GlobalAncova-methods(GlobalAncova)
GlobalAncova-methods()所属R语言包：GlobalAncova
Methods for Function GlobalAncova
方法功能GlobalAncova
译者：生物统计家园网 机器人LoveR
描述----------Description----------
There are three possible ways of using GlobalAncova. The general way is to define formulas for the full and reduced model, respectively, where the formula terms correspond to variables in model.dat. An alternative is to specify the full model and the name of the model terms that shall be tested regarding differential expression. In order to make this layout compatible with the function call in the first version of the package there is also a method where simply a group variable (and possibly covariate information) has to be given. This is maybe the easiest usage in cases where no 'special' effects like e.g. interactions are of interest.
使用GlobalAncova有三种可能的方式。一般的方法是定义公式，为全面和简化模型，公式计算对应变量model.dat。另一种方法是指定完整的模型和模型方面的差异表达方面应测试的名称。为了使这种布局在包的第一个版本的函数调用兼容也有方法给予简单的一组变量（可能协信息）。这也许是最简单的情况下，使用例如像没有特殊的影响相互作用的利益。
方法----------Methods----------
xx = "matrix", formula.full = "formula", formula.red = "formula", model.dat = "ANY", group = "missing", covars = "missing", test.terms = "missing" In this method, besides the expression matrix xx, model formulas for the full and reduced model and a data frame model.dat specifying corresponding model terms have to be given. Terms that are included in the full but not in the reduced model are those whose association with differential expression will be tested. The arguments group, covars and test.terms are '"missing"'
XX =“矩阵”，formula.full =“公式”，formula.red =“的公式”，model.dat =“任何”，组=“失踪”，covars =“失踪”，测试。条件=“失踪”在这种方法中，除了表达矩阵，xx，充分和减少模型和数据框model.dat指定相应的模型计算，必须给模型公式。在全包括，但不是在减少模型的条款，其差异表达的关系将受到考验。的论点group，covars和test.terms是“失踪”
xx = "matrix", formula.full = "formula", formula.red = "missing", model.dat = "ANY", group = "missing", covars = "missing", test.terms = "character" In this method, besides the expression matrix xx, a model formula for the full model and a data frame model.dat specifying corresponding model terms are required. The character argument test.terms names the terms of interest whose association with differential expression will be tested. The basic idea behind this method is that one can select single terms, possibly from the list of terms provided by previous GlobalAncova output, and test them without having to specify each time a model formula for the reduced model. The arguments formula.red, group and covars are '"missing"'
XX =“矩阵”，formula.full =“公式”，formula.red =“失踪”，model.dat =“任何”，组=“失踪”，covars =“失踪”，测试。条款=“字符”，在此方法中，除了表达矩阵xx，一个完整的模型和数据框的模型公式model.dat指定相应的模型计算需要。字符参数test.terms命名权益的条款，其差异表达的关系将受到考验。这种方法背后的基本想法是，人们可以选择单一的条款，可能从以前的GlobalAncova输出规定的名单，他们无需为减少模型的模型公式来指定每次测试。的论点formula.red，group和covars是“失踪”
xx = "matrix", formula.full = "missing", formula.red = "missing", model.dat = "missing", group = "ANY", covars = "ANY", test.terms = "missing" Besides the expression matrix xx a clinical variable group is required. Covariate adjustment is possible via the argument covars but more complex models have to be specified with the methods described above. This method emulates the function call in the first version of the package. The arguments formula.full, formula.red, model.dat and
XX =“矩阵”，formula.full =“失踪”，formula.red =“失踪”，model.dat =“失踪”，组=的“ANY”，covars =“任何”，测试。 =“失踪”除了表达矩阵xx临床变量group是必需的。协变量调整是可能通过参数covars但更复杂的模型必须与上面描述的方法指定。这种方法模拟包的第一个版本的功能调用。参数formula.full，formula.red，model.dat“
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。
|
发表于 2012-2-25 20:40:16
