kibler(rrcovHD)
kibler()所属R语言包：rrcovHD
1985 Auto Imports Database
1985进口汽车数据库
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The original data set kibler.orig consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating and (c) its normalized losses in use as compared to other cars.
原始数据集kibler.orig由三种类型的实体：（一）规范的汽车的各种特性，（b）其指定的保险风险评级及（c）其归在使用中的损失相比，其他车辆。
The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.
第二等级对应程度自动为更多的风险比它的价格表示。汽车最初分配的一个危险因素，其价格与符号。然后，如果它是更大的风险（或更少），该符号被调整通过移动它向上（或向下）的规模。 Actuarians把这个过程称为“这象征”。值+3表示，汽车是有风险的，-3，它可能是非常安全的。
The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.
第三个因素是每一被保险人的车辆年平均相对损失支付。该值被标准化为所有汽车在一个特定的大小分类（双门小，旅行车，运动/特产，等..），每车每年的平均损失。
用法----------Usage----------
data(kibler)
格式----------Format----------
A data frame with 195 observations on the following 14 variables. The original data set (also available as kibler.orig) contains 205 cases and 26 variables of which 15 continuous, 1 integer and 10 nominal. The non-numeric variables and variables with many missing values were removed. Cases with missing values were removed too.
195以下14个变量的观察数据框。原始数据集（也可作为kibler.orig）205例和26其中15个连续的，1个整数和10面值的变量。大量缺失值的非数值型变量和变量被拆除。遗漏值的情况也被拆除。
symboling a numeric vector
symboling一个数值向量
wheel-base a numeric vector
wheel-base一个数值向量
length a numeric vector
length一个数值向量
width a numeric vector
width一个数值向量
height a numeric vector
height一个数值向量
curb-weight a numeric vector
curb-weight一个数值向量
bore a numeric vector
bore一个数值向量
stroke a numeric vector
stroke一个数值向量
compression-ratio a numeric vector
compression-ratio一个数值向量
horsepower a numeric vector
horsepower一个数值向量
peak-rpm a numeric vector
peak-rpm一个数值向量
city-mpg a numeric vector
city-mpg一个数值向量
highway-mpg a numeric vector
highway-mpg一个数值向量
price a numeric vector
price一个数值向量
Details
详细信息----------Details----------
The original data set contains 205 cases and 26 variables of which 15 continuous, 1 integer and 10 nominal. The non-numeric variables and variables with many missing values were removed. Cases with missing values were removed too. Thus the data set remains with 195 cases and 14 variables.
原始数据集包含205例和26个变量，其中15连续，1个整数和10名义。大量缺失值的非数值型变量和变量被拆除。遗漏值的情况也被拆除。因此，数据集仍然为195例和14个变量。
源----------Source----------
www.cs.umb.edu/~rickb/files/UCI/
参考文献----------References----------
of real-valued attributes. Computational Intelligence, Vo.l 5, 51-57.
实例----------Examples----------
data(kibler)
x.sd <- apply(kibler,2,sd)
xsd <- sweep(kibler, 2, x.sd, "/", check.margin = FALSE)
apply(xsd, 2, sd)
x.mad <- apply(kibler, 2, mad)
xmad <- sweep(kibler, 2, x.mad, "/", check.margin = FALSE)
apply(xmad, 2, mad)
x.qn <- apply(kibler, 2, Qn)
xqn <- sweep(kibler, 2, x.qn, "/", check.margin = FALSE)
apply(xqn, 2, qn)
## Display the scree plot of the classical and robust PCA[＃显示卵石图，经典和强大的PCA]
screeplot(PcaClassic(xsd))
screeplot(PcaGrid(xqn))
#########################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
##[＃]
## DD-plots[＃DD图]
##[＃]
usr <- par(mfrow=c(2,2))
plot(SPcaGrid(xsd, lambda=0, method="sd", k=4), main="Standard PCA") # standard[标准]
plot(SPcaGrid(xqn, lambda=0, method="qn", k=4)) # robust, non-sparse[健壮的，非稀疏]
plot(SPcaGrid(xqn, lambda=1,43, method="sd", k=4), main="Stdandard sparse PCA") # sparse[疏]
plot(SPcaGrid(xqn, lambda=2.36, method="qn", k=4), main="Robust sparse PCA") # robust sparse[强大的稀疏]
par(usr)
#########################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
## Table 2 in Croux et al[＃表2克鲁等]
## - to compute EV=Explained variance and Cumulative EV we[ - 计算EV =解释方差和累积EV我们]
## need to get all 14 eigenvalues[，＃需要得到全部14个特征值]
##[＃]
rpca <- SPcaGrid(xqn, lambda=0, k=14)
srpca <- SPcaGrid(xqn, lambda=2.36, k=14)
tab <- cbind(round(getLoadings(rpca)[,1:4], 2), round(getLoadings(srpca)[,1:4], 2))
vars1 <- getEigenvalues(rpca); vars1 <- vars1/sum(vars1)
vars2 <- getEigenvalues(srpca); vars2 <- vars2/sum(vars2)
cvars1 <- cumsum(vars1)
cvars2 <- cumsum(vars2)
ev <- round(c(vars1[1:4], vars2[1:4]),2)
cev <- round(c(cvars1[1:4], cvars2[1:4]),2)
rbind(tab, ev, cev)
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注：
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发表于 2012-9-28 20:35:44
