pottery(rrcov)
pottery()所属R语言包：rrcov
Archaic Greek Pottery data
古希腊陶艺数据的
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The Archaic Greek Pottery data set contains data on fragments of Greek pottery which were classified into two groups according to their origin: Attic or Eritrean. Six chemical variables, metallic oxide constituents, were measured: Si, Al, Fe, Ca and Ti. The main data set consists of 13 Attic objects and 14 Eritrean ones. There is a separate data set with 13 observations which can be used as a test data set. It consists of 4 observations classified as "probably Attic" and the remaining 9 as "probably Eritrean".
古希腊陶器数据集包含的希腊陶器碎片上根据其来源分为两组数据：阁楼或厄立特里亚。六种不同的化学变量，金属氧化物成分，分别测出：硅，铝，铁，钙和Ti。主数据集包含13个的阁楼对象和14厄立特里亚的。有13观测它可以用来作为测试数据集与一个单独的数据集。它由4个观察列为“可能是阁楼”，其余9为“可能厄立特里亚”。
用法----------Usage----------
data(pottery)
pottery
pottery.test
格式----------Format----------
Two data frames with 27 an 13 observations on the following 7 variables.
两个数据框27，13以下7个变量的观察。
SI Si content
SI Si含量
AL Al content
AL Al含量
FE Fe content
FE铁含量
MG Mg content
MG Mg含量
CA Ca content
CA钙含量
TI Ti content
TI Ti含量
origin Origin - factor with two levels: Attic and Eritrean
origin地 - 的因素有两个层次：Attic和Eritrean
Details
详细信息----------Details----------
The Archaic Greek Pottery data set was first published by Stern and Descoeudres (1977) and later reproduced in Cooper and Weeks (1983) for illustration of linear discriminant analisys. The data set was used by Pires and Branco (2010) for illustration of their projection pursuit approach to linear discriminant analysis.
古希腊陶器的数据集是首次出版由斯特恩和Descoeudres的（1977年），后来插图的线性判别analisys的在库珀和周（1983年）为转载。皮雷和布兰科（2010）使用说明的投影寻踪方法的线性判别分析的数据集。
源----------Source----------
STERN, W. B. and DESCOEUDRES, J.-P. (1977) X-RAY FLUORESCENCE ANALYSIS OF ARCHAIC GREEK POTTERY Archaeometry, Blackwell Publishing Ltd, 19, 73–86.
STERN，W. B. DESCOEUDRES的，J.-P. （1977）X-射线荧光分析古希腊陶器考古，Blackwell出版有限公司，19，73-86。
参考文献----------References----------
Data, Models, and Statistical Analysis, (Lanham, MD: Rowman & Littlefield).
Projection-pursuit approach to robust linear discriminant analysis Journal Multivariate Analysis, Academic Press, Inc., 101, 2464–2485.
实例----------Examples----------
data(pottery)
x <- pottery[,c("MG", "CA")]
grp <- pottery$origin
##[＃]
## Compute robust location and covariance matrix and[＃计算协方差矩阵和强大的位置和]
## plot the tolerance ellipses[＃图的公差椭圆]
library(rrcov)
(mcd <- CovMcd(x))
col <- c(3,4)
gcol <- ifelse(grp == "Attic", col[1], col[2])
gpch <- ifelse(grp == "Attic", 16, 1)
plot(mcd, which="tolEllipsePlot", class=TRUE, col=gcol, pch=gpch)
##[＃]
## Perform classical LDA and plot the data, 0.975 tolerance ellipses[＃对经典的LDA和绘制数据，0.975公差椭圆]
## and LDA separation line[＃和LDA分割线]
##[＃]
require(ellipse)
x <- pottery[,c("MG", "CA")]
grp <- pottery$origin
lda <- LdaClassic(x, grp)
lda
e1 <- ellipse(lda@cov, centre=lda@center[1,])
e2 <- ellipse(lda@cov, centre=lda@center[2,])
plot(CA~MG, data=pottery, col=gcol, pch=gpch,
xlim=c(min(MG,e1[,1], e2[,1]), max(MG,e1[,1], e2[,1])),
ylim=c(min(CA,e1[,2], e2[,2]), max(CA,e1[,2], e2[,2])))
ab <- lda@ldf[1,] - lda@ldf[2,]
cc <- lda@ldfconst[1] - lda@ldfconst[2]
abline(a=-cc/ab[2], b=-ab[1]/ab[2], col=2, lwd=2)
lines(e1, type="l", col=col[1])
lines(e2, type="l", col=col[2])
##[＃]
## Perform robust (MCD) LDA and plot data, classical and[＃执行稳健（MCD）LDA和绘图数据，古典与]
## robust separation line[＃强大的分割线]
##[＃]
require(ellipse)
plot(CA~MG, data=pottery, col=gcol, pch=gpch)
lda <- LdaClassic(x, grp)
ab <- lda@ldf[1,] - lda@ldf[2,]
cc <- lda@ldfconst[1] - lda@ldfconst[2]
abline(a=-cc/ab[2], b=-ab[1]/ab[2], col=2, lwd=2)
abline(a=-cc/ab[2], b=-ab[1]/ab[2], col=4, lwd=2)
rlda <- Linda(x, grp, method="mcd")
rlda
ab <- rlda@ldf[1,] - rlda@ldf[2,]
cc <- rlda@ldfconst[1] - rlda@ldfconst[2]
abline(a=-cc/ab[2], b=-ab[1]/ab[2], col=2, lwd=2)
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。
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发表于 2012-9-28 20:50:04
