trioFS(trio)
trioFS()所属R语言包：trio
Trio Feature Selection
三重奏的特征选择
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs a trioFS (trio Feature Selection) analysis as proposed by Schwender et al. (2011) based on bagging/subsampling with base learner trio logic regression (Li et al., 2011).
执行trioFS（三人特征选择）分析所提出的Schwender等。 （2011年）的基础上装袋/二次抽样碱基学习者三人逻辑回归（Li等人，2011）。
用法----------Usage----------
## Default S3 method:[默认方法]
trioFS(x, y, B = 20, nleaves = 5, replace = TRUE, sub.frac = 0.632,
control = lrControl(), fast = FALSE, addMatImp = TRUE, addModels = TRUE,
verbose = FALSE, rand = NA, ...)
## S3 method for class 'trioPrepare'
trioFS(x, ...)
## S3 method for class 'formula'[类formula的方法]
trioFS(formula, data, recdom = TRUE, ...)
参数----------Arguments----------
参数：x
either an object of class trioPrepare, i.e. the output of trio.prepare, or a binary matrix consisting of zeros and ones. If the latter, then each column of x must correspond to a binary variable (e.g., codng for a dominant or a recessive effect of a SNP), and each row to a case or a pseudo-control, where each trio is represented by a block of four consecutive rows of x containing the data for the case and the three matched pseudo-controls (in this order) so that the first four rows of x comprise the data for the first trio, rows 5-8 the data for the seocnd trio, and so on. Missing values are not allowed. A convenient way to generate this matrix is to use the function trio.prepare. Afterwards, trioLR can be directly applied to the output of trio.prepare.
无论是类的一个对象trioPrepare，即输出的trio.prepare，或一个二进制矩阵，由零和一。如果是后者，那么每个列x必须对应于一个二进制变量（例如，codng显性或隐性的SNP效果），并且每行的情况下或一个伪控制，其中每个三重奏表示由四个连续的行的块x包含的数据的情况下，三个匹配的伪控制（以该顺序），使得前四行x包括的数据所第一个三重奏，行5-8的seocnd三人的数据，依此类推。遗漏值是不允许的。一个方便的方法来生成这个矩阵是使用的功能trio.prepare。之后，trioLR可以直接应用于输出trio.prepare。
参数：y
a numeric vector specifying the case-pseudo-control status for the observations in x (if x is a binary matrix). Since in trio logic regression, cases are coded by a 3 and pseudo-controls by a 0, y is given by rep(c(3, 0, 0, 0), n.trios), where n.trios is the number of trios for which genotype data is stored in x. Thus, the length of y must be equal to the number of rows in x. No missing values are allowed in y. If not specified, y will be automatically generated.
一个数字矢量指定x（x是一个二进制矩阵）的意见的情况下，伪控制状态的。因为在三人逻辑回归，情况编码一个3，0的y，其中rep(c(3, 0, 0, 0), n.trios)是一个n.trios和伪控制三重奏的基因型数据被存储在x数目。因此，y的长度必须等于在x的行数。无遗漏值将被允许在y。如果未指定，y将自动生成。
参数：B
number of bootstrap samples or subsamples used in trioFS
用于trioFS的的bootstrap样本或子样本数
参数：nleaves
maximum number of leaves, i.e.\ variables, in the logic tree considered in each of the B trio logic regression models (please note in trio logic regression the model consists only of one logic tree).
叶片的最大数量，即\变量，在逻辑树认为在每个B三人逻辑回归模型（请注意在三人逻辑回归模型只包含一个逻辑树）。
参数：replace
should sampling of the trios be done with replacement? If TRUE, a Bootstrap sample of size n.trios is drawn from the n.trios trios in each of the B iterations. If FALSE, ceiling(sub.frac * n.trios) of the trios are drawn without replacement in each iteration.
应该做的三重奏采样更换吗？如果TRUE，引导样品的大小n.trios来自n.trios三重奏在每个B迭代。如果FALSE，ceiling(sub.frac * n.trios)绘制的三重奏在每次迭代中无需更换。
参数：sub.frac
a proportion specifying the fraction of trios that are used in each iteration to fit a trio logic regression model if replace = FALSE. Ignored if replace = TRUE.
比例的三重奏中所使用的每一次迭代，如果replace = FALSE三人逻辑回归模型，以适应指定的分数。如果忽略replace = TRUE。
参数：control
a list of control parameters for the search algorithms and the logic trees considered when fitting the trio logic regression model, where the parameters for an MC logic regression are ignored. For details and the parameters, see lrControl, which is the function that should be used to specify control.
一个控制参数列表的搜索算法和逻辑树时，会考虑装修的三重奏逻辑回归模型中，MC逻辑回归的参数将被忽略。的详细信息和参数，请参阅lrControl，这是应该使用的函数，该函数指定control。
参数：fast
should a greedy search be used instead of simulated annealing, i.e. the standard search algorithm in (trio) logic regression?
一个贪婪的搜索，而不是使用模拟退火法，即标准的搜索算法（三人）逻辑回归？
参数：addMatImp
should the matrix containing the improvements due to the interactions in each of the iterations be added to the output, where the importance of each interaction is computed by the average over the B improvements due to this interaction?
