vim.logicFS(logicFS)
vim.logicFS()所属R语言包：logicFS
Importance Measures
重要性措施
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the value of the single or the multiple tree measure, respectively, for each prime implicant contained in a logic bagging model to specify the importance of the prime implicant for classification, if the response is binary. If the response is quantitative, the importance is specified by a measure based on the log2-transformed mean square prediction error.
单个或多个树措施，分别计算价值，本原蕴涵在逻辑装袋模型指定分类的主要蕴含的重要性，如果是二进制的响应中包含的每个。如果反应是定量的，指定的log2转化的平均平方预测误差为基础的措施的重要性。
用法----------Usage----------
vim.logicFS(log.out, useN = TRUE, onlyRemove = FALSE, prob.case = 0.5,
addInfo = FALSE, addMatImp = TRUE)
参数----------Arguments----------
参数：log.out
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：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.
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。
参数：onlyRemove
should in the single tree case the multiple tree measure be used? If TRUE, the prime implicants are only removed from the trees when determining the importance in the single tree case. If FALSE, the original single tree measure is computed for each prime implicant, i.e.\ a prime implicant is not only removed from the trees in which it is contained, but also added to the trees that do not contain this interaction. Ignored in all other than the classification case.
单树的情况下应该使用多个树措施？如果TRUE，首要implicants是只拆除确定在单树情况的重要性时，从树上。如果FALSE，原单树措施每个首要蕴含计算，即\素蕴含不仅从包含它的树木，但也加入到不包含这种互动的树木。在所有的分类情况比其他忽略。
参数：prob.case
a numeric value between 0 and 1. If the logistic regression approach of logic regression is used (i.e.\ if the response is binary, and in logic.bagging ntrees is set to a value larger than 1, or glm.if.1tree is set to TRUE), then an observation will be classified as a case (or more exactly as 1), if the class probability of this observation estimated by the logic bagging model is larger than prob.case.
0和1之间的数值。如果使用逻辑回归Logistic回归方法（即\的反应，如果是二进制，logic.baggingntrees设置一个值较大，超过1glm.if.1tree设置为 TRUE），然后观察将被列为情况（或更准确地为1），如果逻辑装袋模型估计这个观察类的概率是较大的比prob.case。
参数：addInfo
should further information on the logic regression models be added?
应该增加逻辑回归模型的进一步信息？
参数：addMatImp
should the matrix containing the improvements due to the prime implicants in each of the iterations be added to the output? (For each of the prime implicants, the importance is computed by the average over the B improvements.) Must be set to TRUE, if standardized importances should be computed using vim.norm, or if permutation based importances should be computed using vim.signperm.
应包含在每个迭代的改进，由于黄金implicants矩阵被添加到输出？标准化的重要性，应计算（对于首要implicants，重要性B改善，平均计算。）必须设置TRUE，vim.norm，或者如果置换基础的重要性，应计算使用vim.signperm。
值----------Value----------
An object of class logicFS containing
一个类的对象logicFS含有
参数：primes
the prime implicants,
首要implicants，
参数：vim
the importance of the prime implicants,
的首要implicants的重要性，
参数：prop
the proportion of logic regression models containing the prime implicants,
的逻辑回归含有首要implicants模型的比例，
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),
更多的参数（如果addInfo = TRUE）
参数：mat.imp
the matrix containing the improvements if addMatImp = TRUE, otherwise, NULL,
矩阵的改进，否则，如果addMatImp = TRUENULL，
参数：measure
the name of the used importance measure,
所使用的重要性措施的名称，
参数：useN
the value of useN,
useN的价值，
参数：threshold
NULL,
为NULL，
参数：mu
NULL.
NULL。
作者（S）----------Author(s)----------
Holger Schwender, <a href="mailto:holger.schwender@udo.edu">holger.schwender@udo.edu</a>
参考文献----------References----------
Using Logic Regression. Biostatistics, 9(1), 187-198.
参见----------See Also----------
logic.bagging, logicFS, vim.norm, vim.signperm
logic.bagging，logicFS，vim.norm，vim.signperm
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-2-25 23:36:05
