csm(wild1)
csm()所属R语言包：wild1
Cause-specific estimates of mortality
死亡原因估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
An implementation of the nonparametric cumulative incidence function estimator described by Heisey and Patterson (2006). Permits staggered entry ("left truncation" in survival literature).
非参数累积发生率函数估计的实现描述的海西和Patterson（2006）。许可证交错项（“左截断”的生存文学）。
用法----------Usage----------
csm(entry, exit, event, fate, cause, alpha = 0.10)
参数----------Arguments----------
参数：entry
A data vector of class "numeric", integer, or "chron", representing times of entry for individuals in the risk set.
数据向量类"numeric"，integer或"chron"，表示时间的条目个人的风险集中。
参数：exit
A data vector of class "numeric", integer, or "chron", representing times of exit for individuals in the risk set.
数据向量类"numeric"，integer或"chron"，为个人的风险集中的退出表示时间。
参数：event
A data vector of class "numeric", integer, or "logical" describing departures of individuals from the risk set (0 or FALSE denotes censoring; 1 or TRUE denotes an event).
数据向量类"numeric"，integer或"logical"描述个人的离开从风险组（0或FALSE表示审查，“1 或TRUE表示事件）。
参数：fate
A data vector of class "numeric", "character", or "factor" describing fates of individuals.
数据向量类"numeric"，"character"或"factor"描述个人的命运。
参数：cause
A vector of class "numeric", integer, or "character" specifying fates attributed to the cause of interest. For example, several fates [c("hunting","poaching","vehicle strike")] might be included in an estimate of anthropogenic mortality.
一个向量类"numeric"，integer或"character"指定的命运归结为利益的原因。例如，一些命运[c("hunting","poaching","vehicle strike")可能人为死亡率的估计。
参数：alpha
Optional alpha level used to compute 100*(1-alpha/2)% upper and lower bounds (a 100*(1-alpha)% confidence interval) for the cumulative incidence function. Default is 0.10 (90% confidence interval).
可选的alpha水平来计算100 *（1-α/ 2）％的上限和下限（100 *（1-α）％置信区间）的累积发生率函数。默认值是0.10（90％置信区间）。
值----------Value----------
An object of classes "dataframe" and "csm".<br> <br> Columns include the following:<br> <br> <table summary="R valueblock"> <tr valign="top"><td> time </td> <td> Event time</td></tr> <tr valign="top"><td> n.event.all </td> <td> Number of events (all causes) occurring at time</td></tr> <tr valign="top"><td> n.risk.all </td> <td> Number of individuals at risk at time</td></tr> <tr valign="top"><td> survival.all </td> <td> Kaplan-Meier estimate of survival (all causes)</td></tr> <tr valign="top"><td> n.event.s </td> <td> Number of events due to fates in cause at time</td></tr> <tr valign="top"><td> n.risk.s </td> <td> Number of individuals at risk at time</td></tr> <tr valign="top"><td> survival.s </td> <td> Kaplan-Meier survival estimate obtained by censoring mortalities due to fates not in cause</td></tr> <tr valign="top"><td> mort.rate </td> <td> Interval mortality rate</td></tr> <tr valign="top"><td> CIF </td> <td> Cumulative incidence function estimate of mortality</td></tr> <tr valign="top"><td> cumvar </td> <td> Variance of CIF</td></tr> <tr valign="top"><td> SE </td> <td> Standard error of CIF</td></tr> <tr valign="top"><td> ucl </td> <td> Upper 100*(1-alpha)% confidence limit for CIF</td></tr> <tr valign="top"><td> lcl </td> <td> Lower 100*(1-alpha)% confidence limit for CIF</td></tr> </table>
对象的类"dataframe"和"csm"。的参考<BR>列包括以下内容：<BR> <BR> <table summary="R valueblock"> <tr valign="top"> <TD> time </ TD> <TD>活动时间</ TD> </ TR> <tr valign="top"> <TD> n.event.all </ TD> <TD>数事件（各种原因）发生在time</ TD> </ TR> <tr valign="top"> <TD> n.risk.all </ TD> <TD>的高危人群 time</ TD> </ TR> <tr valign="top"> <TD> survival.all </ TD> <TD> Kaplan-Meier法估计生存（所有原因）</ TD> < / TR> <tr valign="top"> <TD> n.event.s </ TD> <TD>的事件，由于命运causetime</ TD> </ TR> <tr valign="top"> <TD> n.risk.s </ TD> <TD>的个人风险time</ TD> </ TR> <TR VALIGN =“顶“> <TD> survival.s </ TD> <TD> Kaplan-Meier生存估计通过审查死亡率由于命运并不在cause </ TD> </ TR> <TR VALIGN =”顶部“> <TD> mort.rate </ TD> <TD>间隔死亡率</ TD> </ TR> <tr valign="top"> <TD> CIF </ TD> <TD >累积发生率函数估计的死亡率</ TD> </ TR> <tr valign="top"> <TD> cumvar </ TD> <TD>方差CIF </ TD> < / TR> <tr valign="top"> <TD> SE </ TD> <TD>的标准误差CIF</ TD> </ TR> <tr valign="top"> <TD> ucl </ TD> <TD>上100 *（1-α）％可信限为CIF </ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>下100 *（1-α）％置信区间为CIF </ TD> </ TR> </ TABLE>
注意----------Note----------
Modified from SPLUS code provided by Heisey and Patterson (2006) to 1) run in R, 2) permit more flexible input, 3) check data for a number of foreseeable errors, 4) support grouping of fates as a single cause, and 5) facilitate operations with output.
修改从海西和Patterson（2006）：1）在R，2）S-PLUS提供的代码允许更灵活的输入，3）检查数据的一些可预见的错误，4）支持分组的命运，作为一个单一的原因，的输出），便于操作。
Intervals defined by entry and exit are open on the left and closed on the right, i.e., event time is given by exit and interval is (entry, exit].
给出了entry和间隔是exit，exit和(entry, exit]是离开的权利，即关闭事件时间定义区间。
（作者）----------Author(s)----------
Glen A. Sargeant<br>
U.S. Geological Survey<br>
Northern Prairie Wildlife Research Center<br>
<a href="mailto:glen_sargeant@usgs.gov">glen_sargeant@usgs.gov</a>
参考文献----------References----------
Heisey, D. M., and B. R. Patterson. 2006. A review of methods to estimate cause-specific mortality in presence of competing risks. Journal of Wildlife Management 70(6):1544-1555
参见----------See Also----------
chron
chron
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。
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发表于 2012-10-1 22:31:43
