optimal boundary is defined by the parameter values achieving the largest expected utility, restricted to parameter values with an estimated power greater or equal than powmin. Here power is defined as the expected number of true discoveries divided by the expected number of differentially expressed entities.
直观地，我们的目标是停止收集新的数据时获得一次一批数据的预期收益是小的，即低于一定的边界。我们考虑两个简单的边界参数形式（线性和反平方根），它允许轻松地评估每个网格内的参数值的边界的预期效用。实现预期效用最大，估计功率大于或比powmin平等的限制参数值由参数值的最优边界定义。在这里，权力被定义为预计数除以预期的差异表达的实体的真实发现。
The routine evaluates the expected utility, as well as expected FDR, FNR, power and sample size for each specified boundary, and also reports the optimal boundary.
常规评估的预期效用，以及预期FDR，FNR的，功率为每个指定的边界和样本大小，还报告了最佳的边界。
值----------Value----------
A list with two components:
两部分组成名单：
参数：opt
Vector with optimal stopping boundary (b), estimated expected utility (u), false discovery rate (fdr), false negative rate (fnr), power (power) and the expected sample size measured as the number of batches (time).
向量最优停止边界（b），估计预期效用（u），错误发现率（fdr），假阴性率（fnr），电源（power）和一批批（time）作为衡量预期的样本大小。
参数：grid
data.frame with all evaluated boundaries (columns b0 and b1) and their respective estimated expected utility, false discovery rate, false negative rate, power and expected sample size (measured as the number of batches).
data.frame所有评估的界限（列b0和b1）和各自的估计预期效用，虚假的发现率，假阴性率，功率和预期的样本大小（批次的数量来衡量）。
作者（S）----------Author(s)----------
David Rossell.
参考文献----------References----------
high-throughput hypothesis testing experiments. http://sites.google.com/site/rosselldavid/home.
data analysis. Annals of Applied Statistics, 2009, 3, 1035-1051.
参见----------See Also----------
forwsimDiffExpr
forwsimDiffExpr
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 2012-2-25 18:22:14
