TML.censored(RobustAFT)
TML.censored()所属R语言包：RobustAFT
Truncated Maximum Likelihood Regression With Censored Observations
截断极大似然回归删失观察
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes the truncated maximum likelihood estimates of accelerated failure time regression described in Locatelli et al. (2010). The error distribution is assumed to follow approximately a Gaussian or a log-Weibull distribution. The cut-off values for outlier rejection are fixed or adaptive.
此函数计算Locatelli的等的加速失败时间回归，被截断的最大似然估计。 （2010年）。的误差分布假定约遵循高斯或log的Weibull分布。截止拒绝离群值是固定的或自适应。
用法----------Usage----------
TML.censored(formula, delta, data, errors = "Gaussian", initial = "S",
input = NULL, otp = "fixed", cu = NULL, control.S=list(),
control.ref=list(), control.tml=list())
参数----------Arguments----------
参数：formula
A formula, i.e., a symbolic description of the model to be adjusted (cf. glm or lm).
Aformula，即，象征性的描述模型进行调整（见glm或lm）。
参数：data
An optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which robaft is called.
一个可选的数据框包含在模型中的变量。如果没有找到data，变量environment(formula)，通常是robaft被称为环境。
参数：delta
Vector of 0 and 1.
向量的0和1。
0: censored observation.
0：审查观察。
1: complete observation.</ul>
1：完整的观察。</ ul>
参数：errors
"Gaussian": the error distribution is assumed to be Gaussian.
“高斯”：误差分布被假定为高斯。
"logWeibull" : the error distribution is assumed to be log-Weibull.
“logWeibull”：，误差分布被假定为log威布尔。
参数：initial
"S": initial S-estimate.
“S”：最初的S-估计。
"input": the initial estimate is given on input.</ul>
“输入”：初步估计给定的输入。</ ul>
参数：input
A list(theta=c(...),sigma=...): initial input estimates where theta is a vector of p coefficients and sigma a scalar scale.<br> Required when initial="input".
一个列表（θ= C（...），σ= ...）：最初的输入估计theta是一个矢量的p系数和标准差一个标量规模。参考需要时，初始=“输入”。
参数：otp
"adaptive": adaptive cut-off.
“自适应”：自适应切断。
"fixed": non adaptive cut-off.</ul>
“固定”：非自适应切断。</ ul>
参数：cu
Preliminary minimal upper cut-off. The default is 2.5 in the Gaussian case and 1.855356 in the log-Weibull case.
初步，最小上切断。在高斯的情况下的默认值是2.5和1.855356log威布尔情况下。
参数：control.S
A list of control parameters for the computation of the initial S estimates. See the function TML.censored.control.S for the default values.
控制参数计算的初始S估计的列表。见的功能TML.censored.control.S为默认值。
参数：control.ref
A list of control parameters for the refinement algorithm of the initial S estimates. See the function TML.censored.control.ref for the default values.
列表控制参数的初始S估计的改进算法。见的功能TML.censored.control.ref为默认值。
参数：control.tml
AA list of control parameters for the computation of the final estimates. See the function TML.censored.control.tml for the default values.
AA的控制参数计算的最终估计。见的功能TML.censored.control.tml为默认值。
值----------Value----------
TML.censored returns an object of class "TML". The function summary can be used to obtain or print a summary of the results. The generic extractor functions fitted, residuals and weights can be used to extract various elements of the object returned by TML.censored. The function update can be used to update the model.
TML.censored返回一个对象类的“TML”。函数summary可以用来获取或打印的汇总结果。通用提取功能fitted，residuals和weights可以用于提取各种元素的TML.censored返回的对象。功能update可以用来更新模型。
An object of class "TML" is a list with at least the following components: <table summary="R valueblock"> <tr valign="top"><td>th0 </td> <td> Initial coefficient estimates.</td></tr> <tr valign="top"><td>v0 </td> <td> Initial scale estimate.</td></tr> <tr valign="top"><td>nit.ref </td> <td> Reached number of iteration in the refinement step for the initial estimates.</td></tr> <tr valign="top"><td>th1 </td> <td> Final coefficient estimates.</td></tr> <tr valign="top"><td>v1 </td> <td> Final scale estimate.</td></tr> <tr valign="top"><td>nit.tml </td> <td> Number of iterations reached in IRLS algorithm for the final estimates.</td></tr> <tr valign="top"><td>tu,tl </td> <td> Final cut-off values.</td></tr> <tr valign="top"><td>alpha </td> <td> Estimated proportion of retained observations.</td></tr> <tr valign="top"><td>tn </td> <td> Number of retained observations.</td></tr> <tr valign="top"><td>weights </td> <td> Vector of weights (0 for rejected observations, 1 for retained observations).</td></tr> <tr valign="top"><td>COV </td> <td> Covariance matrix of the final estimates (th1[1],...,th1[p],v1) (where p=ncol(X)).</td></tr> <tr valign="top"><td>residuals </td> <td> Residuals of noncensored observations are calculated as response minus fitted values. For censored observations, the the expected residuals given that the response is larger than the recorded censored value are provided.</td></tr> <tr valign="top"><td>fitted.values </td> <td> The fitted mean values.</td></tr> <tr valign="top"><td>call </td> <td> The matched call.</td></tr> <tr valign="top"><td>formula </td> <td> The formula supplied.</td></tr> <tr valign="top"><td>terms </td> <td> The terms object used.</td></tr> <tr valign="top"><td>data </td> <td> The data argument.</td></tr> </table>
