probmap(spdep)
probmap()所属R语言包：spdep
Probability mapping for rates
概率映射率
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function returns a data frame of rates for counts in populations at risk with crude rates, expected counts of cases, relative risks, and Poisson probabilities.
该函数返回一个数据框计数率在高危人群原油价格，预计计数的情况下，相对风险和泊松概率。
用法----------Usage----------
probmap(n, x, row.names=NULL, alternative="less")
参数----------Arguments----------
参数：n
a numeric vector of counts of cases
一个数值向量计数的情况下，
参数：x
a numeric vector of populations at risk
一个数值向量高危人群
参数：row.names
row names passed through to output data frame
行名通过输出数据框
参数：alternative
default “less”, may be set to “greater”
默认的“少”，可以被设置为“大于”的
Details
详细信息----------Details----------
The function returns a data frame, from which rates may be mapped after class intervals have been chosen. The class intervals used in the examples are mostly taken from the referenced source.
该函数返回一个数据框，费率可能会被映射类的时间间隔后，已被选定。在实施例中所使用的类间隔大多采取从参考源。
值----------Value----------
参数：raw
raw (crude) rates
原料（原油）价格
参数：expCount
expected counts of cases assuming global rate
预计计数的情况下，假设全球速度
参数：relRisk
relative risks: ratio of observed and expected counts of cases multiplied by 100
相对风险：观察和计数的情况下，预期比乘以100
参数：pmap
Poisson probability map values: probablility of getting a more “extreme” count than actually observed - one-tailed, default alternative observed “less” than expected
泊松概率图的值：probablility得到了更“极端”的数量比实际观测到的 - 单尾，默认的另类观察“少”比预期的
（作者）----------Author(s)----------
Roger Bivand <a href="mailto:Roger.Bivand@nhh.no">Roger.Bivand@nhh.no</a>
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
example(auckland)
res <- probmap(auckland$M77_85, 9*auckland$Und5_81)
rt <- sum(auckland$M77_85)/sum(9*auckland$Und5_81)
ppois_pmap <- numeric(length(auckland$Und5_81))
for (i in seq(along=ppois_pmap)) {
ppois_pmap[i] <- poisson.test(auckland$M77_85[i], r=rt,
T=(9*auckland$Und5_81[i]), alternative="less")$p.value
}
all.equal(ppois_pmap, res$pmap)
brks <- c(-Inf,2,2.5,3,3.5,Inf)
cols <- grey(6:2/7)
plot(auckland, col=cols[findInterval(res$raw*1000, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Crude (raw) estimates of infant mortality per 1000 per year")
brks <- c(-Inf,47,83,118,154,190,Inf)
cols <- cm.colors(6)
plot(auckland, col=cols[findInterval(res$relRisk, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Standardised mortality ratios for Auckland child deaths")
brks <- c(0,0.05,0.1,0.2,0.8,0.9,0.95,1)
cols <- cm.colors(7)
plot(auckland, col=cols[findInterval(res$pmap, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Poisson probabilities for Auckland child mortality")
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 2012-9-30 14:43:43
