aareg(survival)
aareg()所属R语言包：survival
Aalen's additive regression model for censored data
阿伦的添加剂删失数据的回归模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns an object of class "aareg" that represents an Aalen model.
返回"aareg"表示阿伦模型，一个类的对象。
用法----------Usage----------
aareg(formula, data, weights, subset, na.action,
qrtol=1e-07, nmin, dfbeta=FALSE, taper=1,
test = c('aalen', 'variance', 'nrisk'),
model=FALSE, x=FALSE, y=FALSE)
参数----------Arguments----------
参数：formula
a formula object, with the response on the left of a "~" operator and the terms, separated by + operators, on the right. The response must be a Surv object. Due to a particular computational approach that is used, the model MUST include an intercept term. If "-1" is used in the model formula the program will ignore it.
一个公式对象，与上一个〜运算符和条件的权利，+运营商分开，左侧的响应。响应必须是一个Surv对象。由于使用的一个特定的计算方法，模型必须包括截距项。如果“-1”的模型公式中使用的程序将忽略它。
参数：data
data frame in which to interpret the variables named in the formula, subset, and weights arguments. This may also be a single number to handle some speci al cases – see below for details. If data is missi ng, the variables in the model formula should be in the search path.
数据框在解释formula名为变量，subset，weights参数。这也可能是一个单一的数字来处理某些特定人的情况 - 详情见下文。 data如果米西议员，模型公式中的变量，应该在搜索路径。
参数：weights
vector of observation weights. If supplied, the fitting algorithm minimizes the sum of the weights multiplied by the squared residuals (see below for additional technical details). The length of weights must be the same as the number of observations. The weights must be nonnegative and it i s recommended that they be strictly positive, since zero weights are ambiguous. To exclude particular observations from the model, use the subset argument instead of zero weights.
矢量观测的权重。如果提供，拟合算法最小化残差平方（更多的技术细节见下文）乘以权数的总和。 weights长度必须是相同的若干意见。的重量必须是非负的，并建议他们是严格正的，因为零权含糊不清。从模型中排除特定的意见，使用subset参数，而不是零权。
参数：subset
expression specifying which subset of observations should be used in the fit. Th is can be a logical vector (which is replicated to have length equal to the numb er of observations), a numeric vector indicating the observation numbers to be i ncluded, or a character vector of the observation names that should be included. All observations are included by default.
表达式，指定应在适合用于观测的子集。钍是可以逻辑向量（复制到长度等于观测麻木ER），数字矢量显示观察号码我ncluded的，或观察，应包括姓名的特征向量。所有的意见，包括默认情况下。
参数：na.action
a function to filter missing data. This is applied to the model.fr ame after any subset argument has be en applied. The default is na.fail, which returns a n error if any missing values are found. An alternative is na.excl ude, which deletes observations that contain one or more missing values.
一个函数来过滤丢失的数据。这是适用于后，任何model.fr ame说法已成为连接应用subset。默认是的na.fail，它会返回一个错误，如果发现任何缺失值。另一种方法是的na.excl ude，删除包含一个或多个缺失值的观测。
参数：qrtol
tolerance for detection of singularity in the QR decomposition
QR分解检测奇异的宽容
参数：nmin
minimum number of observations for an estimate; defaults to 3 times the number of covariates. This essentially truncates the computations near the tail of the data set, when n is small and the calcualtions can become numerically unstable.
估计观测的最低数量，默认为3次共变数。这在本质上截断数据集的尾部附近的计算，当n是小和calcualtions的可以成为数值不稳定。
参数：dfbeta
should the array of dfbeta residuals be computed. This implies computation of the sandwich variance estimate. The residuals will always be computed if there is a cluster term in the model formula.
dfbeta残差计算阵列。这意味着夹心方差估计的计算。残差将永远如果有cluster长期在模型公式计算。
参数：taper
allows for a smoothed variance estimate. Var(x), where x is the set of covariates, is an important component of the calculations for the Aalen regression model. At any given time point t, it is computed over all subjects who are still at risk at time t. The tape argument allows smoothing these estimates, for example taper=(1:4)/4 would cause the variance estimate used at any event time to be a weighted average of the estimated variance matrices at the last 4 death times, with a weight of 1 for the current death time and decreasing to 1/4 for prior event times. The default value gives the standard Aalen model.
允许平滑方差的估计。 VAR（X），其中x是协变量的集合，是阿伦回归模型计算的重要组成部分。在任何给定的时间点t，计算风险仍然在时间t的所有科目。磁带参数允许平滑这些估计，例如taper=(1:4)/4会导致在任何事件发生时使用的是在最后4死亡时间的估计方差矩阵的加权平均方差估计，目前体重为1死亡时间和事先的活动时间减少到1/4。默认值给出的标准阿伦模型。
参数：test
selects the weighting to be used, for computing an overall “average” coefficient vector over time and the subsequent test for equality to zero.