含有改善由于在每个迭代中的相互作用的矩阵应该被添加到输出端，其中在B改善由于这种相互作用的平均计算的重要性的每个交互？
参数：addModels
should the B trio logic regression models be added to the output
B三人逻辑回归模型被添加到输出
参数：verbose
should some comments on the progress the trioFS analysis be printed?
一些意见的进展trioFS分析印制的？
参数：rand
positive integer. If specified, the random number generator is set into a reproducible state.
正整数。如果指定，随机数生成器设置成一个可重复的状态。
参数：formula
an object of class formula describing the model that should be fitted.
类formula描述的模式，应安装一个对象。
参数：data
a data frame containing the variables in the model. Each row of data must correspond to an observation, and each column to a binary variable (coded by 0 and 1) or a factor (for details, see recdom) except for the column comprising the response, where no missing values are allowed in data. For a description of the specification of the response, see y.
一个数据框包含在模型中的变量。的每一行data必须符合观察，每列一个二元变量（由0和1的编码）或因子（有关详细信息，请参阅recdom）除列，包括响应，没有缺失值被允许在data。如果在响应的说明书的描述，请参阅y。
参数：recdom
a logical value or vector of length ncol(data) comprising whether a SNP should be transformed into two binary dummy variables coding for a recessive and a dominant effect. If recdom is TRUE (and a logical value), then all factors/variables with three levels will be coded by two dummy variables as described in make.snp.dummy. Each level of each of the other factors (also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable. If recdom isFALSE (and a logical value), each level of each factor is coded by an indicator variable. If recdom is a logical vector, all factors corresponding to an entry in recdom that is TRUE are assumed to be SNPs and transformed into two binary variables as described above. All variables corresponding to entries of recdom that are TRUE (no matter whether recdom is a vector or a value) must be coded either by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant), or alternatively by the number of minor alleles, i.e. 0, 1, and 2, where no mixing of the two coding schemes is allowed. Thus, it is not allowed that some SNPs are coded by 1, 2, and 3, and others are coded by 0, 1, and 2.
逻辑值或向量的长度ncol(data)包括一个SNP是否应转变为两个二进制编码的隐性和显性效应的虚拟变量。如果recdom是TRUE（和逻辑值），然后所有三个层次的因素/变量将被编码的两个虚拟变量所描述的make.snp.dummy。每一级的每个其他因素（也指定只显示了两个基因型的SNP的因素）编码的一个指标变量。如果recdom是FALSE（和逻辑值），每级各因素的指标变量进行编码。如果recdom是一个逻辑向量，对应的所有因素中的条目recdom，TRUE被认为是个SNP位点，并转化为上述两个二元变量。对应的条目的所有变量recdom是TRUE（无论是否recdom是一个向量或值）必须进行编码，也可以由整数1（编码的纯合子的参考基因型） ，2（杂合子），和3（纯合的变体），或者通过次要等位基因，即0，1和2，其中没有混合的两个编码方案允许的数目。因此，这是不允许的一些SNPs编码由1，2，和3，以及其他的编码由0，1，和2。
参数：...
for the trioPrepare and the formula method, optional parameters to be passed to the low level function trioFS.default, i.e. all arguments of trioFS.default except for x and y. Otherwise, ignored.
低级别的功能trioPrepare，即所有参数的formula的trioFS.default和trioFS.default和x方法，可选的参数被传递到y。否则，忽略不计。
值----------Value----------
An object of class trioFS consisting of
类的一个对象trioFS组成的
参数：vim
a numeric vector containing the values of the importance measure for the found interactions,
含有为所找到的相互作用的重要性度量的值的一个数值向量，
参数：prop
a numeric vector consisting of the percentage of models that contain the respective found interactions,
一个数值向量组成的百分比包含各自的发现相互作用的模型，
参数：primes
a character vector naming the found interactions,
，一个字符向量命名找到的相互作用，
参数：param
a list of parameters used in the trioFS analysis, i.e. B, nleaves, and the sampling method,
使用的参数列表在trioFS分析，即B，nleaves，以及抽样的方法，
参数：mat.imp
if addMatImp = TRUE, a matrix containing the B improvements for each found interaction,
如果addMatImp = TRUE，B改进每一个找到的互动矩阵，
参数：logreg.model
if addModel = TRUE, the B trio logic regression models,
如果addModel = TRUE，B三人逻辑回归模型，
参数：inbagg
if addModel = TRUE, a list of length B in which each object specifies the trios used to fit the corresponding trio logic regression model.
如果addModel = TRUE，列表长度B中的每个对象指定使用的三重奏，以适应相应的的三人逻辑回归模型。
（作者）----------Author(s)----------
Holger Schwender, <a href="mailto:holger.schwender@udo.edu">holger.schwender@udo.edu</a>
参考文献----------References----------
Liang, K.Y., Pulver, A.E., and Ruczinski, I. (2010). Detection of SNP-SNP Interactions in Trios of Parents with Schizophrenic Children. Genetic Epidemiology, 34, 396-406.
in Case-Parent Trios. Annals of Human Genetics, 75, 122-132.
参见----------See Also----------
trioLR, print.trioFS, trio.prepare
trioLR，print.trioFS，trio.prepare
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 2012-10-1 12:07:50
