dissmfac(TraMineR)
dissmfac()所属R语言包：TraMineR
Multi-factor ANOVA from a dissimilarity matrix
从相异度矩阵的多因素方差分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Perform a multi-factor analysis of variance from a dissimilarity matrix.
从相异度矩阵进行多因素方差分析。
用法----------Usage----------
dissmfacw(formula, data, R = 1000, gower = FALSE, squared = FALSE,
weights = NULL)
dissmfac(formula, data, R = 1000, gower = FALSE, squared = TRUE,
permutation = "dissmatrix")
参数----------Arguments----------
参数：formula
A regression-like formula. The left hand side term should be a dissimilarity matrix or a dist object.
的回归公式。左手侧的术语应该是一个的相异矩阵或一个dist对象。
参数：data
A data frame from which the variables in formula should be taken.
一个数据框中的变量formula应采取。
参数：R
Number of permutations used to assess significance.
用来评估意义的排列数目。
参数：gower
Logical: Is the dissimilarity matrix already a Gower matrix?
逻辑：是的相异度矩阵一个高尔矩阵？
参数：squared
Logical: Should we square the provided dissimilarities?
逻辑：我们应该正视所提供的异同？
参数：weights
Optional numerical vector of case weights.
可选数值的情况下，权重向量。
参数：permutation
Deprecated. Kept for backward compatibility.
已过时。保持向后兼容性。
Details
详细信息----------Details----------
This method is, in some way, a generalization of dissassoc to account for several explanatory variables. The function computes the part of discrepancy explained by the list of covariates specified in the formula. It provides for each covariate the Type-II effect, i.e. the effect measured when removing the covariate from the full model with all variables included. For a single factor dissmfac is slower than dissassoc. Moreover, the latter performs also tests for homogeneity in within-group discrepancies (equality of variances) with a generalization of Levene's and Bratlett's statistics.
这种方法是，在某些方面，考虑到几个解释变量的一个推广dissassoc。该函数计算的formula在指定的协变量列表中所解释的部分差异。它提供的每个协变量的II-型效应，即从完整的模型，所有的变量包括除去协变量时测得的效果。对于单因素dissmfac是慢比dissassoc。此外，后者执行测试同质，组内差异（平等的方差）的泛化列文的和Bratlett的统计。
Part of the function is based on the Multivariate Matrix Regression with qr decomposition algorithm written in SciPy-Python by Ondrej Libiger and Matt Zapala (See <CITE>Zapala and Schork</CITE>, 2006, for a full reference.) The algorithm has been adapted for Type-II effects and extended to account for case weights.
QR分解算法写在翁德热Libiger与SciPy的，python和马特萨帕拉（见萨帕拉和朔尔克<CITE> </ CITE>，2006年，为一个完整的参考）。该算法具有的部分功能为基础的多元矩阵回归被改编为-II型的影响，并扩展到帐户的情况下，权重。
值----------Value----------
A dissmultifactor object with the following components:
Adissmultifactor对象有以下组件：
参数：mfac
The part of variance explained by each variable (comparing full model to model without the specified variable) and its significance using permutation test
部分方差解释每个变量（没有指定的变量比较完整的模型，模型）及其意义的置换检验
参数：call
Function call
函数调用
参数：perms
Permutation values as a boot object
一个boot对象的置换值
参考文献----------References----------
Discrepancy analysis of complex objects using dissimilarities. In F. Guillet, G. Ritschard, D. A. Zighed and H. Briand (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence, Volume 292, pp. 3-19. Berlin: Springer.
Analyse de dissimilarit茅s par arbre d'induction. In EGC 2009, Revue des Nouvelles Technologies de l'Information, Vol. E-15, pp. 7-18.
Austral Ecology 26, 32-46.
comment on distance-based redundancy analysis. Ecology 82(1), 290-297.
testing associations between gene expression patterns and related variables. Proceedings of the National Academy of Sciences of the United States of America 103(51), 19430-19435.
参见----------See Also----------
dissvar to compute a pseudo variance from dissimilarities and for a basic introduction to concepts of discrepancy analysis. <br> dissassoc to test association between objects represented by their dissimilarities and a covariate. <br> disstree for an induction tree analysis of objects characterized by a dissimilarity matrix. <br> disscenter to compute the distance of each object to its group center from pairwise dissimilarities.
dissvar的来计算伪方差从不同点和差异分析概念的基本介绍。参考dissassoc，以测试他们的不同点和协变量所代表的对象之间的关联。参考disstree感应树的相异度矩阵分析对象的特点。参考disscenter计算两两相异的每个对象组中心的距离。
实例----------Examples----------
## Define the state sequence object[＃定义的状态序列对象]
data(mvad)
mvad.seq <- seqdef(mvad[, 17:86])
## Compute dissimilarities (any dissimilarity measure can be used)[＃计算的不同点（可用于任何相异措施）]
mvad.ham <- seqdist(mvad.seq, method="HAM")
## And now the multi-factor analysis[＃现在的多因素分析]
print(dissmfac(mvad.ham ~ male + Grammar + funemp +
gcse5eq + fmpr + livboth, data=mvad, R=10))
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 2012-10-1 11:33:28