选择要使用的比重，计算随着时间的推移和后续测试平等零一个整体的“平均水平”的系数向量。
参数：model, x, y
should copies of the model frame, the x matrix of predictors, or the response vector y be included in the saved result.
应模型框架的副本，包括在X矩阵的预测，或响应向量y保存的结果。
Details
详情----------Details----------
The Aalen model assumes that the cumulative hazard H(t) for a subject can be expressed as a(t) + X B(t), where a(t) is a time-dependent intercept term, X is the vector of covariates for the subject (possibly time-dependent), and B(t) is a time-dependent matrix of coefficients. The estimates are inheritly non-parametric; a fit of the model will normally be followed by one or more plots of the estimates.
阿伦模型假定累积性危害的H（t）为主体的可表示为A（T）+ XB（T），（t）是时间依赖的截距项，其中X是变项为向量主题（可能是时间依赖性）和B（t）是时间依赖的系数矩阵。估计是inheritly非参数;一个合适的模型，通常会由一个或多个图的估计。
The estimates may become unstable near the tail of a data set, since the increment to B at time t is based on the subjects still at risk at time t. The tolerance and/or nmin parameters may act to truncate the estimate before the last death. The taper argument can also be used to smooth out the tail of the curve. In practice, the addition of a taper such as 1:10 appears to have little effect on death times when n is still reasonably large, but can considerably dampen wild occilations in the tail of the plot.
估计可能成为不稳定的一个数据集的尾部附近，因为在时间t到B的增量仍然在时间t的风险科目的基础上。宽容和/或无机氮参数可能会采取行动，截断前的最后死亡的估计。 taper参数也可以用来平滑曲线的尾部。此外，如锥度为1:10，在实践中，出现死亡时间当n仍然是相当大的影响不大，但可以大大挫伤野生occilations在图的尾部。
值----------Value----------
an object of class "aareg" representing the fit.
类"aareg"代表适合的对象。
参考文献----------References----------
Statistics in Medicine, 8:907-925.
survival analysis. Statistics in Medicine. 12:1569-1588.
参见----------See Also----------
print.aareg, summary.aareg, plot.aareg
print.aareg，summary.aareg，plot.aareg
举例----------Examples----------
# Fit a model to the lung cancer data set[适合肺癌的数据集模型]
lfit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung,
nmin=1)
## Not run: [＃无法运行：]
lfit
Call:
aareg(formula = Surv(time, status) ~ age + sex + ph.ecog, data = lung, nmin = 1
)
n=227 (1 observations deleted due to missing values)
138 out of 138 unique event times used
slope coef se(coef) z p
Intercept 5.26e-03 5.99e-03 4.74e-03 1.26 0.207000
age 4.26e-05 7.02e-05 7.23e-05 0.97 0.332000
sex -3.29e-03 -4.02e-03 1.22e-03 -3.30 0.000976
ph.ecog 3.14e-03 3.80e-03 1.03e-03 3.70 0.000214
Chisq=26.73 on 3 df, p=6.7e-06; test weights=aalen
plot(lfit[4], ylim=c(-4,4)) # Draw a plot of the function for ph.ecog[绘制函数图ph.ecog]
## End(Not run)[＃结束（不运行）]
lfit2 <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung,
nmin=1, taper=1:10)
## Not run: lines(lfit2[4], col=2) # Nearly the same, until the last point[＃不运行：＃线（lfit2 [4]，COL = 2）几乎相同，直到最后一点]
# A fit to the mulitple-infection data set of children with[设置一个适合多张感染数据与子女]
# Chronic Granuomatous Disease. See section 8.5 of Therneau and Grambsch.[，慢性Granuomatous疾病。见第Therneau和Grambsch 8.5。]
fita2 <- aareg(Surv(tstart, tstop, status) ~ treat + age + inherit +
steroids + cluster(id), data=cgd)
## Not run: [＃无法运行：]
n= 203
69 out of 70 unique event times used
slope coef se(coef) robust se z p
Intercept 0.004670 0.017800 0.002780 0.003910 4.55 5.30e-06
treatrIFN-g -0.002520 -0.010100 0.002290 0.003020 -3.36 7.87e-04
age -0.000101 -0.000317 0.000115 0.000117 -2.70 6.84e-03
inheritautosomal 0.001330 0.003830 0.002800 0.002420 1.58 1.14e-01
steroids 0.004620 0.013200 0.010600 0.009700 1.36 1.73e-01
Chisq=16.74 on 4 df, p=0.0022; test weights=aalen
## End(Not run)[＃结束（不运行）]
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。
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发表于 2012-2-16 19:31:59
