which.influence(rms)
which.influence()所属R语言包：rms
Which Observations are Influential
哪些意见是影响
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Creates a list with a component for each factor in the model. The names of the components are the factor names. Each component contains the observation identifiers of all observations that are "overly influential" with respect to that factor, meaning that |dfbetas| > u for at least one beta i associated with that factor, for a given cutoff. The default cutoff is .2. The fit must come from a function that has resid(fit, type="dfbetas") defined.
每个因素在模型中的一个组成部分，建立一个列表。的部件的名称是因子名称。每个组件包含的所有观测到这个因素是“过于影响力的”观察标识符，这意味着|dfbetas| > u至少一个beta i相关的因素，对于一个给定的cutoff 。默认cutoff是.2。适当人选必须来自resid(fit, type="dfbetas")定义一个函数，这个函数。
show.influence, written by Jens Oehlschlaegel-Akiyoshi, applies the result of which.influence to a data frame, usually the one used to fit the model, to report the results.
show.influence，书面由延Oehlschlaegel昭义，适用的结果which.influence到数据框，通常是一个用于拟合模型，将结果报告。
用法----------Usage----------
which.influence(fit, cutoff=.2)
show.influence(object, dframe, report=NULL, sig=NULL, id=NULL)
参数----------Arguments----------
参数：fit
fit object
适合对象
参数：object
the result of which.influence
结果which.influence
参数：dframe
data frame containing observations pertinent to the model fit
数据框包含的模型拟合相关的观察
参数：cutoff
cutoff value
临界值
参数：report
other columns of the data frame to report besides those corresponding to predictors that are influential for some observations
除了那些相对应的一些观察是有影响的预测变量的其它列的数据框的报告
参数：sig
runs results through signif with sig digits if sig is given
通过signif与sig数字如果sig给定的运行结果
参数：id
a character vector that labels rows of dframe if row.names were not used </table>
字符向量标记行的dframe如果row.names不使用</ TABLE>
值----------Value----------
show.influence returns a marked dataframe with the first column being a count of influence values
show.influence与第一列是一个计数的影响值，返回一个显着的数据框
（作者）----------Author(s)----------
Frank Harrell<br>
Department of Biostatistics, Vanderbilt University<br>
f.harrell@vanderbilt.edu
<br>
Jens Oehlschlaegel-Akiyoshi<br>
Center for Psychotherapy Research<br>
Christian-Belser-Strasse 79a<br>
D-70597 Stuttgart Germany<br>
oehl@psyres-stuttgart.de
参见----------See Also----------
residuals.lrm, residuals.cph, residuals.ols, rms, lrm, ols, cph
residuals.lrm，residuals.cph，residuals.ols，rms，lrm，ols，cph
实例----------Examples----------
#print observations in data frame that are influential,[打印观测数据框的影响力，]
#separately for each factor in the model[分别为在模型中的每个因子]
x1 <- 1:20
x2 <- abs(x1-10)
x3 <- factor(rep(0:2,length.out=20))
y <- c(rep(0:1,8),1,1,1,1)
f <- lrm(y ~ rcs(x1,3) + x2 + x3, x=TRUE,y=TRUE)
w <- which.influence(f, .55)
nam <- names(w)
d <- data.frame(x1,x2,x3,y)
for(i in 1:length(nam)) {
print(paste("Influential observations for effect of ",nam[i]),quote=FALSE)
print(d[w[[i]],])
}
show.influence(w, d) # better way to show results[更好的方式来显示结果]
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 2012-9-27 19:18:25
