imports85(RRF)
imports85()所属R语言包：RRF
The Automobile Data
汽车数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This is the "Automobile" data from the UCI Machine Learning Repository.
这是“汽车”的数据来自于UCI机器学习库。
用法----------Usage----------
data(imports85)
格式----------Format----------
imports85 is a data frame with 205 cases (rows) and 26 variables (columns). This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. 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.
imports85是一个数据框有205例（行）和26个变量（列）。此数据集包含三种类型的实体：（a）规格的汽车的各种特性，（b）其指定的保险风险评级，（c）其归在使用中的损失相比其他汽车。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。调用该进程的Actuarians symboling“。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。
（作者）----------Author(s)----------
Andy Liaw
源----------Source----------
Originally created by Jeffrey C. Schlimmer, from 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook, Personal Auto Manuals, Insurance Services Office, and Insurance Collision Report, Insurance Institute for Highway Safety.
最初由杰弗里·C. Schlimmer，从1985年的模型导入汽车和卡车规格，1985年Ward的汽车年鉴“，个人汽车手册，保险服务处，及保险碰撞报告，美国公路安全保险研究所。
The original data is at http://www.ics.uci.edu/~mlearn/MLSummary.html.
原始数据是http://www.ics.uci.edu/~mlearn / MLSummary.html。
参考文献----------References----------
Yearbook.
160 Water Street, New York, NY 10038
Watergate 600, Washington, DC 20037
参见----------See Also----------
RRF
RRF
实例----------Examples----------
data(imports85)
imp85 <- imports85[,-2] # Too many NAs in normalizedLosses.[太多的NAS normalizedLosses。]
imp85 <- imp85[complete.cases(imp85), ]
## Drop empty levels for factors.[＃删除空的因素。]
imp85[] <- lapply(imp85, function(x) if (is.factor(x)) x[, drop=TRUE] else x)
stopifnot(require(RRF))
price.rf <- RRF(price ~ ., imp85, do.trace=10, ntree=100)
print(price.rf)
numDoors.rf <- RRF(numOfDoors ~ ., imp85, do.trace=10, ntree=100)
print(numDoors.rf)
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-28 21:07:22
