measure_risk(sdcMicro)
measure_risk()所属R语言包：sdcMicro
Measure Risk for weighted or unweighted data / Individual risk to household risk / Compute a risk threshold based on a global risk value
衡量风险加权或不加权的数据/个人到家庭风险的风险/一个全球性的风险值的基础上计算的风险阈值
译者：生物统计家园网 机器人LoveR
描述----------Description----------
measure risk like in mu-argus / To be used when risk of disclosure for individuals within a family is considered to be statistical independent. This is the assumption made in mu-Argus risk model. / To be used when the risk measurement at individual level can be used to calculate the file level risk by taking the average of the individual risk.
衡量风险亩乌鳢/风险披露在一个家庭内的个人被认为是统计独立时所使用的一样。这是在亩阿格斯风险模型所作的假设。 /在个人层面上的风险测量时所使用的平均个人的风险，可用于计算文件级别的风险。
用法----------Usage----------
measure_risk(data,variables,weight_variable=NULL,missing=-999,l_recurs_c=2,ldiv_index=NULL)
## S3 method for class 'measure_risk'
print(x, ...)
measure_hier(data,variables,weight_variable=NULL,hid,missing=-999)
## S3 method for class 'measure_hier'
print(x, ...)
measure_thres(data,variables,weight_variable=NULL,missing=-999)
## S3 method for class 'measure_thres'
print(x, ...)
参数----------Arguments----------
参数：data
Input data
输入数据
参数：variables
Names of key variables
关键变量的名称
参数：weight_variable
name of variable containing sample weights
名称的变量，包含样本权重
参数：hid
Household ID
家庭ID
参数：missing
A integer value to be used as missing value in the C++ routine
在C + +程序的缺失值被用来作为一个整数值，
参数：l_recurs_c
L-Diversity Constant
L-多样性常数
参数：ldiv_index
indices of the variables used for l-diversity
升多样性的指数，其中所使用的变量
参数：x
Output of measure_risk, measure_hier or measure_thres
输出measure_risk，measure_hier或measure_thres的
参数：...
currently unused
目前未使用的
值----------Value----------
xx
XX
（作者）----------Author(s)----------
Alexander Kowarik, Bernd Prantner, IHSN C++ source
参考文献----------References----------
参见----------See Also----------
measure_hier
measure_hier
实例----------Examples----------
# measure_risk[measure_risk]
data(testdata)
res <- measure_risk(testdata,c("urbrur","roof","walls","water","sex"))
print(res)
head(res$Res)
resw <- measure_risk(testdata,c("urbrur","roof","walls","water","sex"),weight_variable="sampling_weight")
print(resw)
head(resw$Res)
res1 <- measure_risk(testdata,c("urbrur","roof","walls","water","sex"),ldiv_index=c(1))
print(res1)
head(res1$Mat_Risk)
res2 <- measure_risk(testdata,c("urbrur","roof","walls","water","sex"),ldiv_index=c(1,2))
print(res2)
head(res2$Mat_Risk)
# measure_hier[measure_hier]
resh <- measure_hier(testdata,c("urbrur","roof","walls","water","sex"),weight_variable="sampling_weight",hid="ori_hid")
print(resh)
# measure_thres[measure_thres]
rest <- measure_thres(testdata,c("urbrur","roof","walls","water","sex"),weight_variable="sampling_weight")
print(rest)
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 2012-9-29 23:18:44
