designMA(daMA)
designMA()所属R语言包：daMA
DESIGN OF FACTORIAL MICROARRAY EXPERIMENTS
阶乘的芯片实验设计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
designMA computes efficient factorial microarray experimental designs for two-colour microarrays based on a list of user-defined design matrices, a matrix describing the experimental questions (contrasts), a vector to discern vectorial contrasts from contrasts given in matrix form
designMA效率因子的基因芯片实验设计计算两色的芯片基础上的用户自定义的设计矩阵，矩阵描述实验问题（对比），矢量矢量对比辨别矩阵形式给出的对比
用法----------Usage----------
designMA(design.list, cmat, cinfo, type = c("d", "e", "t"), tol = 1e-06)
参数----------Arguments----------
参数：design.list
a named list of design matrices. Each design matrix should have nrow = number of arrays and ncol= number of experimental conditions. With p columns, the first two columns describe the dye labeling (green and red), the remaining columns describe the experimental conditions.
一个名为设计矩阵列表。每一个设计矩阵应该有数组和数量NCOL =实验条件NROW =。与p列，前两列描述染料标记（绿色和红色），其余各列描述的实验条件。
参数：cmat
a matrix describing the experimental questions (contrasts) to be analysed in the experiment. The matrix can be composed of vectorial contrasts (a single row of the matrix) and of contrasts in matrix form (several rows of the matrix), e.g. an A \times B interaction effect in a 3 \times 2 design. All contrasts have to be combined into one matrix (using rbind for instance).
矩阵描述在实验分析实验问题（对比）。矩阵可组成矢量对比（单列的矩阵）和对比矩阵形式（矩阵的几行），例如A \times B3 \times 2设计中的互动效应。所有对比都必须结合成一个矩阵（例如使用rbind）。
参数：cinfo
a vector describing the grouping of the contrast matrix rows in vector or matrix form. E.g. if the design matrix contains three contrasts in vector form, cinfo = rep(1,3), if it contains two vectorial contratst and one as matrix with three rows, cinfo=c(1,1,3).
向量描述分组对比矩阵的行向量或矩阵形式。例如如果设计矩阵包含三个对比向量形式，CINFO =（1,3）代表，如果它包含两个的矢量contratst和三行为基质，CINFO = C（1,1,3）。
参数：type
a quoted letter indicating the optimality criterion that shoul be used. "d" - determinant, "e" - eigenvalue, "t" - trace.
一个引用的信中表示，建议立即进行删除用于最优标准。 “D” - 行列式，“E” - 特征值，“T” - 跟踪。
参数：tol
A value indicating the tolerance for contrast estimability check.
一个值，该值指示的对比估计性检查的耐受性。
Details
详情----------Details----------
The choice of the optimality criterion influences the design defined as best. We propose the trace criterion because of its straightforward interpretability. For a detailed description of optimality criteria cf. Pukelsheim, F. "Optimal Design of Experiments", New York 1993.
选择最优标准的影响定义为最佳的设计。我们建议，因为它简单的解释性跟踪标准。为最优标准比照的详细说明。 pukelsheim，F“实验的优化设计”，1993年纽约。
值----------Value----------
a list with the following components
以下组件的列表
参数：alleff
a matrix giving the absolute efficiency values (cols) for each contrast (rows). NA if contrast is not estimatable.
一个矩阵，使每个对比的绝对效率值（列）（行）。那幺相反，如果不是estimatable。
参数：alleffrel
a matrix giving the relative efficiency values (cols) for each contrast (rows). The values are obtained by dividing the absolute values by the by the maximal efficiency value for a given contrast. NA if contrast is not estimatable.
一个矩阵，使每个对比的相对效率值（列）（行）。由一个给定的对比度最大效率值的绝对值除以得到的值。那幺相反，如果不是estimatable。
参数：alleffave
a vector giving the average efficiency for each design over all contrasts.
每超过所有对比的设计提供一个向量的平均效率。
参数：effdesign
the name of the design with the highest alleffave value.
设计与最高alleffave价值的名称。
参数：df
a vector with the degrees of freedom of the F-statistics obtained by the designs.
设计获得的F-统计的自由程度与向量。
作者（S）----------Author(s)----------
Jobst Landgrebe (jlandgr1@gwdg.de) and Frank Bretz (bretz@bioinf.uni-hannover.de)
参考文献----------References----------
two colour factorial microarray experiments", submitted. http://www.microarrays.med.uni-goettingen.de/
举例----------Examples----------
## Not run: designs.composite #look at comlpex composite designs[＃无法运行：＃designs.composite看comlpex复合设计]
## Not run: t.eff.3x2.B.AB <- designMA(designs.composite,[＃无法运行：t.eff.3x2.B.AB < - designMA（designs.composite]
cmatB.AB,cinfoB.AB,type="t")# compute design efficiencies for[计算设计效率为]
# a \eqn{3 \times 2} factorial experiment[\ EQN {3 \ 2倍}因子实验]
# using 18 microarrays and asking for [使用18个芯片和要求]
# the main effect B and the interaction effect \eqn{A \times B}[主要影响B和互动效应\ EQN为{A \次B}]
## End(Not run)[＃结束（不运行）]
## Not run: t.eff.3x2.all <- designMA(designs.composite,[＃无法运行：t.eff.3x2.all < - designMA（designs.composite]
cmat,cinfo,type="t")
## End(Not run) #compute design efficiencies design for[＃结束（不执行）＃计算设计效率设计]
# a \eqn{3 \times 2} factorial[\ EQN {3 \倍2}的阶乘]
# experiment using 18[实验中使用18]
# microarrays and asking for [芯片和要求]
# the the simple B[在简单的B]
# effects, the main effects[的影响，主要影响]
# A, B and the interaction[A，B和相互作用]
# effect \eqn{A \times B}[效果\ EQN为{A \次B}]
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 2012-2-25 16:14:02
