vim.set(logicFS)
vim.set()所属R语言包：logicFS
VIM for SNPs and Sets of Variables
VIM的单核苷酸多态性和组变量
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Quantifies the importances of SNPs or sets of variables, respectively, contained in a logic bagging model.
量化的SNP或组变量的重要性，分别包含在逻辑装袋模型。
用法----------Usage----------
vim.snp(object, useN = NULL, iter = NULL, standardize = NULL,
mu = 0, addMatImp = FALSE, prob.case = 0.5, rand = NA)
vim.set(object, set = NULL, useN = NULL, iter = NULL, standardize = NULL,
mu = 0, addMatImp = FALSE, prob.case = 0.5, rand = NA)
参数----------Arguments----------
参数：object
an object of class logicBagg, i.e.\ the output of logic.bagging.
一个类对象logicBagg，即\logic.bagging的输出。
参数：set
either a list or a character or numeric vector. If NULL (default), then it will be assumed that data, i.e.\ the data set used in the application of logic.bagging, has been generated using make.snp.dummy or similar functions for coding variables by binary variables, i.e.\ with a function that splits a variable, say SNPx, into the dummy variables SNPx.1, SNPx.2, ... (where the “." can also be any other sign, e.g., an underscore). If a character or a numeric vector, then the length of set must be equal to the number of variables used in object, i.e.\ the number of columns of data in the logicBagg object, and must specify the set to which a variable belongs either by an integer between 1 and the number of sets, or by a set name. If a variable should not be included in any of the sets, set the corresponding entry of set to NA. Using this specification of set it is not possible to assign a variable to more than one sets. For such a case, set set to a list (as follows). If set is a list, then each object in this list represents a set of variables. Therefore, each object must be either a character or a numeric vector specifying either the names of the variables that belongs to the respective set or the columns of data that contains these variables. If names(set) is NULL, generic names will be employed as names for the sets. Otherwise, names(set) are used.
一个列表或一个字符或数字向量。 NULL如果（默认），然后将假定data，即\中的应用使用数据集logic.bagging，已产生使用make.snp.dummy或类似功能编码二进制变量的变量，即\分割变量与函数，说成假人的变量SNPx.1，SNPx.2 SNPX，... （“。”也可以是任何其他标志，如下划线）。如果一个字符或数字向量，然后的set长度必须等于object，即\列数data<X中使用的变量的数量>对象，必须指定一个变量属于1套，之间的整数，或由一个集名称的集合。如果一个变量不应包括在任何套，设置相应的条目logicBaggset。使用本规范NA这是不可能指定一个变量多套。对于这样的情况下，设置一个列表set（如下）。如果set是一个列表，然后在此列表中的每个对象代表一组变量。因此，每个对象必须是一个字符或一个数值向量指定的变量属于各自的组或列的名称set包含这些变量。如果datanames(set)，通用名称将受聘为集的名称。否则，NULL使用。
参数：useN
logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If FALSE, the proportion of correctly classified oob observations is used instead. If NULL (default), then the specification of useN in object is used.
逻辑指定的数量，如果正确分类袋意见，应在计算的重要措施。如果FALSE，OOB意见正确归类的比例来代替。如果NULL（默认），然后规范useNobject使用的。
参数：iter
integer specifying the number of times the values of the variables in the respective set are permuted in the computation of the importance of this set. If NULL (default), the values of the variables are not permuted, but all variables belonging to the set are removed from the model
整数，指定时间在各自组中的变量的值的数量置换计算，在这一集的重要性。如果NULL（默认），变量的值不置换，但属于集合的所有变量从模型中删除
参数：standardize
should a standardized version of the importance measure for a set of variables be returned? By default, standardize = TRUE is used in the classification and the (multinomial) logistic regression case, and standarize is set to FALSE in the linear regression case. For details, see mu.
应该返回的一组变量的标准化版本的重要措施？默认情况下，standardize = TRUE使用的分类（多项）logistic回归的情况下，standarize设置为FALSE中的线性回归的情况下。有关详细信息，请参阅mu。
参数：mu
a non-negative numeric value. Ignored if standardize = FALSE. Otherwise, a t-statistic for testing the null hypothesis that the importance of the respective set is equal to mu is computed.
一个非负的数值。如果standardize = FALSE忽略。否则，测试的零假设，即各自一套的重要性等于mu t-统计计算。
参数：addMatImp
should the matrix containing the improvements due to each of the sets in each of the logic regression models be added to the output?
应该被添加到输出矩阵包含在每个逻辑回归模型的改进，由于各套？
参数：prob.case
a numeric value between 0 and 1. If the logistic regression approach of logic regression has been used in logic.bagging, then an observation will be classified as a case (or more exactly, as 1), if the class probability of this observation is larger than prob.case. Otherwise, prob.case is ignored.
0和1之间的数值。如果使用逻辑回归Logistic回归方法已在logic.bagging，然后观察将被列为情况（或者更确切地说，为1），如果这个观察类的概率比prob.case 。否则，prob.case被忽略。
参数：rand
an integer for setting the random number generator in a reproducible state.
重现状态设置随机数发生器的整数。
值----------Value----------
An object of class logicFS containing
一个类的对象logicFS含有
参数：vim
the importances of the sets of variables,
变量的重要性，
参数：prop
NULL,
NULL
参数：primes
the names of the sets of variables,
变量的名称，
参数：type
the type of model (1: classification, 2:linear regression, 3: logistic regression),
类型分类（1：2：线性回归，logistic回归）模型，
参数：param
further parameters (if addInfo = TRUE in the previous call of logic.bagging), or NULL (otherwise),
更多的参数（如果addInfo = TRUE在以前调用logic.bagging），或NULL（否则）
参数：mat.imp
either a matrix containing the improvements due to the sets of variables for each of the models (if addMatImp = TRUE), or NULL (if addMatImp = FALSE),
无论是矩阵包含改善由于每个模型变量的集合（如果addMatImp = TRUE），或NULL（如果addMatImp = FALSE）
参数：measure
the name of the used importance measure,
所使用的重要性措施的名称，
参数：threshold
NULL if standardize = FALSE, otherwise the 1-0.05/m quantile of the t-distribution with B-1 degrees of freedom, where m is the number of sets and B is the number of logic regression models composing object,
NULL如果standardize = FALSE，否则1-0.05/m与B-1自由度，t分布的分位数m套<X >是逻辑回归模型的组成B
参数：mu
mu (if standardize = TRUE), or NULL (otherwise),
mu（如果standardize = TRUE），或NULL（否则）
参数：iter
iter.
iter。
作者（S）----------Author(s)----------
Holger Schwender, <a href="mailto:holger.schwender@udo.edu">holger.schwender@udo.edu</a>
参考文献----------References----------
Biostatistics, 12, 18-32.
参见----------See Also----------
logic.bagging, logicFS, vim.logicFS, vim.input, vim.ebam, vim.chisq
logic.bagging，logicFS，vim.logicFS，vim.input，vim.ebam，vim.chisq
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 23:36:35