“TML”类的一个对象是一个至少有以下部分组成：<table summary="R valueblock"> <tr valign="top"> <TD> th0 </ TD> <TD>初始系数估计值。</ TD> </ TR> <tr valign="top"> <TD>v0 </ TD> <TD>初始规模的估计。</ TD> </ TR> <TR VALIGN “顶”> <TD> nit.ref </ TD> <TD>达到迭代次数的细化步骤的初步估计。</ TD> </ TR> <tr valign="top"> <TD > th1 </ TD> <TD>最终的系数估计值。</ TD> </ TR> <tr valign="top"> <TD>v1 </ TD> <TD>最终规模估计。</ TD> </ TR> <tr valign="top"> <TD>nit.tml </ TD> <TD>的最终估计达到IRLS算法的迭代。</ TD> < / TR> <tr valign="top"> <TD>tu,tl </ TD> <TD>最后的临界值。</ TD> </ TR> <tr valign="top"> <TD >alpha </ TD> <TD>保留意见的比例估算。</ TD> </ TR> <tr valign="top"> <TD> tn </ TD> <TD>保留意见的数量。</ TD> </ TR> <tr valign="top"> <TD>weights </ TD> <TD>（0被拒绝的意见，保留意见权重向量） </ TD> </ TR> <tr valign="top"> <TD>COV </ TD> <TD>协方差矩阵的最终估计值（TH1 [1]，...，TH1 [ P]，V1）（其中p = NCOL（X））。</ TD> </ TR> <tr valign="top"> <TD> residuals </ TD> <TD>残差的noncensored观察计算公式为响应减去拟合值。对于审查意见，预期残差的反应是更大的比记录的审查。</ TD> </ TR> <tr valign="top"> <TD> fitted.values </ TD> <TD>设备齐全的平均值。</ TD> </ TR> <tr valign="top"> <TD>call </ TD> <TD>匹配的呼叫。</ TD> </ TR > <tr valign="top"> <TD> formula </ TD> <TD>提供的计算公式。</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>terms 对象。</ TD> </ TR> <tr valign="top"> <TD> terms</ TD> <TD>的data 。</ TD> </ TR> </ TABLE>
参考文献----------References----------
Locatelli I., Marazzi A., Yohai V. (2010). Robust accelerated failure time regression. Computational Statistics and Data Analysis, 55, 874-887.
参见----------See Also----------
TML.censored.control.ref, TML.censored.control.tml, TML.censored.control.S, TML.noncensored
TML.censored.control.ref，TML.censored.control.tml，TML.censored.control.S，TML.noncensored
实例----------Examples----------
# This is the example described in Locatelli et al. (2010). [这是洛卡泰利等人所描述的例子。 （2010年）。]
# The estimates are slighty different than those of the paper due to changes [估计是略低不同于那些纸张由于改变]
# in the algorithm for the final estimate.[最终估计的算法。]
#[]
data(MCI)
attach(MCI)
# Exploratory Analysis[探索性分析]
plot(Age,log(LOS),type= "n",cex=0.7)
# (1) filled square : regular, complete[（1）填方：定期，完整的]
# (2) empty square : regular, censored[（2）空方：定期，删]
# (3) filled triangle : emergency, complete[（3）填充三角形的紧急情况下，]
# (4) empty triangle : emergency, censored[（4）空三角：紧急情况下，审查]
points(Age[Dest==1 & TypAdm==0], log(LOS)[Dest==1 & TypAdm==0], pch=15,cex=0.7) # (1)[（1）]
points(Age[Dest==0 & TypAdm==0], log(LOS)[Dest==0 & TypAdm==0], pch=0, cex=0.7) # (2) [（2）]
points(Age[Dest==1 & TypAdm==1], log(LOS)[Dest==1 & TypAdm==1], pch=17,cex=0.7) # (3) [（3）]
points(Age[Dest==0 & TypAdm==1], log(LOS)[Dest==0 & TypAdm==1], pch=2, cex=0.7) # (4) [（4）]
# Maximum Likelihood[最大似然法（Maximum Likelihood）]
ML <- survreg(Surv(log(LOS), Dest) ~ TypAdm*Age, dist="gaussian")
summary(ML)
B.ML <- ML$coef
S.ML <- ML$scale
abline(c(B.ML[1] ,B.ML[3] ),lwd=1,col="grey",lty=1)
abline(c(B.ML[1]+B.ML[2],B.ML[3]+B.ML[4]),lwd=1,col="grey",lty=1)
# Robust Accelerated Failure Time Regression with Gaussian errors[强大的加速失效时间回归与高斯错误]
ctrol.S <- list(N=150, q=5, sigma0=1, MAXIT=100, TOL=0.001,seed=123)
ctrol.ref <- list(maxit.sigma=2,tol.sigma=0.0001,maxit.Beta=2,tol.Beta=0.0001,
Maxit.S=50, tol.S.sigma=0.001, tol.S.Beta=0.001,alg.sigma=1,nitmon=FALSE)
ctrol.tml <- list(maxit.sigma=50,tol.sigma=0.0001,maxit.Beta=50,tol.Beta=0.0001,
Maxit.TML=50, tol.TML.sigma=0.001, tol.TML.Beta=0.001, alg.sigma=1,nitmon=FALSE)
WML<-TML.censored(log(LOS)~TypAdm*Age,data=MCI,delta=Dest,otp="adaptive",
control.S=ctrol.S,control.ref=ctrol.ref,control.tml=ctrol.tml)
summary(WML)
B.WML<-coef(WML)
abline(c(B.WML[1] ,B.WML[3] ),lty=1, col="red")
abline(c(B.WML[1]+B.WML[2],B.WML[3]+B.WML[4]),lty=1, col="red")
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注：
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发表于 2012-9-27 22:04:59
