R语言 robCompositions包 robCompositions-package()函数中文帮助文档(中英文对照)

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robCompositions-package(robCompositions)
robCompositions-package()所属R语言包：robCompositions

                                         Robust Estimation for Compositional Data.
                                         稳健估计的成分数据。

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The package contains methods for imputation  of compositional data including robust methods, (robust) outlier detection for compositional data,  (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis (Fisher rule) and (robust)  Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (alr, clr, ilr, and their inverse transformations).
的包包含方法归集的成分数据，包括可靠的方法，（强大）的组成数据的异常值检测（强大）主成分分析的成分数据，（强大）成分数据的因子分析，判别分析（强大）（费舍尔规则）和安德森 - 达令河（强大）的正态性检验的成分数据以及流行数比转换（ALR，CLR，劳资关系，和逆变换）。


Details

详细信息----------Details----------


（作者）----------Author(s)----------



Matthias Templ, Peter Filzmoser, Karel Hron,

Maintainer: Matthias Templ <templ@tuwien.ac.at>




参考文献----------References----------

Data Monographs on Statistics and Applied Probability. Chapman \& Hall Ltd., London (UK). 416p. \
Outlier detection for compositional data using robust methods. Math. Geosciences, 40 233-248.
Principal Component Analysis for Compositional Data with Outliers. Environmetrics, 20 (6), 621–632.
Computers and Geosciences, 35 (9), 1854–1861.
Computational Statistics and Data Analysis, 54 (12), 3095–3107.  
Statistical Data Analysis Explained.  Applied Environmental Statistics with R.  John Wiley and Sons, Chichester, 2008.

实例----------Examples----------


## k nearest neighbor imputation[＃k近邻归集]
data(expenditures)
expenditures[1,3]
expenditures[1,3] <- NA
impKNNa(expenditures)$xImp[1,3]

## iterative model based imputation[＃迭代模型为基础的归集]
data(expenditures)
x <- expenditures
x[1,3]
x[1,3] <- NA
xi <- impCoda(x)$xImp
xi[1,3]
s1 <- sum(x[1,-3])
impS <- sum(xi[1,-3])
xi[,3] * s1/impS

xi <- impKNNa(expenditures)
xi
summary(xi)
plot(xi, which=1)
plot(xi, which=2)
plot(xi, which=3)

## pca[＃PCA]
data(expenditures)
p1 <- pcaCoDa(expenditures)
p1
plot(p1)

## outlier detection[＃孤立点检测]
data(expenditures)
oD <- outCoDa(expenditures)
oD
plot(oD)

## transformations[＃转换]
data(arcticLake)
x <- arcticLake
x.alr <- alr(x, 2)
y <- invalr(x.alr)
invalr(alr(x, 3))
data(expenditures)
x <- expenditures
y <- invalr(alr(x, 5))
head(x)
head(y)
invalr(x.alr, ivar=2, useClassInfo=FALSE)

data(expenditures)
eclr <- clr(expenditures)
inveclr <- invclr(eclr)
head(expenditures)
head(inveclr)
head(invclr(eclr$x.clr))

require(MASS)
Sigma <- matrix(c(5.05,4.95,4.95,5.05), ncol=2, byrow=TRUE)
z <- invilr(mvrnorm(100, mu=c(0,2), Sigma=Sigma))

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