R语言 Sleuth2包 case1902()函数中文帮助文档(中英文对照)

loveR

case1902(Sleuth2)
case1902()所属R语言包：Sleuth2

                                        Death Penalty and Race
                                         死刑和种族

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Lawyers collected data on convicted murderers in the state of Georgia to see whether convicted murderers who were black were more likely to receive the death penalty than convicted murderers who were white, after accounting for aggravation level of the murder. They categorized murders into 6 progressively more serious types. Category 1 comprises barroom brawls, liquor-induced arguments lovers' quarrels, and similar crimes. Category 6 includes the most vicious, cruel, cold=blooded, unprovoked crimes.
律师收集到的数据在佐治亚州被定罪的杀人犯，看是否有谋杀罪的是黑色的更有可能收到的是白色的，占加重的谋杀后，比杀人犯的死刑。他们分类，分为6个种类越来越严重的谋杀案。第1类包括条形间斗殴，酒引起的参数恋人的争吵，类似的罪行。 6类，包括最恶毒的，残忍的，冷=热血沸腾，无端的罪行。


用法----------Usage----------


case1902



格式----------Format----------

A data frame with 12 observations on the following 4 variables.   
一个数据框有12以下4个变量的观察。

Aggravationthe aggravation level of the crime, a factor with levels "1", "2", "3", "4", "5" and "6"  
Aggravation的犯罪，加重的因素与水平"1"，"2"，"3"，"4"，"5"和"6"

Victima factor indicating race of murder victim, with levels "White" and "Black"
Victim的一个因素，指出谋杀的受害者的种族，同级别"White"和"Black"

Deathnumber in the aggravation and victim category who received the death penalty  
Death的加剧和受害者类被判死刑

Nodeathnumber in the aggravation and victim category who did not receive the death penalty   
Nodeath没有收到死刑的人的加重和受害者类


源----------Source----------

Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed), Duxbury.
拉姆齐，F.L.和Schafer，D.W. （2002年）。的统计的猎犬：A课程方法的数据分析（第二版），达克斯伯里。


参考文献----------References----------

Woodworth, G.C. (1989). Statistics and the Death Penalty, Stats 2: 9–12.

实例----------Examples----------


str(case1902)

# Add smidgeon to denominator because of zeros[分母，因为零smidgeon]
empiricalodds <- with(case1902, Death/(Nodeath + .5))
plot(empiricalodds ~ as.numeric(Aggravation), case1902, log="y", pch=ifelse(Victim=="White", 21, 19), xlab="Aggravation Level of the Murder", ylab="Odds of Death Penalty")
legend(3.8,.02,legend=c("White Victim Murderers","Black Victim Murderers"),pch=c(21,19))

fitbig <- glm(cbind(Death,Nodeath) ~ Aggravation*Victim, case1902, family=binomial)
# No evidence of overdispersion; no statistically significant evidence[无统计学显着的证据没有证据偏大;]
# of interactive effect [交互效应]
anova(fitbig, test="Chisq")
fitlinear <- glm(cbind(Death,Nodeath) ~ Aggravation + Victim, case1902, family=binomial)
summary(fitlinear)

# Mantel Haenszel Test, as an alternative[曼特尔Haenszel法测试，作为一种替代]
table1902   <- with(case1902, rbind(Death,Nodeath))
dim(table1902) <- c(2,2,6)
mantelhaen.test(table1902)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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